Automated grading of student responses still faces numerous challenges, particularly when dealing with complex and ambiguous answers. In particular, large models are prone to scoring bias when handling uncertain responses, and few-shot reasoning methods often lack stability, which limits their applicability in real educational scenarios. To tackle these challenges, we propose the Contrastive Error Mining and FineTuning (CEM-FT) framework, which automatically identifies high-value hard samples by analyzing scoring disagreements between a full fine-tuned model and a few-shot model. A lightweight LoRA adapter is then trained on these samples to refine model performance with minimal computational overhead. Experiments on the SciEntsbank, Beetle, and Mohler datasets show that CEM-FT can improve QWK by up to 3.9% compared to the fine-tuned Qwen model on SciEntsbank datasets, which is a significant improvement over the few-shot baseline. The proposed framework substantially enhances both scoring accuracy and consistency, providing a practical, robust solution for reliable automated assessment with large language models.

Learning path recommendation seeks to provide students with a structured sequence of learning items (e.g., knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relations, which present two major limitations: (1) Prerequisite relations between knowledge concepts are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. (2) Relying on a single sequentially dependent knowledge structure based on prerequisite relations implies that a confusing knowledge concept can disrupt subsequent learning processes, which is referred to as blocked learning. To address these two challenges, we propose a novel approach, GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation (KnowLP), which enhances learning path recommendations by incorporating both prerequisite and similarity relations between knowledge concepts. Specifically, we introduce a knowledge structure graph generation module EDU-GraphRAG that constructs knowledge structure graphs for different educational datasets, significantly improving the applicability of learning path recommendation methods. We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module that utilizes similarity relations as fallback relations when prerequisite relations become ineffective, thereby alleviating the blocked learning. Finally, we conduct extensive experiments on three benchmark datasets, demonstrating that our method not only achieves state-of-the-art performance but also generates more effective and longer learning paths.

Cognitive diagnosis aims to infer students' mastery levels over knowledge components from their learning interactions, supporting personalized education applications. However, existing models encode responses as binary correctness labels, discarding information about which specific option a student selected. This input-level information loss limits their ability to distinguish qualitatively different error types. As a result, providing interpretable diagnostic outputs becomes challenging. To address these limitations, we propose SACD, a semantic-augmented cognitive diagnosis framework that integrates LLM-based semantic analysis with student behavioral modeling. SACD comprises the following key components. First, an LLM-based exercise diagnostic generator analyzes exercise content and produces structured semantic annotations for each answer choice, capturing the specific misconceptions each option represents. Second, a kernel-based alignment mechanism projects semantic embeddings and behavioral representations into a unified kernel space, enabling effective fusion of heterogeneous information. Third, an interpretable diagnosis layer predicts student performance and generates fine-grained mastery estimates, which LLMs further process to produce actionable learning plans. Extensive experiments on three real-world datasets demonstrate that SACD achieves superior prediction accuracy while enabling interpretable, actionable diagnostics.

Knowledge tracing (KT) aims to personalize online education on large-scale web-based platforms by modeling students' evolving knowledge states from their interaction sequences. However, most KT models rely on a single encoder architecture (e.g., self-attention or RNN), with fixed inductive biases that fails to capture the diversity of learning behaviors. Specifically, student learning unfolds across multiple timescales, and interaction sequences contain diverse frequency components ranging from short-term variations to long-term trends. Our data-driven analysis reveals that existing encoders exhibit characteristic frequency biases (e.g., self-attention tends to emphasize low-frequency patterns), highlighting the limitations of any single architecture. To address this problem, we propose FA-KT, a frequency-aware mixture of heterogeneous experts framework. FA-KT combines self-attention, Mamba, CNN, and LSTM experts, each with complementary frequency biases. A frequency-aware router analyzes each sequence's frequency characteristics and adaptively combines experts to create dynamic, personalized encoders for individual students. Across five benchmark datasets, FA-KT consistently outperforms 20 strong KT baselines in predicting future performance. Code is available at https://pykt.org/.

This study explores the design space of two-handed input (i.e., clicking or tapping with the thumb) on the touchpads of controllers for virtual reality (VR) interaction. Four experiments were conducted to fulfill this purpose. Experiment 1 investigated how users employed two VR controllers to perform four representative interaction tasks in VR and identified 14 potentially usable two-handed operations that involved tapping or clicking. Experiments 2 and 3 analyzed user performance of the 14 operations, providing insights into their interaction characteristics in terms of completion time, accuracy, and subjective feedback. In Experiment 4, we designed a command-input technique based on the proposed operations. We verified its effectiveness compared to context menus and marking menus in a VR text entry scenario. Our technique generally had shorter times and similar accuracy to the two menu types. Our work contributes to the design of VR interactions using two-handed controllers.

Knowledge tracing (KT) models students' knowledge states and predicts their future performance based on their historical interaction data. However, attention based KT models struggle to accurately capture diverse forgetting behaviors in ever-growing interaction sequences. First, existing models use uniform time decay matrices, conflating forgetting representations with problem relevance. Second, the fixed-length window prediction paradigm fails to model continuous forgetting processes in expanding sequences. To address these challenges, this paper introduces LefoKT, a unified architecture that enhances attention based KT models by incorporating proposed relative forgetting attention. LefoKT improves forgetting modeling through relative forgetting attention to decouple forgetting patterns from problem relevance. It also enhances attention based KT models' length extrapolation capability for capturing continuous forgetting processes in ever-growing interaction sequences. Extensive experimental results on three datasets validate the effectiveness of LefoKT.

Knowledge tracing (KT) involves using the historical records of student-learning interactions to anticipate their performance on forthcoming questions. Central to this process is the modeling of human cognition to gain deeper insights into how knowledge is acquired and retained. Human cognition is characterized by two key features: long-term cognitive trends, reflecting the gradual accumulation and stabilization of knowledge over time, and short-term cognitive fluctuations, which arise from transient factors such as forgetting or momentary lapses in attention. Although existing attention-based KT models effectively capture long-term cognitive trends, they often fail to adequately address short-term cognitive fluctuations. These limitations lead to overly smoothed cognitive features and reduced model performance, especially when the test data length exceeds the training data length. To address these problems, we propose FlucKT, a novel short-term cognitive fluctuations enhanced attention network for KT tasks. FlucKT improves the attention mechanism in two ways: First, by using a decomposition-based layer with causal convolution to separate and dynamically reweight long-term and short-term cognitive features. Second, by introducing a kernelized bias attention score penalty to enhance focus on short-term fluctuations, improving length generalization capabilities. Our contributions are validated through extensive experiments on three real-world datasets, demonstrating significant improvements in length generalization and prediction performance.

Step-level reward models (SRMs) can significantly enhance mathematical reasoning performance through process supervision or step-level preference alignment based on reinforcement learning. The performance of SRMs is pivotal, as they serve as critical guidelines, ensuring that each step in the reasoning process is aligned with desired outcomes. Recently, AlphaZero-like methods, where Monte Carlo Tree Search (MCTS) is employed for automatic step-level preference annotation, have proven particularly effective. However, the precise mechanisms behind the success of SRMs remain largely unexplored. To address this gap, this study delves into the counterintuitive aspects of SRMs, particularly focusing on MCTS-based approaches. Our findings reveal that the removal of natural language descriptions of thought processes has minimal impact on the efficacy of SRMs. Furthermore, we demonstrate that SRMs are adept at assessing the complex logical coherence present in mathematical language while having difficulty in natural language. These insights provide a nuanced understanding of the core elements that drive effective step-level reward modeling in mathematical reasoning. By shedding light on these mechanisms, this study offers valuable guidance for developing more efficient and streamlined SRMs, which can be achieved by focusing on the crucial parts of mathematical reasoning.

Mathematical reasoning remains a challenging area for large language models (LLMs), prompting the development of math-specific LLMs such as LLEMMA, DeepSeekMath, and Qwen2-Math, among others. These models typically follow a two-stage training paradigm: pre-training with math-related corpora and post-training with problem datasets for supervised fine-tuning (SFT). Despite these efforts, the improvements in mathematical reasoning achieved through continued pre-training (CPT) are often less significant compared to those obtained via SFT. This study addresses this discrepancy by exploring alternative strategies during the pre-training phase, focusing on the use of problem-solving data over general mathematical corpora. We investigate three primary research questions: (1) Can problem-solving data enhance the model's mathematical reasoning capabilities more effectively than general mathematical corpora during CPT? (2) Are synthetic data from the same source equally effective, and which synthesis methods are most efficient? (3) How do the capabilities developed from the same problem-solving data differ between the CPT and SFT stages, and what factors contribute to these differences? Our findings indicate that problem-solving data significantly enhances the model's mathematical capabilities compared to general mathematical corpora. We also identify effective data synthesis methods, demonstrating that the tutorship amplification synthesis method achieves the best performance. Furthermore, while SFT facilitates instruction-following abilities, it underperforms compared to CPT with the same data, which can be partially attributed to its poor learning capacity for more challenging problem-solving data. These insights provide valuable guidance for optimizing the mathematical reasoning capabilities of LLMs, culminating in our development of a powerful mathematical base model called MathGPT-8B.

Knowledge tracing (KT) is an essential task in online education systems. It aims to predict the future performance of students based on their historical learning interaction data. Despite significant advancements in attention-based KT models, they still face some limitations: inaccurate input representation and excessive student forgetting modeling. These limitations often lead to the attention noise problem: the model assigns non-negligible attention weight to some information that is cognitively irrelevant in nature, thereby generating interference signals. To address this problem, we propose a novel KT model, i.e., DenoiseKT. DenoiseKT effectively models the difficulty of the questions and utilizes graph neural network to capture the complex relationship between questions, thereby refining the representations of input features. Additionally, the denoised attention mechanism introduces a weight factor to reduce the model's attention weight distribution on irrelevant information. We extensively compare DenoiseKT with 22 state-of-the-art KT models on 4 widely-used public datasets. Experimental results show that DenoiseKT can effectively solve the attention noise problem and outperform other models. The source code of DenoiseKT is available at https://pykt.org.

With the widespread adoption of online education platforms, an increasing number of students are gaining new knowledge through Massive Open Online Courses (MOOCs). Exercise recommendation have made strides toward improving student learning outcomes. However, existing methods not only struggle with high dropout rates but also fail to match the diverse learning pace of students. They frequently face difficulties in adjusting to inactive students' learning patterns and in accommodating individualized learning paces, resulting in limited accuracy and diversity in recommendations. To tackle these challenges, we propose Neural Re-ranking for Diversified Exercise Recommendation (in short, NR4DER). NR4DER first leverages the mLSTM model to improve the effectiveness of the exercise filter module. It then employs a sequence enhancement method to enhance the representation of inactive students, accurately matches students with exercises of appropriate difficulty. Finally, it utilizes neural re-ranking to generate diverse recommendation lists based on individual students' learning histories. Extensive experimental results indicate that NR4DER significantly outperforms existing methods across multiple real-world datasets and effectively caters to the diverse learning pace of students.

Knowledge tracing (KT) aims to predict students' future performance based on their past learning activities. However, no one is perfect. Factors such as carelessness, fatigue, and stress often cause students to make mistakes on problems they have already mastered, leading to anomalies in their historical learning data. These anomalies disrupt inherent patterns in the data, misleading the KT model. Extracting cognitive patterns that accurately reflect students' knowledge mastery from such error-prone data remains a significant challenge. Against this background, this paper proposes a novel KT method named RoubstKT, inspired by educational measurement theory and frequency-based decomposition. A cognitive decoupling analyzer is proposed to decouple the student's cognitive pattern and random factors from the data through smoothing and subtraction operations, then recombine them using a gating mechanism or adaptive parameter fusion strategy. To more effectively diagnose students' knowledge mastery, we employ a decay-based attention mechanism that focuses on random behaviors at adjacent time steps. We conducted comprehensive experiments based on real-world datasets and targeted datasets with added random noise. The experimental results demonstrated the effectiveness of the proposed method.

Recommending suitable exercises and providing the reasons for these recommendations is a highly valuable task, as it can significantly improve students' learning efficiency. Nevertheless, the extensive range of exercise resources and the diverse learning capacities of students present a notable difficulty in recommending exercises. Collaborative filtering approaches frequently have difficulties in recommending suitable exercises, whereas deep learning methods lack explanation, which restricts their practical use. To address these issue, this paper proposes KG4EER, an explainable exercise recommendation with a knowledge graph. KG4EER facilitates the matching of various students with suitable exercises and offers explanations for its recommendations. More precisely, a feature extraction module is introduced to represent students' learning features, and a knowledge graph is constructed to recommend exercises. This knowledge graph, which includes three primary entities - knowledge concepts, students, and exercises - and their interrelationships, serves to recommend suitable exercises. Extensive experiments conducted on three real-world datasets, coupled with expert interviews, establish the superiority of KG4EER over existing baseline methods and underscore its robust explainability.

Knowledge tracing (KT) involves utilizing historical data from students’ learning interactions to model their mastery of knowledge over time, with the aim of predicting their future performance in interactions. Recently, significant advancements have been achieved through the application of various deep learning methodologies to address the KT challenge. However, a considerable proportion of deep learning-based knowledge tracing (DLKT) approaches exhibit striking similarities in their methodologies, and model designs, and even the outcomes demonstrate minimal divergence. In addition, the evaluation procedures employed in current DLKT studies are not standardized, resulting in substantial inconsistencies in the reported area under the curve (AUC) outcomes, despite analyzing the same model on identical datasets. To address the two aforementioned problems, this paper proposes a generalized DLKT framework and represents the existing DLKT models with five components, i.e., multimodal data encoder, student knowledge memory, auxiliary knowledge base, learning outcome objective, and computational efficiency and scalability. Furthermore, we develop and open source a standardized DLKT benchmark platform namedpyKT,1that consists of a standardized set of integrated data preprocessing procedures on 9 popular datasets across different domains, and 21 frequently compared DLKT model implementations. WithpyKT, we conduct empirical and reproducible research to assess the performance of prevalent DLKT algorithms in an unbiased and clear setting over multiple data sources. Finally, we discuss the applications of KT techniques in the educational sector and their future development directions.

Accurately judging student on-going performance is crucial for adaptive teaching. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in online one-on-one classes. As a step toward this direction, we introduce the Multi-turn Classroom Dialogue (MCD) dataset as a benchmark testing the capabilities of machine learning models in classroom conversation understanding of student performance judgment. Our dataset contains aligned multi-turn spoken language of 5000+ unique samples of solving grade-8 math questions collected from 500+ hours' worth of online one-on-one tutoring classes. In our experiments, we assess various state-of-the-art models on the MCD dataset, highlighting the importance of understanding multi-turn dialogues and handling noisy ASR transcriptions. Our findings demonstrate the dataset's utility in advancing research on automated student performance assessment. To encourage reproducible research, we make our data publicly available at https://github.com/ai4ed/MCD.

Knowledge tracing (KT) is a prediction task that aims to predict students’ future performance based on their past learning data. The rapid progress in attention mechanisms has led to the emergence of various high-performing attention based KT models. However, in online or personalized education settings, students’ varying learning paths result in different lengths of student interaction sequences, which poses a significant challenge for attention based KT models as their context window sizes are fixed during both training and prediction stages. We refer to this as the length extrapolation of KT model. In this paper, we propose extraKT to facilitate better extrapolation that learn from student interactions with a short context window and continue to perform well across various longer context window sizes at prediction stage. Specifically, we negatively bias attention scores with linearly decreasing penalties that are proportional to query-key distance, which efficiently represents short-term forgetting characteristics of student knowledge states. We conduct comprehensive and rigorous experiments on three real-world educational datasets. The results show that our extraKT model exhibits robust length extrapolation capability and outperforms state-of-the-art baseline models in terms of AUC and accuracy. To encourage reproducible research, we merge our data and code to the publicly available pyKT benchmark at https://github.com/pykt-team/pykt-toolkit.

Multi-Modal Large Language Models (MM-LLMs) have demonstrated powerful reasoning abilities in various visual question-answering tasks. However, they face the challenge of lacking rigorous reasoning and precise arithmetic, when solving geometry questions. To address this challenge, we propose a novel prompting method, namely Reason-and-Execute (R&E), to enhance the accuracy of solving geometry questions by MM-LLMs. Specifically, the R&E method includes two templates: reasoning template and execution template. We first adopt a reverse-thinking approach to construct a rigorous reasoning template so that it guides MM-LLMs to start reasoning from the most relevant domain knowledge of the question and ultimately identify the arithmetic requirements. We then make use of program-assisted thought to construct execution template in order to guide MM-LLMs to understand the arithmetic requirements from reasoning template and generate executable code block. The answer is finally obtained by executing the code block. We evaluate our prompting method on 9 models in answering questions on 6 datasets (including four geometry datasets and two science datasets) compared to Chain-of-Thought (CoT) and Program-Aided Language (PAL) prompting methods. R&E method shows up to 12.8% improvement compared to CoT and PAL, proving strong reasoning and arithmetic abilities for solving geometry questions of our method. Moreover, we further analyze the answering accuracy from the different perspectives on solving geometric questions, including domain knowledge, geometry shapes, question length, and language. Through multiple analysis, our method is able to enhance the ability of MM-LLMs to solve geometry questions.

As Virtual Reality (VR) applications gain popularity, the need for a secure, usable, and memorable user authentication method becomes crucial. However, security and privacy in such VR applications are often ignored. Current methods are insufficient in preventing man-in-the-room (MITR) attacks, which allow attackers to observe user interactions in VR while remaining invisible, and inputted passwords can easily be stolen. In this study, we propose a dynamic combination of multi-attribute authentication methods for VR, where various 3D objects and their attributes can be created and displayed. Users must select combinations of 3D objects and their attributes provided by our designed principles for identity authentication. We explore the impact of method parameters on security and provide three specific parameter schemes to deploy the practical authentication system. We designed three user studies to evaluate the usability, security, and memorability of our authentication system. The results show that the proposed scheme can effectively resist both shoulder surfing and MITR attacks with unsuccessful attack rates of 100% and 95.83%, respectively. Furthermore, this research provides suggestions to secure VR applications while maintaining usability and enhancing the memorability of the authentication method.

Despite being widely applied, pre-trained language models have been proven vulnerable to backdoor attacks. Backdoor attacks are designed to introduce targeted vulnerabilities into models by poisoning a subset of training samples through trigger injection and label modification. Traditional textual backdoor attacks suffer several flaws: the triggers lead to abnormal natural language expressions, and poisoned sample labels are mistakenly labeled. These flaws reduce the stealthiness of the attack and can be easily detected by defense models. In this study, we introduce Cbat, a novel and efficient method to perform clean-label backdoor attack with text style, which does not require external trigger, and the poisoned samples are correctly labeled. Specifically, we develop a sentence rewriting model by leveraging the powerful few-shot learning capability of prompt tuning to generate clean label poisoned samples. Cbat then injects text style as an abstract trigger into the victim model through poisoned samples. We also introduce an algorithm for defending against backdoor attacks, named CbatD, which effectively erases the poisoned samples by locating the lowest training loss and calculating feature relevance. The experiments on text classification tasks demonstrate that our Cbat and CbatD show overall competitive performance in textual backdoor attack and defense. It is noteworthy that Cbat attained leading results in the clean-label backdoor attack benchmark without triggers.

With the pervasiveness of GPS-enabled devices, mobile users can directly visit the best travel routes matching their interests and obtain a better user experience via location-based travel recommendation services. As the number of queries grows, the travel agency for location-based travel recommendations tends to outsource its recommendation services to the cloud server. Since the travel agency’s popular travel routes and raw trajectory data from mobile users contain sensitive information, privacy protection should be guaranteed. Although some schemes have been proposed to solve the privacy problems, no previous works related to the location-based recommendation are proposed over mobile users’ raw trajectories. To solve this problem, we propose a privacy-preserving travel recommendation scheme based on stay points over the raw encrypted trajectory data. Specifically, we first propose an adapted longest common subsequence computation algorithm to measure the similarity of two trajectories. Second, to support some computations under ciphertext, we design several secure two-party computation (S2PC) primitives (e.g., secure division, secure mean coordinate, and secure comparison) based on the Paillier cryptosystem. Third, we implement secure stay points extraction and adapted longest common subsequence computation protocols via these secure computation primitives. Finally, we analyze the security of our proposed scheme in the semi-honest model and show that the privacy of mobile users’ trajectories, query results, and the travel agency’s popular travel routes are well protected. Meanwhile, we evaluate the performance of each secure computation primitive and conduct extensive experiments on synthetic datasets, and the experimental results show that our scheme is practical in the real applications.

Mobile crowdsensing (MCS) utilizes ubiquitous mobile devices to collect massive amounts of data and offer various high-quality services. During the data collection and upload process, bilateral access control is implemented to recruit qualified data providers and prevent unauthorized access to collected data. However, the efficiency of existing bilateral access control schemes applicable in the data collection phase is dissatisfactory, as their ciphertext sizes are linear with the number of attributes. Additionally, data confidentiality and authenticity, as well as lightweight encryption and decryption processes, are crucial for the deployment of MCS since the former eliminate the risks of data abuse and false data injection, and the latter are typically limited in their computation and communication resources. To reduce the resource consumption of these devices, we present EBAC-CC, an efficient bilateral access control with constant-size ciphertexts that ensures data confidentiality and authenticity and allows for flexible threshold bilateral access control. Besides, offline/online techniques and outsourced decryption are employed to quickly generate ciphertexts and recover perceptual data, which also alleviates their computation burdens. We also prove its privacy and authenticity in the standard model and evaluate its efficacy theoretically and experimentally, demonstrating its superiority over other bilateral access control schemes.

Knowledge tracing (KT) is a crucial technique to predict students’ future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students’ knowledge state variations at a fine-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at https://pykt.org/.

The development of online intelligent educational systems has revolutionized personalized learning, presenting an opportunity for the integration of knowledge tracing (KT). KT is an essential task that leverages students’ historical interactions to model their knowledge states, enabling accurate predictions of their future performance. The application of deep learning models on KT tasks, also known as deep learning based knowledge tracing (DLKT) models, has accelerated the process of KT tasks in recent years. Although DLKT models have achieved promising results, there is a huge challenge to avoid models generating wrong estimations that yield bad guidance in educational contexts. Hence, in this work, we propose a simulation framework to explore the feasibility of minimizing the evaluation cost while guaranteeing prediction performance. In the framework, we initially design a simple yet efficient DLKT model that learns the actual process of students’ knowledge acquisition. We then select the reliable predictions generated by the proposed model and assign the unreliable ones to teaching professionals based on confidence estimations. We present the results of a proof-of-concept experiment on three real-world publicly available datasets to demonstrate that our framework can obtain the balance of human cost and automatic evaluation accuracy, which can be flexibly deployed to real-world educational contexts in the future. To encourage reproducible research, we make our code publicly available at https://github.com/gwbnwnwh/human-in-the-loop.

Accurately judging students' ongoing performance is very crucial for real-world educational scenarios. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in the online learning environment. We propose a novel neural network armed with a multi-granularity attention mechanism to capture the personalized pedagogical instructions from the very noisy teacher-student dialogue transcriptions. We conduct experiments on a real-world educational dataset and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.

Effective exercise recommendation is crucial for guiding students' learning trajectories and fostering their interest in the subject matter. However, the vast exercise resource and the varying learning abilities of individual students pose a significant challenge in selecting appropriate exercise questions. Collaborative filtering-based methods often struggle with recommending suitable exercises, while deep learning-based methods lack explanation, limiting their practical adoption. To address these limitations, this paper proposes KG4Ex, a knowledge graph-based exercise recommendation method. KG4Ex facilitates the matching of diverse students with suitable exercises while providing recommendation reasons. Specifically, we introduce a feature extraction module to represent students' learning states and construct a knowledge graph for exercise recommendation. This knowledge graph comprises three key entities (knowledge concepts, students, and exercises) and their interrelationships, and can be used to recommend suitable exercises. Extensive experiments on three real-world datasets and expert interviews demonstrate the superiority of KG4Ex over existing baseline methods and highlight its strong explainability.

The vision community is undergoing the unprecedented progress with the emergence of Vision-Language Pretraining Models (VLMs). Prompt learning plays as the holy grail of accessing VLMs since it enables their fast adaptation to downstream tasks with limited resources. Whereas existing researches milling around single-prompt paradigms, rarely investigate the technical potential behind their multi-prompt learning counterparts. This paper aims to provide a principled retrospect for vision-language multi-prompt learning. We extend the recent constant modality gap phenomenon to learnable prompts and then, justify the superiority of vision-language transfer with multi-prompt augmentation, empirically and theoretically. In terms of this observation, we propose an Energy-based Multi-prompt Learning (EMPL) to generate multiple prompt embeddings by drawing instances from an energy-based distribution, which is implicitly defined by VLMs. So our EMPL is not only parameter-efficient but also rigorously lead to the balance between in-domain and out-of-domain open-vocabulary generalization. Comprehensive experiments have been conducted to justify our claims and the excellence of EMPL.

Knowledge tracing (KT) is a task that predicts students' future performance based on their historical learning interactions. With the rapid development of deep learning techniques, existing KT approaches follow a data-driven paradigm that uses massive problem-solving records to model students' learning processes. However, although the educational contexts contain various factors that may have an influence on student learning outcomes, existing public KT datasets mainly consist of anonymized ID-like features, which may hinder the research advances towards this field. Therefore, in this work, we present, \emph{XES3G5M}, a large-scale dataset with rich auxiliary information about questions and their associated knowledge components (KCs)\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.}. The XES3G5M dataset is collected from a real-world online math learning platform, which contains 7,652 questions, and 865 KCs with 5,549,635 interactions from 18,066 students. To the best of our knowledge, the XES3G5M dataset not only has the largest number of KCs in math domain but contains the richest contextual information including tree structured KC relations, question types, textual contents and analysis and student response timestamps. Furthermore, we build a comprehensive benchmark on 19 state-of-the-art deep learning based knowledge tracing (DLKT) models. Extensive experiments demonstrate the effectiveness of leveraging the auxiliary information in our XES3G5M with DLKT models. We hope the proposed dataset can effectively facilitate the KT research work.

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interaction sequences. With the advanced capability of capturing contextual long-term dependency, attention mechanism becomes one of the essential components in many deep learning based KT (DLKT) models. In spite of the impressive performance achieved by these attentional DLKT models, many of them are often vulnerable to run the risk of overfitting, especially on small-scale educational datasets. Therefore, in this paper, we propose sparseKT, a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. Specifically, we incorporate a k-selection module to only pick items with the highest attention scores. We propose two sparsification heuristics: (1) soft-thresholding sparse attention and (2) top-K sparse attention. We show that our sparseKT is able to help attentional KT models get rid of irrelevant student interactions and improve the predictive performance when compared to 11 state-of-the-art KT models on three publicly available real-world educational datasets. To encourage reproducible research, we make our data and code publicly available at https://github.com/pykt-team/pykt-toolkit1..

In order to improve the security of IoT information systems, this paper proposes the Blockchain-based Framework for Securing IoT Information (BFSII), which is built on consortium blockchain and the edge IoT architectures. This paper addresses data security in smart hotels as a research scenario. The majority of data generated by IoT devices in smart hotels contains users' private information, which is susceptible to alteration and leakage during transmission and storage. The BFSII solution leverages the decentralized nature of blockchain to enhance data traceability and tamper-proof capabilities. And it uses edge IoT architecture and consortium blockchain to improve system operational efficiency. Sensitive data generated by IoT devices are protected in BFSII. The experiment's findings show that BFSII can boost smart hotel system security while maintaining operational effectiveness. The information management system of smart hotels is provided with an inventive and secure solution by the BFSII framework.

The primary aim of cognitive diagnosis is to predict students' performance and knowledge structures by analyzing their learning behavior and answering results, thereby enabling educators can provide personalized instruction. Scholars have proposed many cognitive diagnostic models. However, most of the models do not fully extract and utilize the relevant data and parameters of cognitive diagnosis. Moreover, some models only rely on artificially designed simple functions to analyze the cognitive process of students, which cannot fully capture the complex relationship between students and the exercises. To address these limitations, this paper proposes a neural cognitive diagnostic model named NCDFSA, which focuses on students' attention to knowledge concepts. The model utilizes neural networks to diagnose students' knowledge and considers students' implicit relationships with knowledge concepts. We introduce the concept of attention matrix(AM) and define the importance of knowledge concepts by the frequency of usage of knowledge concepts to improve the prediction effect. This paper compares NCDFSA with existing classical models on four real datasets and finds that the model has higher accuracy and rationality in predicting student performance.

Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge.

Knowledge tracing (KT) is the problem of predicting students’ future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components1 (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed AT-DKT to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., question tagging (QT) prediction task and individualized prior knowledge (IK) prediction task. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students’ global historical performance by progressively predicting student-level prior knowledge that is hidden in students’ historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that AT-DKT outperforms all sequential models with more than 0.9% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of AT-DKT. To encourage reproducible research, we make our data and code publicly available at https://github.com/pykt-team/pykt-toolkit 2.

The Chinese radiology report summarization is a crucial component in smart healthcare that employs language models to summarize key findings in radiology reports and communicate these findings to physicians. However, most language models for radiology report summarization utilize a softmax transformation in their output layer, leading to dense alignments and strictly positive output probabilities. This density is inefficient, reducing model interpretability and giving probability mass to many unrealistic outputs. To tackle this issue, we propose a novel approach named nucleusmax. Nucleusmax is able to mitigate dense outputs and improve model interpretability by truncating the unreliable tail of the probability distribution. In addition, we incorporate nucleusmax with a copy mechanism, a useful technique to avoid professional errors in the generated diagnostic opinions. To further promote the research of radiology report summarization, we also have created a Chinese radiology report summarization dataset, which is freely available. Experimental results showed via both automatic and human evaluation that the proposed approach substantially improves the sparsity and overall quality of outputs over competitive softmax models, producing radiology summaries that approach the quality of those authored by physicians. In general, our work demonstrates the feasibility and prospect of the language model to the domain of radiology and smart healthcare.

Forward secure searchable encryption (FSSE) scheme allows one data user to search on encrypted databases while resisting the file injection attack. The data utilization can be further improved by extending the single-user scenario to the multi-user scenario. However, there are some issues needed to be considered when a data owner shares data with multiple data users. First, the public cloud server can not be completely trusted as it may be dishonest returning incorrect or incomplete results. Second, authorized users may trade their private keys for financial benefit. To our knowledge, state-of-the-art searchable encryption schemes only consider part of the following desirable features: the verifiability of results, the resistance to file injection attacks, the traceability and revocation of malicious users who abuse their private keys in the multi-user setting. Based on these motivations, we first propose enabling traceable and verifiable multi-user FSSE, which achieves the above functionalities. Besides, we carry out the security proof which demonstrates that our scheme can meet the requirements of security. We also assess the performance from theoretical analysis and experimental analysis, which shows that compared with other similar schemes, our scheme has richer functionalities with comparable efficiency.

Ateniese et al. introduced the primitive of matchmaking encryption (ME) at CRYPTO 2019 and left open several important questions, which include extending ME to fuzzy cases or giving an efficient ME in the identity-based setting without relying on random oracles. The main challenge is to achieve fuzzy bilateral access control while providing identity privacy of the sender and receiver, message confidentiality and authenticity without random oracles. In this work, we resolve the question by formalizing the first fuzzy identity-based ME (IB-ME) and presenting a concrete construction. Specifically, we propose the formal syntax definition of fuzzy IB-ME. In fuzzy IB-ME, the identities of senders and receivers are characterized by attribute sets. A ciphertext can be correctly decrypted if the overlaps between the attribute set of the sender or receiver and the attribute set specified by the other party are simultaneously greater than a threshold, which can be applied to many attractive applications such as fuzzy bilateral access control in online social dating. Then, we present concrete details of fuzzy IB-ME based on fuzzy identity-based encryption, which does not rely on other cryptographic tools such as two-input functional encryption and non-interactive zero-knowledge proof systems. In this process, fuzzy bilateral access control and identity privacy are achieved through the formalism of arranged ME and the splitting technique while message authenticity is provided through the authentication and binding of the encryption key. The identity privacy of the sender and receiver, confidentiality, and authenticity of messages are reduced to the decisional bilinear Diffie-Hellman, decision linear, and computational bilinear Diffie-Hellman assumptions in the selective model without random oracles. Finally, we implement the scheme and evaluate its performance through theoretical analyses and experiments to demonstrate its efficiency.

Deep neural networks (DNNs) are easily fooled by adversarial examples. Most existing defense strategies defend against adversarial examples based on full information of whole images. In reality, one possible reason as to why humans are not sensitive to adversarial perturbations is that the human visual mechanism often concentrates on most important regions of images. A deep attention mechanism has been applied in many computer fields and has achieved great success. Attention modules are composed of an attention branch and a trunk branch. The encoder/decoder architecture in the attention branch has potential of compressing adversarial perturbations. In this article, we theoretically prove that attention modules can compress adversarial perturbations by destroying potential linear characteristics of DNNs. Considering the distribution characteristics of adversarial perturbations in different frequency bands, we design and compare three types of attention modules based on frequency decomposition and reorganization to defend against adversarial examples. Moreover, we find that our designed attention modules can obtain high classification accuracies on clean images by locating attention regions more accurately. Experimental results on the CIFAR and ImageNet dataset demonstrate that frequency reorganization in attention modules can not only achieve good robustness to adversarial perturbations, but also obtain comparable, even higher classification, accuracies on clean images. Moreover, our proposed attention modules can be integrated with existing defense strategies as components to further improve adversarial robustness.

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location data contains the sensitive information, privacy protection becomes an essential requirement for these services. Although some related works have been proposed to provide privacy protection, the privacy of data and queries, accuracy of query results, and multiple features of location data are not considered by them simultaneously. In this paper, we propose a privacy-preserving optimal location query scheme PPOLQ that supports multiple-condition filter and queries over multiple data providers in outsourced environments. Specifically, we first design a secure division protocol and a secure inner product protocol based on the Paillier algorithm and the random masking technique, respectively. After that, based on the proposed algorithms, the additive homomorphic encryption, and the secure two-party computation techniques, we develop a privacy-preserving optimal location query scheme. Finally, we analyze the security of our proposed algorithms and scheme in the semi-honest model. Meanwhile, we implement all algorithms and the proposed scheme, and our implementation is open source at Gitee. We also evaluate their performances using synthetic datasets, and extensive experiments show that our scheme is practical for the real-world applications.

Online dialogic instructions are a set of pedagogical instructions used in real-world online educational contexts to motivate students, help understand learning materials, and build effective study habits. In spite of the popularity and advantages of online learning, the education technology and educational data mining communities still suffer from the lack of large-scale, high-quality, and well-annotated teaching instruction datasets to study computational approaches to automatically detect online dialogic instructions and further improve the online teaching effectiveness. Therefore, in this paper, we present a dataset of online dialogic instruction detection, DialogID, which contains 30,431 effective dialogic instructions. These teaching instructions are well annotated into 8 categories. Furthermore, we utilize the prevalent pre-trained language models (PLMs) and propose a simple yet effective adversarial training learning paradigm to improve the quality and generalization of dialogic instruction detection. Extensive experiments demonstrate that our approach outperforms a wide range of baseline methods. The data and our code are available for research purposes from: https://github.com/ai4ed/DialogID.

The Knowledge Tracing (KT) task aims to trace the changes of students' knowledge state in real time according to students' historical learning behavior, and predict students' future learning performance. The modern KT models have two problems. One is that these KT models can't reflect students' actual knowledge level. Most KT models only judge students' knowledge state based on their performance in exercises, and poor performance will lead to a decline in knowledge state. However, the essence of students' learning process is the process of acquiring knowledge, which is also a manifestation of learning behavior. Even if they answer the exercises incorrectly, they will still gain knowledge. The other problem is that many KT models don't pay enough attention to the impact of students' forgetting behavior on the knowledge state in the learning process. In fact, learning and forgetting behavior run through students' learning process, and their effects on students' knowledge state shouldn't be ignored. In this paper, based on educational psychology theory, we propose a knowledge tracing model with learning and forgetting behavior (LFBKT). LFBKT comprehensively considers the factors that affect learning and forgetting behavior to build the knowledge acquisition layer, knowledge absorption layer and knowledge forgetting layer. In addition, LFBKT introduces difficulty information to enrich the information of the exercise itself, while taking into account other answering performances besides the answer. Experimental results on two public datasets show that LFBKT can better trace students' knowledge state and outperforms existing models in terms of ACC and AUC.

Cognitive diagnosis is a crucial task in the field of educational measurement and psychology, which aims to diagnose the strengths and weaknesses of participants. Existing cognitive diagnosis methods only consider which of knowledge concepts are involved in the knowledge components of exercises, but ignore the fact that different knowledge concepts have different effects on practice scores in actual learning situations. Therefore, researchers need to reshape the learning scene by combining the multi-factor relationships between knowledge components. In this paper, in order to more comprehensively simulate the interaction between students and exercises, we developed a neural network-based CDMFKC model for cognitive diagnosis. Our method not only captures the nonlinear interaction between exercise characteristics, student performance, and their mastery of each knowledge concept, but also further considers the impact of knowledge concepts by designing the difficulty and discrimination of knowledge concepts, and uses multiple neural layers to model their interaction so as to obtain accurate and interpretable diagnostic results. In addition, we propose an improved CDMFKC model with guessing parameter and slipping parameter designed by knowledge concept proficiency and student proficiency vectors. We validate the performance of these two diagnostic models on six real datasets. The experimental results show that the two models have better effects in the aspects of accuracy, rationality and interpretability.

Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. First, data preprocessing procedures in existing works are often private and custom, which limits experimental standardization. Furthermore, existing DLKT studies often differ in terms of the evaluation protocol and are far away real-world educational contexts. To address these problems, we introduce a comprehensive python based benchmark platform, \textsc{pyKT}, to guarantee valid comparisons across DLKT methods via thorough evaluations. The \textsc{pyKT} library consists of a standardized set of integrated data preprocessing procedures on 7 popular datasets across different domains, and 10 frequently compared DLKT model implementations for transparent experiments. Results from our fine-grained and rigorous empirical KT studies yield a set of observations and suggestions for effective DLKT, e.g., wrong evaluation setting may cause label leakage that generally leads to performance inflation; and the improvement of many DLKT approaches is minimal compared to the very first DLKT model proposed by Piech et al. \cite{piech2015deep}. We have open sourced \textsc{pyKT} and our experimental results at \url{https://pykt.org/}. We welcome contributions from other research groups and practitioners.

Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the training dataset. Efforts have been made to hinder the attack but unfortunately, they may lead to a major overhead or impaired usability. In this article, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense aginist membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another relate and similar dataset, and derives a model that underlyingly extracts the features from both datasets. Seeing that the model is obfuscated, membership inference fails, while the extracted features provide supports for usability. Extensive experiments have been conducted to validates our intuition. The model trained by DAMIA has a negligible footprint to the usability and introduces slight overhead compared with other defenses. Our experiment also excludes factors that may hinder the performance of DAMIA, and comparisons with other defenses, providing a potential guideline to vendors and researchers to benefit from our solution in a timely manner.

With the growing popularity of the Internet-of-Things (IoT), a massive amount of purpose-specific, heterogeneous, inexpensive devices have been deployed. To allow these devices to perform their duties and collaborate efficiently, designing a secure and dependable communication channel is necessary. Pairing, as the fundamental procedure for establishing a trustworthy communication channel, has received extensive attention from security researchers. Previous secure pairing protocols depend on auxiliary peripherals (e.g., displays, speakers) to share the secret message, while for those products featuring with low-price, manufacturers would probably adopt insecure pairing methods to reduce the cost, so the devices may be subject to various attacks. To mitigate such a situation, we design a peripheral-free secure pairing protocol, termed SwitchPairing. Our protocol only requires users to connect the pre-pairing devices to the same power source, then randomly presses and releases the switch to generate a shared secret. It does not require additional peripherals and can defense eavesdropping and replay attacks innately. We implement a prototype via two CC2640R2F development boards and invite volunteers to participate in the experiments about bench-marking security and usability. The result of our experiments show that our protocol can fulfill the security and efficient requirement of various IoT applications.

Deep learning can achieve higher accuracy than traditional machine learning algorithms in a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn tremendous attention from information security community, in which neither training data nor the training model is expected to be exposed. Federated learning is a popular learning mechanism, where multiple parties upload local gradients to a server and the server updates model parameters with the collected gradients. However, there are many security problems neglected in federated learning, for example, the participants may behave incorrectly in gradient collecting or parameter updating, and the server may be malicious as well. In this article, we present a distributed, secure, and fair deep learning framework named DeepChain to solve these problems. DeepChain provides a value-driven incentive mechanism based on Blockchain to force the participants to behave correctly. Meanwhile, DeepChain guarantees data privacy for each participant and provides auditability for the whole training process. We implement a prototype of DeepChain and conduct experiments on a real dataset for different settings, and the results show that our DeepChain is promising.

Industrial automation and control systems (IACS) are tremendously employing supervisory control and data acquisition (SCADA) network. However, their integration into IACS is vulnerable to various cyber-attacks. In this article, we first present population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect the cyber-attacks of SCADA-based IACS. In PEO-DBN method, PEO algorithm is employed to determine the DBN's parameters, including number of hidden units and the size of mini-batch and learning rate, as there is no clear knowledge to set these parameters. Then, to enhance the performance of single method for cyber-attacks detection, the ensemble learning scheme is introduced for aggregation of the proposed PEO-DBN method, called EnPEO-DBN. The proposed detection methods are evaluated on gas pipeline system dataset and water storage tank system dataset from SCADA network traffic by comparing with some existing methods. Through performance analysis, simulation results show the superiority of PEO-DBN and EnPEO-DBN.

Gait recognition aims to recognize persons' identities by walking styles. Gait recognition has unique advantages due to its characteristics of non-contact and long-distance compared with face and fingerprint recognition. Cross-view gait recognition is a challenge task because view variance may produce large impact on gait silhouettes. The development of deep learning has promoted cross-view gait recognition performances to a higher level. However, performances of existing deep learning-based cross-view gait recognition methods are limited by lack of gait samples under different views. In this paper, we take a Multi-view Gait Generative Adversarial Network (MvGGAN) to generate fake gait samples to extend existing gait datasets, which provides adequate gait samples for deep learning-based cross-view gait recognition methods. The proposed MvGGAN method trains a single generator for all view pairs involved in single or multiple datasets. Moreover, we perform domain alignment based on projected maximum mean discrepancy to reduce the influence of distribution divergence caused by sample generation. The experimental results on CASIA-B and OUMVLP dataset demonstrate that fake gait samples generated by the proposed MvGGAN method can improve performances of existing state-of-the-art cross-view gait recognition methods obviously on both single-dataset and cross-dataset evaluation settings.

For a linear locally recoverable (LRC) code with length n, dimension k and locality r, its minimum distance d satisfies d ≤ n-k+2-⌈k/r⌉. A code attaining this bound is called optimal. Many families of optimal locally recoverable codes have been constructed by using different techniques in finite fields or algebraic curves. However only optimal LRC codes with lengths n >> q and minimum distances restricted to few constants smaller than 9, have been given in previous constructions. No optimal LRC code over a general finite field Fqwith the length n ~ q2and the minimum distance d ≥ 9 has been constructed. In this article we present a general construction of optimal LRC codes over arbitrary finite fields. Over any given finite field Fq, for any given r ∈ {1,2,...,q-1} and given d satisfying 3 ≤ d ≤ min{r+1,q+1-r}, we construct explicitly an optimal LRC code with length n=q(r+1), locality r and minimum distance d. We also give an asymptotic bound of q-ary LRC codes with locality r (r ≤ q-1), which is better than some known previous asymptotic bounds in some parameter range. Moreover many long LRC codes with locality r (r ≤ q-1) and small defect s=n-k+2-⌈k/r⌉-d are also constructed from algebraic curves with many rational points.

Generalized spatial modulation (GSM), which is a novel multiple-input multiple-output (MIMO) transmission technique, has attracted massive research attention in recent years. In this paper, we first utilize the orthogonal least squares (OLS) based detector for GSM detection. Then, we develop a sufficient condition of successful detection for the OLS based detector based on the restricted isometry property (RIP) of the channel matrix. Moreover, we prove that our sufficient condition is optimal. Finally, numerical simulations are conducted to illustrate that the proposed OLS based detector has better detection performance than the orthogonal matching pursuit (OMP) based detector with more or less the same time complexity.

Sample balancing includes sample selection and sample reweighting. Sample selection aims to remove some bad samples that may lead to bad local optima. Sample reweighting aims to assign optimal weights to samples to improve performance. In this article, we integrate a sample selection method based on self-paced learning into deep learning frameworks and study the influence of different sample selection strategies on training deep networks. In addition, most of the existing sample reweighting methods mainly take per-class sample number as a metric, which does not fully consider sample qualities. To improve the performance, we propose a novel metric based on the multiview semantic encoders to reweight the samples more appropriately. Then, we propose an optimization mechanism to embed sample weights into loss functions of deep networks, which can be trained in end-to-end manners. We conduct experiments on the CIFAR data set and the ImageNet data set. The experimental results demonstrate that our proposed sample balancing method can improve the performances of deep learning methods in several visual recognition tasks.

The lattice L(A) of a full-column rank matrix A ∈ Rm×nis defined as the set of all the integer linear combinations of the column vectors of A. The successive minima λi(A), 1 ≤ i ≤ n, of lattice L(A) are important quantities since they have close relationships with the following problems: shortest vector problem, shortest independent vector problem, and successive minima problem. These problems arise from many practical applications, such as communications and cryptography. This paper first investigates some properties of λi(A). Specifically, we develop lower and upper bounds on λi(A), where A are, respectively, the Cholesky factor of G1+ G2and (G1+ G2)-1for two given symmetric positive definitive matrices G1and G2. The bounds are, respectively, expressed as the successive minima of L(A1) and L(A2), and L(A1) and L(A2), where A1, A2, A1and A2are, respectively, the Cholesky factors of G1, G2, G1-1, and G2-1. Then, we show how some properties of λi(A) are used to design a suboptimal integer-forcing strategy for cloud radio access network. Our approach provides much higher time efficiency while keeping the same achievable rate as the algorithm reported by Bakoury and Nazer (I. E. Bakoury and B. Nazer, “Integer-forcing architectures for uplink cloud radio access networks,” in Proc. 55th Annu. Allerton Conf. Commun. Control Comput., Oct. 2007, pp. 67-75). Simulation tests are performed to illustrate our main results.

In digital signature, strong unforgeability requires that an attacker cannot forge a new signature on any previously signed/new messages, which is attractive in both theory and practice. Recently, a strongly unforgeable certificateless signature (CLS) scheme without random oracles was presented. In this paper, we firstly show that the scheme fails to achieve strong unforgeability by forging a new signature on a previously signed message under its adversarial model. Then, we point out that the scheme is also vulnerable to the malicious-but-passive key generation center (MKGC) attacks. Finally, we propose an improved strongly unforgeable CLS scheme in the standard model. The improved scheme not only meets the requirement of strong unforgeability but also withstands the MKGC attacks. To the best of our knowledge, we are the first to prove a CLS scheme to be strongly unforgeable against the MKGC attacks without using random oracles.