In recent years, the adoption of cloud services has been expanding at an unprecedented rate. As more and more organizations migrate or deploy their businesses to the cloud, a multitude of related cybersecurity incidents such as data breaches are on the rise. Many inherent attributes of cloud environments, for example, data sharing, remote access, dynamicity and scalability, pose significant challenges for the protection of cloud security. Even worse, cyber threats are becoming increasingly sophisticated and covert. Attack methods, such as Advanced Persistent Threats (APTs), are continually developed to bypass traditional security measures. Among the emerging technologies for robust threat detection, system provenance analysis is being considered as a promising mechanism, thus attracting widespread attention in the field of incident response. This paper proposes a new few-shot learning-based attack detection with improved data context intelligence. We collect operating system behavior data of cloud systems during realistic attacks and leverage an innovative semiotics extraction method to describe system events. Inspired by the advances in semantic analysis, which is a fruitful area focused on understanding natural languages in computational linguistics, we further convert the anomaly detection problem into a similarity comparison problem. Comprehensive experiments show that the proposed approach is able to generalize over unseen attacks and make accurate predictions, even if the incident detection models are trained with very limited samples.

In recent years, the adoption of cloud services has been expanding at an unprecedented rate. As more organizations migrate or deploy their businesses to the cloud, a multitude of related cybersecurity incidents, such as data breaches, are on the rise. Several inherent attributes of cloud environments, including data sharing, remote access, dynamic scalability, and scalability, pose significant challenges for the protection of cloud security. Even more concerning is the growing threat of Advanced Persistent Threats (APTs), which have become increasingly sophisticated and stealthy. As a more complex form of multi-stage attacks (MSAs), APTs follow a multi-step process that spreads malicious actions across different stages and blends them with legitimate operations, making intrusion detection particularly challenging. In this paper, we conduct an empirical study on the multi-stage characteristics of APT attacks specifically within cloud environments. Drawing from real-world attack scenarios, we analyze the behavioral patterns of APT attacks, evaluate the existing countermeasures, and identify ongoing challenges in defending against them. Our findings expose the limitations of conventional intrusion detection approaches and highlight the critical need for fine-grained and behavior-aware security mechanisms specifically designed for cloud environments. This study provides a deeper understanding of how APTs adapt their tactics to exploit cloud-specific vulnerabilities and offers insights for improving threat detection and response in modern cloud infrastructures.

Brain tumors, characterized by the uncontrolled growth of cells within the brain, pose a formidable challenge in medical diagnostics due to their complex nature and the critical need for precise intervention. Magnetic Resonance Imaging (MRI) plays a pivotal role in the early detection and classification of brain tumors, offering detailed insights into tumor types without invasive procedures. However, the manual interpretation of MRI scans is labor-intensive, subject to human error, and heavily dependent on the expertise of radiologists. These challenges underscore the urgent need for advanced computational tools that enhance diagnostic accuracy and efficiency. This paper introduces XcepKNN, a novel architecture that integrates a K-Nearest Neighbor (KNN) classifier within the Xception deep learning framework, specifically designed to improve the classification of brain tumor MRI images. The hybrid model leverages the depthwise separable convolutions of Xception to extract detailed features from MRI data, while the embedded KNN classifier utilizes these features to accurately identify and classify various brain tumor types. The fusion of these techniques facilitates a more nuanced analysis of MRI images, enhancing the model’s ability to distinguish between tumor categories with high precision. Our extensive validation on a dataset of 7,023 MRI images demonstrates that XcepKNN significantly outperforms traditional models in terms of accuracy, precision, recall, and F1 scores. By providing an open-source implementation, this study contributes to the field of medical image analysis, offering a reliable tool for researchers and clinicians alike to improve the diagnostic processes for brain tumors.

Employee turnover in the restaurant industry poses significant operational and financial challenges, primarily due to the high costs associated with hiring and training new staff, as well as the negative impact on team cohesion and customer service. This paper employs a comprehensive datadriven approach using machine learning (ML) and deep learning (DL) techniques to analyze employee turnover, aiming to identify and understand the key predictors and underlying patterns. Utilizing a dataset of 778 responses from restaurant employees, the study explores various factors, including job satisfaction, income levels, and perceptions of management. Through rigorous data preprocessing and the application of advanced analytical models such as Decision Trees, Random Forests, and Deep Neural Networks, the study enhances the predictive accuracy and reliability of turnover predictions. The results not only highlight the effectiveness of specific models but also shed light on significant turnover predictors. This research contributes to the development of targeted strategies for reducing turnover rates, ultimately aiding in improving both operational efficiency and employee retention in the restaurant industry.

Payment channel networks (PCNs) have emerged as a viable solution to the scalability problem of blockchain systems. In PCNs, two peers can open a payment channel to transfer funds without publishing every transaction to a global blockchain. Payments can be routed between two unchanneled peers via a payment path, i.e., a sequence of adjacent payment channels. The routing protocol is the core of a PCN, since it regulates path discovery for transaction senders and receivers. Lots of routing algorithms have been proposed, most of which, however, lack either utility, i.e., a low payment success rate, or privacy, i.e., leaking channel balances. In this work, we first identify the utility-privacy tradeoff in existing routing algorithms. Aiming to trade privacy for utility, we propose a noised PCN by introducing a probabilistic model on the publicly-revealed channel balances. This enables us to express the reliability of a successful transaction for a given channel/path, Channel owners are allowed to select their own noise mechanisms, which directly link to routing fees they will charge. We further apply utility theory to design routing algorithms which achieve a balance between utility and privacy. Extensive simulations using a Lightning Network (LN) simulator called CLoTH have been conducted to validate the improvement in the payment success rate of the proposed architecture under different settings.

In this study, we explore the role of contextual information in enhancing ChatGPT's capabilities in bug fixing. Our focus is specifically on the “Wrong Answer” problem, where a program executes without error but fails to produce the correct output. Our approach draws inspiration from human debugging practices, which heavily rely on understanding both the intended task of the program and the specific scenarios in which it fails, such as unit test cases. We evaluate ChatGPT's performance with various types and levels of contextual data. The results reveal three key insights. First, providing the model with a mix of correct and incorrect test cases sharpens its debugging skills. Second, giving ChatGPT detailed descriptions of the problems substantially enhances its ability to identify and resolve errors. Third, merging detailed problem descriptions with various test cases leads to a synergistic outcome. This combined approach significantly elevates the efficiency of the bug-fixing process compared to employing each type of contextual information individually. Our paper presents a thorough analysis based on these findings. It offers an extensive exploration of why and how contextual information can be strategically utilized to enhance ChatGPT's debugging effectiveness. Furthermore, this investigation enriches our comprehension of the underlying mechanisms by which contextual cues amplify the model's capacity for solving problems.

In this study, we present a novel attention-based encoder-decoder model for monocular depth estimation, show-casing exceptional robustness and accuracy across standard and noise-injected variants of the KITTI dataset. By inte-grating Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) blocks, our approach significantly outperforms existing state-of-the-art methods, especially in en-vironments affected by real-world noise such as changes in brightness, saturation, and RGB channels. The model's superior performance, validated through rigorous testing, marks a signif-icant step forward in the field, offering promising applications in autonomous driving, augmented reality, and beyond. Our work demonstrates the potential of attention mechanisms in enhancing depth estimation models to reliably interpret complex scenes, paving the way for advancements in depth-dependent technologies operating in dynamic and challenging conditions.

Convolutional neural networks (CNNs) have gained increasing popularity and versatility in recent decades, finding applications in diverse domains. These remarkable achievements are greatly attributed to the support of extensive datasets with precise labels. However, annotating image datasets is intricate and complex, particularly in the case of multi-label datasets. Hence, the concept of partial-label setting has been proposed to reduce annotation costs, and numerous corresponding solutions have been introduced. The evaluation methods for these existing solutions have been primarily based on accuracy. That is, their performance is assessed by their predictive accuracy on the test set. However, we insist that such an evaluation is insufficient and one-sided. On one hand, since the quality of the test set has not been evaluated, the assessment results are unreliable. On the other hand, the partial-label problem may also be raised by undergoing adversarial attacks. Therefore, incorporating robustness into the evaluation system is crucial. For this purpose, we first propose two attack models to generate multiple partial-label datasets with varying degrees of label missing rates. Subsequently, we introduce a lightweight partial-label solution using pseudo-labeling techniques and a designed loss function. Then, we employ D-Score to analyze both the proposed and existing methods to determine whether they can enhance robustness while improving accuracy. Extensive experimental results demonstrate that while certain methods may improve accuracy, the enhancement in robustness is not significant, and in some cases, it even diminishes.

Along with the increasing popularity of smart home IoT devices, more users are turning to smart home automation platforms to control and automate their IoT devices. However, IoT automation is vulnerable to spoofed event attacks. Given that IoT devices are intricately linked with the physical environment and operate autonomously, event-based attacks can pose serious safety and security challenges. Our observations show that many IoT events are accompanied by visual modifications in objects such as shape alterations (for example, contact sensor events correspond with door movement) or changes in color/brightness (for example, a functioning microwave oven with the internal light switched on). These alterations can be detected by the commonly deployed smart cameras, providing a visually rich but challenging to manipulate channel for verifying IoT events. We introduce IoTSentry, the first system of its kind to extract high-level semantic information from streaming video data and pixels for IoT event verification. We have designed a Siamese deep neural network to identify variations in the appearance of IoT devices and interior objects. These are used as the yardstick for verifying IoT events received at IoT automation platforms. Upon assessing IoTSentry with 21 IoT devices (8 types), the results demonstrate that IoTSentry can be trained within 120 seconds, yielding an accuracy rate of over 96.7% in recognizing device states. We have deployed the 21 IoT devices and IoTSentry on two real-world smart home test sites. Over the course of our one-week evaluation, IoTSentry consistently achieved an average detection rate of 99.24% in identifying attack instances. Moreover, it triggered no more than 2 false alarms per day on each test site.

Payment Channel Networks (PCNs) have been introduced as a viable solution to the scalability problem of the popular blockchain. In PCNs, a payment channel allows its end nodes to pay each other without publishing every transaction to the blockchain. A transaction can be routed in the network if there is a path of channels with sufficient funds, and the intermediate routing nodes can ask the transaction sender for a compensatory fee. However, a channel may eventually become depleted and cannot support further payments in a certain direction, as transaction flows from that direction is heavier than flows from the other direction. In this paper, we discuss a PCN node’s possible roles and objectives, and analyze the strategies nodes should take under different roles by considering nodes’ benefits and the network’s performance. Then, we examine two basic network structures (ring and chord) and determine the constraints under which they constitute a Nash equilibrium. Based on the theoretical results, we propose a balance-aware fee-incentivized routing algorithm to guarantee cost-efficient routing, fair fee charging, and the network’s long lasting good performance in general PCNs. Testbed-based evaluation is conducted to validate our theoretical results and to show the feasibility of our proposed approach.

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that can be used for provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. Also, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. We demonstrate the details of the dataset events and dependency analysis.

Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In theory, this is a supervised learning method that requires a large amount of labeling. However, annotating data is time-consuming and may be infeasible for huge labeling spaces. In addition, label imbalance can limit the performance of multi-label classifiers, especially when some labels are missing. Therefore, it is meaningful to study how to train neural networks using partial labels. In this work, we address the issue of label imbalance and investigate how to train classifiers using partial labels in large labeling spaces. First, we introduce the pseudo-labeling technique, which allows commonly adopted networks to be applied in partially labeled settings without the need for additional complex structures. Then, we propose a novel loss function that leverages statistical information from existing datasets to effectively alleviate the label imbalance problem. In addition, we design a dynamic training scheme to reduce the dimension of the labeling space and further mitigate the imbalance. Finally, we conduct extensive experiments on some publicly available multi-label datasets such as COCO, NUS-WIDE, CUB, and Open Images to demonstrate the effectiveness of the proposed approach. The results show that our approach outperforms several state-of-the-art methods, and surprisingly, in some partial labeling settings, our approach even exceeds the methods trained with full labels.

Programming courses in colleges often involve a myriad of coding assignments, which brings heavy grading workloads for instructors. To alleviate this problem, automatic programming evaluation tools are becoming more of a requirement than an option. However, after considering the actual requirements in our teaching practice, we have noticed that the current solutions still suffer from shortcomings and limitations. In the process of addressing the challenges, we propose and implement a brand new code assessment application based on PowerShell, which shows both extendibility and configurability. In particular, we integrate both black-box testing and the lexical analysis into the system, thus achieving a customized solution to meet specific requirements. This paper presents the architecture and design of our automatic code assessment application. Furthermore, we conduct empirical evaluations on the proposed system following the Technology Acceptance Model, and also investigate the drawbacks of manual assessment of coding assignments in terms of reliability and fairness. Finally, the evaluations demonstrate the effectiveness of our proposed auto-grader in facilitating the code assessment targeting college-level programming courses.

As open source software is widely used, the vulnerabilities contained therein are also rapidly propagated to a large number of innocent applications. Even worse, many vulnerabilities in open-source projects are secretly fixed, which leads to affected software being unaware and thus exposed to risks. For the purpose of protecting deployed software, designing an effective patch classification system becomes more of a need than an option. To this end, some researchers take advantage of the recent advancements in natural language processing to learn both commit messages and code changes. However, they often incur high false positive rates. Not only that, existing works cannot yet answer how much the textual description (such as commit messages) alone can influence the final triage. In this paper, we propose a Transformer based patch classifier, which does not use any code changes as inputs. Surprisingly, the extensive experiment shows the proposed approach can significantly outperform other state-of-the-art work with a high precision of 93.0% and low false positive rate. Therefore, our research further confirms the critical importance of well-crafted commit messages for the later software maintenance. Finally, our case study also identifies 48 silent security patches, which can benefit those affected software.

Existing symbolic execution typically assumes the analyzer can control the I/O environment and/or access the library code, which, however, is not the case when programs run on a remote proprietary execution environment managed by another party. For example, SmartThings, one of the most popular IoT platforms, is such a cloud-based execution environment. For programmers who write automation applications to be deployed on IoT cloud platforms, it raises significant challenges when they want to systematically test their code and find bugs. We propose fuzzing-assisted remote dynamic symbolic execution, which uses dynamic symbolic execution as backbone and utilizes fuzzing when necessary to automatically test programs running in a remote proprietary execution environment over which the analyzer has little control. As a case study, we enable it for analyzing smart apps running on SmartThings. We have developed a prototype and the evaluation shows that it is effective in testing smart apps and finding bugs.

By adding carefully crafted perturbations to input images, adversarial examples (AEs) can be generated to mislead neural-network-based image classifiers. L2 adversarial perturbations by Carlini and Wagner (CW) are among the most effective but difficult-to-detect attacks. While many countermeasures against AEs have been proposed, detection of adaptive CW-L2 AEs is still an open question. We find that, by randomly erasing some pixels in an L2 AE and then restoring it with an inpainting technique, the AE, before and after the steps, tends to have different classification results, while a benign sample does not show this symptom. We thus propose a novel AE detection technique, Erase-and-Restore (E&R), that exploits the intriguing sensitivity of L2 attacks. Experiments conducted on two popular image datasets, CIFAR-10 and ImageNet, show that the proposed technique is able to detect over 98% of L2 AEs and has a very low false positive rate on benign images. The detection technique exhibits high transferability: a detection system trained using CW-L2 AEs can accurately detect AEs generated using another L2 attack method. More importantly, our approach demonstrates strong resilience to adaptive L2 attacks, filling a critical gap in AE detection. Finally, we interpret the detection technique through both visualization and quantification.

Internet of Things (IoT) are densely deployed in smart environments, such as homes, factories and laboratories, where many people have physical access to IoT devices. How to authenticate users operating on these devices is thus an important problem. IoT devices usually lack conventional user interfaces, such as keyboards and mice, which makes traditional authentication methods inapplicable. We present a virtual sensing technique that allows IoT devices to virtually sense user 'petting' (in the form of some very simple touches for about 2 seconds) on the devices. Based on this technique, we build a secure and intuitive authentication method that authenticates device users by comparing the petting operations sensed by devices and those captured by the user wristband. The authentication method is highly secure as physical operations are required, rather than based on proximity. It is also intuitive, adopting very simple authentication operations, e.g., clicking buttons, twisting rotary knobs, and swiping touchscreens. Unlike the state-of-the-art methods, our method does not require any hardware modifications of devices, and thus can be applied to commercial off-the-shelf (COTS) devices. We build prototypes and evaluate them comprehensively, demonstrating their high effectiveness, security, usability, and efficiency.

The k -regret query aims to return a size- k subset S of a database D such that, for any query user that selects a data object from this size- k subset S rather than from database D , her regret ratio is minimized. The regret ratio here is modeled by the relative difference in the optimality between the locally optimal object in S and the globally optimal object in D . The optimality of a data object in turn is modeled by a utility function of the query user. Unlike traditional top- k queries, the k -regret query does not minimize the regret ratio for a specific utility function. Instead, it considers a family of infinite utility functions F , and aims to find a size- k subset that minimizes the maximum regret ratio of any utility function in F . Studies on k -regret queries have focused on the family of additive utility functions, which have limitations in modeling individuals’ preferences and decision-making processes, especially for a common observation called the diminishing marginal rate of substitution (DMRS). We introduce k -regret queries with multiplicative utility functions, which are more expressive in modeling the DMRS, to overcome those limitations. We propose a query algorithm with bounded regret ratios. To showcase the applicability of the algorithm, we apply it to a special family of multiplicative utility functions, the Cobb-Douglas family of utility functions, and a closely related family of utility functions, the Constant Elasticity of Substitution family of utility functions, both of which are frequently used utility functions in microeconomics. After a further study of the query properties, we propose a heuristic algorithm that produces even smaller regret ratios in practice. Extensive experiments on the proposed algorithms confirm that they consistently achieve small maximum regret ratios.

Ultrasound-guided medical interventions are broadly applied in diagnostics and therapy, e.g., regional anesthesia or ablation. A guided intervention using 2-D ultrasound is challenging due to the poor instrument visibility, limited field of view, and the multi-fold coordination of the medical instrument and ultrasound plane. Recent 3-D ultrasound transducers can improve the quality of the image-guided intervention if an automated detection of the needle is used. In this paper, we present a novel method for detecting medical instruments in 3-D ultrasound data that is solely based on image processing techniques and validated on various ex vivo and in vivo data sets. In the proposed procedure, the physician is placing the 3-D transducer at the desired position, and the image processing will automatically detect the best instrument view, so that the physician can entirely focus on the intervention. Our method is based on the classification of instrument voxels using volumetric structure directions and robust approximation of the primary tool axis. A novel normalization method is proposed for the shape and intensity consistency of instruments to improve the detection. Moreover, a novel 3-D Gabor wavelet transformation is introduced and optimally designed for revealing the instrument voxels in the volume, while remaining generic to several medical instruments and transducer types. Experiments on diverse data sets, including in vivo data from patients, show that for a given transducer and an instrument type, high detection accuracies are achieved with position errors smaller than the instrument diameter in the 0.5-1.5-mm range on average.

We present a cascaded framework for robust and accurate facial feature extraction. In this framework, we propose the following three model-based algorithms: (1) constrained global deformation using a sparse feature representation; (2) component texture fitting using direct parameter estimation by SVR, and (3) component feature refinement by direct optimization. The algorithms capture different characteristics of facial features, giving various extraction performances in terms of robustness (convergence) and accuracy. To achieve both high accuracy and robustness, we cascade these algorithms into a chain, where each algorithm progressively 'pulls' the model closer to the correct position. Experiments show that the combined algorithm achieves a large convergence area and high accuracy.

We propose a fast facial feature extraction technique for an embedded face recognition system. The novel key element is a combination of a statistical shape model and the application of a Haar-wavelet based feature matching. Our statistical face model is based on the active shape model (ASM). However ASM lacks robustness to illumination changes and it has a limited convergence area. Instead of a 1D profile analysis, we propose a 2D texture pattern search-and-fitting scheme, which provides more robustness and faster convergence than conventional ASM. Furthermore, we employ Haar-wavelets to model local-facial textures, which yields two improvements: faster processing and more robustness with respect to low-quality images. Our proposed approach shows good results dealing with test face images, which are quite dissimilar with the faces used for statistical training. The convergence area of our proposed method almost quadruples compared to ASM, and the extraction accuracy is also improved. The total processing requires 30 - 70 ms, which is comparable to ASM, but faster than the active appearance model (AAM).

We propose a novel multistage facial feature extraction approach using a combination of 'global' and 'local' techniques. At the first stage, we use template matching, based on an Edge-Orientation-Map for fast feature position estimation. Using this result, a statistical framework applying the Active Shape Model (ASM) is initialized and deformed to fit the real face image. In our proposal, we use a 2-D pattern search-and-fitting scheme guiding the deformation process, which provides more robustness and faster convergence than the traditional ASM. Our proposed approach for feature extraction shows good results dealing with a test set composed of faces images which are quite dissimilar with the faces used for the statistical training of the face model. The convergence area of our proposed technique almost quadruples compared to the ASM, while the amount of faces doubles for which the convergence is reached. The total processing for feature extraction takes less than 1 second for 250x250 face images on a Pentium-IV PC (1.7GHz).