Reliable verifiable data has become a key driver of capability gains in modern language models, enabling stable reinforcement learning with verifiable rewards and effective distillation that transfers competence across math, coding, and agentic tasks.Yet constructing generalizable synthetic verifiable data remains difficult due to hallucination-prone generation, and weak or trivial verification artifacts that fail to separate strong from weak solutions.Existing approaches often rely on task-specific heuristics or post-hoc filters that do not transfer across domains and lack a principled, universal evaluator of verifiability.In this work, we introduce an evolutionary, task-agnostic, strategy-guided, executably-checkable data synthesis framework that, from minimal seed supervision, jointly synthesizes problems, diverse candidate solutions, and verification artifacts, and iteratively discovers strategies via a consistencybased evaluator that enforces agreement between human-annotated and strategy-induced checks.This pipeline upgrades filtering into principled synthesis: it reliably assembles coherent, verifiable training instances and generalizes without domain-specific rules.Our experiments demonstrate the effectiveness of the proposed approach under both RLVR and model distillation training paradigms.The results show that training with our synthesized data yields significant improvements on both the LiveCodeBench and AgentBench-OS tasks, highlighting the robust generalization of our framework 1 .

We introduce a self-supervised, Multi-Task Framework that simultaneously recognises human emotion and synthesises emotion-consistent motion within a single skeleton-based networkan ability that is particularly valuable for resource-constrained Internet-of-Things (IoT) devices that must both perceive and respond in situ. A masked-motion auto-encoder with factorised space-time attention first reconstructs hidden bodypart tokens to mine fine-grained spatio-temporal cues without labels; the same encoder then feeds a lightweight classification head that predicts eight Ekman-style emotions. Evaluated on the Emilya benchmark (8206 clips, 8 daily actions × 8 emotions), our model attains 88.7% accuracy and 0.886 macro-F1, matching state-of-the-art recognition-only GCNs while also generating realistic, emotion-specific motions. Visualisations show expansive, dynamic poses for joy and contracted, slow movements for sadness; ablations reveal that the joint reconstruction loss and factorised attention contribute roughly 2 pp and markedly boost minority classes such as pride and shame. Because the classifier is ultra-compact and the encoder can be quantised or partitioned, the framework prepares itself for future deployment on low-power IoT hardware, enabling edge devices, robots, and wearables to sense and express affect in real time.

Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isolated aspects of coding, such as single-file code generation or repository issue debugging, falling short of measuring the full spectrum of challenges raised by real-world programming activities. In this case study, we explore the performance of LLMs across the entire software development lifecycle with DevEval, encompassing stages including software design, environment setup, implementation, acceptance testing, and unit testing. DevEval features four programming languages, multiple domains, high-quality data collection, and carefully designed and verified metrics for each task. Empirical studies show that current LLMs, including GPT-4, fail to solve the challenges presented within DevEval. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications.

First-order logic (FOL) reasoning, which involves sequential deduction, is pivotal for intelligent systems and serves as a valuable task for evaluating reasoning capabilities, particularly in chain-of-thought (CoT) contexts. Existing benchmarks often rely on extensive human annotation or handcrafted templates, making it difficult to achieve the necessary complexity, scalability, and diversity for robust evaluation. To address these limitations, we propose a novel framework called ProverGen that synergizes the generative strengths of Large Language Models (LLMs) with the rigor and precision of symbolic provers, enabling the creation of a scalable, diverse, and high-quality FOL reasoning dataset, ProverQA. ProverQA is also distinguished by its inclusion of accessible and logically coherent intermediate reasoning steps for each problem. Our evaluation shows that state-of-the-art LLMs struggle to solve ProverQA problems, even with CoT prompting, highlighting the dataset's challenging nature. We also finetune Llama3.1-8B-Instruct on a separate training set generated by our framework. The finetuned model demonstrates consistent improvements on both in-distribution and out-of-distribution test sets, suggesting the value of our proposed data generation framework. Code available at: \url{https://github.com/opendatalab/ProverGen}

Locking protocol is an essential component in resource management of real-time systems, which coordinates mutually exclusive accesses to shared resources from different tasks. Although the design and analysis of locking protocols have been intensively studied for sequential real-time tasks, there has been a little work on this topic for parallel real-time tasks. In this article, we study the analysis of parallel real-time tasks using spin locks to protect accesses to shared resources in three commonly used request serving orders (unordered, FIFO-order, and priority-order). A remarkable feature making our analysis method more accurate is to systematically analyze the blocking time which may delay a task's finishing time, where the impact to the total workload and the longest path length is jointly considered, rather than analyzing them separately and counting all blocking time as the workload that delays a task's finishing time, as commonly assumed in the state-of-the-art.

Locking protocol is an essential component in resource management of real-time systems, which coordinates mutually exclusive accesses to shared resources from different tasks. OpenMP is a promising framework for multi-core realtime embedded systems as well as provides spin locks to protect shared resources. In this paper, we propose a resource model for analyzing OpenMP programs with spin locks. Based on our resource model, we also develop a technique for analyzing the blocking time which impacts the total workload. Notably, the resource model provides detailed resource access behavior of the programs, making our blocking analysis more accurate. Further, we derive the schedulability analysis for real-time OpenMP tasks with spin locks protecting shared resources. Experiments with realistic OpenMP programs are conducted to evaluate the performance of our method.

User profile can be used to characterize a person and help us better understand him/her, which further can be utilized to provide enhanced personalized services. When using mobile phone, some of one's information are unavoidably and unobtrusively passed or stored, which makes it possible to draw the user profile. In this paper, we propose to infer user profile, including age, gender, and personality traits based on mobile phone sensory data. Specifically, we capture data when unlocking screen, playing games as well as some basic mobile phone information, app usage, and screen status by using common available sensors in commodity mobile phones. By analyzing the differences in users' phone usage, we extracted features for user profile inference. Random Forest regression and random forest classification models are separately used to estimate age and gender of the user while support vector regression algorithm is applied to identify personality traits. In addition, we evaluate the model through real-life experiments conducted with a total of 84 phone users. Experimental results show that our approach effective, achieving an RSME of 4.3696 in age estimation and precision of 91.70% in gender detection. As for personality traits identification, the root mean square errors of openness, conscientiousness, extraversion, agreeableness, and neuroticism are 0.29, 0.3506, 0.465, 0.3022, and 0.452, respectively.

This paper builds a Food Delivery Network (FooDNet in short) using spatial crowdsourcing (SC). It investigates the participation of urban taxis to support on demand take-out food delivery. Unlike existing SC-enabled service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, two on demand food delivery problems under different situations are studied in our work: (1) for O-OTOD, the food is opportunistically delivered by taxis when carrying passengers, and the optimization goal is to minimize the number of selected taxis to maintain a relatively high incentive to the participated drivers; (2) for D-OTOD, taxis dedicatedly deliver food without taking passengers, and the aim is to minimize the number of selected taxis (i.e., to raise the reward for each participant) and the total traveling distance to reduce the cost. A two-stage approach, including the construction algorithm and the Adaptive Large Neighborhood Search (ALNS) algorithm based on simulated annealing, is proposed to solve the problem. We have conducted extensive experiments based on the real-world datasets, including city-wide restaurant data, cell tower data, and the large-scale taxi trajectory data with 10,000 taxis in the city of Chengdu, China. Experimental results demonstrate that our proposed algorithms are more effective and efficient than baselines, fulfilling the food delivery service using a smaller number of taxis within the given time.

Human computer operations such as writing documents and playing games have become popular in our daily lives. These activities (especially if identified in a non-intrusive manner) can be used to facilitate context-aware services. In this paper, we propose to recognize human computer operations through keystroke sensing with a smartphone. Specifically, we first utilize the microphone embedded in a smartphone to sense the input audio from a computer keyboard. We then identify keystrokes using fingerprint identification techniques. The determined keystrokes are then corrected with a word recognition procedure, which utilizes the relations of adjacent letters in a word. Finally, by fusing both semantic and acoustic features, a classification model is constructed to recognize four typical human computer operations: 1) chatting; 2) coding; 3) writing documents; and 4) playing games. We recruited 15 volunteers to complete these operations, and evaluated the proposed approach from multiple aspects in realistic environments. Experimental results validated the effectiveness of our approach.

Monitoring group mobility and structure is crucial for understanding group activities and social relations. In this paper, we develop algorithms for fine-grained mobility classification and structure recognition of social groups utilizing mobile devices. First, we present a method that recognizes four levels of group mobility, including stationary, strolling, walking, and running. Second, using multiple types of mobile sensors, a novel relative position relationship estimation algorithm is developed to understand different moving group structures. We have conducted real-life experiments in which 12 volunteers moved in different small groups either in an office building or a shopping mall with various speeds and structures. Experimental results show that our approach achieves an accuracy of 99.5 percent in group mobility level classification and about 80 percent in group structure recognition.

This paper builds a Food Delivery Network (FooDNet) that investigates the usage of urban taxis to support on demand take-out food delivery by leveraging spatial crowdsourcing. Unlike existing service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, we study the food delivery problem in association with the Opportunistic Online Takeout Ordering & Delivery service (O-OTOD). Specifically, the food is delivered incidentally by taxis when carrying passengers in the O-OTOD problem, and the optimization goal is to minimize the number of selected taxis to maintain a relative high incentive to the participated drivers. The two-stage method is proposed to solve the problem, consisting of the construction algorithm and the Large Neighborhood Search (LNS) algorithm. Preliminary experiments based on real-world taxi trajectory datasets verify that our proposed algorithms are effective and efficient.

Pedestrians distracted by smartphones are easy to meet with various dangers when crossing or walking on the street, such as the obstacles on the sidewalk (e.g., temporary parking and road repairing). Existing works about pedestrian safety are mostly based on the sensing capabilities from a single device. The surrounding information that can be learned, however, is quite limited or incomplete. Therefore, in many cases the dangers cannot be detected and the pedestrians cannot be alerted. In this paper, a novel system called CrowdWatch is proposed, which leverages mobile crowd sensing and crowd intelligence aggregation to detect temporary obstacles and make effective alerts for distracted walkers. To detect obstacles, we first study the regular rules of pedestrians' avoidance behaviors from the aspects of turn-making and visual contexts. The Dempster-Shafer evidence theory is then used to fuse the behavior and visual contexts, and further calculate the confidence of obstacle existence. Afterwards, we leverage the features of pedestrians' traces to characterize an appropriate dangerous area, which is used to alert distracted walkers. The conducted experiments with 36 participants and different obstacle settings indicate that the crowd-intelligencebased obstacle detection method is effective and the accuracy of reminding attains 83.3%.

Monitoring group mobility and structure is crucial for public safety management and emergency evacuation. In this paper, we propose a fine-grained mobility classification and structure recognition approach for social groups based on hybrid sensing using mobile devices. First, we present a method which classifies group mobility into four levels, including stationary, strolling, walking and running. Second, by combining mobile sensing and Wi-Fi signals, a novel relative position relationship estimation algorithm is developed to understand moving group structures of different shapes. We have conducted real-life experiments in which eight volunteers form two to three small groups moving in a teaching building with different speed and structures. Experimental results show that our approach achieves an accuracy of 99.5% in mobility classification and about 80% in group structure recognition.

With eXtensible Markup Language (XML) becoming a ubiquitous language for data storage and transmission in various domains, effectively safeguarding the XML document containing sensitive information is a critical issue. In this study, the authors propose a new access control model with filtering privacy. Based on the idea of separating the structure and content of the XML document, they provide a method to extract the main structure of the XML document and use matrix to save the structure information, at the same time, the start–end region encoding is used to combine the corresponding structure and content skillfully. These not only save the storage space but also efficiently speed up the search and make it convenient to find the relevant elements, especially the finding of the related content. In order to evaluate the security and efficiency of this model, the security analysis and simulation experiment verify its performance in this work.

Hierarchical wireless sensor networks (HSNs) have been widely used in many applications, especially in military areas. They usually consist of different types of nodes and behave better in performances and reliability than traditional flat wireless sensor networks (FSNs). In this paper, a novel key pre-distribution scheme is proposed for a three-tier HSN. Shamir's secret sharing technique is implemented in intracluster pairwise key establishment. Compared with existing key management schemes, our scheme guarantees a fully connected network with less storage requirement and communication overhead of sensors. Besides, it substantially improves the network resilience against nodes capture attack and collusion attack.

This paper describes the design of a 16/spl times/16 complex-number multiplier developed as part of the arithmetic datapath of a complex-number digital signal processor. The complex-number multiplier internally uses binary signed digits for fast multiplication and compact layout. It employs the traditional three-multiplication scheme while minimizing the logic and delay associated with the three extra pre-multiplication binary additions which that scheme requires. The minimization comes from producing the redundant binary sum for each of the pre-multiplication binary additions with minimal hardware, and then recoding the redundant sums as radix-4 multiplier operands. The radix-4 operands halve the number of summands to be added in each of the three real multiplier units. Furthermore, an additional factor of two reduction in the number of summands is effectuated by our coding scheme for representing binary signed digits. The result is a fast and compact complex-number multiplier.>