arXiv Computer Science @arxiv_cs@qoto.org

This article present a new, direct and simple formula for constructing Mignotte sequences.

The opioid crisis remains a critical public health challenge in the United States. Despite national efforts which reduced opioid prescribing rates by nearly 45\% between 2011 and 2021, opioid overdose deaths more than tripled during this same period. Such alarming trends raise important questions about what underlying social factors may be driving opioid misuse. Using county-level data across the United States, this study begins with a preliminary data analysis of how the rates of thirteen social vulnerability index variables manifest in counties with both anomalously high and low mortality rates, identifying patterns that warrant further investigation. Building on these findings, we further investigate the importance of the thirteen SVI variables within a machine learning framework by employing two predictive models: XGBoost and a modified autoencoder. Both models take the thirteen SVI variables as input and predict county-level opioid-related mortality rates. This allows us to leverage two distinct feature importance metrics: information gain for XGBoost and a Shapley gradient explainer for the autoencoder. These metrics offer two unique insights into the most important SVI factors in relation to opioid-related mortality. By identifying the variables which consistently rank as most important, this study highlights key social vulnerability factors that may play critical roles in the opioid crisis.

The demand for computing education increases due to the rapid development of technology and its involvement in most daily activities. Academic institutes offer a variety of computing majors, such as Computer Engineering, Computer Science, Information Systems, Information Technology, Software Engineering, Cybersecurity, and Data Science. Since a major objective of earning a bachelor's degree is to improve career opportunities, it is crucial to understand how the job market perceives these computing majors. This study analyzed the relationships between various computing majors and the job market in Saudi Arabia, using LinkedIn public profile data, discovering insights into the strong relationship between the focus of certain computing majors and the employment of relevant job positions. Moreover, job category trends were analyzed over the past ten years, observing that demands for System Admin and Technical Support positions declined while demands for Business Analysis and Artificial Intelligence and Data Science inclined. This study also compared earned professional certifications between different computing major graduates that correspond to job position findings.

Video and audio are closely correlated modalities that humans naturally perceive together. While recent advancements have enabled the generation of audio or video from text, producing both modalities simultaneously still typically relies on either a cascaded process or multi-modal contrastive encoders. These approaches, however, often lead to suboptimal results due to inherent information losses during inference and conditioning. In this paper, we introduce SyncFlow, a system that is capable of simultaneously generating temporally synchronized audio and video from text. The core of SyncFlow is the proposed dual-diffusion-transformer (d-DiT) architecture, which enables joint video and audio modelling with proper information fusion. To efficiently manage the computational cost of joint audio and video modelling, SyncFlow utilizes a multi-stage training strategy that separates video and audio learning before joint fine-tuning. Our empirical evaluations demonstrate that SyncFlow produces audio and video outputs that are more correlated than baseline methods with significantly enhanced audio quality and audio-visual correspondence. Moreover, we demonstrate strong zero-shot capabilities of SyncFlow, including zero-shot video-to-audio generation and adaptation to novel video resolutions without further training.

The gps2gtfs package addresses a critical need for converting raw Global Positioning System (GPS) trajectory data from public transit vehicles into the widely used GTFS (General Transit Feed Specification) format. This transformation enables various software applications to efficiently utilize real-time transit data for purposes such as tracking, scheduling, and arrival time prediction. Developed in Python, gps2gtfs employs techniques like geo-buffer mapping, parallel processing, and data filtering to manage challenges associated with raw GPS data, including high volume, discontinuities, and localization errors. This open-source package, available on GitHub and PyPI, enhances the development of intelligent transportation solutions and fosters improved public transit systems globally.

Programming is a crucial skill in today's world and being taught worldwide at different levels. However, in the literature there is little research investigating a formal approach to embedding public engagement into programming module design. This paper explores the integration of public engagement into an introductory programming module, at the University of Warwick, UK, as part of the Digital and Technology Solutions (DTS) degree apprenticeship. The module design follows a 'V' model, which integrates community engagement with traditional programming education, providing a holistic learning experience. The aim is to enhance learning by combining programming education with community engagement. Apprentices participate in outreach activities, teaching programming and Arduino hardware to local secondary school students. This hands-on approach aligns with Kolb's experiential learning model, improving communication skills and solidifying programming concepts through teaching. The module also includes training in safeguarding, presentation skills, and storytelling to prepare apprentices for public engagement. Pedagogical techniques in the module include live coding, group exercises, and Arduino kit usage, as well as peer education, allowing apprentices to learn from and teach each other. Degree apprentices, who balance part-time studies with full-time employment, bring diverse knowledge and motivations. The benefit of public engagement is that it helps bridge their skills gap, fostering teamwork and creating a positive learning environment. Embedding public engagement in programming education also enhances both technical and soft skills, providing apprentices with a deeper understanding of community issues and real-world applications. Our design supports their academic and professional growth, ensuring the module's ongoing success and impact.

This study investigates the potential of using ChatGPT as a teachable agent to support students' learning by teaching process, specifically in programming education. While learning by teaching is an effective pedagogical strategy for promoting active learning, traditional teachable agents have limitations, particularly in facilitating natural language dialogue. Our research explored whether ChatGPT, with its ability to engage learners in natural conversations, can support this process. The findings reveal that interacting with ChatGPT improves students' knowledge gains and programming abilities, particularly in writing readable and logically sound code. However, it had limited impact on developing learners' error-correction skills, likely because ChatGPT tends to generate correct code, reducing opportunities for students to practice debugging. Additionally, students' self-regulated learning (SRL) abilities improved, suggesting that teaching ChatGPT fosters learners' higher self-efficacy and better implementation of SRL strategies. This study discussed the role of natural dialogue in fostering socialized learning by teaching, and explored ChatGPT's specific contributions in supporting students' SRL through the learning by teaching process. Overall, the study highlights ChatGPT's potential as a teachable agent, offering insights for future research on ChatGPT-supported education.

Images serve as a crucial medium for communication, presenting information in a visually engaging format that facilitates rapid comprehension of key points. Meanwhile, during transmission and storage, they contain significant sensitive information. If not managed properly, this information may be vulnerable to exploitation for personal gain, potentially infringing on privacy rights and other legal entitlements. Consequently, researchers continue to propose some approaches for preserving image privacy and publish reviews that provide comprehensive and methodical summaries of these approaches. However, existing reviews tend to categorize either by specific scenarios, or by specific privacy objectives. This classification somewhat restricts the reader's ability to grasp a holistic view of image privacy protection and poses challenges in developing a total understanding of the subject that transcends different scenarios and privacy objectives. Instead of examining image privacy protection from a single aspect, it is more desirable to consider user needs for a comprehensive understanding. To fill this gap, we conduct a systematic review of image privacy protection approaches based on privacy protection goals. Specifically, we define the attribute known as privacy sensitive domains and use it as the core classification dimension to construct a comprehensive framework for image privacy protection that encompasses various scenarios and privacy objectives. This framework offers a deep understanding of the multi-layered aspects of image privacy, categorizing its protection into three primary levels: data-level, content-level, and feature-level. For each category, we analyze the main approaches and features of image privacy protection and systematically review representative solutions. Finally, we discuss the challenges and future directions of image privacy protection.

Digital libraries provide different access paths, allowing users to explore their collections. For instance, paper recommendation suggests literature similar to some selected paper. Their implementation is often cost-intensive, especially if neural methods are applied. Additionally, it is hard for users to understand or guess why a recommendation should be relevant for them. That is why we tackled the problem from a different perspective. We propose XGPRec, a graph-based and thus explainable method which we integrate into our existing graph-based biomedical discovery system. Moreover, we show that XGPRec (1) can, in terms of computational costs, manage a real digital library collection with 37M documents from the biomedical domain, (2) performs well on established test collections and concept-centric information needs, and (3) generates explanations that proved to be beneficial in a preliminary user study. We share our code so that user libraries can build upon XGPRec.

Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on early-stage dementia detection. We provide a new spontaneous speech corpus for this challenge. This corpus includes answers from three prompts designed by neurologists to better capture the cognition of speakers. Our baseline models achieved an F1-score of 55.0% on the classification task and an RMSE of 2.98 on the regression task.

Despite increasing mobile Internet penetration in developing regions, mobile users continue to experience a poor web experience due to two key factors: (i) lack of locally relevant content; (ii) poor web performance due to complex web pages and poor network conditions. In this paper, we describe our design, implementation and deployment experiences of GAIUS, a mobile content ecosystem that enables efficient creation and dissemination of locally relevant web content into hyperlocal communities in emerging markets. The basic building blocks of GAIUS are a lightweight content edge platform combined with a mobile application that collectively provide a Hyperlocal Web abstraction for mobile users to create and consume locally relevant content and interact with other users via a community abstraction. The GAIUS platform uses MAML, a web specification language that dramatically simplifies web pages to reduce the complexity of Web content within the GAIUS ecosystem, improve page load times and reduce network costs. In this paper, we describe our experiences deploying GAIUS across a large user base in India, Bangladesh and Kenya.

The Harmonized Tariff System (HTS) classification industry, essential to e-commerce and international trade, currently lacks standardized benchmarks for evaluating the effectiveness of classification solutions. This study establishes and tests a benchmark framework for imports to the United States, inspired by the benchmarking approaches used in language model evaluation, to systematically compare prominent HTS classification tools. The framework assesses key metrics--such as speed, accuracy, rationality, and HTS code alignment--to provide a comprehensive performance comparison. The study evaluates several industry-leading solutions, including those provided by Zonos, Tarifflo, Avalara, and WCO BACUDA, identifying each tool's strengths and limitations. Results highlight areas for industry-wide improvement and innovation, paving the way for more effective and standardized HTS classification solutions across the international trade and e-commerce sectors.

This study examined the impact of Code.org's block-based coding curriculum on primary school students' computational thinking, motivation, attitudes, and academic performance. Twenty students participated, and a range of tools was used: the Programming Computational Thinking Scale (PCTS) to evaluate computational thinking, the Instructional Materials Motivation Survey (IMMS) for motivation, the Attitude Scale of Computer Programming Learning (ASCOPL) for attitudes, and the Programming Achievement Test (PAT) for programming performance. The results revealed significant improvements in computational thinking, motivation, attitudes, and programming performance, with strong positive correlations among these factors. ANOVA analysis highlighted significant differences in computational concepts, perspectives, and motivational factors like attention and confidence, emphasizing their interdependence in programming success. This study highlights the interconnectedness of these factors and their importance in supporting programming achievement in primary school students, addressing gaps in the literature on block-based programming education.

Ensuring compliance of norms and policies when working on administrative law cases can be difficult to manage for government organisations. Automating this process could save a lot of time, effort and ensure compliance. Prior research resulted in a method to formalize sources of norms. These can be turned into executable specifications using the domain-specific language eFLINT, which can be used for automating compliance. However, the current interface of eFLINT prevents adaption by legal experts. The aim of this research was to bridge this gap by developing a prototype based on eFLINT, for automating compliance within government organisations. To get a better understanding of the needs and requirements of potential users, qualitative research was conducted. This consisted of semi-structured interviews to gather requirements, which were analyzed using a thematic analysis method. Based on the analyzed data, a design for the interface of the prototype was made. The final prototype was evaluated in a user end study which included a cognitive walkthrough and user testing. The prototype proved to be a good first step in the right direction with a lot of room for further development.

Wearable robotics have the capacity to assist stroke survivors in assisting and rehabilitating hand function. Many devices that use surface electromyographic (sEMG) for control rely on extrinsic muscle signals, since sEMG sensors are relatively easy to place on the forearm without interfering with hand activity. In this work, we target the intrinsic muscles of the thumb, which are superficial to the skin and thus potentially more accessible via sEMG sensing. However, traditional, rigid electrodes can not be placed on the hand without adding bulk and affecting hand functionality. We thus present a novel sensing sleeve that uses textile electrodes to measure sEMG activity of intrinsic thumb muscles. We evaluate the sleeve's performance on detecting thumb movements and muscle activity during both isolated and isometric muscle contractions of the thumb and fingers. This work highlights the potential of textile-based sensors as a low-cost, lightweight, and non-obtrusive alternative to conventional sEMG sensors for wearable robotics.

Ensuring Artificial General Intelligence (AGI) reliably avoids harmful behaviors is a critical challenge, especially for systems with high autonomy or in safety-critical domains. Despite various safety assurance proposals and extreme risk warnings, comprehensive guidelines balancing AI safety and capability remain lacking. In this position paper, we propose the \textit{AI-\textbf{$45^{\circ}$} Law} as a guiding principle for a balanced roadmap toward trustworthy AGI, and introduce the \textit{Causal Ladder of Trustworthy AGI} as a practical framework. This framework provides a systematic taxonomy and hierarchical structure for current AI capability and safety research, inspired by Judea Pearl's ``Ladder of Causation''. The Causal Ladder comprises three core layers: the Approximate Alignment Layer, the Intervenable Layer, and the Reflectable Layer. These layers address the key challenges of safety and trustworthiness in AGI and contemporary AI systems. Building upon this framework, we define five levels of trustworthy AGI: perception, reasoning, decision-making, autonomy, and collaboration trustworthiness. These levels represent distinct yet progressive aspects of trustworthy AGI. Finally, we present a series of potential governance measures to support the development of trustworthy AGI.\footnote{In this paper, trustworthiness is generally considered a broad form of safety, and no explicit distinction is made between the two. However, in some contexts, safety and trustworthiness are treated as distinct: safety involves assurance of correct behavior, while trustworthiness refers to user confidence in the system's decision-making. In such cases, different terms or both may be used depending on the context.

Recently, it has been experimentally demonstrated that individual memory units coupled in certain topology can provide the intended performance. However, experimental or simulation based evaluation of different coupled memory topologies and materials are costly and time consuming. In this paper, inspired by Glauber dynamics models in non-equilibrium statistical mechanics, we propose a physically accurate generic mathematical framework for analyzing retention times of various coupled memory topologies and materials. We demonstrate efficacy of the proposed framework by deriving closed form expressions for a few popular coupled and uncoupled memory topologies, which match simulations. Our analysis also offers analytical insights helping us estimate the impact of materials and topologies on retention time.

AI infrastructures, predominantly GPUs, have delivered remarkable performance gains for deep learning. Conversely, scientific computing, exemplified by quantum chemistry systems, suffers from dynamic diversity, where computational patterns are more diverse and vary dynamically, posing a significant challenge to sponge acceleration off GPUs. In this paper, we propose Matryoshka, a novel elastically-parallel technique for the efficient execution of quantum chemistry system with dynamic diversity on GPU. Matryoshka capitalizes on Elastic Parallelism Transformation, a property prevalent in scientific systems yet underexplored for dynamic diversity, to elastically realign parallel patterns with GPU architecture. Structured around three transformation primitives (Permutation, Deconstruction, and Combination), Matryoshka encompasses three core components. The Block Constructor serves as the central orchestrator, which reformulates data structures accommodating dynamic inputs and constructs fine-grained GPU-efficient compute blocks. Within each compute block, the Graph Compiler operates offline, generating high-performance code with clear computational path through an automated compilation process. The Workload Allocator dynamically schedules workloads with varying operational intensities to threads online. It achieves highly efficient parallelism for compute-intensive operations and facilitates fusion with neighboring memory-intensive operations automatically. Extensive evaluation shows that Matryoshka effectively addresses dynamic diversity, yielding acceleration improvements of up to 13.86x (average 9.41x) over prevailing state-of-the-art approaches on 13 quantum chemistry systems.

In the Internet of Vehicles (IoV), Age of Information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, the vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the Age of Information and optimize the system's average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles' data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system's AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system.