Reviewerly/exHarmony huggingface dataset:
First three years of the international verification of neural networks competition (VNN-COMP)

Abstract This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments.
Privacy-preserving contact tracing

Digital contact tracing apps represent a significant contribution of computer science to the global fight with COVID-19 pandemic. Digital contact tracing apps are designed towarn people if they have been in contact with an infected person. They have proven useful to slow down transmission for many infectious diseases, nonetheless they also rose many privacy concerns. In this paper, we explore the main privacy concerns in digital contact tracing.
Exploring the Benefits and Drawbacks of AR and VR Technologies for Learners of Mathematics: Recent Developments

Despite the growing interest in the field, the overall impact of augmented reality (AR) and virtual reality (VR) on mathematics learning remains unclear, with previous studies reporting mixed results. Moreover, to date, no systematic review has evaluated the potential of AR/VR in mathematics education, including its benefits and drawbacks for learners. To address this gap, the present systematic literature review aims to identify research trends, determine characteristics and methodologies, and explore the potential benefits and drawbacks of AR/VR technologies in mathematics learning based on existing empirical studies. In accordance with the PRISMA guidelines, we analyzed 59 peer-reviewed journal articles published in English that focused on AR/VR implementation in mathematics education. The review determined that geometry was the most widely studied topic of mathematics, with several studies focusing on the use of AR/VR to assist students with learning disabilities. The present review offers evidence for the potential of AR/VR potential in consolidating learners’ socio-emotional, cognitive/meta-cognitive, and pedagogical development in mathematics learning. Nevertheless, a few issues, including technological glitches, cost, start-up effort, health issues, and unfamiliarity with AR/VR, pose challenges to the successful application of AR/VR in the classroom. This systematic review contributes to the existing body of knowledge in the field and recommends avenues for future research.
Battle of the Wordsmiths: Comparing ChatGPT, GPT-4, Claude, and Bard

Although informal evaluations of modern LLMs can be found on social media, blogs, and news outlets, a formal and comprehensive comparison among them has yet to be conducted. In response to this gap, we have undertaken an extensive benchmark evaluation of LLMs and conversational bots. Our evaluation involved the collection of 1002 questions encompassing 27 categories, which we refer to as the “Wordsmiths dataset.” These categories include reasoning, logic, facts, coding, bias, language, humor, and more. Each question in the dataset is accompanied by an accurate and verified answer. We meticulously assessed four leading chatbots: ChatGPT, GPT-4, Bard, and Claude, using this dataset. The results of our evaluation revealed the following key findings: a) GPT-4 emerged as the top-performing chatbot across all categories, achieving a success rate of 84.1%. On the other hand, Bard faced challenges and achieved a success rate of 62.4%. b) Among the four models evaluated, one of them responded correctly approximately 93% of the time. However, all models were correct only about 44%. c) Bard is less correlated with other models while ChatGPT and GPT-4 are highly correlated in terms of their responses. d) Chatbots demonstrated proficiency in language understanding , facts, and self awareness. However, they encountered difficulties in areas such as math, coding, IQ, and reasoning. e) In terms of bias, discrimination, and ethics categories, models generally performed well, suggesting they are relatively safe to utilize. To make future model evaluations on our dataset easier, we also provide a multiple-choice version of it (called Wordsmiths-MCQ). The understanding and assessment of the capabilities and limitations of modern chatbots hold immense societal implications. In an effort to foster further research in this field, we have made our dataset available for public access, which can be found at Wordsmiths.
Differentially private knowledge transfer for federated learning

Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exchanging model parameters, rather than raw data. However, model parameters may encode not only non-private knowledge but also private information of local data, thereby transferring knowledge via model parameters is not privacy-secure. Here, we present a knowledge transfer method named PrivateKT, which uses actively selected small public data to transfer high-quality knowledge in federated learning with privacy guarantees. We verify PrivateKT on three different datasets, and results show that PrivateKT can maximally reduce 84% of the performance gap between centralized learning and existing federated learning methods under strict differential privacy restrictions. PrivateKT provides a potential direction to effective and privacy-preserving knowledge transfer in machine intelligent systems.
Blockchain and Internet of Things in smart cities and drug supply management: Open issues, opportunities, and future directions

Blockchain-based drug supply management (DSM) requires powerful security and privacy procedures for high-level authentication, interoperability, and medical record sharing. Researchers have shown a surprising interest in Internet of Things (IoT)-based smart cities in recent years. By providing a variety of intelligent applications, such as intelligent transportation, industry 4.0, and smart financing, smart cities (SC) can improve the quality of life for their residents. Blockchain technology (BCT) can allow SC to offer a higher standard of security by keeping track of transactions in an immutable, secure, decentralized, and transparent distributed ledger. The goal of this study is to systematically explore the current state of research surrounding cutting-edge technologies, particularly the deployment of BCT and the IoT in DSM and SC. In this study, the defined keywords “blockchain”, “IoT”, drug supply management”, “healthcare”, and “smart cities” as well as their variations were used to conduct a systematic search of all relevant research articles that were collected from several databases such as Science Direct, JStor, Taylor & Francis, Sage, Emerald insight, IEEE, INFORMS, MDPI, ACM, Web of Science, and Google Scholar. The final collection of papers on the use of BCT and IoT in DSM and SC is organized into three categories. The first category contains articles about the development and design of DSM and SC applications that incorporate BCT and IoT, such as new architecture, system designs, frameworks, models, and algorithms. Studies that investigated the use of BCT and IoT in the DSM and SC make up the second category of research. The third category is comprised of review articles regarding the incorporation of BCT and IoT into DSM and SC-based applications. Furthermore, this paper identifies various motives for using BCT and IoT in DSM and SC, as well as open problems and makes recommendations. The current study contributes to the existing body of knowledge by offering a complete review of potential alternatives and finding areas where further research is needed. As a consequence of this, researchers are presented with intriguing potential to further create decentralized DSM and SC apps as a result of a comprehensive discussion of the relevance of BCT and its implementation.
Automated Vehicle Counting from Pre-Recorded Video Using You Only Look Once (YOLO) Object Detection Model

Different techniques are being applied for automated vehicle counting from video footage, which is a significant subject of interest to many researchers. In this context, the You Only Look Once (YOLO) object detection model, which has been developed recently, has emerged as a promising tool. In terms of accuracy and flexible interval counting, the adequacy of existing research on employing the model for vehicle counting from video footage is unlikely sufficient. The present study endeavors to develop computer algorithms for automated traffic counting from pre-recorded videos using the YOLO model with flexible interval counting. The study involves the development of algorithms aimed at detecting, tracking, and counting vehicles from pre-recorded videos. The YOLO model was applied in TensorFlow API with the assistance of OpenCV. The developed algorithms implement the YOLO model for counting vehicles in two-way directions in an efficient way. The accuracy of the automated counting was evaluated compared to the manual counts, and was found to be about 90 percent. The accuracy comparison also shows that the error of automated counting consistently occurs due to undercounting from unsuitable videos. In addition, a benefit-cost (B/C) analysis shows that implementing the automated counting method returns 1.76 times the investment.
A survey on deep learning-based image forgery detection

Image is known as one of the communication tools between humans. With the development and availability of digital devices such as cameras and cell phones, taking images has become easy anywhere. Images are used in many medical, forensic medicine, and judiciary applications. Sometimes images are used as evidence, so the authenticity and reliability of digital images are increasingly important. Some people manipulate images by adding or deleting parts of an image, which makes the image invalid. Therefore, image forgery detection and localization are important. The development of image editing tools has made this issue an important problem in the field of computer vision. In recent years, many different algorithms have been proposed to detect forgery in the image and pixel levels. All these algorithms are categorized into two main methods: traditional and deep-learning methods. The deep learning method is one of the important branches of artificial intelligence science. This method has become one of the most popular methods in most computer vision problems due to the automatic identification and prediction process and robustness against geometric transformations and post-processing operations. In this study, a comprehensive review of image forgery types, benchmark datasets, evaluation metrics in forgery detection, traditional forgery detection methods, discovering the weaknesses and limitations of traditional methods, forgery detection with deep learning methods, and the performance of this method is presented. According to the expansion of deep-learning methods and their successful performance in most computer vision problems, our main focus in this study is forgery detection based on deep-learning methods. This survey can be helpful for a researcher to obtain a deep background in the forgery detection field.
A systematic literature review on wireless security testbeds in the cyber-physical realm

The Cyber-Physical System (CPS) lies in the core of Industry 4.0, accelerating the convergence of formerly barricaded operational technology systems with modern information technology ones. Nevertheless, the increased connectivity in terms of both wired and wireless links and associated attack surfaces that comes along, requires higher security for safeguarding critical industrial systems and manufacturing lines from cyberattacks. In this rapidly evolving ecosystem, security testbeds have emerged as a versatile, cost-effective solution for investigating potential attack vectors and devising appropriate countermeasures, without putting the real system at risk. The present work seeks to address a prominent literature gap, namely, the lack of a systematic review regarding the use of wireless-oriented security testbeds in CPS. We contribute an overarching, manifold review on this topic from 2016 onward, examining the various literature works from diverse angles, namely, the wireless technologies used, the implemented attacks, the employed security controls, and more. The analysis is done on a per-sector basis, including water and wastewater systems, healthcare, transportation, agriculture, energy, maritime, unmanned aircraft systems, and others. Finally yet importantly, we discuss key takeaways, open issues, and challenges. The key observations of our analysis, including almost 50 articles, can be wrapped up into two salient points: on the one hand, wireless technologies are increasingly penetrating into the CPS domain as an orthogonal, versatile solution to their wired counterparts, but on the other, they widen the window of opportunity for threat actors targeting wireless links. In this context, testbed thoroughness and security as a trade-off seem to be of major importance, alongside a modular, possibly sector-neutral reference architecture that overarches the peculiarities of CPS. Overall, to our knowledge, this work provides the first full-fledged survey on the use of wireless-oriented security testbeds in CPS, and it is therefore anticipated to serve as a groundwork and touchstone for several stakeholders at different levels.
Creation of Optical Cat and GKP States Using Shaped Free Electrons

A new approach to generating quantum states of light most suitable for robust quantum computing draws on one of the most basic interactions in physics---the interaction between free electrons and photons.
Learning hierarchical time series data augmentation invariances via contrastive supervision for human activity recognition

Human activity recognition (HAR) using wearable sensors is always a research hotspot in ubiquitous computing scenario, in which feature learning has played a crucial role. Recent years have witnessed outstanding success of contrastive learning in image data, which learns invariant representations by adding contrastive loss to the last layer of deep neural networks. However, the advantages of contrastive loss have been rarely leveraged in time series data for activity recognition. A fundamental obstacle to contrastive learning in HAR is that image-based augmentation could not fit well with sensor data, which raises a critical issue: the distortions induced by augmentation might be further enlarged by intermediate layers of a network and thus severely harm semantic structure of original activity instance. In this paper, taking an inspiration from deeply-supervised learning, we propose a novel approach called Contrastive Supervision by considering “where” to contrast, which aims to learn time series augmentation invariances by forcing positive pairs nearby and negative pairs far apart at different depths of neural network. Our approach can be seen as a generalization of contrastive learning in a deeply-supervised setting, where the contrastive loss is used to supervise the intermediate layers instead of only the last layer, allowing us to effectively leverage label information so as to better fuse the multi-level features. Experiments on popular benchmarks demonstrate that our approach can learn better representations and improve classification accuracy without additional inference cost for various HAR tasks in supervised and semi-supervised learning paradigms.
The defeat of the Winograd Schema Challenge

The Winograd Schema Challenge—a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge—was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-trained transformer-based language models and fine-tuned on these kinds of problems, achieved better than 90% accuracy. In this paper, we review the history of the Winograd Schema Challenge and discuss the lasting contributions of the flurry of research that has taken place on the WSC in the last decade. We discuss the significance of various datasets developed for WSC, and the research community's deeper understanding of the role of surrogate tasks in assessing the intelligence of an AI system.
Building Trust in Fintech: An Analysis of Ethical and Privacy Considerations in the Intersection of Big Data, AI, and Customer Trust

This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature-review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership, and control. The findings emphasize the importance of safeguarding customer data, complying with data protection laws, and promoting corporate digital responsibility. The study provides practical suggestions for companies, including the use of encryption techniques, transparency regarding data collection and usage, the provision of customer opt-out options, and the training of staff on data-protection policies. However, the study is limited by its exclusion of non-English-language studies and the need for additional resources to deepen the findings. To overcome these limitations, future research could expand existing knowledge and collect more comprehensive data to better understand the complex issues examined.
AttrLeaks on the Edge: Exploiting Information Leakage from Privacy-Preserving Co-inference

Collaborative inference (co-inference) accelerates deep neural network inference via extracting representations at the device and making predictions at the edge server, which however might disclose the sensitive information about private attributes of users (e.g., race).Although many privacy-preserving mechanisms on co-inference have been proposed to eliminate privacy concerns, privacy leakage of sensitive attributes might still happen during inference.In this paper, we explore privacy leakage against the privacy-preserving co-inference by decoding the uploaded representations into a vulnerable form.We propose a novel attack framework named AttrLeaks, which consists of the shadow model of feature extractor (FE), the susceptibility reconstruction decoder, and the private attribute classifier.Based on our observation that values in inner layers of FE (internal representation) are more sensitive to attack, the shadow model is proposed to simulate the FE of the victim in the blackbox scenario and generates the internal representations.Then, the susceptibility reconstruction decoder is designed to transform the uploaded representations of the victim into the vulnerable form, which enables the malicious classifier to easily predict the private attributes.Extensive experimental results demonstrate that AttrLeaks outperforms the state of the art in terms of attack success rate.
Efficient Methods for Natural Language Processing: A Survey

Abstract Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.
Video-Based Human Activity Recognition Using Deep Learning Approaches

Due to its capacity to gather vast, high-level data about human activity from wearable or stationary sensors, human activity recognition substantially impacts people’s day-to-day lives. Multiple people and things may be seen acting in the video, dispersed throughout the frame in various places. Because of this, modeling the interactions between many entities in spatial dimensions is necessary for visual reasoning in the action recognition task. The main aim of this paper is to evaluate and map the current scenario of human actions in red, green, and blue videos, based on deep learning models. A residual network (ResNet) and a vision transformer architecture (ViT) with a semi-supervised learning approach are evaluated. The DINO (self-DIstillation with NO labels) is used to enhance the potential of the ResNet and ViT. The evaluated benchmark is the human motion database (HMDB51), which tries to better capture the richness and complexity of human actions. The obtained results for video classification with the proposed ViT are promising based on performance metrics and results from the recent literature. The results obtained using a bi-dimensional ViT with long short-term memory demonstrated great performance in human action recognition when applied to the HMDB51 dataset. The mentioned architecture presented 96.7 ± 0.35% and 41.0 ± 0.27% in terms of accuracy (mean ± standard deviation values) in the train and test phases of the HMDB51 dataset, respectively.
Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models

Incident management for cloud services is a complex process involving several steps and has a huge impact on both service health and developer productivity. On-call engineers require significant amount of domain knowledge and manual effort for root causing and mitigation of production incidents. Recent advances in artificial intelligence has resulted in state-of-the-art large language models like GPT-3.x (both GPT-3.0 and GPT-3.5), which have been used to solve a variety of problems ranging from question answering to text summarization. In this work, we do the first large-scale study to evaluate the effectiveness of these models for helping engineers root cause and mitigate production incidents. We do a rigorous study at Microsoft, on more than 40,000 incidents and compare several large language models in zero-shot, fine-tuned and multi-task setting using semantic and lexical metrics. Lastly, our human evaluation with actual incident owners show the efficacy and future potential of using artificial intelligence for resolving cloud incidents.
Peptide conformational sampling using the Quantum Approximate Optimization Algorithm

Abstract Protein folding has attracted considerable research effort in biochemistry in recent decades. In this work, we explore the potential of quantum computing to solve a simplified version of protein folding. More precisely, we numerically investigate the performance of the Quantum Approximate Optimization Algorithm (QAOA) in sampling low-energy conformations of short peptides. We start by benchmarking the algorithm on an even simpler problem: sampling self-avoiding walks. Motivated by promising results, we then apply the algorithm to a more complete version of protein folding, including a simplified physical potential. In this case, we find less promising results: deep quantum circuits are required to achieve accurate results, and the performance of QAOA can be matched by random sampling up to a small overhead. Overall, these results cast serious doubt on the ability of QAOA to address the protein folding problem in the near term, even in an extremely simplified setting.