Reviewerly/exHarmony huggingface dataset:
A Study on Cloud Environment: Confidentiality Problems, Security Threats, and Challenges

In terms of computing services, cloud computing has expanded significantly. It is a set of computing tools that may be accessed through the Internet. The cloud computing security, a field rife with challenges and critical significance, is still in its early stages of growth, and many research questions remain unanswered. The IT industry has benefited immensely from cloud computing. It is like the next step in the development of the Internet, with a controlled and powerful data storage network with enormous capabilities and applications. It allows authorities to gain access to a user's file or data without having to install the information on the user's computer using Internet services. Cloud computing allows for on-demand community access or pooled access to computing resources that have been configured. Having multiple operating systems on a computer allows you to get the most out of that resource. It refers to the development of virtual version resources such as storage devices, a virtual operating system, or the deployment of an application or software on the cloud in this context. Cloud computing is no exception; it has both advantages and disadvantages. Cloud protection, manageability, honesty, fraud, data loss, and other issues are some of the implications. In the presented paperwork, we reviewed different challenges and issues faced in the data security over cloud with an objective to identify security challenges for adapting cloud computing.
Mixed Cooperative-Competitive Communication Using Multi-agent Reinforcement Learning

By using communication between multiple agents in multi-agent environments, one can reduce the effects of partial observability by combining one agent’s observation with that of others in the same dynamic environment. While a lot of successful research has been done towards communication learning in cooperative settings, communication learning in mixed cooperative-competitive settings is also important and brings its own complexities such as the opposing team overhearing the communication. In this paper, we apply differentiable inter-agent learning (DIAL), designed for cooperative settings, to a mixed cooperative-competitive setting. We look at the difference in performance between communication that is private for a team and communication that can be overheard by the other team. Our research shows that communicating agents are able to achieve similar performance to fully observable agents after a given training period in our chosen environment. Overall, we find that sharing communication across teams results in decreased performance for the communicating team in comparison to results achieved with private communication.
Low-Latency Hardware Masking of PRINCE

Efficient implementation of Boolean masking in terms of low latency has evolved into a hot topic due to the necessity of embedding a physically secure and at-the-same-time fast implementation of cryptographic primitives in e.g., the memory encryption of pervasive devices. Instead of fully minimizing the circuit's area and randomness requirements at the cost of latency, the focus has changed into finding optimal tradeoffs between the circuit area and the execution time. The main latency bottleneck in hardware masking lies in the need for registers to stop the propagation of glitches and maintain non-completeness. Usually, an exponentially growing number of shares (hence an extremely large circuit), as well as a high demand for fresh randomness, are the result of avoiding registers in a securely masked hardware implementation of a block cipher. In this paper, we present several first-order secure and low-latency implementations of PRINCE. In particular, we show how to realize the masked variant of round-based PRINCE with only a single register stage per cipher round. We compare the resulting architectures, based on the popular TI and GLM masking scheme based on the area, latency, and randomness requirements and point out that both designs are suited for specific use cases.
Balancing Exploration and Exploitation in Forward Model Learning

Forward model learning algorithms enable the application of simulation-based search methods in environments for which the forward model is unknown. Multiple studies have shown great performance in game-related and motion control applications. In these, forward model learning agents often required less training time while achieving a similar performance than state-of-the-art reinforcement learning methods. However, several problems can emerge when replacing the environment’s true model with a learned approximation. While the true forward model allows the accurate prediction of future time-steps, a learned forward model may always be inaccurate in its prediction. These inaccuracies become problematic when planning long action sequences since the confidence in predicted time-steps reduces with increasing depth of the simulation. In this work, we explore methods for balancing risk and reward in decision-making using inaccurate forward models. Therefore, we propose methods for measuring the variance of a forward model and the confidence in the predicted outcome of planned action sequences. Based on these metrics, we define methods for learning and using forward models under consideration of their current prediction accuracy. Proposed methods have been tested in various motion control tasks of the Open AI Gym framework. Results show that the information on the model’s accuracy can be used to increase the efficiency of the agent’s training and the agent’s performance during evaluation.
Graph Signal Denoising Method via Hybrid Neumann-Series and Edge-Variant Graph Filters

In this study, a Tikhonov-based graph signal denoising method is presented. First, the signal denoising problem is formulated as a minimization problem and its optimal solution is required to compute the matrix inverse which only allows the centralized processing. To avoid solving matrix inverse and obtain the distributed implementation, two methods are studied. One is the Neumann-series (NS) method; the other is the edge-variant (EV) filter. As a result, EV filter has faster convergence speed than NS method. However, at steady state, NS method has smaller approximation error than EV filter. Thus, a hybrid denoising method of NS and EV filters is proposed in this paper to get fast convergence speed and smaller approximation error simultaneously. The data from sensor network and social network are used to examine the correctness of the proposed graph signal denoising approach.
A PCP Theorem for Interactive Proofs and Applications

The celebrated PCP Theorem states that any language in $$\mathrm {NP}$$ can be decided via a verifier that reads O(1) bits from a polynomially long proof. Interactive oracle proofs (IOP), a generalization of PCPs, allow the verifier to interact with the prover for multiple rounds while reading a small number of bits from each prover message. While PCPs are relatively well understood, the power captured by IOPs (beyond $$\mathrm {NP}$$ ) has yet to be fully explored. We present a generalization of the PCP theorem for interactive languages. We show that any language decidable by a k(n)-round IP has a k(n)-round public-coin IOP, where the verifier makes its decision by reading only O(1) bits from each (polynomially long) prover message and O(1) bits from each of its own (random) messages to the prover. Our result and the underlying techniques have several applications. We get a new hardness of approximation result for a stochastic satisfiability problem, we show IOP-to-IOP transformations that previously were known to hold only for IPs, and we formulate a new notion of PCPs (index-decodable PCPs) that enables us to obtain a commit-and-prove SNARK in the random oracle model for nondeterministic computations.
An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.
Lossless Image Set Compression Using Animated FLIF

Many image datasets are available on the Internet, contributing to the development of computer vision. While huge datasets are useful for research, they are time-consuming to transfer due to their large data volume. In particular, lossless compression has a worse compression ratio than lossy compression. It is considered that a higher compression ratio can be achieved by encoding multiple images together exploiting the features in the dataset rather than encoding each image individually. In this paper, we propose a new method for efficient lossless compression of image sets by combining a minimum spanning tree (MST) and the Free Lossless Image Format (FLIF). The experimental results show that the compression ratio of the proposed method is better than that of the HEVC-based method. We also show that the compression ratio can be further improved by extending the entropy coder of FLIF, but the effect of the compression ratio improvement depends on the characteristics of the images in the set.
Evaluation of Traditional and Deep Learning Human Detection Techniques Applied to Surveillance: A Performance Comparison at Distinct Object Sizes

Making computers capable of identifying and localizing people in images and videos is a topic that has been attracting the attention of many researchers in recent years. Several applications, including surveillance systems, can benefit from this capacity. There is no study that provides an unbiased comparison between the most representative types of methods, traditional and recent ones, focusing on human detection and specifically within a context of surveillance, where the size of the objects is mostly small relative to the image size. This paper aims to compare the performance of a set of representative human detection techniques applied to surveillance systems in terms of Average Precision (AP) and speed. Two main types of human detection methods are analyzed: the traditional ones, represented by HOG and Haar Cascades, and the deep learning ones, represented by Faster R-CNN, YOLO, and Mobile SSD. The comparison was performed using the VIRAT Ground Dataset, a large-scale real-world surveillance video dataset. When humans were small relative to the overall image size, the Haar Cascades method had an AP of 9.09, a value around six times higher than the other ones. The results indicate that detecting humans when they are far from the camera in videos and images is still a challenge to be overcome, and that, in some aspects, the traditional approaches outperform the recent deep learning ones.
The Role of Environmental Deviations in Engineering Robust Systems

Software systems are developed with various assumptions about the environment. However, over time, the environment may evolve and deviate from its expected behavior, possibly undermining desired requirements of the system. In this paper, we argue that identifying and treating environmental deviations as a first-class concept throughout the development cycle is a key to the successful engineering of robust software systems. We describe some examples of environmental deviations in different domains, discuss their implications on development activities, and also put forward research challenges that we believe the requirements engineering community is particularly well-positioned to tackle.
Ensemble-Based Semi-Supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving

Manual annotation of soiling on surround view cameras is a very challenging and expensive task. The unclear boundary for various soiling categories like water drops or mud particles usually results in a large variance in the annotation quality. As a result, the models trained on such poorly annotated data are far from being optimal. In this paper, we focus on handling such noisy annotations via pseudo-label driven ensemble model which allow us to quickly spot problematic annotations and in most cases also sufficiently fixing them. We train a soiling segmentation model on both noisy and refined labels and demonstrate significant improvements using the refined annotations. It also illustrates that it is possible to effectively refine lower cost coarse annotations.
Design of Human-computer Interaction System Using Gesture Recognition Algorithm from the Perspective of Machine Learning

The human-computer interaction (HCI) system is the hub of conversation between man and machine. The previous HCI system is challenging to meet the needs of modern society with the continuous progress of computer technology. A new HCI technology different from the past needs to be put forward to solve the problem that the development of computer technology and outdated HCI technology lag behind the times. Machine learning aims to build a behavior method that can obtain information from data and predict the data. Machine learning features are used and integrated into gesture algorithms through the basic principle of a finger-guessing game. Gesture estimation is adopted to detect joint gesture features efficiently, and a convolutional neural network is employed to plan and process the joint features to solve the bottleneck of poor segmentation of gesture images in different environments. HCI system based on gesture recognition algorithm is designed from the perspective of machine learning. The final experimental results show that this method has good recognition accuracy for the performance of different scales of various gestures, and the accuracy of recognition results can reach 70%. It suggests that the system is theoretically conditional on making the recognition result reach the most authentic level. It is demonstrated that gesture recognition is of excellent use value in this HCI system, and the HCI system established here also has an efficient reference value.
Preparation and Planning of the Development of a Proficiency Test in the Field of Digital Forensics

This paper presents the planning and preparation of a proficiency test for the field of digital forensics. The paper provides an elaboration of the idea and procedure of creating a forensic experiment, the result of which will be a test of expertise applicable for various organizations engaged in digital forensics. The whole process of planning, selecting forensic tools, defining forensic procedures, and producing test forensic images is shown, explaining and elaborating the criteria used in the selection, and the expected results. The proficiency test is planned for a smart electric bicycle.
Application and Cultivation on Mathematical Thinking in Artificial Intelligence

Mathematical thinking is the basis of the development of artificial intelligence, which is the ability to think and solve problems from the mathematical viewpoint. Based on the analysis of the connotation and function of mathematical thinking, this paper discusses mathematical thinking required in artificial intelligence, then expounds the mathematical thinking cultivation method in artificial intelligence. Finally, the typical application of mathematical thinking in artificial intelligence is deeply discussed. Through the research on mathematical thinking in artificial intelligence, it is conducive to further embody the function of mathematical thinking, and promote artificial intelligence development in the theory and the practice level.
SmartWarden: Automated Intrusion Detection System for Smart Contracts

Blockchain technology has gained much relevance in recent years, specifically the smart contract functionality, due to its great potential to decentralize different required scenarios. This technology brings many advantages, in particular, smart contracts provide blockchain with very great versatility. However, these smart contracts can bring some threats to the blockchain. This study has focused on the development of an intrusion detection system (IDS) based on the path study. This IDS defends the smart contracts, monitoring the transactions received by the targeted smart contract and generating the paths where the transactions are coming. These generated paths will have some features that are extracted and analyzed. Our approach suggests implementing an automated IDS on smart contracts to defend them from potential threats.
Spelling Checking and Error Corrector System for Marathi Language Text Using Minimum Edit Distance Algorithm

The performance of any search engine, social media word processor depends deeply on the spelling checkers, grammar checkers etc. Spelling checker is the application used to correct the spelling mistakes done by users unintentionally. The minimum edit distance is one of the string-matching algorithms used in various applications like text mining, spell checking, bioinformatics and so on. In this paper, we proposed the minimum edit distance algorithm (MED) which correct the spelling mistakes in Marathi language text. It corrects the spelling errors by performing various operations like substitution, insertion, and deletion of characters. The algorithm detects non-word spelling errors and generates a suitable suggestion set for misspelled words by matching them with the corpus. While doing this, the misspelled word length is a key component to searching in the corpus so that, the searching complexity is minimum. In this paper, we have implemented a minimum edit distance algorithm and evaluated their performance with accuracy measure. The accuracy of the system is 85.5%. The performance of this algorithm is evaluated by suggestion generation accuracy for given misspelled words.
Attention based Neural Machine Translation with Sequence to Sequence Learning on Low Resourced Indic Languages

Deep Learning techniques are powerful in mimicking humans in a particular set of problems. They have achieved a remarkable performance in complex learning tasks. Deep learning-inspired Neural Machine Translation (NMT) is a proficient technique that outperforms traditional machine translation tasks. Performing machine-aided translation on Indic languages has always been a challenging task considering their rich and diverse grammar. The neural machine translation has shown quality results compared to the traditional machine translation approaches. The task becomes problematic when it comes to low-resourced language like Sanskrit. This paper has presented our work on developing a dataset with Sanskrit as a language pair and an attention mechanism-based neural machine translation model trained on the Sanskrit-Hindi bilingual dataset. We have shown the significance of the attention mechanism to overcome the problem of long-term dependencies. The attention mechanism has shown promising results on low-resourced Indic languages.
A Performance Metrics Estimation of Spade, Prefix Span, Fast, and Lapin Algorithms

Sequential pattern mining extracts the core of patterns from a wide number of social media platforms, including Google, Yahoo, Amazon, Flipkart, and others. In different applications such as DNA analysis, stock market, intrusion detection, and so on, sequential pattern algorithms are extremely beneficial in extracting the intelligent patterns. The performance mestrics estimation of Prefix Span, SPADE and LAPIN and FAST algorithms is calculated. For real-time applications of DNA analysis, stock market analysis, user behavior prediction, and online business expansion, the best algorithm should be selected. FAST and LAPIN algortihms deliver best performance, when correlated with other algorithms. FAST performance is better at sparse dataset and LAPIN outperforms better at dense dataset.
arttech: Performance and Embodiment in Technology for Resilience and Mental Health

This workshop explores socially expressive technology design using collaborative principles of theater, dance and music. Building on shared roots of making something together, technologies (including virtual agents, physical devices, storytelling multimedia) and artistic provocation can create playful, productive and healthy experiences for communities. How do we: Improve collaboration between performance art and creative technology? Design apps and artistic collaboration/learning for mental health? Use theater performativity and technology to enhance our lives? As smart devices become ubiquitous, yet solidarity and cooperation remain elusive in competitive/hierarchical societies, understanding how performance art and technology collaborate can improve how we work, play and care for our and each other's health. This workshop is a forum and creative space to discuss and play with the future of performative expression and collaborative computing. We welcome researchers and practitioners from varied disciplines including technical implementation, design, theater, dance, improv, physiology and psychology. The program features collaborative creation modules across the expressive human spectrum (movement, sound, text) using digital dramaturgy (Budde, 2017) and pleasure of learning (Brecht, 2001). It also includes participants’ presentations, expert talks and videos exploring the expressive side of computing technology. Accepted applicants are invited to share perspectives and collaborate on creative exercises on art/artefacts/artificiality.
Lessons Learned from a Study on Distractions in Virtual Learning Environments: Reliability, Ecological Validity, and an Elusive Social Component

Abstract Virtual Reality has long been proposed to combine the reliability of controlled laboratory settings with the ecological validity of real life. While the technological development steadily pushes towards even more realistic renderings of the real world—the elusiveness of social and emotional elements gradually becomes more evident. This is not the least true for behavioral studies in rich sociocultural contexts. This article examines the outcomes of a study on distractions, taking place in a socially rich context—the classroom. The study made use of a Virtual Reality environment simulating a junior high school lesson, where the Distraction condition consisted of peers watching nonrelevant content on their laptops. In the control condition these laptops were closed. No significant distraction effects were found, neither on learning nor behavior. Given the strong support in the literature for such effects, the study design, including technical aspects, is scrutinized and discussed. We specifically highlight the difficulty of simulating a social relationship between the participant and agents in VR, which in this case makes the distraction stimulus significantly weaker. It is argued that the distraction effect of nearby peers’ laptop use relies (partly) on shared attention with social agents with an established social relation and common interests.