Reviewerly/exHarmony huggingface dataset:
Performance analysis of SHA 2 and SHA 3

Since its inception in early 2009, Bitcoin has attracted a substantial amount of users and the popularity of this decentralized virtual currency is rapidly increasing day by day. Over the years, an arms race for mining hardware has resulted with miners requiring more and more hashing power in order to remain alive in the Bitcoin mining arena. The hashing rate and the energy consumption of the mining devices used are of utmost importance for the profit margin in Bitcoin mining. As Bitcoin mining is fundamentally all about computing the double SHA256 hash of a certain stream of inputs many times, a lot of research has been aimed towards hardware optimisations of the SHA256 Hash Standard implementations. However, due to attacks on Blockchain, the SHA 256 Hashing algorithm has come under threat. Most of the widely used cryptographic hash functions are under attack today. With the need to maintain a certain level of security, NIST had selected new cryptographic hash function through public competition. The winning algorithm, Keccak will not only have to establish a strong security, but also has to exhibit good performance and capability to run. In this context, we have analyzed SHA-3 finalists along with the used standard SHA-2. The performances of respective algorithms are evaluated by computing cycles per byte. The empirical analysis shows that two SHA-3 finalists viz. Skein and BLAKE perform better which are nearly same as the performance of SHA-2.
Communication-efficient Subspace Methods for High-dimensional Federated Learning

As an emerging technique to employ machine learning processes within an edge computing infrastructure, federated learning (FL) has aroused great interests in both industry and academia. In this paper, we consider a potential challenge of FL in a wireless setup, whereby uplink communication from edge devices to the central server has limited capacity. This is particularly important for machine learning tasks (such as training deep neural networks) in FL with extremely high-dimensional domains that can substantially increase the communication burden. To tackle this challenge, we first propose a basic method called Subspace Stochastic Gradient Descent for Federated Learning (FL-SSGD) to introduce the idea of subspace methods. Through theoretical analysis, we show that by choosing appropriate subspace matrices in FL-SSGD, we can reduce uplink communication costs compared to classical FedAvg method. To improve FL-SSGD, we then propose another method called Subspace Stochastic Variance Reduced Gradient for Federated Learning (FL-SSVRG) that has a faster convergence rate with less assumptions on objective functions. By conducting experiments of a nonconvex machine learning problem in two FL setups, we demonstrate the advantages of our methods compared to other communication-efficient methods.
False Data Injection Attack on Power System Data- Driven Methods Based on Generative Adversarial Networks

With the rapid development of power systems, power system models have become more and more complex, which limits the application of traditional analysis methods. Thus, data-driven methods play an increasingly important role in the power system. However, due to the shortcomings of data-driven methods such as poor interpretability and strong dependence on input data, the power system data-driven methods are vulnerable to malicious attacks. This paper proposes a false data injection attack strategy for power system data-driven methods based on generative adversarial networks. In this strategy, the generator is used to generate a disturbance vector, which is superimposed on the original measurement input to construct an attack vector, which has a significant impact on the prediction results of the data-driven model. The method described in this article is implemented and verified in new England IEEE 39-bus system, and the experimental results prove the effectiveness of the method.
Management Information Systems in Knowledge Society

The technical and practical dimensions of the management field are deeply related to information and decision, two main components of any managerial process. The decision, as a managerial tool, is the very reason for the existence of information. Same as the real system, in which the managerial processes take place, both components are the result of the activity of two relatively distinct systems—the information system and the decisional system. Due to strong dependencies and mutual correlations, they constitute the information-decision dual as an image/reflection of the real economic system for the manager. In the current context of technology development, this dual is increasingly built through information technology and communication specific tools, in a knowledge-based social development. Although management as a science has emerged relatively independent of the concerns in the information and decision domains, there is an obvious functional interconnection between the two fields. The aim of this paper is to present a progressive analysis of developments related to information systems in the last sixty years. Thus, in the first part, we will analyze from different perspectives the four generations of information systems development. In the last part, we will present a typological synthesis of information systems and the specific architectural concepts in the ecosystem of the knowledge society. The most important aspects regarding security, control, and audit of information systems, will also be examined. In the end of the paper, we will highlight the characteristics of the relationship between the knowledge society and the age of agile development for information systems.
AutoRat: Automated Risk Assessment Tool for Network Mapper Scanning

Network vulnerability discovery can generally be done using vulnerability scanning tools that are able to identify possible threats or flaws of network services, ports, devices, protocols, operating systems, and software connected to a network. Most existing tools give a list of vulnerabilities on network-related services and they are used in another stage of the analysis such as risk assessment, penetration test, and re-programming. However, the automatic compilation of the detected vulnerabilities into the risk assessment is crucial for timely risk mitigation and optimized penetration test process. This paper proposes a design and implementation of a risk assessment tool called Auto-RAT which delivers an automatic risk assessment from Nmap, a network enumeration tool supporting the penetration test process. To enable the usability and immediate risk analysis of the Nmap, Auto-RAT is designed to realize the process of Nmap and its result for the automatic compilation into the risk assessment engine. We propose a set of algorithms to convert Nmap scan results into risk assessment model where the risk scoring is calculated using the Common Vulnerability Scoring System (CVSS). Finally, the system generates final risk assessment score and possible risk mitigation strategies for the system evaluation. We run the simulation by comparing the overlapped results between our AutoRAT and Nessus scan result to demonstrate that AutoRAT can effectively reduce the set of vulnerabilities that will be executed in the penetration test process.
Detection of SQL Injection attack Using Machine Learning

Many software systems had a web component, and that web-based component, or web application, was accessible to the general public over the Internet. It is vulnerable to attack by the adversary. It is not uncommon for web and mobile applications to have a lackadaisical flaw that adversely affects their security and privacy. Database vulnerability attacks are becoming more common and harmful. It is critical to understand software defects and, more importantly, prevent these security issues. SQL injection and XSS scan the same security code, often employed in online applications developed in a weak and stable typed language. So we looked for a big point where an attacker may use a native notion or existing ways to access or hack data from the database. What kind of issue will arise if an attacker tries to make a database vulnerable by injecting SQL that affects the results.
A Reversible Watermarking for 2D Engineering Graphics Based on Difference Expansion With Adaptive Interval Partitioning

To resolve the distortion resulting from the mismatch between the interval partition and the watermark distribution in the reversible watermarking based on difference expansion, an investigation is made to difference expansion with adaptive interval partitioning. It is found that the conventional floating number-based methods with uniform interval partitioning are not fully considering different watermark distributions, and the embedding distortion can be optimized. Thus, a reversible watermarking based on difference expansion with adaptive interval partitioning is put forward for 2D engineering graphics. Each interval is adaptively partitioned into <inline-formula><tex-math notation="LaTeX">$2^{s}$</tex-math></inline-formula> sub-intervals, and the watermark is embedded via transforming the positions of the vertices to its corresponding sub-intervals. The watermark extraction is accomplished by acquiring the indices of the sub-intervals of the watermarked vertices, and the graphics recovery is achieved by moving the watermarked vertices to the reverse direction. Moreover, a new polar coordinate system is built to be invariant to overall geometric transformation and vertices rearrangement. Experimental results and analysis indicate that it maintains good authentication ability and semi-fragility. Furthermore, its imperceptibility is significantly improved compared with the existing watermarking schemes under the same experimental conditions.
Towards Handling Sudden Changes in Feature Maps During Depth Estimation

Depth estimation aims to predict depth map from RGB images without high cost equipments. Deep learning based depth estimation methods have shown their effectiveness. However in existing methods, depth information is represented by a per-pixel depth map. Such depth map representation is fragile facing different kinds of depth changes. This paper proposes a Compressive Sensing based Depth Representation (CSDR) scheme, which formulates the problem of depth estimation in pixel space into the task of fixed-length vector regression in representation space. In this way, deep model training errors will not directly interfere depth estimation, and distortions in estimated depth maps can be restrained in the greatest extent. In addition, we improve depth estimation from two other aspects: model structure and loss function. To capture the features in different scales, we propose a Multiscale Encoder \& Multiscale Decoder (MEMD) structure as the vector regression model. To further deal with depth change, we also modify the loss function, where the curvature difference between ground truth and estimation is directly incorporated. With the support of CSDR, MEMD and the curvature loss, the proposed approach achieves superior performance on a challenging depth estimation dataset: NYU-Depth-v2. A range of experiments support our claim that regression in CSDR space performs better than traditionally direct depth map estimation in pixel space.
Opinion mining on COVID-19 vaccines in India using deep and machine learning approaches

Since the COVID-19 outbreak, considering the people’s opinion has been perceived as the most crucial challenge for the government to combat the pandemic, such as implementing a national lockdown, instituting a quarantine procedure, providing health services, and more. Furthermore, the government made many critical decisions based on public opinion to combat coronavirus. Opinion mining or sentiment analysis has arisen as a method for mining people’s views on several issues using machine learning techniques. With the support of machine learning methods, this paper extracted the Indian people’s opinions on vaccines through Twitter tweets. More than four lakh vaccine-related tweets from May 04 to May 11, 2021, and from Aug 13 to Aug 21, 2021, were analyzed using state-of-the-art machine learning and deep learning approaches. The BERT and RoBERTa models produced promising results compared to other models on the collected twitter dataset.
A Hybrid Covariate Microaggregation Approach for Privacy-Preserving Logistic Regression

Abstract Distributed data networks (DDNs) with horizontally partitioned datasets are viable resources for multicenter research studies and pharmacosurveillance. Within DDNs, maintaining confidentiality and limiting the disclosure of sensitive information is critical. Consequently, data sharing between partners within the same network is either restricted or completely prohibited during statistical modeling. Current privacy-preserving methods for logistic regression span two extreme paradigms: meta-analysis (MA), which combines estimates based on partner-specific estimates, is convenient for the analytical center (AC) but requires separate implementations of the analysis by each data node; while distributed regression (DR), which provides overall estimates based on partner-specific data summaries, produces rigorous solutions but is an iterative process that is both time and resource consuming. A practical middle ground that combines the convenience of MA and the rigor of DR is lacking. We propose a likelihood-based approach for logistic regression modeling that combines the rigor of DR and the convenience of MA. The two-stage approach has an equivalent estimation performance as DR but foregoes its multiple iterative steps through an MA update step, and is therefore more user-friendly. The approach uses only aggregate-level covariates to estimate a starting pooled effect estimate and within-node data summaries for a single-shot update of the pooled estimate without requiring individual covariate values at the AC. We call the approach hybrid Pooled Logistic Regression (hPoLoR) and show that it conveniently provides accurate and efficient estimates of the standard individual-level log odds ratios and standard errors without revealing personal data. Hence hPoLoR provides a rigorous yet convenient and application-friendly alternative to MA and DR. The method is demonstrated through extensive simulations and application to the JCUSH data.
The GNS1 Algorithm for Graph Isomorphism

This paper presents a new algorithm that was developed for graph isomorphism, the main goal being obtaining the correct results with the best execution times. The things described are the utility and domains of usage for the algorithm, the nomenclature, the input data, the preconditions and graph definitions, the access of GNS1 to the query graphs and the data graph, the implementation details for the algorithm structure, methods and pruning techniques, then a series of test cases, the system specifications, the acknowledgment, the conclusions and the references. Motif graph finding has gathered increased popularity due to its vast domain of applicability. GNS1 is a backtracking algorithm with new pruning techniques for reducing the search space. It returns to the user all occurrences of a query graph found in a data graph while at the same time having much lower execution times than the STwig [1] and VF2 [2–4] algorithms. The algorithms can be used in a multitude of domains.
Vision Video Making with Novices: A Research Preview

[Context and motivation] It is crucial to develop a vision of what a new piece of software will do and how it will affect personal tasks, processes, and the environment. Vision videos have been proposed for visualizing a tentative vision very early in a project, long before prototypes are in reach. Such a video can support the elicitation and validation of (software) product visions. [Question/problem] However, vision videos often have to be produced by people without prior knowledge in video production. Can video novices be adequately supported through an adapted vision video creation process? What materials, documents, and steps are crucial for planning, recording, and finalizing a video? [Principal ideas/results] We derived recommendations for Affordable Vision Videos. In this paper, we describe how the recommended process and techniques performed when we created a vision video with the "Viva con Agua" (VcA) NGO. [Contribution] The lessons learned from this case study helped us improve our recommendations for requirements engineers who are not proficient in directing videos but consider using them as a good medium for expressing visions and soliciting rich feedback.
Robots and Events: Participant Preferences for Robot Services

Robots are rapidly taking over tasks in the workplace. However, there has been little or no research on the specifics of how the public views the use of robotic technologies in events. In this research note, we use data from a large-scale international survey to learn about how the general public views the use of robots in events and what attitudes and demographics are associated with acceptance of greater use of robots as labor in events. The findings show that the public is most supportive of using robots to supply information at events and least supportive of using robots as entertainers at events. However, regressions illustrate a general willingness to have robots do tasks for events and a generally positive attitude towards robots. These attitudes are best correlated with the acceptance of higher ratios of robots in the labor force at events.
Hand Gesture Recognition

Hand gesture recognition (HGR) is a natural kind of human-machine interaction that has been used in a variety of settings. In this chapter, the authors address work in the field of HGR applications in industrial robots, with an emphasis on processing stages and approaches in gesture-based human-robot interaction (HRI), which may be beneficial to other researchers. They examine several similar research in the field of HGR that use a variety of techniques, including sensor-based and vision-based approaches. They discovered that the 3D vision-based HGR technique is a hard but interesting study field after comparing the two methodologies. Hand gesture recognition has a major application for disabled peoples. The person who is not able to speak can use these kinds of systems to control the devices or various applications in his/her machine. This technology also has vast potential in the gaming industry. The gaming industry has already adapted this technique.
ChiTransformer: Towards Reliable Stereo from Cues

Current stereo matching techniques are challenged by restricted searching space, occluded regions and sheer size. While single image depth estimation is spared from these challenges and can achieve satisfactory results with the extracted monocular cues, the lack of stereoscopic relationship renders the monocular prediction less reliable on its own especially in highly dynamic or cluttered environments. To address these issues in both scenarios, we present an optic-chiasm-inspired self-supervised binocular depth estimation method, wherein vision transformer (ViT) with a gated positional cross-attention (GPCA) layer is designed to enable feature-sensitive pattern retrieval between views, while retaining the extensive context information aggregated through self-attentions. Monocular cues from a single view are thereafter conditionally rectified by a blending layer with the retrieved pattern pairs. This crossover design is biologically analogous to the optic-chasma structure in human visual system and hence the name, Chi-Transformer. Our experiments show that this architecture yields substantial improvements over state-of-the-art self-supervised stereo approaches by 11%, and can be used on both rectilinear and non-rectilinear (e.g., fisheye) images. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/ISL-CV/ChiTransformer.git
The effectiveness of an augmented reality app in online civil engineering learning

In this study, an AR-based mobile learning application is proposed to assist online civil engineering course learning during the COVID-19 pandemic. A quasi-experiment has been conducted, and feedback from both the teacher and students has been analysed to examine the effectiveness of the proposed approach in terms of learning achievements. The subjects were 46 sophomores who majored in civil engineering in one class taught by one instructor in a southern U.S. state university. The quasi-experimental results showed that the proposed approach could not significantly improve the students’ online learning achievements. However, the feedbacks brought some explanation to this non-significant result. They indicated that students found this mobile AR app to be an interesting, helpful, practical, and effective approach in their online learning that helped them gain more in-depth knowledge than traditional teacher-centred classroom instruction.
An intelligent decision making method based on Bayesian policy reuse framework

Transfer learning plays an important role in multi-agent reinforcement learning. Bayesian policy reuse(BPR) algorithm is an excellent transfer learning algorithm and has been successfully used in repeated stochastic games. BPR can quickly detect the opponents and select the optimal policy. BPR's main limitation is the need of an offline learning phase where the models (priori knowledge) can be obtained. However, BPR may perform poorly when there is a lack of prior knowledge or the prior knowledge is inaccurate. To solve this problem, we propose an algorithm that combine the no-regret with BPR. Our approach can be exploited to improve performance when the priori knowledge is accurate, and to achieve less poor performance when the priori knowledge is inaccurate. Our approach has robust theoretical guarantees and is validated on the repeated stochastic games.
An NLP Framework to Detect Ailment based on Summary of Patient Symptoms Supporting Multilinguality for Rural Area

India is home to approximately 1.4 billion people, has a diverse mixture of both urban and rural regions. Compared to the medical development in the urban sector, the rural sector is trailing behind by a lot. The advancement of technology in the 21st century has brought various sectors close to one another. With the help of this technology, we can bridge the gap between the two sectors in the medical domain. The medical sector is a sector that requires human intervention irrespective of however advance the technology has become. To have a balanced amount of technology and human mediation and to utilize both their knowledge efficiently and feasibly, we have come up with a solution that integrates both these aspects and tries to give the best possible accurate solution. Our module gives a probable detection of ailment based on symptoms which helps speed up doctor’s analysis. It also takes into consideration, the diversity of languages that India has, and it takes care that the communication with the patient is in a language that he/she understands. Sentiment Analysis is also performed to detect mental health issues among rural areas to create more awareness and to create normalcy regarding the same.