Reviewerly/exHarmony huggingface dataset:
Cognitive Complexity Beyond Generalization: A Subjective Rating for the Human Comprehension

The cognitive complexity of a software determines the comprehension effort of a particular individual faces when designing, developing and maintaining a software. The comprehension level tends to be varied with each human resulting the cognitive complexity a subjective measurement. Expressing the cognitive complexity as a form of metric quantifies the comprehensibility as a generic value, which does not imply the subjectivity of human factor. This study elaborates the significance of expressing the cognitive complexity as a form of a subjective rating. The cognitive complexity rating has been pioneered with respect to the human and programming dependent factors related to human cognition. The Divisive hierarchical clustering algorithm has been used to train and predict the cognition rating per user. It has been clearly elaborated the subjectivity of the cognitive ratings over the quantitative and static complexity values of current cognitive and software complexity metrics. Thereby, the concept of cognition rates has been proposed as a preliminary step of determining and expressing the cognitive complexity.
Theoretical Understanding of the Information Flow on Continual Learning Performance

Continual learning (CL) requires a model to continually learn new tasks with incremental available information while retaining previous knowledge. Despite the numerous previous approaches to CL, most of them still suffer forgetting, expensive memory cost, or lack sufficient theoretical understanding. While different CL training regimes have been extensively studied empirically, insufficient attention has been paid to the underlying theory. In this paper, we establish a probabilistic framework to analyze information flow through layers in networks for sequential tasks and its impact on learning performance. Our objective is to optimize the information preservation between layers while learning new tasks. This manages task-specific knowledge passing throughout the layers while maintaining model performance on previous tasks. Our analysis provides novel insights into information adaptation within the layers during incremental task learning. We provide empirical evidence and practically highlight the performance improvement across multiple tasks. Code is available at https://github.com/Sekeh-Lab/InformationFlow-CL.
MLPSI: Multi-party Privacy Set Intersection with Linear Complexity

Multi-party privacy set intersectionenables multiple parties to compute the intersection of their datasets without leaking the data privacy. Among existing MPSI protocols, practical multi-party private set intersection from symmetric-key techniques(KMPRT) is a paradigm protocol leveraging the oblivious evaluation of programmable pseudorandom function (OPPRF). OPPRF or oblivious pseudorandom function (OPRF) are usually used to generate pseudorandom numbers, which require a lot of online interactions. To avoid such frequent interactions, this paper propose a new protocol named Privacy Set Intersection with Linear Complexity (MLPSI). The MLPSI integrates a Zero Sharing (ZS) and Garbled Bloomfilter(GBF), which will significantly reduces the communication and computing overhead.
Deepfakes and Information Security Issues

In the era of information technology development, new threats to information security have appeared. One of the new trends in this area is the deepfake. This article examines the essence of the term «deepfake», the history of its origin and the features of the impact on information. Algorithms for its creation and distribution are also considered. An assessment is given to the problem of the threat of the spread of deepfakes in the media, including on the Internet and on television. Possible solutions to the identified problem are considered.
RISK-ORIENTED APPROACH IN ASSESSING THE QUALITY OF TRAINING OF UNIVERSITY SPECIALISTS

The paper deals with the issues of information technologies implementing in the study of risks and improving the quality of training of the University of Technology specialists. The main types of risks in university are analyzed, including issues of competitiveness, software, quality of teaching and insufficient funding of budget training. The main indicators of the educational process of the university versus regional indicators are considered. The assessment of the influence of the enrollees’ level by the Unified State Exam and the current state of the university teaching staff on the quality of graduates is given. The role of information technologies in improving the quality of training and reducing the risks of the educational process is analyzed.
Environmental Consequences of Institutional Facts in Artificial Institutions

In multi-agent systems, the agents may have goals that depend on the common interpretation of actions and other agents acting in the system. These goals are thus social goals. Artificial institutions are used to provide such a social interpretation by assigning statuses to the concrete elements that compose the system. These statuses are supposed to enable the assignee element to perform functions that are not exclusively inherent to their design features. However, the consequences in the environment of the enabled functions are not explicit in the existing models of artificial institutions. As a consequence, (i) agents may have difficulties reasoning about the achievement of their social goals and (ii) these institutions are not well instrumented to receive incoming agents in the case of open systems. Considering these problems, this paper proposes the addition of purposes in artificial institution models to express the consequences in the environment of the constitution of status-functions helping the agents to reason about the fulfillment of their social goals. We evaluate the proposal in some scenarios, showing how the agents can use purposes to reason about the satisfaction of their social goals in institutional contexts and how the institution can be flexible enough to support new agents operating in the system.
Carbon-Neutral Distributed Ledger

This work introduces a carbon-neutral approach for Distributed Ledger. With computationally intensive consensus models creating exceptional levels of wasted energy worldwide, it is crucial that distributed ledger technology progresses in a direction that alleviates this trend. This work offers both a novel consensus model and a user incentive that departs from the usage of computational processes for consensus. This work considers the need for longevity of distributed ledger and reflects the desires of the different user archetypes engaged in the recent upswing of cryptocurrency. We anticipate that some users engage for investment or growth speculation, some engage for transactional purposes, and others engage for analytics and transaction verification. We introduce a sustainable distributed ledger that provides engagement considerations for each archetype while eliminating wasteful side effects of computational proof-of-work algorithms. The incentive for contribution included for this novel distributed ledger is based on participation over time. This work shows the ability to incentivize each of the archetypes aligned for the purpose of maintaining ledger data and transactions over time. In the model introduced, the key user engagement focus is redirected from computational waste found in current cryptocurrencies to a model that incentivizes constant uptime to receive tokens released for use over time. In addition, we demonstrate that current incentive models which result in global energy waste are converted to a user incentive where all rewards are aligned to maintain continually available data storage.
Learning Feature Alignment Architecture for Domain Adaptation

In domain adaptation, where the feature distributions of the source and target domains are different, various distance-based methods have been proposed to handle the domain shift by minimizing the discrepancy between the source and target domains. These methods use hand-crafted bottleneck networks, which might hinder the alignment of hidden feature representations extracted from both domains. In this paper, we propose a new method called Alignment Architecture Search with Population Correlation (AASPC) to automatically learn the architecture of the bottleneck network that can align the source and target domains. The proposed AASPC method introduces a new similarity function called Population Correlation (PC) to measure the domain discrepancy. The proposed AASPC method leverages PC to learn the alignment architecture and domaininvariant feature representation. Experiments on several benchmark datasets, including Office-31, Office-Home, and VisDA-2017, show the effectiveness of the proposed AASPC method.
A Solution for the Offline Double-Spending Issue of Digital Currencies

Digital currencies are legally valid legal tender issued by the country’s central bank, equivalent to the paper currencies currently on the market, with value characteristics and legal solvency, manufactured and managed by the central bank. User privacy and the security against the double spending issues are among the most important factors of the electronic currency. Features such as the anonymity, one time and the unforgeability are of great importance to the digital currency in order to replace the existing paper money in the market. In this paper, we effectively achieve the one-time feature of the digital currencies based on the one-time signature power delegation technique, so as to successfully avoid the “double spending” issue when users are offline.
3D Shape and Texture Features Fusion using Auto-Encoder for Efficient Face Recognition

Face recognition is one of the most widely used biometrics for identifying people. However, face images suffer from several issues that could affect the achieved results, especially in a crowded environment. Such as facial expression, occlusion, low resolution, noise, illumination and pose variation. In this paper, we propose a robust image representation system for face recognition. First, 3D face data reconstructed from 2D images are used instead of 3D capture. This is accomplished by modeling the difference in the texture map of the 3D aligned input and reference images. Then, fusing shape and texture local binary patterns (LBP) on a mesh for face recognition using the Mesh-LBP. Finally, we used a deep Auto-Encoder to create a compact data representation based on the obtained face images descriptors from the Mesh-LBP. Through experiments conducted on the Multi-PIE and Bosphorus databases, we show that our method is very competitive against state-of-the-art methods.
Fairness-Aware Active Learning for Decoupled Model

Active learning (AL) aims to actively label training data for supervised learners to efficiently improve accuracy of the trained models. However, traditional AL techniques ignore model fairness, which is an important social norm in modern machine learning applications. In this paper, we propose the first fairness-aware active learning technique for decoupled model called D-FA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L. It labels data that receive disagreed predictions from the decoupled models, and we hypothesize it can simultaneously improve model fairness and model accuracy. We present theoretical justification of D-FA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L, proving it can reduce model unfairness under proper conditions. In experiments, we show D-FA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L can reduce model unfairness effectively, and the reduction is more efficient than both classic and state-of-the-art AL techniques on three data sets. In the meantime, D-FA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L is able to maintain similar improvement rate on model accuracy.
PhishRescue: A Stacked Ensemble Model to Identify Phishing Website Using Lexical Features

The increased usage of the internet due to the development of network technologies has increased the probability of social engineering attacks in different sectors such as financial institutions, webmail, payment, social media, retail, shipping, and cryptocurrency. Out of five common social engineering attacks such as phishing, pretexting, baiting, quid pro quo, and tailgating, phishing is the most familiar one. Massive loss of money has occurred because of this attack. So, several approaches have been proposed till now to fight this attack. Among all the approaches, a machine learning algorithm is an appropriate method to identify phishing attacks because of its ability to find patterns in URL (Uniform Resource Locator) to detect phishing sites. This study uses two datasets of URLs from Kaggle. During preprocessing, we removed unnecessary values and then extracted the appropriate attributes to accurately classify the phishing URL from all types of URLs using regular expressions in natural language techniques. Finally, we used six individual classification algorithms: Extra Tree, Logistic Regression, Gaussian Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, and three ensemble methods such as Bagging Stacking and Voting for dataset training and testing. The results of our tests show that the proposed model achieves an accuracy of 85.08% for binary classification and 81.31% for multi-class classification for the first dataset and an accuracy of 95.99% for binary classification for the second dataset
Efficient NIZK Arguments with Straight-Line Simulation and Extraction

Non-interactive zero-knowledge (NIZK) arguments allow a prover to convince a verifier about the truthfulness of an $$\mathcal {N}\mathcal {P}$$ -statement by sending just one message, without disclosing any additional information. In several practical scenarios, the Fiat-Shamir transform is used to convert an efficient constant-round public-coin honest-verifier zero-knowledge proof system into an efficient NIZK argument system. This approach is provably secure in the random oracle model, crucially requires the programmability of the random oracle and extraction works through rewinds. The works of Lindell [TCC 2015] and Ciampi et al. [TCC 2016] proposed efficient NIZK arguments with non-programmable random oracles along with a programmable common reference string. In this work we show an efficient NIZK argument with straight-line simulation and extraction that relies on features that alone are insufficient to construct NIZK arguments (regardless of efficiency). More specifically we consider the notion of quasi-polynomial time simulation proposed by Pass in [EUROCRYPT 2003] and combine it with simulation and extraction with non-programmable random oracles thus obtaining a NIZK argument of knowledge where neither the zero-knowledge simulator, nor the argument of knowledge extractor needs to program the random oracle. Still, both the simulator and the extractor are straight-line. Our construction uses as a building block a modification of the Fischlin’s transform [CRYPTO 2005] and combines it with the concept of dense puzzles introduced by Baldimtsi et al. [ASIACRYPT 2016]. We also argue that our NIZK argument system inherits the efficiency features of Fischlin’s transform, which represents the main advantage of Fischlin’s protocol over existing schemes.
Development of Application Software for Generating Music Composition Inspired by Nature Using Deep Learning

Music composition for small-scale gaming companies to generate theme music to generate unique notes for each level of the game demands the requirement of machine learning-based automated nature-inspired music composition. It is worth mentioning that even great musicians usually get inspired by nature to compose the musical notes for the film. In this paper, an attempt is planned to develop the application software to generate musical notes using Deep learning. Deep learning algorithm can help to get predictions, classifications, and decisions based on the data without extracting the features from the data. We propose to use a Deep learning algorithm to hunt tunes from nature.
AI SoC-Based Accelerator for Speech Classification Accélérateur de classification de la parole basé sur un AI SoC

Speech classification acceleration using field-programmable gate arrays (FPGAs) is a well-studied field and enables the potential to gain both speed and better energy efficiency over other processor-intensive classifiers. System-on-chip (SoC) architecture allows for an integrated system between programmable logic and processor and for increased bandwidth communications to on-chip peripherals and memory. This article serves as an investigation of the utility of an edge-based support-vector machine (SVM) implemented onto a Zynq-XC7Z020 multiprocessor system on a chip (MPSoC) for the acceleration of three speech class pairs. The system allows for a parallelized structure, which yielded a faster classifier model. The results were found to be an acceleration factor of 2.08 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> . This appears to have come at the cost of a decrease in prediction accuracy, lowering from 92.5% to 83.5% positive prediction percentage likely due to decreased data resolution. The resolution used in this model was a 16-bit fixed-point format for the hardware interpretation and a floating-point format for the software benchmark. The resource usage of the FPGA was also analyzed for both overlays and can yield a 21% reduction in CPU usage.
Shedding Light in the Dark Forest: A Theory of Liability For Cryptocurrency "MEV" Sandwich Attacks

On public, permissionless blockchains like Ethereum, space is scarce and crypto traders must compete to use it for executing their transactions.That means those who control this space in the form of blocks resemble landlords who can extract rent."Maximal Extractable Value" (MEV) refers to trading strategies that exploit the ability to decide what transactions go into a block.Those who control the contents of a block (validators) can obtain rents not only for including transactions in a block, but also for ordering them in profitable ways-say, by letting transactions "front-run" others.Since rising to prominence in 2019, MEV has quickly become a major market phenomenon, generating $600 million in profit between 2020 and 2022 alone, while affecting tens of billions of dollars in transaction value.MEV is often condemned.Techniques like "sandwich attacks" which involve trading ahead of other users' trades, have been described as toxic, fraudulent, manipulative -even theft.However, this broad denunciation of MEV is too quick, as the technical nuances of how each kind of MEV extraction operates are determinative of the legal risk it entails.The legality of MEV extraction under U.S. financial laws has yet to be subject to sustained scholarly analysis, and the present Article aims to fill this gap.We undertake the first systematic analysis of how U.S. securities and commodities law, particularly the broad anti-manipulation rules wielded by the SEC (Rule 10b-5) and CFTC (Rule 180.1), apply to core MEV extraction techniques on Ethereum.
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applications where conventional measures are not well suited. Video Moment Retrieval (VMR) is one such application, and the current practice is to use R@K, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> for evaluating VMR systems. However, this measure has two disadvantages. First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-K ranked list by treating the list as a set. Second, it binarizes the Intersection over Union (IoU) of each retrieved video moment using the threshold <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> and thereby ignoring fine-grained localisation quality of ranked moments. We propose an alternative measure for evaluating VMR, called Average Max IoU (AxIoU), which is free from the above two problems. We show that AxIoU satisfies two important axioms for VMR evaluation, namely, Invariance against Redundant Moments and Monotonicity with respect to the Best Moment, and also that R@ K, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> satisfies the first axiom only. We also empirically examine how Ax-IoU agrees with R@K, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> , as well as its stability with respect to change in the test data and human-annotated temporal boundaries.
A Cloud Resource Scheduling Method Based on Multi-dimensional Service Evaluation

With the continuous evolution of cloud computing technology, cloud resources can carry complex and diverse service applications. Different services have different requirements on cloud resources, and task scheduling is difficult, affecting the stability of cloud environment. In order to improve the overall utilization level of cloud resources, this paper proposes a cloud resource scheduling method based on multidimensional service evaluation (CSMSE), which monitors global real-time resources, allocates cloud resources according to service classification results, and achieves global optimal scheduling. Finally, we choose CloudSim for experiment. The results show that the algorithm can effectively improve the utilization of cloud resources compared with the traditional scheduling method, and guarantee the priority scheduling of services with high development possibility.
COMPARISON OF LINEAR REGRESSION MODELS OF THERMOPHYSICAL PROPERTIES WITH MODELS BASED ON MACHINE LEARNING

The paper compares classical models for determining the thermophysical properties of steels based primarily on empirical equations derived using linear regression methods with models created using machine learning methods.The selected investigated quantities include phase transformation temperatures, specific heat capacity, coefficient of thermal expansion.The results of both approaches are verified on the measured data by methods of thermal analysis such as differential scanning calorimetry, differential thermal analysis and dilatometry.The methods are evaluated both in terms of the accuracy of predictions and in terms of the adequacy of use for a specific purpose, or in terms of the complexity of creating and using the model.
Exploration of Data Acquisition and Processing Technology Course Education Based on OBE Concept

This paper proposes a practical teaching program of data acquisition and processing technology course based on the concept of Outcome based education (OBE). It takes students as the main body and adopts reverse thinking to reform and construct the current curriculum system. Meanwhile, the analysis and summary of the teaching effects presented after the improvement demonstrate that the program is effective in improving teaching quality, developing students' engineering thinking logic, and enhancing students' ability to solve practical engineering problems. Students have shown great performance in many professional competitions, and their final grades have also steadily increased year by year.