Reviewerly/exHarmony huggingface dataset:
Cybercrime and Electronic Evidence in India: a Comprehensive Analysis

As technology continues to advance, cybercrime has become a global issue of significant concern. With its rapidly expanding digital landscape, India has also witnessed an increase in cybercrime. The purpose of this research paper is to provide a comprehensive analysis of cybercrime in India, with a particular emphasis on electronic evidence. It examines the nature and types of cybercrimes prevalent in the country, the legal framework governing cybercrime, and the difficulties and strategies associated with the collection and preservation of electronic evidence. The findings highlight the need for robust cybercrime legislation, enhanced law enforcement capabilities, and the development of a secure and efficient framework for managing electronic evidence. This research paper seeks to contribute to the understanding of cybercrime trends in India by analysing the current landscape of cybercrime and the challenges associated with electronic evidence.
An Optimal Universal Construction for the Threshold Implementation of Bijective S-Boxes

Threshold implementation is a method based on secret sharing to secure cryptographic ciphers (and in particular S-boxes) against differential power analysis side-channel attacks which was proposed by Nikova, Rechberger, and Rijmen in 2006. Until now, threshold implementations were only constructed for specific types of functions and some small S-boxes, but no generic construction was ever presented. In this paper, we present the first universal threshold implementation with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t+2$ </tex-math></inline-formula> shares that is applicable to any bijective S-box, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> is its algebraic degree (or is larger than the algebraic degree). While being universal, our construction is also optimal with respect to the number of shares, since the theoretically smallest possible number, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t+1$ </tex-math></inline-formula> , is not attainable for some bijective S-boxes. Our results enable low latency secure hardware implementations without the need for additional randomness. In particular, we apply this result to find two uniform sharings of the AES S-box. The first sharing is obtained by using the threshold implementation of the inversion in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{2^{8}}$ </tex-math></inline-formula> and the second by using two threshold implementations of two cubic power permutations that decompose the inversion. Area and performance figures for hardware implementations are provided.
A Polynomial Time Algorithm for Constructing Optimal Binary AIFV-2 Codes

Huffman Codes are optimal Instantaneous Fixed-to-Variable (FV) codes in which every source symbol can only be encoded by one codeword. Relaxing these constraints permits constructing better FV codes. More specifically, recent work has shown that AIFV- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> codes can beat Huffman coding. AIFV- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> codes construct an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> -tuple of different coding trees between which the code alternates and are only almost instantaneous (AI). This means that decoding a word might require a delay of a finite number of bits. Current algorithms for constructing optimal AIFV- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> codes are iterative processes that construct progressively “better sets” of code trees. The processes have been proven to finitely converge to the optimal code but with no known bounds on the convergence rate. This paper derives a geometric interpretation of the space of binary AIFV-2 codes, permitting the development of the first polynomially time-bounded procedure for constructing optimal AIFV codes. This binary-search like procedure will run in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{3} b)$ </tex-math></inline-formula> time, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of symbols in the source alphabet and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> is the maximum number of bits used to encode any one input probability.
Blockchain and ESG: A Comprehensive Guide to Data Integrity in Modern Business

Environmental, Social, and Governance (ESG) factors have become crucial in the modern business landscape as stakeholders increasingly focus on long-term value creation. However, ESG reporting faces challenges in ensuring the reliability and integrity of non-financial metrics. Blockchain technology, often referred to as the "trust machine," offers a decentralized solution for ESG data management, integrating data from various sources and ensuring timestamping and tamper-resistance. While the potential benefits of blockchain are significant, concerns regarding the environmental impact of cryptocurrencies and data quality should be considered. Several case studies demonstrate the successful application of blockchain in enhancing ESG data integrity. Blockchain offers enhanced trust, accuracy, and regulatory compliance in ESG reporting, but a balanced perspective is required, acknowledging criticisms and concerns. The future of blockchain in ESG data management holds great potential with emerging trends like IoT and intelligent automation. Critical thinking and informed decision-making are essential for harnessing the full potential of blockchain in the pursuit of reliable and trustworthy ESG data.
Analytical Study of Cyber Security Risks and Challenges in the Education Sector

These days, we are witnessing the good sized of the virtual revolution and its applications in all fields, because the way of networking supplied the alternate of information, stories, and in all sectors, understanding with a whole lot of those superb functions, we discover that today’s virtual global has delivered a large type of dangers of its personal, which encompass assaults and malicious applications like viruses, worms, spyware documents, and plenty of others. and the training sector higher education isn't always far from the ones bad impacts that avoid the education process and affect its infrastructure and its outer edge, in conjunction with university college students, teachers, administrative assistants, the environment and teaching techniques. For this danger, we need to interest and cyber protection education and adopt study cyber safety coverage in higher training sectors institution that allows you to shield this important place. This study paper will find out the importance of cyber security on this vicinity and provide the strategies that students, education sector can utilize to sell cyber security all through higher training institutions an analytical way.
Combat Prototypes

Combat prototypes test whether fighting another player or non-player character feels fair and realistic for that particular game setting. In a realistic military simulation, weapons would need to look and act like their real world counterparts. However, in a fantasy or science fiction setting, weapons would need to look and act like what most people believe about those game settings.
Randomness in Games

Randomness adds uncertainty in games. Some games have no randomness whatsoever such as pure skill games like chess or Go. Other games are completely random such as children’s games like Candy Land or Chutes and Ladders. When games lack randomness, the game outcome depends entirely on each player’s skill. When games embrace total randomness, the game outcome depends entirely on chance.
Pedagogical possibilities of educational cooperation in the development of divergent thought of students

Importance. Within the framework of the competence approach, the higher school faces the task of training specialists with non-standard, versioned, multivariate thinking, called divergent. However, the scientific and methodic support for the development of this type of thinking in the conditions of vocational training at the university has not been developed enough. Therefore, in order to determine the pedagogical conditions necessary for the development of the above-mentioned phenomenon and the development of technology for its development in the process of educational cooperation, it is necessary to determine the potential of educational cooperation available for the development of divergent thinking of university students. The purpose of the research is to study educational cooperation and its types to substantiate the possibilities of its use in the context of the development of divergent thinking of university students. Research methods. The content analysis of scientific literature on the research topic, generalization, systematization, structuring of information, the method of logical and semantic modeling are applied. Results and Discussion. Antagonistic factors influencing the development of divergent thinking of students (cooperation-competition, activity-passivity, motivation-demotivation) are identified and described. The analysis of the specifics of educational cooperation is carried out. A logical and semantic model of the typology of educational cooperation has been developed, reflecting the variety of forms and methods of organizing joint activities of interaction participants and the possibility of combining them. Based on the developed typology, the possibilities of educational cooperation for the development of divergent thinking of university students are considered. Conclusion. Educational cooperation contributes to the development of such basic characteristics of divergent thinking as flexibility, non-standard, variability, as well as abilities that ensure divergent productivity. Practical application of the obtained results is possible for the development of pedagogical conditions and technology for the development of divergent thinking of students in the process of educational cooperation.
Introduction to SAGIN Security

Space-air-ground Integrated Network (SAGIN) is expected to play an increasing role in providing real-time, flexible, and integrated communication and data transmission services in an efficient manner. Nowadays, SAGIN has been widely developed for position navigation, environment monitor, traffic management, counter-terrorism, etc. However, security is a major concern in SAGIN, since the satellites, spacecrafts, and aircrafts are susceptible to a variety of traditional / specific network-based attacks, including eavesdropping, session hijacking, and illegal accessing. In this chapter, we review the network architecture and security issues of SAGIN. We discuss various security requirements that should be considered when designing SAGIN, and survey existing solutions proposed to meet these requirements. This section identifies key challenges and key technologies that still require extensive research and development for securing SAGIN.
Anomaly Detection in Ultrasonic Monitoring System Using Unsupervised Machine Learning

The development of monitoring and control systems must be carried out to ensure the efficiency and safety of industrial systems. The detection of anomalies in the monitoring system is of great importance for the predictive maintenance of pressure vessels and pipelines. In this sense, it is crucial to analyze the sensor data and investigate the latest updates in machine learning (ML) technology to detect the various system anomalies immediately. Ultrasonic (UT) monitoring system is one of the traditional non-destructive testing (NDT) methods that is widely used to monitor the various industrial components. The traditional algorithms based on setting a threshold value is not able to detect outliers in the monitoring system dataset. This study investigates this problem by evaluating the performance of various unsupervised ML algorithms and applying them to detect anomalies in multivariate time series of UT wall thickness monitoring system. The following methods are evaluated: Interquartile range, distance-based method, density-based method, and isolation forest. The results provide important insights that the established algorithms can be effectively implemented for anomaly detection.
Digitalization in Education (Terminological Aspect)

The given paper is devoted to a study of education terminology in the context of digitalization. The peculiarities of education terminology are considered on the base of the English language. Special focus is given to the research of the coronavirus pandemic impact on the increasing role of digitalization in education. Moreover, the author pays attention to the study of the reflection of the impact mentioned in the education terminology.
Nonparametric Empirical Bayes Prediction in Mixed Models

Mixed models are classical tools in statistics for modeling repeated data on subjects, such as data on patients, customers or firms collected over time. These models extend conventional linear models to include latent parameters, called random effects, that capture between-subject variation and accommodate dependence within the repeated measurements of a subject. Traditionally, predictions in mixed models are conducted by assuming that the random effects have a zero mean Normal distribution, which leads to the classical Best Linear Unbiased Predictor (BLUP) of the random effects in these models. However, such a distributional assumption on the random effects is restrictive and may lead to inefficient predictions, especially when the true random effect distribution is far from Normal. In this article, we develop a novel framework, EBPred, for empirical Bayes prediction in mixed models. The predictions from EBPred rely on the Best Predictor (BP) of the random effects, which are constructed without any parametric assumption on the distribution of the random effects and offer a natural extension to the BLUP when the true random effect distribution is not Normal. We develop theory to show that the corresponding predictions from EBPred are asymptotically optimal in terms of mean squared error for prediction. Extensive simulation studies demonstrate that EBPred outperforms existing predictive rules in mixed models and the efficiency gain is substantial in many settings. The method is illustrated on an application involving the prediction of bank stock returns and exhibits superior prediction performance relative to extant approaches.
Toys, Design and Technology: Intergenerational Connects and Embodied Cultural Practices

Toys represent symbolic, material, and cultural manifestations of design and technology. Developing an understanding of technologies through the lens of indigenous technological knowledge systems affords critical advantages, besides enabling dialogue on discursive practices around technologies. Indian toys are a case in point. An exhaustive analysis of the vast repertoire of toys from the rich, diverse, and evolving Indian culture is difficult to achieve. Hence, data is drawn from an earlier study involving pre-service teachers examining Indian childhoods through toys. The range of indigenous toys elicited is analysed using a heuristic framework developed to parse the diversity of toys based on two dimensions of the nature of toy functioning (static vs. dynamic) and influence on toy production (realistic vs. creative ingenuity). Besides aiding reflection on aspects of culture and cognition, the analysis of indigenous toys helps unpack complex, multi-layered interactions between humans, technologies, and societies. By stimulating context and culture, indigenous toys can facilitate the reflexive orientations in teaching and learning technology. The study offers possibilities for designing authentic engagements for learners, teachers, and teacher educators from the indigenous knowledge systems informed standpoint.
WildWood: A New Random Forest Algorithm

We introduce WildWood (WW), a new ensemble algorithm for supervised learning of Random Forest (RF) type. While standard RF algorithms use bootstrap out-of-bag samples to compute out-of-bag scores, WW uses these samples to produce improved predictions given by an aggregation of the predictions of all possible subtrees of each fully grown tree in the forest. This is achieved by aggregation with exponential weights computed over out-of-bag samples, that are computed exactly and very efficiently thanks to an algorithm called context tree weighting. This improvement, combined with a histogram strategy to accelerate split finding, makes WW fast and competitive compared with other well-established ensemble methods, such as standard RF and extreme gradient boosting algorithms.
Goal-Conditioned Reinforcement Learning With Disentanglement-Based Reachability Planning

Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem by leveraging planning algorithms to plan intermediate subgoals to augment GCRL. Their methods need two crucial requirements: (i) A state representation space to search valid subgoals, and (ii) a distance function to measure the reachability of subgoals. However, they struggle to scale to high-dimensional state space due to their non-compact representations. Moreover, they cannot collect high-quality training data through standard GC policies, which results in an inaccurate distance function. Both affect the efficiency and performance of planning and policy learning. In the letter, we propose a goal-conditioned RL algorithm combined with Disentanglement-based Reachability Planning (REPlan) to solve temporally extended tasks. In REPlan, a Disentangled Representation Module (DRM) is proposed to learn compact representations which disentangle robot poses and object positions from high-dimensional observations in a self-supervised manner. A simple Reachability Discrimination Module (REM) is also designed to determine the temporal distance of subgoals. Moreover, REM computes intrinsic bonuses to encourage the collection of novel states for training. We evaluate our REPlan in three vision-based simulation tasks and one real-world task. The experiments demonstrate that our REPlan significantly outperforms the prior state-of-the-art methods in solving temporally extended tasks.
Advancing the JPEG Compatibility Attack: Theory, Performance, Robustness, and Practice

The JPEG compatibility attack is a steganalysis method for detecting messages embedded in the spatial representation of an image under the assumption that the cover image was a decompressed JPEG. This paper addresses a number of open problems in previous art, namely the lack of theoretical insight into how and why the attack works, low detection accuracy for high JPEG qualities, robustness to the JPEG compressor and DCT coefficient quantizer, and real-life performance evaluation. To explain the main mechanism responsible for detection and to understand the trends exhibited by heuristic detectors, we adopt a model of quantization errors of DCT coefficients in the recompressed image, and within a simplified setup, we analyze the behavior of the most powerful detector. Empowered by our analysis, we resolve the performance deficiencies using an SRNet trained on a two-channel input consisting of the image and its SQ error. This detector is compared with previous state of the art on four content-adaptive stego methods and for a wide range of payloads and quality factors. The last sections of this paper are devoted to studying robustness of this detector with respect to JPEG compressors, quantizers, and errors in estimating the JPEG quantization table. Finally, to demonstrate practical usability of this attack, we test our detector on stego images outputted by real steganographic tools available on the Internet.
Limits of Data Driven Steganography Detectors

While deep learning has revolutionized image steganalysis in terms of performance, little is known about how much modern data driven detectors can still be improved. In this paper, we approach this difficult and currently wide open question by working with artificial but realistic looking images with a known statistical model that allows us to compute the detectability of modern content-adaptive algorithms with respect to the most powerful detectors. Multiple artificial image datasets are crafted with different levels of content complexity and noise power to assess their influence on the gap between both types of detectors. Experiments with SRNet as the heuristic detector indicate that independent noise contributes less to the performance gap than content of the same MSE. While this loss is rather small for smooth images, it can be quite large for textured images. A network trained on many realizations of a fixed textured scene will, however, recuperate most of the loss, suggesting that networks have the capacity to approximately learn the parameters of a cover source narrowed to a fixed scene.
Analysis and Mitigation of the False Alarms of the Reverse JPEG Compatibility Attack

The Reverse JPEG Compatibility Attack can be used for steganalysis of JPEG images compressed with Quality Factor 100 by detecting increased variance of decompression rounding errors. In this work, we point out the dangers associated with this attack by showing that in an uncontrolled environment, the variance can be elevated simply by using a different JPEG compressor. If not careful, the steganalyst can wrongly misclassify cover images. In order to deal with the diversity associated to the devices or softwares generating JPEGs, we propose in this paper to build a deep learning detector trained on a huge dataset of downloaded images. Experimental evaluation shows that such a detector can provide operational false alarms as small as 10-4, while still correctly classifying 90% of stego images. Furthermore, it is shown that this performance is directly applicable to other image datasets. As a side product, we indicate that the attack is not applicable to images developed with a specific JPEG compressor based on the trunc quantization function.