Reviewerly/exHarmony huggingface dataset:
Analysis of Bilibili's Transformation from the Perspective of Communication

Bilibili is now a highly aggregated cultural community for China's younger generation and one of the most popular video sites in China, known as "China's YouTube." In its thirteen years of development, Bilibili has undergone many changes, and its IPO marked the beginning of Bilibili's journey from subculture to mass appeal. Today, Bilibili has more and more functions, and its video content has become diversified to cover many circles of interest. This research will use Bilibili as the object of study and analyze the desirability of the transformation methods used by Bilibili using communication theory. This research finds that Bilibili's transformation changes are particularly evident in video content and marketing methods. Based on Bilibili's development ideas and business data, the author identifies two primary purposes for Bilibili's transformation. In addition, social integration theory and use and satisfaction theory can effectively explain some of Bilibili's transformation methods, providing references for Bilibili itself and other companies.
Survey on Blockchain Cryptocurrency Wallet

Normal cash has developed and appears numerous downsides such as inaccessibility. It is inclined to burglary and is intensely directed by government offices. Cryptocurrencies have risen as a egotistic money related framework. They depend upon secure disseminated ledger data structure. Mining plays a critical portion in this framework. Basically, our cryptocurrency could be a conveyed database that keeps up tamper-proof information structure pieces containing his bunches of person exchanges. Blockchain innovation can be a widely emerging approach to data innovations. Bitcoin as a cryptocurrency has made several considerations since it was one of its earliest implementations. They discuss the key elements driving the development of sophisticated cryptocurrencies alongside Ethereum, a blockchain implementation with a focus on informed contracts. In its most basic form, our cryptocurrency may be thought of as a distributed database that keeps track of tamper-proof data structure blocks comprising batches of individual transactions.
Catch-23: The New C Standard Sets the World on Fire

A new major revision of the C programming language standard is nearly upon us. C23 introduces pleasant conveniences, retains venerable traps for the unwary, and innovates a gratuitous catastrophe. A few steps forward, much sideways shuffling, and a drunken backward stumble into the fireplace come together in the official dance of C standardization, the Whiskey Tango Foxtrot.
Secure multi-factor access control mechanism for pairing blockchains

Digitalization has made it easier for centric systems to transition from single-factor to two-factor authentication. However, several access control practices struggle to authenticate users correctly due to their weak and outdated authorization systems. Moreover, the majority of cloud-centric models suffer from security and single-point-of-failure issues. Consequently, several systems have migrated their access control services to the blockchain. A good illustration is using two-factor authentication techniques to increase transaction security in Bitcoin systems. However, with this holistic trend of 2FAs, multi-factor authentication has yet to see this trend. In this regard, the study proposes an access control technique that combines multiple factors of knowledge, inherent, and possession to authenticate users to the blockchain. The multiple factors derive a time-based access code for users to generate private keys. The efficiency of the proposed method is tested with a Py-Eth-pairing library to determine the computation cost and transmission overhead. Furthermore, we use the EIP (Ethereum Improvement Proposals) library to assess the gas cost and determine the throughput. Our findings show the suggested approach to achieving the lowest operational cost, making it scalable on the blockchain while demonstrating its practicability through the model framework.
A Knowledge Concept Recommendation Model Based on Tensor Decomposition and Transformer Reordering

To help students choose the knowledge concepts that meet their needs so that they can learn courses in a more personalized way, thus improving the effectiveness of online learning, this paper proposes a knowledge concept recommendation model based on tensor decomposition and transformer reordering. Firstly, the student tensor, knowledge concept tensor, and interaction tensor are created based on the heterogeneous data of the online learning platform are fused and simplified as an integrated tensor; secondly, we perform multi-dimensional comprehensive analysis on the integrated tensor with tensor-based high-order singular value decomposition to obtain the student personalized feature matrix and the initial recommendation sequence of knowledge concepts, and then obtain the latent embedding matrix of knowledge concepts via Transformer that combine initial recommendation sequence of knowledge concepts and knowledge concept learning sequential information; finally, the final Top-N knowledge concept recommendation list is generated by fusing the latent embedding matrix of knowledge concepts and the students’ personalized feature matrix. Experiments on two real datasets show that the model recommendation performance of this paper is better compared to the baseline model.
Mining actionable combined high utility incremental and associated sequential patterns

High utility sequential pattern (HUSP) mining aims to mine actionable patterns with high utilities, widely applied in real-world learning scenarios such as market basket analysis, scenic route planning and click-stream analysis. The existing HUSP mining algorithms mainly attempt to improve computation efficiency while maintaining the algorithm stability in the setting of large-scale data. Although these methods have made some progress, they ignore the relationship between additional items and underlying sequences, which directly leads to the generation of redundant sequential patterns sharing the same underlying sequence. Hence, the mined patterns' actionability is limited, which significantly compromises the performance of patterns in real-world applications. To address this problem, we present a new method named Combined Utility-Association Sequential Pattern Mining (CUASPM) by incorporating item/sequence relations, which can effectively remove redundant patterns and extract high discriminative and strongly associated sequential pattern combinations with high utilities. Specifically, we introduce the concept of actionable combined mining into HUSP mining for the first time and develop a novel tree structure to select discriminative high utility sequential patterns (HUSPs) for downstream tasks. Furthermore, two efficient strategies (i.e., global and local strategies) are presented to facilitate mining HUSPs while guaranteeing utility growth and high levels of association. Last, two parameters are introduced to evaluate the interestingness of patterns to choose the most useful actionable combined HUSPs (ACHUSPs). Extensive experimental results demonstrate that the proposed CUASPM outperforms the baselines in terms of execution time, memory usage, mining high discriminative and strongly associated HUSPs.
Does Corruption Hinder Domestic Investment in the MENA Region: Evidence from a Panel-Country Analysis

Abstract This study aims to investigate the impact of different facets of corruption on domestic investment (DI) in the Middle East and North Africa (MENA) economies. The main purpose of this empirical research to assess the influence of bribes, abuse of public office and nepotism on domestic investment in 14 MENA countries over the period 2005–2022. The results of 2SLS model indicate that corruption could affect negatively on domestic investments through two channels, namely: illegal payments (bribes) and the abuse of public office for private gain. In this case, the regulators and decision makers have to formulate new strategies that aimed particularly to fight these kinds of corruption to increase domestic investment.
Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by CMA-ES

Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimization approach. Recently, an algorithmic level decomposition of program evolution has been introduced that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem-solving was adapted to evolve these programs. The applicability of the approach and the effectiveness of the used multi-agent protocol as well as of the evolved genetic programs for the case of full enumeration in local agent decisions has already been successfully demonstrated. Symbolic regression, n-parity, and classification problems were used for this purpose. As is typical of decentralized systems, agents have to solve local sub-problems for decision-making and for determining the best local contribution to solving program evolution. So far, only a full enumeration of the solution candidates has been used, which is not sufficient for larger problem sizes. We extend this approach by using CMA-ES as an algorithm for local decisions. The superior performance of CMA-ES is demonstrated using Koza’s computational effort statistic when compared with the original approach. In addition, the distributed modality of the local optimization is scrutinized by a fitness landscape analysis.
Sharpening Your Tools

This article presents our experience updating the high-performance Digital forensics tool BE (bulk_extractor) a decade after its initial release. Between 2018 and 2022, we updated the program from C++98 to C++17. We also performed a complete code refactoring and adopted a unit test framework. DF tools must be frequently updated to keep up with changes in the ways they are used. A description of updates to the bulk_extractor tool serves as an example of what can and should be done.
Using Transition Systems to Formalize Ideas from Vedānta

Abstract Vedānta is one of the oldest philosophical systems. While there are many detailed commentaries on Vedānta, there are very few mathematical descriptions of the different concepts developed there. This article shows how ideas from theoretical computer science can be used to explain Vedānta. The standard ideas of transition systems and modal logic are used to develop a formal description for the different ideas in Vedānta. The generality of the formalism is illustrated via a number of examples including saṃsāra , Patañjali’s Yogasūtra s, karma , the three avasthā s from the Māṇḍūkya Upaniṣad and the key difference between advaita and dvaita in relation to mokṣa .
Encryption of Dynamic Areas of Images in Video based on Certain Geometric and Color Shapes

The paper is devoted to the search for new approaches to encrypting selected objects in an image. Videos were analyzed, which were divided into frames, and in each video frame, the necessary objects were detected for further encryption. Images of objects with a designated geometric shape and color characteristics of pixels were considered. To select objects, a method was used based on the calculation of average values, the analysis of which made it possible to determine the convergence with the established image. Dividing the selected field into subregions with different shapes solves the problem of finding objects of the same type with different scales. In addition, the paper considers the detection of moving objects. The detection of moving objects is carried out based on determining the frame difference in pixel codes in the form of a rectangular shape. Cellular automata technology was used for encryption. The best results were shown by the transition rules of elementary cellular automata, such as: 90, 105, 150, and XOR function. The use of cellular automata technologies made it possible to use one key sequence to encrypt objects on all video frames of the video. Encryption results are different for the same objects located in different places of the same video frame and different video frames of the video sequence. The video frame image is divided into bit layers, the number of which is determined by the length of the code of each pixel. Each bit layer is encrypted with the same evolution, which is formed by one initial key bit sequence. For each video frame, a different part of the evolution is used, as well as for each detected object in the image. This approach gives different results for any objects that have a different location both on the video frame image and in different video frames. The described methods allow you to automate the process of detecting objects on video and encrypting them.
Bias Mitigation for Capturing Potentially Illegal Hate Speech

Abstract Hate speech is a persistent issue in social media. Researchers have analyzed and developed detection methods for hate speech on the basis of example data, even though the phenomenon is only rather vaguely defined. This paper provides an approach to identify hate speech in terms of German laws, which are used as a basis for annotation guidelines applied to real world data. We annotate six labels in a corpus of 1,385 German short text messages: four subcategories of illegal hate speech, offensive language and a neutral class. We consider hate speech expressions as illegal if the linguistic content could be interpreted in a given context possibly violating a specific law. This interpretation and a check by lawyers would be the next step which is not yet included in our annotation. In this paper, I report on strategies to avoid certain biases in data for illegal hate speech. These strategies may serve as a model for building a larger dataset. In experiments, I investigate the capability of a Transformer-based neural network model to learn our classification. The results show that this multiclass classification is still difficult to learn, probably due to the small size of the dataset. I suggest that it is crucial to be aware of data biases and to apply bias mitigation techniques when training hate speech detection systems on such data. Data and scripts of the experiments are made publicly available.
The Voting algorithm is robust to various noise models

A simple Voting algorithm has been shown to be effective at solving the OneMax problem in the presence of high levels of posterior noise in our previous research. In this paper, we extend this analysis to several different noise models, and show that the Voting algorithm remains robust in all of them. We consider the prior noise model and the partial evaluation of randomly selected bits. The Voting algorithm has superior runtime bounds on these problems compared to other published algorithms. We also introduce a new variant of partial evaluation, and further consider the simple model when a comparison-based oracle produces incorrect results with a fixed probability.
A Data Augmentation Method for Data-Driven Component Segmentation of Engineering Drawings

Abstract We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.
Nonparametric Generation of Synthetic Data Using Copulas

This article presents a novel nonparametric approach to generate synthetic data using copulas, which are functions that explain the dependency structure of the real data. The proposed method addresses several challenges faced by existing synthetic data generation techniques, such as the preservation of complex multivariate structures presented in real data. By using all the information from real data and verifying that the generated synthetic data follows the same behavior as the real data under homogeneity tests, our method is a significant improvement over existing techniques. Our method is easy to implement and interpret, making it a valuable tool for solving class imbalance problems in machine learning models, improving the generalization capabilities of deep learning models, and anonymizing information in finance and healthcare domains, among other applications.
Exploratory Factorial Model of Collaboration in the Covid-19 Era

The pandemic impacted the educational systems through anti-COVID-19 policies that are based on the distancing and confinement of people. In this sense, academic collaboration has been reported by the literature from 2019 to 2022 as an asynchronous Internet user strategy. It is a dual factorial structure that explains entrepreneurship and innovation, but does not anticipate scenarios of high risk and intensive use of Internet technologies, devices and networks in synchronous or asynchronous mode. Therefore, the objective of the study was to establish an explanatory model of the impact of the pandemic on academic collaboration. A cross-sectional, psychometric and exploratory study was carried out with a sample of 177 women from a municipality in central Mexico, considering their participation in social services and professional practices. A structure of four factors was found: Internet user, face-to-face, synchronous and asynchronous, which explained the total variance. In reference to the state of the art that exalts a two-factor model, its extension is recommended to anticipate scenarios of increasing risk.
Automation of Quantifying Security Risk Level on Injection Attacks Based on Common Vulnerability Scoring System Metric

An injection attack is a cyber-attack that is one of The Open Web Application Security Project Top 10 Vulnerabilities. These attacks take advantage of insufficient user input validation into the system through the input surface of a Web application as that user in the browser. The company’s cyber security team must filter thousands of attacks to prioritize which attacks are considered the most dangerous to be mitigated first. This activity of filtering thousands of attacks takes much time because you have to check these attacks one by one. Therefore, a method is needed to assess how dangerous a cyber-attack is that enters an organization’s or company’s server. Injection attack detection can be done by analyzing the request data in the web server log. Our research attempts to perform quantification modeling of the variations of two types of injection attacks, SQL Injection (SQLi) and Cross-Site Scripting (XSS), using Common Vulnerability Scoring System Metrics (CVSS). CVSS metrics are generally used to calculate the level of dangerous weakness in the system. This metric is never used to calculate the level of how dangerous an attack is. The modeling that we have made shows that SQLi and XSS attacks have many variations in levels ranging from low to high levels. We discovered that when classified with Common Weakness Enumeration Database, SQLi and XSS attacks CVE values would have high-level congruence with almost 94% value between one another vector on CVSS.
Interpretable Architecture Neural Networks for Function Visualization

In many scientific research fields, understanding and visualizing a black-box function in terms of the effects of all the input variables is of great importance. Existing visualization tools do not allow one to visualize the effects of all the input variables simultaneously. Although one can select one or two of the input variables to visualize via a 2D or 3D plot while holding other variables fixed, this presents an oversimplified and incomplete picture of the model. To overcome this shortcoming, we present a new visualization approach using an Interpretable Architecture Neural Network (IANN) to visualize the effects of all the input variables directly and simultaneously. We propose two interpretable structures, each of which can be conveniently represented by a specific IANN, and we discuss a number of possible extensions. We also provide a Python package to implement our proposed method. The supplemental materials are available online.
Flexible multi-client functional encryption for set intersection

A multi-client functional encryption (MCFE) scheme [Goldwasser-Gordon-Goyal 2014] for set intersection is a cryptographic primitive that enables an evaluator to learn the intersection from all sets of a predetermined number of clients, without need to learn the plaintext set of each individual client. Using these schemes, it is impossible to compute the set intersections from arbitrary subsets of clients, and thus, this constraint limits the range of its applications. To provide such a possibility, we redefine the syntax and security notions of MCFE schemes, and introduce flexible multi-client functional encryption (FMCFE) schemes. We extend the aIND security of MCFE schemes to aIND security of FMCFE schemes in a straightforward way. For a universal set with polynomial size in security parameter, we propose an FMCFE construction for achieving aIND security. Our construction computes set intersection for n clients that each holds a set with m elements, in time O(nm). We also prove the security of our construction under DDH1 that it is a variant of the symmetric external Diffie-Hellman (SXDH) assumption.
Automatic product copywriting for e‐commerce

Abstract Product copywriting is a critical component of e‐commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying the proposed Automatic Product Copywriting Generation (APCG) system into the JD.com e‐commerce product recommendation platform. It consists of two main components: (1) natural language generation, which is built from a transformer–pointer network and a pretrained sequence‐to‐sequence model based on millions of training data from our in‐house platform; and (2) copywriting quality control, which is based on both automatic evaluation and human screening. For selected domains, the models are trained and updated daily with the updated training data. In addition, the model is also used as a real‐time writing assistant tool on our live broadcast platform. The APCG system has been deployed in JD.com since February 2021. By September 2021, it has generated 2.53 million product descriptions, and improved the overall averaged click‐through rate (CTR) and the conversion rate (CVR) by 4.22 and 3.61%, compared to baselines, respectively, on a year‐on‐year basis. The accumulated gross merchandise volume (GMV) made by our system is improved by 213.42%, compared to the number in February 2021.