Reviewerly/exHarmony huggingface dataset:
MODEL FOR CALCULATING THE COSTS OF A BUG BOUNTY PROGRAM FOR TESTING SECURITY VULNERABILITIES

The article describes the ways of researching bug bounties of programs and proposes a new approach for calculating the score of the found vulnerabilities. The paper begins with an introduction to the understanding of vulnerability management processes and the concept of an attack surface. The paper analyzes the statistics of all vulnerabilities found in information systems over the past ten years, which are divided according to the standard CVSS score. The types and vectors of attacks are analyzed in the example of the financial sector. Additionally, hacking and incidents are categorized by attack vectors in the financial sector. The following is the ratio of the most popular types and vectors of attacks to the criticality of information systems. A rating of critical and high vulnerabilities of one of the bug bounty platforms is presented with a detailed description of the types of attacks and exploitation techniques. An integral part of the vulnerability management process is the categorization of importance and impact on the organization. Possible life cycle scenarios for the identified vulnerability in an information system are also presented through the eyes of the owner of the vulnerability information and the owner of such an information system. A comparative quantitative and qualitative analysis of the maturity of bug bounty programs from the moment of launch and over the years, as well as the factors influencing the maturity of the program, are carried out. The statistics of vulnerabilities found in public bug bounty programs over the past six years are analyzed. The author proposes her approach to calculating the effective cost of a bug bounty program and conducts an experimental test on three programs. The factors influencing the calculation of the effective cost of vulnerabilities are highlighted. Approaches to vulnerability assessment and validation by bug bounty platforms and the stages of arbitration between the owner of the information system and the vulnerability researcher are considered. The study concludes with recommendations for achieving a higher level of maturity in vulnerability management processes. The forging highlights the continuity of the emergence and disappearance of additional factors in vulnerability management processes, in which bug bounty programs are an integral part. The interdependence of the maturity of the company’s processes and its bug bounty program requires the attraction of sufficient resources for its effectiveness.
Crop Recommendation Systems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review

Data-driven approaches and resource management to improve yield are becoming increasingly frequent in agriculture with the progress in technology. Based on a broad variety of environmental variables, this research compares two graph-based crop recommendation algorithms, GCN and GNN. Our methods select the optimal crop for a season based on nitrogen, potassium and phosphorus levels, as well as temperature, humidity, soil pH and rainfall. We assess the dataset’s complexity using GCN and GNN, which can handle graph-based structured data well. We utilize supervised learning to structure input information as nodes in a graph with edges reflecting plausible feature relationships to predict the optimal crop based on environmental conditions. Our experiment creates a graph via data preprocessing. Crop recommendation effectiveness is assessed using F1-score, recall, accuracy and precision for both models. To prevent overfitting and ensure generalizability, we employ k-fold cross-validation. Our crop suggestion comparison of GCN vs. GNN shows their pros and cons. Due to its concentration on graph convolution and feature aggregation, GCN captures localized connections in the feature graph better than GNN, which competes in situations needing larger feature interactions. This research advances graph-based models in agriculture and highlights their potential to enhance precision agriculture. We prioritize choosing the optimum graph-based model based on the dataset’s nature and inherent links to optimize crop management and resource allocation.
Improve Code Summarization via Prompt-Tuning CodeT5

Code comments are crucial in software engineering, aiding in program maintenance and code reuse. The process of generating clear and descriptive code comments, outlining code functionality, is called code summarization. Existing code summarization methods are typically trained using transformer-based models. However, these trained models often possess limited parameters and lack specific training tasks, hindering their ability to capture code semantics effectively. This paper uses a high-capacity pre-trained model, CodeT5, for code summarization. CodeT5 is designed with an encoder-decoder architecture that excels in code summarization tasks. Furthermore, we adopt a novel paradigm, "pre-train, prompt, predict", to unlock the knowledge embedded within CodeT5. We devise a prompt template to convert input code into code prompts and fine-tune CodeT5 with these prompts—a process we term prompt tuning. Our effectiveness experiments demonstrate that prompt tuning CodeT5 with only 40% of the dataset can achieve comparable performance to fine-tuning CodeT5 with 100% of the dataset. This means our approach is applicable in few-shot learning scenarios. Additionally, our prompt learning method is not sensitive to the size of the tuning dataset. Our practicality experiments show that the performance of prompt-tuned CodeT5 far surpasses that of transformer-based models trained on code-comment datasets collected from Stack Overflow.
Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance Strategy

Because of the low convergence accuracy of the basic Harris Hawks algorithm, which quickly falls into the local optimal, a Harris Hawks algorithm combining tuna swarm algorithm and differential mutation strategy (TDHHO) is proposed. The escape energy factor of nonlinear periodic energy decline balances the ability of global exploration and regional development. The parabolic foraging approach of the tuna swarm algorithm is introduced to enhance the global exploration ability of the algorithm and accelerate the convergence speed. The difference variation strategy is used to mutate the individual position and calculate the fitness, and the fitness of the original individual position is compared. The greedy technique is used to select the one with better fitness of the objective function, which increases the diversity of the population and improves the possibility of the algorithm jumping out of the local extreme value. The test function tests the TDHHO algorithm, and compared with other optimization algorithms, the experimental results show that the convergence speed and optimization accuracy of the improved Harris Hawks are improved. Finally, the enhanced Harris Hawks algorithm is applied to engineering optimization and wireless sensor networks (WSN) coverage optimization problems, and the feasibility of the TDHHO algorithm in practical application is further verified.
Multi-View Feature Fusion Model for Software Bug Repair Pattern Prediction

Many search-based Automatic Program Repair (APR) techniques employ a set of repair patterns to generate candidate patches. Regarding repair pattern selection, existing search-based APR techniques either randomly select a repair pattern from the repair pattern set to apply or prioritize all repair patterns based on the bug's context information. In this paper, we introduce PatternNet, a multi-view feature fusion model capable of predicting the repair pattern for a reported software bug. To accomplish this task, PatternNet first extracts multi-view features from the pair of buggy code and bug report using different models. Specifically, a transformer-based model (i.e., UniXcoder) is utilized to obtain the bimodal feature representation of the buggy code and bug report. Additionally, an Abstract Syntax Tree (AST)-based neural model (i.e., ASTNN) is employed to learn the feature representation of the buggy code. Second, a co-attention mechanism is adopted to capture the dependencies between the statement trees in the AST of the buggy code and the textual tokens of the reported bug, resulting in co-attentive features between statement trees and reported bug's textual tokens. Finally, these multi-view features are combined into a unified representation using a feature fusion network. We quantitatively demonstrate the effectiveness of PatternNet and the feature fusion network for predicting software bug repair patterns.
The Coevolution of Knowledge, Knowledge Management, and Social Competence in Higher Education

Universities are considered as learning institutions and their output is knowledge. Their main objectives are to promote knowledge and to integrate three main roles: (1) teaching and learning toward an award; (2) research and publication; and (3) activities centred toward work-based learning. Researchers generally categorize knowledge in three dimensions, cognitive, functional and social competence which are clearly consistent with the French paradigm- savoir, savoir faire, and savoir être. Delamare Le Deist and Winterton (2007) acknowledged that knowledge, that is, understanding is captured by cognitive competence, skills are captured by functional competence and behavioral and attitudinal competencies are captured by social competencies. This chapter describes some basic concepts of social competence in the tertiary education and examines the relationship that exists among knowledge, knowledge management, and social competence. Achieving personal goals and at the same time maintaining positive relationship over time and across situations is one of the main definition of social competence, as brought forward by Rubin and Rose-krasner (1992). Social competence also embraces all the social, emotional and cognitive knowledge and skills individuals require to achieve their goals and to be effective in their relations with others (Kostlenik et al., 2014).
Forensic handwriting examination: An updated clinical review

ABSTRACT Handwriting is a unique property of an individual; no two individual has similar handwritings. Forensic handwriting examination is an important part of forensic document examination. Handwriting at crime scene provides evidence or clues about type of crime, nature of crime to the investigating officer and also find out identity of suspect or criminal. Handwriting examination involves the examination of various parameters in the writing which includes handwriting positions, surface used for handwriting, pen pressure, handwriting strokes, slants in writing, letter spacing, line quality, etc., handwriting examination is done based on the principle that no two individuals can write exactly alike. Forensic handwriting analysis is a pattern-based science that examines significant, repeated handwriting traits in questioned writing comparing and evaluating these traits in the known writing. If significant similarities are determined without differences, this may lead to a handwriting recognition. Nowadays, an automated computer-based identification and examination system are developed for forensic document examination which is more feasible to use a computer-assisted system in forensic identification of source issues related with questioned document analysis. Experimental eye-tracking is a technologically novel way of forensic handwriting examination. These studies may have future relevance in developing technology that can be linked to the computer in evaluating handwriting, especially in programming software that evaluates handwriting similar to the way a human examiner evaluates handwriting.
PREDICTION OF SKINCARE SALES TURNOVER USING THE SUPPORT VECTOR METHOD AT THE WIDYA MSGLOW SIDOARJO COMPANY

Every entrepreneur will certainly follow technological developments in the business world. MsGlow is one of theskincare businesses. The skincare business is one of the businesses that must compete with rapid and complexchanges, and this very competitive makes business people have to think of strategies for business continuity in orderto compete and also survive. One way that can be done is to utilize existing sales data. The importance of fast andprecise operational data processing, information system facilities can be an alternative to solving problems in dataprocessing, minimizing errors and accelerating the data processing process. As the number of sales transactionsincreases, there will be a buildup of data that has not been processed optimally. With the above problems, aforecasting system was created that can forecast skincare sales turnover using the Support Vector Machine (SVM)method. In this study, turnover in several areas will be forecasted. The kernel function variations used in SupportVector Machine (SVM) are RBF, Linear, and, Polynomial Degree 2. The results obtained from this research trialshow that the overall forecasting model is good. The accuracy of the three areas obtained with the RBF kernel has arelatively good MAPE. In the accuracy test to predict skincare sales turnover, the three areas got a fairly goodaccuracy value of 94.46%. In the Sidoarjo area, it is predicted that there will be a lot of decrease in turnover in 2023-2024.
DESIGN OF THRIFT SHOP E-CATALOG INFORMATION SYSTEM USING PHP AND JAVASCRIPT (CASE STUDY: MEYTWINS THRIFTING)

Currently, there is a popular trend among the younger generation regarding the use of used clothes. What's interesting is the emergence of the online thrift shop, which is a type of business that is booming in the fashion sector by selling used clothes through online platforms. Generally, these business people get clothing supplies in the form of bales (sacks) imported from abroad. The Online Thrift Shop has received a positive response from many young people by offering clothing that has a unique style in a retro or vintage atmosphere from the 90s, as well as world-famous brands. However, one of the obstacles faced by the online thrift shop is the mismatch between the products received by consumers and what was initially displayed by the seller. There are situations where the goods delivered to consumers do not match the description given by the seller. This phenomenon is one of the shortcomings that need to be considered in online thrift shop operations. There are many online shops that sell goods to consumers that do not match what is offered. This makes consumers' shopping confidence and comfort less comfortable. The aim of the research is to build an information system in the form of an e-catalog that can simplify the buyer process without having to come to the store, as well as provide information related to fashion products that are suitable for consumers or customers. In this research, we used the prototype method, then for the system design tools, we used UML modeling, and for database modeling, we used ERD. Information system built using PHP and Javascript. Then, based on the test results using BlackBox Testing from several respondents, the success rate of the system built was 95%.&#x0D; Keywords: E-Catalog, Prototype, PHP, Javascript, Blackbox Testing
Database System Development for Student Learning Planning Program

Developing a database system for universities is very important. Databases are useful for universities, one of which is data that involves students. The database is also useful for the students themselves. When students carry out the learning process, it will help them monitor the progress of their studies. Apart from that, the university monitors students in taking study programs, especially in the era of independent campus learning which is a program from the government. Apart from that, the database will also identify students who excel so that this can be done to develop learning. More than that, all student academic activities will be captured and become an information center for the university. The research was carried out using the observation method on students who input data for student learning plans, which is carried out every semester. With the government program in the form of independent learning, a system was created that can identify students taking part in the program activities. This database then becomes information for lecturers and universities to be able to determine learning targets and see program results. The development of this database system can also enable students to know their course progress.
Programmer Job Interviews: The Hidden Agenda

Top tech interviews test coding and CS knowledge overtly, but they also evaluate a deeper technical instinct so subtly that candidates seldom notice the appraisal. We'll learn how interviewers create questions to covertly measure a skill that sets the best programmers above the rest. Equipped with empathy for the interviewer, you can prepare to shine on the job market by seizing camouflaged opportunities.
RESEARCH AND DEVELOPMENT OF DEPTH OPTIMIZED CIRCUITS IN QUANTUM APPROXIMATE OPTIMIZATION ALGORITHM

One of the main challenges faced by researchers in the field of quantum computing is the problemof noise in quantum systems. Noise can significantly limit the performance of quantum algorithms.It is in this context that our research, aimed at the development and optimization of quantumalgorithms with a focus on depth, is updated. The depth of quantum circuits is one of the critical parametersin the development of quantum algorithms. Optimized circuits with improved depth have the potential to significantly reduce the impact of noise, which in turn should lead to improved efficiency.We aim to provide solutions that not only address technical constraints, but also provide practicalresults for quantum computing in the context of optimization problems. This study analyzes the use ofa quantum approximate optimization algorithm for solving complex combinatorial optimizationproblems. However, in the process of using this algorithm we encounter a serious limitation – noisein the quantum system, which significantly reduces its efficiency. To overcome the influence of noiseand improve the efficiency of quantum algorithms, several methods have been proposed. This paperpresents a greedy heuristic algorithm aimed at reducing the impact of noise. The main goal of thisalgorithm is to find a spanning tree of minimum height. This, in turn, reduces the overall depth ofquantum circuits and minimizes the number of CNOT gates, which is key to optimizing quantumcomputing. Through numerical analysis, it was demonstrated that the proposed greedy heuristicalgorithm is capable of significantly increasing the probability of successful completion of each iterationin the problem of finding the maximum cut in a graph by 10 times. Moreover, the study confirmsthat the average depth of the quantum circuit generated by the proposed heuristic algorithm is stilllinearly dependent on the size of the input data, but the slope of this linear dependence is reducedfrom 1 to 0.11 by using the proposed method.
HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video

We introduce HOSNeRF, a novel 360° free-viewpoint rendering method that reconstructs neural radiance fields for dynamic human-object-scene from a single monocular in-the-wild video. Our method enables pausing the video at any frame and rendering all scene details (dynamic humans, objects, and backgrounds) from arbitrary viewpoints. The first challenge in this task is the complex object motions in human-object interactions, which we tackle by introducing the new object bones into the conventional human skeleton hierarchy to effectively estimate large object deformations in our dynamic human-object model. The second challenge is that humans interact with different objects at different times, for which we introduce two new learnable object state embeddings that can be used as conditions for learning our human-object representation and scene representation, respectively. Extensive experiments show that HOSNeRF significantly outperforms SOTA approaches on two challenging datasets by a large margin of 40%~50% in terms of LPIPS. The code, data, and compelling examples of 360° free-viewpoint renderings from single videos: https://showlab.github.io/HOSNeRF.
Level, structure and dynamics of drug trafficking

The subject of this study is the level, structure and dynamics of crimes in the field of illicit drug trafficking. Some indicators of official statistics related to the operational situation in the fight against drug trafficking in the country are considered (the number of registered crimes, identification and suppression of drug distribution channels, etc.). The purpose of the study is to conduct a detailed analysis of statistical data on crimes related to drug trafficking, identify current problems in the fight against drug crime and formulate proposals for eliminating them. General scientific methods of cognition were used: the method of statistical analysis; method of analysis, synthesis; comparative method; methods of induction, deduction. An analysis of scientific literature has shown that the issues of drug crime are studied in domestic doctrine by many authors. In theory, issues related to crime rates, the impact of illegal migration on the drug situation in Russia, and the problems of investigating drug crimes committed using information technologies have been worked out. However, some aspects require further study. Within the framework of this study, an attempt was made to identify from the entire array the most pressing problems that require a speedy solution. In particular, it is proposed to reconsider the issue of the availability of electronic devices (vapes, electronic cigarettes) used by adolescents and young people to use psychoactive substances. A ban on the use of such devices should be established at the legislative level. It is concluded that the fight against drug crime must be comprehensive and carried out at several levels, including the prevention of drug crimes among minors.
GENERATION OF PSEUDORANDOM SEQUENCES ON MICROCONTROLLERS WITH LIMITED COMPUTATIONAL RESOURCES, ENTROPY SOURCES, AND STATISTICAL PROPERTIES TESTING

Traditional encryption algorithms cannot be implemented on Internet of Things (IoT) devices due to their constrained computational resources. This necessitates the search and development of cryptographic solutions for securing data processed and transmitted by such devices. When encrypting data on devices with limited computational resources, simple encryption algorithms based on elementary bitwise operations, such as bitwise modulo-2 addition (XOR), can be utilized since these operations execute in a single processor cycle and do not require complex computations. However, a drawback of such operations is their invertibility—knowing the encryption key enables easy decryption by applying the same operation to the ciphertext. Ensuring the reliability of such ciphers requires continuous generation of random encryption keys.This work explores the functionality of the linear congruential method for generating sequences of random numbers. Several entropy sources available on microcontrollers are presented for the initial generator value, along with proposed algorithms for collecting initial data from these sources. The use of noise from unconnected pins of the analog-to-digital converter is suggested as the primary entropy source, while the uninitialized area of the microcontroller’s random-access memory serves as an additional source. A method for generating random sequences using the specified entropy sources is implemented and the algorithm’s performance is evaluated, specifically the key characteristic—randomness of the encryption key. The NIST STS 800-22 test suite is employed for evaluation. In all tests, the random sequence generation algorithm demonstrated results confirming the hypothesis that the sequence can be considered random.