Reviewerly/exHarmony huggingface dataset:
TEAMS FORMATION FOR IT PROJECTS IMPLEMENTATION ON THE BASIS OF THE MODEL OF LIMITED RATIONALITY

The paper presents the possibility of applying the model of limited rationality to solve the forming project teams for the IT systems development and implementation problem.To achieve this goal, the following tasks were set and achieved: a review of standards in the field of information technology, which determine the requirements for labor resources involved in the project work was performed; a model of limited rationality for the selection of executors in the project team, which is based on the parameters and characteristics of the project conditions and the achievement of the overall project goal was presented; a formal description of the selection of executors and generation of a project team for the IT systems development and implementation in accordance with the functional requirements of the project. The main factor in the development and implementation of information systems is a thorough approach to the appointment of workers for project work. It is the formation of a project team in the context of attracting limited labor resources that work on the project during its implementation and the requirements for project tasks, is the main factor in the successful implementation of the whole project. According to flexible methodologies, when the project implementation plan may change at each iteration, the peculiarity of the project teams formation involved in the IT systems development and implementation determines the limitations on the quality requirements for individual project iterations. The use of models of limited rationality allows to balance between the requirements for the whole project and the requirements for specific project tasks in accordance with the possible involvement of executors for the most effective selection. The importance of selection of labor resources in the project team with the appropriate levels of competence and qualification for the possibility of performing all project tasks was substantiated.
The H2020 ANITA platform: generating knowledge about crime through user-centred innovative tools

This paper illustrates a novel user-centred and secure investigation platform to discover relevant data sources disseminated on the Web (including the Dark Web) and to analyse, enrich and correlate them to support knowledge-generation and investigations on illegal trafficking activities. The platform was designed and implemented in the context of the Horizon 2020 project ANITA (https://www.anita-project.eu/) to effectively support Law Enforcement Agencies in better understanding and investigating the online illegal trafficking framework and further improve their capacity to analyse complex criminal scenarios and big amount of relevant data. The paper illustrates the ANITA platform capabilities through some of the &#x2018;horizontal criminal scenarios&#x2019; defined by the Project to explore connections between illegal trafficking activities concerning different goods. These &#x2018;scenarios&#x2019; refer to cases of the same vendor distributing both firearms and synthetic drugs on the same or in different crypto markets, the same profiles found to be active on both some Dark Web crypto markets and Surface platforms or terrorist attacks performed with firearms bought on the Dark Web.
An Gradient-based Continuous-Time Trajectory Optimization method for Real-Time Collision Avoidance on collaborative robot

Collision avoidance is an essential consideration in the growing application of human-robot interaction (HRI) and robot-robot interaction (RRI). Based on predicting the trajectory of other agents, a continuous-time trajectory optimization method based on the gradient for real-time collision avoidance of manipulators is proposed in this paper. The algorithm constructs a gradient field in joint space. It optimizes trajectory by using convex hull characteristics of the B-spline so that the robot can avoid dynamic obstacles and generate a smooth and dynamic feasible trajectory in joint space. The proposed collaborative robot (Co-robot) trajectory planning framework can be calculated in real-time and verified by simulation.
Penerapan Apriori Hybrid Pada Transaksi Penjualan Barang

Ayu Nadi Swalayan is a retail company that produces a lot of every day sales transaction data, and it is stored for years without knowing the benefits and the placement of the goods is still random. From these problems, an effort is needed to process the data so the data is useful in the future. One of the process is using data mining techniques with apriori hybrid algorithm to find association rules for an items combination. Data product sale in a certain period is used to find the association rules. The results of this study are the development of applications that are used to determine consumer spending habits. So that the company can develop a strategy to promote the product sale and close placement for items that are often purchased together. The application testing found the effect of minimum support, minimum confidence on the number of rules, and lift ratio testing. The smaller the minimum support and minimum confidence, the more rules are generated and vice versa. The lift ratio value is directly proportional to the minimum confidence value and inversely proportional to the minimum support value. The higher the minimum confidence value, the higher the lift ratio value and vice versa. The more items in the transaction cause the minimum support threshold to be lowered in order to generate rules for the data analysis process with the hybrid apriori algorithm
A Novel Memory Management for RISC-V Enclaves

Trusted Execution Environment (TEE) is a popular technology to protect sensitive data and programs. Recent TEEs have proposed the concept of enclaves to execute code processing sensitive data, which cannot be tampered with even by a malicious OS. However, due to hardware limitations and security requirements, existing TEE architectures usually offer limited memory management, such as dynamic memory allocation, defragmentation, etc. In this paper, we present Ashman—a novel software-based memory management extension of TEE on RISC-V, including dynamic memory allocation, migration, and defragmentation. We integrate Ashman into a self-designed TEE and evaluate the performance on a real-world development board. Experimental results have shown that Ashman provides memory management functions similar to native user applications while ensuring enclave security without modifying hardware.
Contribution of learning and knowledge technologies in the digital competences of students of higher basic education

This research covers the results of the use of learning and knowledge technologies (LKT). Its objective was to recognize the effects of the application of ICTs in the digital competencies of students in higher basic education at the José María Santana Salazar educational unit in Ecuador from December 2020 to July 2021. The methodology used was a mixed qualitative-quantitative approach, the research was descriptive, the population consisted of teachers and students of the higher sub-level, the sample was selective. Surveys and an interview with an expert provided specific information. The results highlight those teachers believe that they have knowledge on this subject in 38% and consider that students do not have digital competencies in terms of information management and security in 75% and 85% respectively, at the same time they value its importance within learning as an innovative aspect for their task; although their activities are developed without the effective application of these. It is concluded that the effects of the application of ICTs are positive in motivating classes, facilitating the subjects, promoting the attention and commitment of the students; however, the application of these tools is deficient due to the digital gap and the lack of training of teachers.
Anomaly Detection Enables Cybersecurity with Machine Learning Techniques

Over recent years, the rapid development of Machine Learning has greatly stimulated its integration in various industrial applications. A major field in which this radical methodological shift has found considerable success is Industrial Cybersecurity. In this chapter, readers are provided with an overview of Cybersecurity issues for Industrial systems including Wireless Sensor Network, Fraud Detection System, IoT-based Industrial systems, and the Industrial Control Systems (ICSs) in conjunction with the Machine Learning approaches to these issues. Moreover, this chapter also addresses the challenge of handling multivariate time-series data which is the predominant type of data in the context of Cybersecurity in Smart Factory in the era of Industry 4.0. Throughout this chapter, readers can see how anomaly detection schemes for IoT-based systems and IoT-based Industrial systems could be designed from the aspect of effective Machine Learning-based algorithms as well as efficient architectures based on Cloud-Edge Computing. Finally, a further discussion about the challenges along with the difficulties and perspectives in designing an anomaly detection scheme is also presented at the end of the chapter.
Formation of postgraduate students’ professional competences through independent work

The purpose of this research is to get the opinions of the graduate students in order to establish their professional competences through an independent study. The research was carried out in accordance with the qualitative research method. The participant group of the research consisted of 40 students who were in the graduate programmes in various universities in Kazakhstan in the 2021–2022 academic year. Research data were collected with a semi-structured interview form prepared by the researchers. As a result of the research, the status of postgraduate students working in a job related to the education they received during their education was evaluated, and it was determined that the number of students working in an independent job and the number of students not working were close to each other. In the research, it was determined that the majority of students stated that independent work is important in the formation of professional competencies. Graduate students who participated in the research stated that they found their professional competencies somewhat sufficient. The vast majority of postgraduate students are concerned with the development of their professional competences through independent study. They suggested increasing employment opportunities for graduate students, providing students with the opportunity to practice by collaborating with the university and the industry, creating internship opportunities suitable for each programme and opening applied graduate programmes.&#x0D; Keywords: Postgraduate education, independent work, professional competencies, student opinions;
Convolutional Neural Network and its Applications in Artificial Intelligence

Deep learning is an artificial intelligence (AI) function that specially based onConvolutional neural networks (CNNs) techniques that simulate the workings of human brain for processing data and are able to learn without human supervision.This techniques are widely used in many applications of pattern and image recognition with obtaining a number of advantages compared to other traditional techniques.This study covers the basics of CNNs including a description to the functions of its various layers and mathematically learns how to use it in many AI applications.The structure models of these applications that are based on CNNs are proposed to show the improvement in processing time and state-of-the-art precision.This study also provides an overview of different CNNs architectures that are used in famous useful applications for different fields in our life based on computer vision tasks.
Reversible Computing with Fast Fully Static Fully Adiabatic CMOS.

To advance the energy efficiency of general digital computing far beyond the thermodynamic limits that apply to conventional digital circuits will require utilizing the principles of reversible computing. It has been known since the early 1990s that reversible computing based on adiabatic switching is possible in CMOS, although almost all of the "adiabatic" CMOS logic families in the literature are not actually fully adiabatic, which limits their achievable energy savings. The first CMOS logic style that achieved truly, fully adiabatic operation if leakage was negligible (CRL) is not fully static, which leads to a number of practical engineering difficulties in the presence of certain nonidealities. Later, "static" adiabatic logic families were described, but they were not actually fully adiabatic, or fully static, and were much slower. In this paper, we describe a new logic family, Static 2-Level Adiabatic Logic (S2LAL), which is, to our knowledge, the first CMOS logic family that is both fully static, and truly, fully adiabatic (modulo leakage). In addition, S2LAL is, we think, the fastest possible such family (among fully pipelined sequential circuits), having a latency per logic stage of one "tick" (transition time), and a minimum clock period (initiation interval) of 8 ticks. S2LAL requires 8 phases of a trapezoidal power-clock waveform (plus constant power and ground references) to be supplied. We argue that, if implemented in a suitable fabrication process designed to aggressively minimize leakage, S2LAL should be capable of demonstrating a greater level of energy efficiency than any other semiconductor-based digital logic family known today.
Continuum Wavelets and Distributions

The purpose of this work is to obtain a wavelet expansion of information flows, which are distribution flows (in the terminology of Schwartz). The concept of completeness is introduced for a family of abstract functions. Using the mentioned families, nested spaces of distribution flows are constructed. The projection of the enclosing space onto the nested space generates a wavelet expansion. Decomposition and reconstruction formulas for the above expansion are derived. These formulas can be used for wavelet expansion of the original information flow coming from the analog device. This approach is preferable to the approach in which the analog flow is converted into a discrete numerical flow using quantization and digitization. The fact is that quantization and digitization lead to significant loss of information and distortion. This paper also considers the wavelet expansion of a discrete flow of distributions using the Haar type functions.
Nonlinear System Identification Using Dynamic Memristor-Based Reservoir Computing System

Nonlinear systems have attracted a lot of attention because of their widespread existence in nature and life. Among them, the modeling and prediction of nonlinear systems is the focus of the research field of nonlinear systems. Although the traditional neural network has achieved good results, it is not conducive to being applied to practical problems due to the unsatisfactory training speed and large energy consumption. In this paper, considering the nonlinear characteristics of the memristor and the fast training speed of reservoir computing, we combine memristor and reservoir computing. Lorenz time series prediction and second-order nonlinear system modeling tasks are demonstrated. The results show that our model performs well in nonlinear time series prediction and nonlinear system model identification, the feasibility of the method is demonstrated. This is of great significance to the study of nonlinear systems and can be effectively applied to the analysis of nonlinear systems.
APPLYING OF MARKETING COMMUNICATIONS FOR INCREASING STUDENTS SATISFACTION OF HIGHER EDUCATION INSTITUTIONS AS AN INDICATOR OF TRUST IN SOCIETY

The article proposes the hypothesis that one of the indicators of public confidence may be the degree of satisfaction of higher education students with the educational program of the professional field in which they acquire knowledge and skills. Systematization of literature sources and approaches to solving the problem of compliance of educational programs with market expectations has shown that today the issue of modernization and constant implementation of innovations in the educational process remains relevant. The urgency of solving this scientific problem is that the introduction of innovative approaches to increase student satisfaction in each university is the basis for increasing confidence in society, because it increases their professional level and skills are quickly implemented in the labor market and positive feedback from employers. In order to assess the degree of student satisfaction, a marketing study was conducted by questionnaire among applicants for the educational level "Bachelor" and "Master" majoring in "Marketing". The article presents the results of empirical analysis of this marketing research, which shows a high level of student satisfaction with the educational program, but at the same time reflects the desire of students to gain more practical skills through non-formal education such as workshops, round tables, trainings, seminars with practitioners. business games, etc. According to the results of the analysis, it is proposed to create an innovative product within the educational process additional course of non-formal education for students majoring in "Marketing" to improve practical skills, rapid and adaptive soft skills and increase personal professional efficiency and self-management. The results of the study and the proposals provided can be useful for structural units that provide educational services in the field of "Marketing", as well as for institutions that research tools and implement measures to increase public confidence, especially in areas related to youth trust.
Simulating complex networks in phase space: Gaussian boson sampling

We show how phase-space simulations of Gaussian quantum states in a photonic network permit verification of measurable correlations of Gaussian boson sampling (GBS) quantum computers. Our results agree with experiments for up to 100-th order correlations, provided decoherence is included. We extend this to more than 16,000 modes, and describe how to simulate genuine multipartite entanglement.
Domain Adaptation via Maximizing Surrogate Mutual Information

Unsupervised domain adaptation (UDA), which is an important topic in transfer learning, aims to predict unlabeled data from target domain with access to labeled data from the source domain. In this work, we propose a novel framework called SIDA (Surrogate Mutual Information Maximization Domain Adaptation) with strong theoretical guarantees. To be specific, SIDA implements adaptation by maximizing mutual information (MI) between features. In the framework, a surrogate joint distribution models the underlying joint distribution of the unlabeled target domain. Our theoretical analysis validates SIDA by bounding the expected risk on target domain with MI and surrogate distribution bias. Experiments show that our approach is comparable with state-of-the-art unsupervised adaptation methods on standard UDA tasks.
A Simple yet Effective Method for Graph Classification

In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the complexity of such models. Intuitively, given a problem, a simpler data structure comes with a simpler algorithm. Here, we investigate the feasibility of improving graph classification performance while simplifying the learning process. Inspired by structural entropy on graphs, we transform the data sample from graphs to coding trees, which is a simpler but essential structure for graph data. Furthermore, we propose a novel message passing scheme, termed hierarchical reporting, in which features are transferred from leaf nodes to root nodes by following the hierarchical structure of coding trees. We then present a tree kernel and a convolutional network to implement our scheme for graph classification. With the designed message passing scheme, the tree kernel and convolutional network have a lower runtime complexity of O(n) than Weisfeiler-Lehman subtree kernel and other graph neural networks of at least O(hm). We empirically validate our methods with several graph classification benchmarks and demonstrate that they achieve better performance and lower computational consumption than competing approaches.
“Think Before You Speak”: Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs

Multi-action dialog policy (MADP), which generates multiple atomic dialog actions per turn, has been widely applied in task-oriented dialog systems to provide expressive and efficient system responses. Existing MADP models usually imitate action combinations from the labeled multi-action dialog samples. Due to data limitations, they generalize poorly toward unseen dialog flows. While interactive learning and reinforcement learning algorithms can be applied to incorporate external data sources of real users and user simulators, they take significant manual effort to build and suffer from instability. To address these issues, we propose Planning Enhanced Dialog Policy (PEDP), a novel multi-task learning framework that learns single-action dialog dynamics to enhance multi-action prediction. Our PEDP method employs model-based planning for conceiving what to express before deciding the current response through simulating single-action dialogs. Experimental results on the MultiWOZ dataset demonstrate that our fully supervised learning-based method achieves a solid task success rate of 90.6%, improving 3% compared to the state-of-the-art methods. The source code and the appendix of this paper can be obtained from https://github.com/ShuoZhangXJTU/PEDP.
A Study on Prediction Using Machine Learning

Prediction is a statement we make about the future.If a person makes a prediction, he thinks it will happen in the future.Prediction or forecast is a future event or Report of data.They are often, but not always No, based on experience or knowledge.Global in perfect contrast to "rating" There is no consensus; Different authors and disciplines have different meanings.Forecasting encourages children to think seriously and ask questions.This will help students better understand the story, allow them to interact with what they are reading and interact with the text.Making predictions is a valuable technique for improving reading comprehension.He is not ready to predict which books will sell in the coming year.Earthquake Prediction is a false science.Brownian's prediction is no better than a wild guess.Talented readers use context and predictions.Our prediction is correct.This great prediction comes true.Weather forecasting has never been a perfect science.