Reviewerly/exHarmony huggingface dataset:
Influence of External Factors on the Attitude of Students Towards Arduino Micro Development Boards

The fourth industrial revolution has a significant impact on the professional skills set of employees. Creativity, critical thinking and problem-solving skills as well as communication and effective teamwork are some of the required skills. The rapid pace of change in technology and consequently, new required skills urge educational institutions to create an inspiring and suitable learning environment for the 21st-century student.As such, a project-based learning approach was introduced at the School of Computer Science and Information Systems at a higher education institution in South Africa to better prepare students for the current technologically advanced workplace. Students were exposed to hands-on hardware programming using Arduino micro development boards. With no previous experience in the field of hardware programming, students were introduced briefly to some basic concepts and thereafter, they had to develop a solution to solve a specific problem. Apart from hardware programming, skills such as communication, effective teamwork and innovation were required to solve the problem. For Arduino micro development boards to be successfully incorporated in class, it is necessary to determine the student’s acceptance of such technology as a teaching tool towards an Industry 4.0 related qualification. Therefore, the attitude of students towards using Arduino technology in class was evaluated using a questionnaire on completion of the project.The focus of this paper is on measuring the external factors that influence the student’s attitude towards the use of the Arduino micro development boards as a teaching tool in class. For this purpose, the Technology Acceptance Model was used. Statistical analysis was done on collected data to derive relations between various external factors and the attitude of students towards the use of the Arduino micro development boards in class. The results of this paper show that external factors such as enjoyment, subjective norms, and focused immersion have a significant influence on the attitude of students towards using Arduino micro development boards as a teaching tool in class. This serves as evidence that the use of Arduino micro development boards as a teaching tool could be implemented successfully in class in the South African context. However, some of the external factors that were evaluated contributed less to the students’ attitude.
Introduction and scientific goals

This chapter explains the rationale and importance of studying grammatical inference. Section 1.2 highlights the application areas where grammatical inference has been used successfully. Scientific goals and different objectives are listed in Section 1.3. The purpose of this chapter is to set a road map for readers so they can develop an understanding of what this book is about.
Towards the generation of machine learning defect reports

Effective locating and fixing defects requires detailed defect reports. Unlike traditional software systems, machine learning applications are subject defects caused from changes in the input data streams (concept drift) and assumptions encoded into models. Without appropriate training, developers face difficulties understanding and interpreting faults in machine learning (ML). However, little research is done on how to prepare developers to detect and investigate machine learning system defects. Software engineers often do not have sufficient knowledge to fix the issues themselves without the help of data scientists or domain experts. To investigate this issue, we analyse issue templates and check how developers report machine learning related issues in open-source applied AI projects. The overall goal is to develop a tool for automatically repairing ML defects or generating defect reports if a fix cannot be made. Previous research has identified classes of faults specific to machine learning systems, such as performance degradation arising from concept drift where the machine learning model is no longer aligned with the real-world environment. However, the current issue templates that developers use do not seem to capture the information needed. This research seeks to systematically develop a two-way human-machine information exchange protocol to support domain experts, software engineers, and data scientists to collaboratively detect, report, and respond to these new classes of faults.
FlowDecider: End-Host Driven Proactive Load Balancing for Data Center Networks

Due to scale-out architectures of data center networks, there exist multiple paths for flows to traverse between an end-host pair. In such case, balancing traffic load across multiple paths is a key to facilitates efficient bandwidth utilization between any pair of hosts in data center networks. However, there is still a challenge of timeliness for load balancing in data center networks, owing to the passive response mechanism to the network state. As a result, short flows suffer from long queuing delay while the throughputs of long flows are influenced at the same time. To tackle it, this paper proposes an end-host driven load balancing algorithm in data center networks with multipath fat-tree topology, termed as FlowDecider. At the host, FlowDecider detects long flows and splits them into flowcells to pave the way for load balancing. For each flowcell, the one egress port with the least queue length among two random available ports and the last used port is preferred at the switch. Otherwise, short flows are forwarded at the granularity of packet. FlowDecider has good timeliness and requires no additional hardware support. The simulation results show that FlowDecider significantly reduces the average flow completion time and obtains high throughput at the same time.
Information visualization with network services tools

Data visualization finds its application in many disciplines as the modern equivalent of visual communication, and at the same time does not belong to any specific field, but has its own significance in the presentation of information, ready-made data through the use of visualization tools. In the process of scientific communication, the problem arises of the possibility of methods for presenting information that will be understandable to the user. Therefore, in the process of the exponential growth of the documentary flow of information, the need for compression of information, visibility increases, while maintaining the integrity of the display of the main idea and compact generalization of data. The article discusses data visualization as a way of presenting information and options for using interactive network services to visualize contextual information. Using specific examples, the application of such technologies (services) is tested and presented to present ready-made data or information, which is a more visual, accessible way for perception, understanding and memorization. The study of online services as data visualization tools can be applied and have practical value in educational processes. The aim of the study was: search and study of effective methods of data presentation and clearly demonstrate the use of this method through online services as data visualization tools. The relevance of the work consists in the use of methods for presenting information as services and tools for automating the presentation of analysis, stages or results of work.
Chess fortresses, a causal test for state of the art Symbolic [Neuro] architectures

The AI community's growing interest in causality is motivating the need for benchmarks that test the limits of neural network based probabilistic reasoning and state of the art search algorithms. In this paper we present such a benchmark, chess fortresses. To make the benchmarking task as challenging as possible, we compile a test set of positions in which the defender has a unique best move for entering a fortress defense formation. The defense formation can be invariant to adding a certain type of chess piece to the attacking side thereby defying the laws of probabilistic reasoning. The new dataset enables efficient testing of move prediction since every experiment yields a conclusive outcome, which is a significant improvement over traditional methods. We carry out extensive, large scale tests of the convolutional neural networks of Leela Chess Zero [1], an open source chess engine built on the same principles as AlphaZero (AZ), which has passed AZ in chess playing ability according to many rating lists. Our results show that a novel, experimental network, which was intended for efficient endgame play, performed best despite being one of the smallest networks.
Robust Pathological Detector Training Method on Sparsely Annotated Datasets via Spatial Cues

Computer-aided diagnosis of pathological images usually requires detection and examination of all positive cells and lesions to make an accurate diagnosis. Therefore, there is an unprecedented demand for effective and reliable methods of training pathological detectors than ever. To train a reliable detector, the training dataset is required to fully annotate all positive instances, such a requirement is challenge and laborious, and is not guaranteed in most cases. However, sparse annotations will limit the training performance of detectors. Here, we propose a novel module named Collaborative Correction Sibling (CCS), which is embedded into the original object detection network to enhance the training performance on sparse annotations in a pioneering way. Specifically, instance-level annotations in the image space can be calibrated by positive instances’ spatial features provided by CCS. Extensive experiments have been conducted on both cellular-and-lesion-level detection tasks, compared with the state of the art methods, our CCS demonstrates the training effectiveness on pathological images.
3D Human Joints Extraction Using Part Segmentation

We propose an approach of human joints extraction using part segmentation. Six human segmentations obtained by PointNet++ are further utilized to extract the 3D joints. The average error of 14 joints is &lt; 4.2 cm.
Fine-Tuning Dropout Regularization in Energy-Based Deep Learning

Deep Learning architectures have been extensively studied in the last years, mainly due to their discriminative power in Computer Vision. However, one problem related to such models concerns their number of parameters and hyperparameters, which can easily reach hundreds of thousands. Additional drawbacks consist of their need for extensive training datasets and their high probability of overfitting. Recently, a naïve idea of disconnecting neurons from a network, known as Dropout, has shown to be a promising solution though it requires an adequate hyperparameter setting. Therefore, this work addresses finding suitable Dropout ratios through meta-heuristic optimization in the task of image reconstruction. Several energy-based Deep Learning architectures, such as Restricted Boltzmann Machines, Deep Belief Networks, and several meta-heuristic techniques, such as Particle Swarm Optimization, Bat Algorithm, Firefly Algorithm, Cuckoo Search, were employed in such a context. The experimental results describe the feasibility of using meta-heuristic optimization to find suitable Dropout parameters in three literature datasets and reinforce bio-inspired optimization as an alternative to empirically choosing regularization-based hyperparameters.
A Balanced Shadow-Following Coverage Path Planning Approach under Energy Constraints

Area coverage is one of the significant fundamental problems in mobile robotics. In this paper, we study the coverage path planning (CPP) problem of unmanned aerial vehicle(s) (UAVs) with limited energy. Most existing solutions for this problem adopted that the UAV returns to the charging station and replenishes energy, which ensures the complete coverage for the area of interest. However, to guarantee the integrity of the uncovered area, there is usually no gain to the cover task during the process of returning, which leads to poor efficiency. To address this problem, we propose a shadow-following (SF) strategy which performs the cover task when the UAV returns along the shadow path, improving the energy utilization and coverage efficiency. Then, we extend for multi-UAV scenarios and propose a load-balanced (LB) multi-UAV CPP algorithm, which minimizes the number of robots and assigns the balanced task to each UAV. Finally, we validate the results in experiments performed with a UAV and multiple UAVs. The results show that the SF and LB algorithm performs well in the coverage efficiency.
THE INFLUENCE OF SKYPE TOWARDS STUDENTS’ WRITING SKILL

One of the main skill in learning English as second language is writing. Learning writing is necessarily important nowadays, not only for education purposes but also for occupation purposes as well. However, most of the students feel bored in writing; one of the reasons is because the teaching learning process still uses conventional method. Thereby, it seems that writing is difficult and boring because the students score in writing is low. Therefore, the researcher uses Skype as the media in teaching writing as an alternative solution for this problem. The study aim is to find if there is any significant influence of using Skype towards students’ writing skill especially in writing descriptive text. The result of this study showed that there is a significant influence of using Skype towards students’ writing skill.
Application of IoT Technology in Throttle Motor Detection System

In order to improve the automatic production efficiency and realize the detection data sharing, this study designed and developed an automated throttle motor detection system based on the Internet of Things (IoT) technology. The system was established on the basis of the three-layer architecture of IoT technology, and consisted of the front-end detection device, data transmission device, throttle motor detection data (TMDD) server, and remote management subsystem. The sensors in the front-end detection device were used to sample the throttle motor data and display the data locally, and the TMDD server was used to share the data received from different detection lines. The custom communication protocol was used to encode the detection data and parameters to improve the reliability of data transmission. Compared with most existing detection systems, the proposed detection system performs well on the data acquisition, transmission, data storage and management, and completes the throttle motor factory detection and fatigue detection, which effectively simplify the operation of the system and realize the data sharing and online management of different detection points through local net and Internet.
Robust Model Aggregation for Federated Learning with Heterogeneous Clients

This Federated learning has received extensive attention in recent years due to its potential in mitigating risks of intruding privacy. However, most of existing methods don’t provide adequate consideration to the heterogeneity of client. Therefore these parameters are treated equally and aggregated with the same weight. As a result, the overall performance may be degraded because of straggling clients. In this paper, we propose an aggregation algorithm considering the accuracy of local model of heterogeneous clients. The server evaluates the accuracy of the uploaded local model with a benchmark dataset, and then updates model parameters according to the accuracy ratio. Experimental results show the model accuracy of our proposed method performs better than existing methods. At the same time, in the presence of noisy users, it can eliminate their effect by assigning lower aggregation weights to them.
Virtual Training System Based on the Physiological Cycle of the Potato INIAP Suprema

This paper presents the development of a Virtual Reality system oriented to the simulation of the physiological cycle of the INIAP Suprema potato. The developed system allows the user to analyse tuber growth and production under certain conditions such as soil type, seed type and ambient temperature in order to optimize raw material, infrastructure, production time and among other benefits. The conditions set out in the document for virtualization are subject to monitoring and are based on studies previously carried out by agricultural institutions in Ecuador. The immersive system is developed in the graphical environment of Unity 3D using models and scenes that allow the user to visualise the growth simulation, obtain the quantity to be produced, observe the characteristics and physiological conditions of the plant. The results show the efficiency of the system, as the user can combine different conditions for the development of the plant, obtaining a virtual guide to the growth and production of potatoes and can be used as data to obtain quality harvests.
Incorporating Neighborhood Information During NMF Learning

Self-Organizing Map is an algorithm that computes a set of artificial neurons to model the distribution of a data-set. This model is composed of a graph of neurons connected by neighborhood links. The main advantage of a SOM model is the conservation of a low-dimensional topology, which allows a visual representation of the data distribution. It is a non-linear projection that preserves the neighborhood, while reducing both the dimensions and the size of the data. In this paper, we propose a modified version of the convex Non-negative Matrix Factorization to compute a similar projection. Experimental results show that the proposed algorithm significantly decreases the topological error in comparison to SOM, without loss of computational speed.
Cautious Nonlinear Covariance Steering using Variational Gaussian Process Predictive Models

In this work, we consider the problem of steering the first two moments of the uncertain state of an unknown discrete-time stochastic nonlinear system to a given terminal distribution in finite time. Toward that goal, first, a non-parametric predictive model is learned from a set of available training data points using stochastic variational Gaussian process regression: a powerful and highly scalable machine learning tool for learning distributions over arbitrary nonlinear functions. Second, we formulate a tractable nonlinear covariance steering algorithm that utilizes the learned Gaussian process predictive model to compute a feedback policy that will drive the distribution of the state of the system close to the goal distribution. In a greedy approach, we linearize the Gaussian process model at each time step around the latest predicted mean and covariance, solve the linear covariance steering problem, and propagate the state statistics to the next time step using the unscented transform. This process is then repeated in a shrinking-horizon model predictive control fashion. The cautiousness of the Gaussian process predictive model, which captures both the process noise and modeling errors, is demonstrated in numerical simulations.
Security Paradigms In SDLC Requirement Phase — A Comparative Analysis Approach

Security is crucial for the success of any project, as it is the first prerequisite for any project. A number of experts have proposed a number of different security methods, but they all had some various types of issues, like Identification of risks, risk assessments of threats, specification of security measures to be deployed, requirement inspections to identify the flaws in security requirements, and optimization of low-level security requirements are all key examples of security tasks. In this paper, a comparative study is carried out on various types of security methods used in the requirement phase of Software Development Life Cycle (SDLC) processes and on maturity model as well. Firstly, comparison of the Security Assurance Maturity Model (SAMM) with Building Security in Maturity Model (BSIMM) and Capability Maturity Model Integration (CMMI) is conducted for determining which maturity model is the best for implementation. Secondly, comparison between different software security methods is conducted to determine what method is suitable for security implementation in the Software Development Life Cycle requirement phase.
Computer Network Security Defense Model

Abstract With the rapid development of the Internet industry, hundreds of millions of online resources are also booming. In the information space with huge and complex resources, it is necessary to quickly help users find the resources they are interested in and save users time. At this stage, the content industry’s application of the recommendation model in the content distribution process has become the mainstream. The content recommendation model provides users with a highly efficient and highly satisfying reading experience, and solves the problem of information redundancy to a certain extent. Knowledge tag personalized dynamic recommendation technology is currently widely used in the field of e-commerce. The purpose of this article is to study the optimization of the knowledge tag personalized dynamic recommendation system based on artificial intelligence algorithms. This article first proposes a hybrid recommendation algorithm based on the comparison between content-based filtering and collaborative filtering algorithms. It mainly introduces user browsing behavior analysis and design, KNN-based item similarity algorithm design, and hybrid recommendation algorithm implementation. Finally, through algorithm simulation experiments, the effectiveness of the algorithm in this paper is verified, and the accuracy of the recommendation has been improved.
Online Video Game Recommendation System Using Content And Collaborative Filtering Techniques

The virtual amusement quarter is one in all the quickest developing in current years. In the case of video games, the productions of several maximum famous titles are with movie productions. The video game sale is within side of millions, and but there are very few works on the advice of videogames. Video games and online game gamers generate huge quantities of data, as the entirety they do inside a sport is recorded. Its diversification has dramatically extended the quantity of customers engaged in online groups of this amusement area, and consequently, the quantity and sorts of video games available. The statistics context overload underpins the improvement of recommender structures that would leverage the statistics that the online game structures collect, for this reason following the fashion of recent video games coming out each year. Here, a new method of online game recommendation is provided, via the usage of collaborative filtering. In order to enhance the recommendations, a new approach for estimating implicit rankings is anticipated and takes the hours of play and recommends better. The proposed recommender device improves the effects of different strategies provided withinside the kingdom of the art.
Science Education in Libya

This chapter provides a brief introduction to the geographical location of Libya, its population and the current political system. We highlight the levels of the education system in Libya (primary, basic and higher education). This includes the number of enrolled students and the number of dropout rates per year. Training and development in some schools and universities that have established eLearning and emerging technologies to enhance science education teaching or learning are discussed. The assessment methodology is also considered. The chapter also provides some statistics about the economic situation technologies and cultural development in Libya. Challenges, strategies, reflections, issues, and future paths form the latter part of this chapter.