Reviewerly/exHarmony huggingface dataset:
Development of a Low-Cost Autonomous, Multi- Terrain Robot with 3 DOF Arm for Outdoor Surveillance Applications

Military forces introduce new gadgets such as Unmanned Aerial Vehicles (UAV) or robots to reduce the risk of causalities. The paper shows the design of a low-cost, compact, and autonomous military mobile robot specifically designed to assist soldiers during any armed conflicts or attacks. This unmanned ground vehicle should perform several military operations such as transport, search or surveillance, bomb defusing, rescue and attack. The best design is found out using concept scoring and selection from nine different designs showcased in this paper. The robot also includes a 3 DOF robotic arm. The main advantage of the selected robot is that it can move in any terrain more easily with the help of its hybrid wheel-sets, where radials wheels are used for smooth landscapes, which facilitates fast movement. Triangular-shaped tracked wheels that can rotate on their own are selected in rough terrain to overcome the movement difficulty in the rugged surfaces.
Implementation of a teleimmersion homecoming support system for supporting inpatients

This research has developed a teleimmersion homecoming support system for inpatients and their families by combining a head-mounted display, a 360-degree camera, and a remote control robot. This system realizes real-time remote communication between inpatients and their families and provides inpatients with a “homecoming” feeling. Furthermore, inpatients can experience a realistic home space by operating the remote control robot with a 360-degree camera installed at home and enjoying family conversations with the voice call function. To evaluate this system, we conducted a questionnaire survey on the operability, necessity, functionality, and presence of this system. More than 80% of the subjects answered positively in the evaluation of the necessity, functionality, and presence of this system. However, in the operability evaluation, 30% of the subjects answered negatively, so it is necessary to improve the operability in the future.
Disruptive Unicorn of Digital Innovations

This chapter analyzes the disruptive unicorn of the 21st century, characterized by six of the most important digital disruptive innovations that will dominate the business ecosystem. A survey was applied to undergraduate students at Tecnologico de Monterrey in Mexico in 2019 to identify the students' perception of the unicorn. The hypothesis to be evaluated is that the undergraduate student has an unclear perception of the existence of the unicorn that could generate risks related to ethics and business power shifts in the world. An educational challenge is the need to develop the transversal competence of critical thinking related to digital disruptive innovations in the students. The educational strategies must evolve as fast as these digital disruptive innovations in order for the world to have citizens of a technological world in favor of humanity with the power of critical thinking and discernment. This is the challenge for the university educator.
An Anomaly Detection Model for ADS-B Systems Using a LSTM-based Variational Autoencoder

Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation system, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attacks. Therefore, it is necessary to develop a set of ADS-B data anomaly detection models. According to the strong temporal dependence and fast update rate of ADS-B data, we initiatively introduce a novel anomaly detection model based on variational autoencoder and long short-term memory networks (VAE-LSTM). In the model, we focus on reconstructing the expected distribution of ADS-B data, which is called reconstruction probability. At the same time, to model ADS-B data with the temporal dependence, we use the LSTM networks as the main architecture of the model, which is able to make use of long-term temporal dependence and avoid the vanishing gradient problem during training. Experiments show that the proposed model has a higher area under the receiver operating characteristic curve and the precision-recall curve than the other baseline methods from the literature, and reveal that the model has better performance of anomaly detection of ADS-B data.
IS2020:Competency-Based Information Systems Curriculum Guidelines

The Association of Computing Machinery (ACM) and the Association for Information Systems (AIS) engaged in a project to revise the Information Systems Curriculum. The IS2010 model curriculum has been widely used for nearly a decade. However, its value may be decreasing as new approaches to model curricula have been introduced. The AIS and ACM established an exploratory taskforce which found there have been substantial changes in the IS field, and that current graduates' technical skills do not appear to meet industry needs. The IS discipline must express its core in terms of a standard curriculum that meet stakeholder demands. A joint ACM/AIS taskforce on the Information Systems Model Curriculum (IS2020) was created to develop new IS curriculum guidelines. This panel will introduce the work of the IS2020 taskforce. Panelists will present the key points from the final report. This session should be of interest to faculty and administrators developing college-level curricula in IS.
The method of vicinity minutiae decomposition with higher level graphs for fingerprint verification

The subject matter of the paper is the development of fingerprint local structures based on the new method of the minutia vicinity decomposition (MVD) for the solution to the task of fingerprint verification. It is an essential task because it is produced attempts to introduce biometric technology in different areas of social and state life: criminology, access control system, mobile device applications, banking. The goal is to develop real number vectors that can respond to criteria for biometric template protection schemes such as irreversibility with the corresponding accuracy of equal error rate (EER). The problem to be solved is the problem of accuracy in the case of verification because there are false minutiae, disappearing of truth minutiae and there are also linear and angular deformations. The method is the new method of MVD that used the level of graphs with many a point from 7 to 3. This scheme of decomposition is shown in this paper; such a variant of decomposition is never used in science articles. The following results were obtained: description of a new method for fingerprint verification. The new metric for creating vectors of real numbers were suggested – a minimal path for points in the graphs. Also, the algorithm for finding out minimal paths for points was proposed in the graphs because the classic algorithm has a problem in some cases with many points being 6. These problems are crossing and excluding arcs are in the path. The way of sorting out such problems was suggested and examples are given for several points are 20. Results of false rejection rate (FRR), false acceptance rate (FAR), EER are shown in the paper. In this paper, the level of EER is 33 % with full search. 78400 false and 1400 true tests were conducted. The method does not use such metrics as distances and angles, which are used in the classical method of MVD and will be used in future papers. This result is shown for total coincidences of real number, not a similarity that it is used at verifications. It is a good result in this case because the result from the method index-of-max is 40 %.
Research on Automation of Test Skill Evaluation Based on Fuzzy Controller

Abstract The fuzzy control system is composed of an integral part of modern intelligent automatic control, can control of precision is difficult to directly establish the mathematical model of the intelligent control system object data for effective fuzzy control, so fuzzy has wide application field, considering the basic features of fuzzy control arithmetic, more close to the parallel processing of the way of fuzzy data stream, so fuzzy control is more suitable for implementation by a special control circuit, not only can not only improve the speed of data processing, and it also can improve the control system running stability. With the improvement of programming logic and programmable ability of FPGA, FPGA has the necessary ability to implement ASIC directly and meet the requirements of system programming and design on chip. It has become the development trend of FPGA. The fuzzy analog controller can be used as a digital analog controller of a DC/DC fuzzy converter. In the absence of Matlab/Simulinkc environment, a DC/DC fuzzy converter can be modeled by using Maplecs toolbox. The simulation test results firstly verify the practical feasibility of the fuzzy analog controller. Then PLXILINXXC3S500EFPGA chip is used to realize the fuzzy simulation controller, and good simulation results are obtained.
An Edge Video Analysis Solution For Intelligent Real-Time Video Surveillance Systems

Video Analytics has played an essential role in the most varied public safety sectors, mainly when applied to Intelligent Video Surveillance Systems. In this scenario, Edge Video Analytics seeks to migrate part of the workload of the Video Analysis process to devices close to the data source to reduce transmission overhead on the network and overall latency. Therefore, this work proposes an Edge Video Analytics architecture for real-time video monitoring systems. Such architecture divides the analysis process into functional and independent modules, being flexible to support analytics or network functions. We developed a proof of concept to validate the proposed architecture, focusing on detecting and recognizing license plate characters in the edge. In this scenario, between the detection and recognition modules, we used Deep Learning to implement a module responsible for discard plates with distorted identification text to reduce network utilization. Conducted experiments demonstrate that the architecture meets its objectives by reducing an average of 25.64% of the network traffic in its frames flow due to a resolution quality control and 56.65% in its license plate flow due to the filtering step proposed.
Leaper: Video Game Development on Android Devices with Cross-Device Interaction

Cross-device interaction is a technology that has increased the relationship between people and the different devices that exist today. This allows users to manipulate information through gestures or body movements. Likewise, the recent demand for smart devices has motivated users to increase their use in different contexts. That is why in this research we propose a platform video game for Android mobile devices called LEAPER. This is a video game that uses the Screen Sharing, Synchronization and Simultaneity design patterns, which allows the user to use several Android operating system devices to interact with each other. In addition, the connectivity of the devices is established by the Pinch gesture. The videogame was tested by 20 users, which have different models of the Android operating system. These participants have successfully demonstrated the cross-device interaction that LEAPER offers.
World-Around-Us Lesson in the Third Grade: “Diversity of the Animal World (Fauna)”

The article offers for consideration a variant of a lesson on the subject “The World-Around-Us”, which is held in the third grade based on the&#x0D; use of the educational and methodological set “Primary School of the XXI century”. The purpose of the lesson, which is part of the “Kingdoms&#x0D; of Nature” section, is to provide students with new knowledge about the diversity of the animal world. The high working capacity of junior&#x0D; schoolchildren during the entire period of the lesson is ensured by the use of various types of tasks that require constant mental activity, reasonable alternation of material of varying degrees of complexity, and the inclusion of elements of art pedagogy in the educational process. The use of art-pedagogical techniques allows the third graders to be informed as effectively as possible about the real results of their studies. Figuratively speaking, junior schoolchildren “put new knowledge in a portfolio and take it with them”, that is, they discover the expansion of their knowledge on the topic under study as a visual result of their activities in the lesson.
Formation of the ability of junior schoolchildren to work with schemes

Children come across sketches and technical drawings even in preschool age in the process of performing constructive and visual activities; they begin to get acquainted with schemes and drawings only at primary school age. It is therefore no coincidence that one of the tasks of teaching in primary school is the formation of sign and symbolic activity among students, which provides specific ways of transforming educational material and performs the following functions: coding — transmission and reception of information; schematization — the use of symbolic means to display educational material; modeling — highlighting and displaying essential features of an object by operating with symbolic means. The article analyzes the possibilities of forming in junior schoolchildren the ability to work with schemes in the process of teaching construction using the Cuboro constructor. The main studied concepts, stages of training and their content, skills are considered. The possibility of using the Cuboro Draw program in working with younger students is shown. The Cuboro Draw interface and the process of creating schemes in this program are described.
TED Talks Segmentation and Topics Extraction Using Machine Learning

TED Talks are knowledge videos of talks by experts in technology, entertainment, and design (hence TED). These conferences are arranged across the world. Great speakers come forward and share their experiences and knowledge. These talks are limited to a maximum length of 18 minutes and cover a wide range of topics. The videos are stored, and every video has a description of the video content.
Feature Selection Method for L1-norm Twin Support Vector Regression

L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm twin support vector regression is a sparse regression algorithm with certain feature selection ability, but its feature selection is inefficient and cannot be applied to nonlinear problems. To solve this problem, a feature selection method for L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm twin support vector regression (L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -FTSVR) is proposed to automatically select significant features. Feature selection is implemented in L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -FTSVR by introducing a diagonal matrix whose diagonal element is 0 or 1. The feature selection matrix is used to convert the regression upper and lower bound functions into a multi-objective hybrid programming problem, and then the alternate iterative method is used to solve the multi-objective programming problem. Experimental results on several UCI datasets show that the proposed algorithm not only has good regression performance, but also effectively improves feature selection ability.
Involution Based Speech Autoencoder: Investigating the Advanced Vision Operator Performance on Speech Feature Extraction

Feature extraction is an important aspect of deep learning. In recent studies, various methods from image processing have been applied to speech feature extraction. Although the involution operator has achieved great success in vision recognition, the use of the involution operator in speech tasks has not been investigated. To determine whether the performance improvement of this operator on visual tasks is reflected directly in the speech processing domain, this study investigated autoencoder networks operating on Mel-spectrograms utilizing involution and convolution. It was determined that convolution continues to outperform involution as a direct replacement. The reasons for discrepancies in the input feature dimensions of different fields are discussed. Based on its channel-agnostic property, involution in combination with convolution requires additional research.
Side Channel Vulnerability in Parity Computation of Generic Key Reconciliation Process on QKD

Quantum key distribution (QKD) is a key exchange protocol based on quantum physics, and the key reconciliation process is a necessary process that should not be omitted from QKD in correcting discrepancies between the key sent and the one received. In this paper, we propose the single trace side channel attack on the parity sum, which is a common operation in general error-correcting algorithms. We show that one can fully recover the secret key using a power consumption trace captured when a processor computes the parity sum.
Deep Learning Algorithm for Picture Frame Detection on Social Media Videos

This paper introduces a novel method for picture frame detection by using a deep learning algorithm. The detection aims to find the four vertices of multiple picture frames on social media videos. The detection model is based on Key-point RCNN (Region-Based Convolutional Neural Network). Although the Key-point RCNN is suitable for human key points detection, it does not perform well on the vertices detection of picture frames. In this research, a new picture frame (PF) branch is created to replace the Key-point branch of the Key-point RCNN. This PF branch includes more convolutional layers of the neural network and a feature pyramid network (FPN) structure which can extract more detail of features of picture frames. The experiment shows that the new PF branch significantly increase the accuracy. In 12 test videos, number of good performance videos are raised from 1 to 9 for the picture frame detection.
The use of electronic learning centres in public libraries in the city of Johannesburg, South Africa

Purpose This study aimed to establish the use of electronic learning centres in public libraries in the city of Johannesburg, which is under one of the biggest metropolitan municipalities in South Africa. Design/methodology/approach A quantitative research approach was adopted, and questionnaires were used to collect primary data. The target population for this study was the entire users of the electronic learning centres and librarians in the public libraries within the city of Johannesburg. Findings The findings of this study revealed that there are different services, levels of awareness and training. Research limitations/implications This study was limited to public libraries in the city of Johannesburg. Academic, special and mobile libraries were not included in this study because they were not open to the general public. Users who were not using the electronic learning centres were excluded from this study. Practical implications Adequate use and awareness of services of electronic learning centres in public libraries are instrumental in the satisfaction of users’ needs using the electronic learning centres. The suggestions of this study will be useful to decision makers and heads of the electronic learning centres on how to enhance the use of the electronic learning centres in the city of Johannesburg. Originality/value This paper establishes the use of electronic learning centres in public libraries in the city of Johannesburg, which is under one of the biggest metropolitan municipalities in South Africa.
Adaptive method for complete random forest

This paper presents an adaptive method to search best number of trees (Ntree) in complete random forest (CRF). Ada- CRF can automatically determine whether the forest has reached a stable state during the establishment of the forest, thereby avoiding inaccurate results caused by too small Ntree, or low efficiency caused by too large Ntree. As a general sampling method, Ada-CRF can not only effectively compress the amount of data, but also filter label noise to improve data quality. Ada-CRF can identify the noise points in the data by automatically searching for the results of the complete random tree division of the data. To improve the data quality, it filters the noise points of the label and retains valid data.
Low-light Image Enhancement Based on Joint Convolutional Sparse Representation and Adaptive Gradient Constraint

To improve the visual quality of low-light images, we proposed a novel low-light image enhancement algorithm via joint convolution sparse representation and adaptive gradient constraint. The proposed method is based on the Retinex model. When estimating the illuminance image, under the joint representation of the directional convolution analysis sparse representation and the group local convolution composite sparse representation, the structure and texture of the illuminance image are constrained, and the optimized illuminance image is obtained. As for the reflection image, the adaptive gradient constraint is applied to suppress the noise and artifact and enhance the detail as well. Compared with the state-of-arts, the proposed algorithm has achieved great improvement in both subjective and objective evaluations.
An Event-Driven Authentication Approach for Mediation of User Actions

Traditional authentication schemes challenge the user by something only they know, often a username and password, and become more robust with two-factor authentication. However, a new security problem arises when the system or service cannot ensure accountability for all events that occur within some user application. The vulnerability exists in authentication mechanisms that fail to provide security for events that occur after the login stage. This accountability issue leaves users susceptible to physical and cyber-attacks, such as physical compromises or Man-in-the-Middle (MITM) and replay attacks. In these cases the user is held accountable for these actions and the server is unaware that the legitimate user is no longer near the active session. Therefore, an additional authentication mechanism is needed to provide security up to the application layer when critical events are attempted. In this paper we study a practical, user-friendly approach to mediate critical events by authentication to verify the legitimate user is still near the live session. Critical events are authenticated by pairing the PC with the user's mobile smart device over a connection medium to determine if both devices are within an acceptable range. Afterwards, the PC sends a cryptographic challenge that can only be answered by the user's devices using the public key infrastructure and digital signatures. The smartphone replies back to the PC with a challenge, so that both devices can guarantee mutual authentication.
Effect of Channel Consideration on Auto Encoders for Color Image Compression using Deep Learning

In this paper, we have studied the effect of channels consideration on autoencoders for color image compression. The study is made in relation to RGB patch in an image and individual channel patches to know the effectiveness of what criteria is to be used while processing the image for compression. The study reveals that the RGB patch consideration in a color image is better than considering the channels individually. The chaotic (or scramble) image is given as input to autoencoder for compression and this helps to overcome the threat by the intruder and as well protection to data transmitted.