Reviewerly/exHarmony huggingface dataset:
Classical machine learning and deep neural network ensemble model for GPS-based activity recognition

Our KDDI Research team proposes an ensemble model for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge using GPS data. During preprocessing, we corrected the GPS dataset that contains errors and missing values using a Kalman smoother. Since this smoother can be processed offline, it can be used to correct backforward using the recorded future locations in the dataset. Further, by using features that have similar distributions in the train subject’s data and the other subjects’ datasets, we could achieve robust feature selection across multiple subjects. The first stage of our ensemble model employs classical machine learning and deep neural network approaches independently, specifically, LightGBM and LSTM, respectively. The second stage calculates the weighted average of the outputs of both approaches. Our results show the improved accuracy contributed by our ensemble, suggesting that it effectively makes use of both statistical and non-statistical features given the suitable base models. We confirmed that the use of Kalman-smoother, selection of features with similar distributions across subjects and ensemble modeling contributed to improving the accuracy of both train and validation datasets.
Dynamic Frequency–Temperature Characteristic Modeling for Quartz Crystal Resonator Based on Improved Echo State Network

Quartz crystal resonators are the key component of various kinds of electronic systems because they provide the reference frequency source of the system running clocks. However, the frequency stability is often affected by the temperature. Therefore, the frequency-temperature ( f-T ) characteristic modeling has been an important research topic in the frequency control field. The classic f-T modeling method omits the system dynamics and may lead to a large frequency compensation error in the case of rapid temperature changing. To deal with this issue, this article proposes a dynamic f-T modeling method based on improved echo state network (ESN), called residual scaled ESN (RSESN). In the proposed method, the residual modeling framework is designed for purposes of good physical understandability and high prediction precision. This framework uses the static polynomial f-T model to depict the approximated data relationship and applies the complicated network model to compensate the detailed dynamic error. To estimate the dynamic errors, one effective dynamic modeling tool, ESN, is introduced to build the dynamic compensation model for f-T characteristic of quartz crystal resonators. For a better fitting performance, the ESN activation limitations are analyzed and the scaled echo states are constructed in the improved ESN model. The modeling and testing results on the real experiment data show that the proposed method can capture the dynamic information effectively and provide better frequency deviation predictions.
Traffic Accident Analyzer: A Visual Analytics Tool for Traffic Accidents Dataset

The number of traffic accidents in Saudi Arabia is huge, despite all the measures put in place. The current problem in traffic accidents can be best solved by visualization. It will enable users to see the analytics presented clearly and enable decision-makers to gain new insights about the road or identify new patterns faster. This research enables people to capture perceptions of traffic from the large volume of traffic dataset. Traffic Accident Analyzer is proposed as a tool that provides visual analytics for authorities, in order to understand traffic better and limit accidents. Interactive map, word-cloud, histogram, donut chart, and calendar are five visualization techniques that are integrated together into the tool. The approach is illustrated as a case study for traffic accident visualization systems, using a traffic accident dataset from the city of Riyadh for a period of 3 years. The preliminary evaluation of the Traffic Accident Analyzer Tool referred to that the tool is appropriate for the intended purpose, also, the tool is acceptable to users based on usability testing.
OntoBlogDis: A Knowledge-Centric Ontology Driven Socially Aware Framework for Influential Blogger Discovery

Blogging in present-day times has become a common form of voicing opinions and explaining them. Therefore, commercial interventions to advertise products and political interventions, to share a party’s agendas, etc., have been started to come upon blogs. The bloggers also make money on this, and a lot of upcoming bloggers are now finding high-grossing blogs and further write about the same topic or paraphrase it to increase their views in a very short time. This does not help in the growth of this mode of media in a long run. Thus, by finding the influential blogger these problems can be solved. This paper proposes a statistical knowledge engineering-based approach to provide an effective solution to this problem. The proposed OntoBlogDis uses TF-IDF, Synonymization, NPMI for Term Co-occurrence, Static Ontology Matching using ANOVA, Jaccard similarity, and knowledge harvesting from Wikidata. OntoBlogDis when compared to the baselined approach is inferred to be superior and recorded an overall F-measure and FNR are 96.84% and 0.02.
Face Recognition in the Context of Website Authentication

In 1995, developers and programmers have begun focusing on developing websites and data protection. This came as a result of the increase and growth of many digital services such as e-shopping services, bank account management, e-mails, and many other services that require a high level of protection and security. As the penetration internet gowned it throws many challenges towards information protection. Hence the importance of providing protection and security for websites and users to protect their data and privacy, and bearing in mind that authentication is the first and basic step to protect and encrypt information sources, it also provides a safe environment for Internet users, which gives them comfort and security when dealing with these sites. The idea of our research aims for developing a security system that combines some recognition algorithms based on machine learning and deep learning techniques to verify the identity of each user (authentication) for the website and all the services associated with it. Thus, we can achieve the highest level of protection and security from the digital gaps that hackers may exploit. Therefore, the facial expression became the most important technique to identify hackers. The obtained results reveal that deep learning-based techniques for face recognition over a collected dataset are superior to conventional machine learning techniques.
Multi-agent Techniques to Solve a Real-World Warehouse Problem

In recent years, many warehouses applied mobile robots to move products from one location to another. We focus on a traditional warehouse where agents are humans and they are engaged with tasks to navigate to the next destination one after the other. The possible destinations are determined at the beginning of the daily shift. Our real-world warehouse client asked us to minimise the total wage cost, and to minimise the irritation of the workers because of conflicts in their tasks. We extend Multi-Agent Path Finding (MAPF) solution techniques. We define a heuristic optimisation for the assignment of the packages. We have implemented our proposal in a simulation software and we have run several experiments. According to the experiments, the make-span and the wage cost cannot be reduced with the heuristic optimisation, however the heuristic optimisation considerably reduces the irritation of the workers. We conclude our work with a guideline for the warehouse.
Validated Data Quality Assessment with “Skin in the Game”: A Smart Contract Approach

Data Markets are becoming increasingly popular but are very challenging to deploy and maintain successfully. We discuss some of the challenges related to the success of data markets, focusing particularly on the diverse challenge of assessing data quality. We introduce a novel, holistic approach whereby a blockchain-based smart contract called a Quality Assessment contract allows an actor called the quality assessor to assess the quality of a data asset, provide immutable proof of their efforts on the blockchain, and get rewarded for their efforts proportionally to the value of their quality assessment efforts. We discuss how such an approach could be used in practice to assess the quality of different data assets and discuss some architectural considerations for using a quality assessment contract.
Performance Comparison of Quantum Phase Estimation Algorithm with Different Number of Register Qubits on Noisy Quantum Processor

In the process of implementing a quantum algorithm as a quantum circuit, the error rate of an actual quantum computer should be considered. In this paper, we confirm through simulation that the one control qubit quantum phase estimation algorithm performs better than the many control qubit quantum phase estimation algorithm on the noisy quantum processor. We use QISKit to simulate a quantum phase estimation algorithm. We performed the simulation considering the error rate of the IBM Q quantum processor. The error rate of the one control qubit quantum phase estimation algorithm is compared with the many control qubit quantum phase estimation algorithm.
The impact of blockchain and cryptocurrencies on the global financial system: Prospects and contradictions

Subject. This article examines the impact of cryptocurrencies on the global financial system. Objectives. The article aims to identify, systematize, and analyze the influence of cryptocurrencies, as well as the blockchain, on the global financial system, and identify the opportunities and limitations that exist at the present stage. Methods. For the study, I used the methods of logical and statistical analyses, systematization, and forecasting. Results. In accordance with the original hypothesis put forward, the article reveals that cryptocurrencies do not represent such a powerful part of the global financial system today to consider it as a driver of changes in this system, but large enough to provoke a crisis similar to the coronavirus one. Conclusions. Today, cryptocurrencies are one of the promising financial instruments, the influence of which in the global financial system can significantly increase with the appropriate actions of the economic agents themselves. The positive impact on the elements of the global financial system will make cryptocurrencies more instrumental in the global foreign exchange and capital markets.
Development of a Web Application for Sports Skill Acquisition Process Visualization System

We aim to develop a web application system that supports learners by visualizing both cognitive aspects and movements. In this study, we report on the development stage of the system, which aims to automate the server-side process. We used it for training experiments for forward upward circling and confirmed that we could graphically observe the skeletal information and compare movements and speech each time.
Video Surveillance Framework Based on Real-Time Face Mask Detection and Recognition

In this paper we proposed a real-time face mask detection and recognition for CCTV surveillance camera videos. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, face mask detection, and face recognition. In the first step, a set of keyframes are selected using histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (CLAHE), and poses normalization (Angular Affine Transformation). In third step, facial parts are segmented using clustering approach i.e. Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, Pixel-based Feature Extraction is performed using Yolo Nano approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. The final step is face recognition which is implemented by a Kernel-based Extreme Learning Machine with Slime Mould Optimization (SMO). Experiments conducted using Python IDLE 3.8 for the proposed Yolo Nano model and also previous works as GMM with Deep learning (GMM+DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics.
Human Pose Estimation Based Pre-training Model and Efficient High-Resolution Representation

With the continuous development of computer vision technology, deep learning technology has been well applied in human pose estimation. However, the latest human pose estimation algorithms can not solve the problem of interdependence and occlusion between joints, so the key point detection in human pose estimation is still a challenging task. In this paper, a method based on masked pre-training model and lightweight SHNet combined method, random occlusion is performed on the key points of the human body in the picture, and the pre-training model is used to predict them, so that the problems of mutual occlusion and interdependence can be solved. Experiments show that the algorithm is effective in key point detection of MPII and COCO 2017 datasets.
The screen object

This article proposes that psychic experiences of ‘dyslocation’, ‘dysembodiment’ and ‘psychic slippage’ can occur in relation to the object ‘on’ or ‘in’ the screen – ‘the screen object’ – in the context of working remotely using televideo technology with children in psychoanalytic psychotherapy. This is explored in the context of a child’s experience when treatment started in person and then changed to remote working. The loss of the therapeutic setting and frame in mid-treatment can constitute a rupture and a trauma, experienced at a primitive level as the loss of ‘bounded space’ and of psychic orientation to the object. This experience may evoke psychotic or borderline-type anxieties associated with disembodiment, depersonalisation, derealisation or annihilation. Concepts from Linguistics of deixis, endophora and exophora – concerning orientation in place, time and person relative to context – are applied to the situation of remote working with televideo technology, to highlight tensions in this setting in the way emotional meaning and understanding are derived from the surrounding context. The screen as part of the setting/frame gives rise to ‘slippages’ of various kinds. In order to explore the theme of ‘the screen object’, the author draws on experimental psychology research on infant responses to televideo technology; psychological or psychiatric theories of disembodiment; and psychoanalytic theory related to the ‘body ego’ and ‘skin-ego’, the psychoanalytic setting, and remote or virtual psychoanalysis. It is speculated that elements of the psychic experience of ‘the screen object’ are likely to be present to some degree in all remote working using video technology, whether in response to a traumatic disruption to treatment in person as discussed in this article, or whether remote treatment is the agreed treatment from the beginning.
A Faster Read and Less Storage Algorithm for Small Files on Hadoop

Massive small files access is the main challenge for the Hadoop Distributed File System. To solve these problems, we present a new Algorithm of archive file, A Faster Read and Less Storage Algorithm for Small Files on Hadoop. A new logical file name is used to identify the file which generated by the <file ID, blcok ID> pair in the name node. Our experiments show that the algorithm is around 76.6% faster than original HDFS in the time of file storing, and around 31.9.6% faster than original HDFS in the time of file reading, around 73.9% less than original HDFS in the memory consumption of namenode.
Capsule Interpretability in Object Detection

Great progress has been made in the field of target detection in recent years. Most of these improvements come from the use of more complex convolutional neural networks. However, how to introduction explanations for individual classifications of features has not been well explored. To overcome the lack of interpretability, we can either propose capsule to have build-in explanations. In this work, we propose interpretability of capsule to replace the original feature extraction. In the proposed model, feature explanations can be created effectively and efficiently. Our model also demonstrates some benefits, experimental results verify the interpretability of the learned features which can help us understand what is the important factors in object detection.
TokenCheck: Towards Deep Learning Based Security Vulnerability Detection In ERC-20 Tokens

The use of Ethereum based tokens in blockchain applications have been on the rise in recent times and accordingly, the need for proper analysis of token source codes for security vulnerabilities has become paramount. Existing symbolic analysis tools have demonstrated to be efficient in detecting many of the security vulnerabilities, but by virtue of the complex nature of the analysis they perform to detect vulnerable paths, there is a considerable increase in search time with an increase in depth. Cryptocurrencies have recently achieved the milestone of a USD 2 trillion market cap and with such a high volume of assets involved, the need for an efficient and scalable security vulnerability detection tool in an ever-increasing list of tokens becomes of utmost priority. This paper proposes a deep learning based approach for the prediction of security vulnerabilities in ERC-20 token smart contracts. The proposal proposed by this paper is based on the use of Long Short-Term Memory neural network architecture on smart contract opcodes which are in form of sequential data. The proposed solution achieves an accuracy of 93.26% when tested on ERC-20 smart contracts collected from Ethereum mainnet and thus proves to be an efficient alternative for existing symbolic tools.
Policy evaluation for reinforcement learning over asynchronous multi-agent networks

This paper proposes a fully asynchronous algorithm for policy evaluation of multi-agent reinforcement learning over networks. Without any form of coordination, agents can communicate with neighbors and compute their local variables using (possibly) delayed information at any time. Thus, the proposed scheme fully takes advantage of the distributed setting. We prove that our method converges to a neighborhood of the optimum at a linear rate, showing the computational advantage by reducing the amount of synchronization. Numerical experiments show that our method is robust to straggler agents.
A Proposed Program for the Activities of the School Media Literacy in the Development of Some Dimensions of Learning for Students in the Third-Grade Primary Considering the Vision of Egypt 2030.

Children and youth are living today in a multi-device, multi-platform, multi-channel world, and school children are more affected by different means of media. The current study aims to investigate the effectiveness of the media literacy activities in developing the learning dimensions of the third-grade students in the light of the vision of Egypt 2030? After the application of the study tools on an experimental sample of (40) children in the third-grade primary, the results showed that the use of the activities of the school media literacy to the dimensions of learning in the light of Egypt's vision led to the improvement of the following learning skills "procedural skills and professional skills, Skills of co-existence and cooperation with others. In addition of improving the following sub-skills (Introduce new and unique ideas – problems solving Respect for opinion and other opinion - Define clear goals - Respect diversity - Selfexpression - Verbal communication - Negotiation and good inquiry - Distinguish between similarities and differences).
Improved Research on Target Unreachable Problem of Path Planning Based on Artificial Potential Field for an Unmanned Aerial Vehicle

Artificial potential field (APF) method is a simple and effective method in robot path planning algorithm and has been widely used to plan path for unmanned aerial vehicle (UAV). However, the traditional APF has problems such as the local minima and the target unreachability. These problems can be solved by improving repulsive potential and attractive potential. So, following researches are carried out in this paper. Firstly, a kind of improved repulsive potential is introduced to solve the traditional target unreachable problem due to the goal with obstacles nearby. Then, one improved attractive potential is proposed to solve the common local minima problem in APF method. However, the improved attractive potential may cause a rotation around the target. To solve this problem, attractive potential is improved again. Finally, a series of simulation experiments are carried out and the simulation results show that the proposed method can plan an effective path and has a strong robustness.
Selection and Verification of Privacy Parameters for Local Differentially Private Data Aggregation

Acquiring and aggregating data from a group of individuals is crucial for studying their general behavior. Differentially Private (DP) techniques, characterized by the parameter ϵ, help to protect Individually Identifiable Data (IID) of individuals participating in such data collection. However, such techniques affect the usefulness of the data leading to a trade-off between usefulness and privacy, thereby making the selection of ϵ an important problem before data acquisition. In this work, we use a mathematical formalism to estimate usefulness and privacy for sum query as aggregate analysis for the local model of privacy. The mathematical relation enables the application of a variety of optimization techniques, discussed in the work, to select an optimal value of ϵ. Existing methods for selecting ϵ are based on financial parameters, but they heavily rely on past data and domain knowledge which may not be available in many cases. To address this, we have provided Knee-point based recommendations along with a selection criterion to choose the method of recommendation depending on the availability of information. This allows analysts to take enlightened decisions while negotiating the value of ϵ. Our experiments on synthetic and real-world datasets unambiguously demonstrate the strength of the mathematical model and the recommended values