Reviewerly/exHarmony huggingface dataset:
Double Replay Buffers with Restricted Gradient

In this paper we consider the problem of how to balance exploration and exploitation in deep reinforcement learning (DRL). We propose a generative method called double replay buffers with restricted gradient (DRBRG). DRBRG divides the replay buffer in experience replay into two parts: the exploration buffer and the exploitation buffer. The two replay buffers with different retention policies can increase sample diversity to prevent over-fitting caused by exploiting. In order to avoid the deviation of the current policy from the past behaviors by exploring, we introduce a gradient penalty to limit the policy change into a trust region. We compare our method with other methods using experience replay on continuous-action environments. Empirical results show that our method outperforms existing methods both in training performance and generalization performance.
Hindsight-Combined and Hindsight-Prioritized Experience Replay

Reinforcement learning has proved to be of great utility; execution, however, may be costly due to sampling inefficiency. An efficient method for training is experience replay, which recalls past experiences. Several experience replay techniques, namely, combined experience replay, hindsight experience replay, and prioritized experience replay, have been crafted while their relative merits are unclear. In the study, one proposes hybrid algorithms – hindsight-combined and hindsight-prioritized experience replay – and evaluates their performance against published baselines. Experimental results demonstrate the superior performance of hindsight-combined experience replay on an OpenAI Gym benchmark. Further, insight into the nonconvergence of hindsight-prioritized experience replay is presented towards the improvement of the approach.
Segmenting Epipolar Line

Identifying feature correspondence between two images is a fundamental procedure in three-dimensional computer vision. Usually the feature search space is confined by the epipolar line. Using the cheirality constraint, this paper finds that the feature search space can be restrained to one of two or three segments of the epipolar line that are defined by the epipole and a so-called virtual infinity point.
Development and Evaluation of Mixed Reality System for Improving Eating Experience by Co-eating with a Robot

Eating with others enriches our dining experience and has many social functions. However, more and more people tend to eat alone these days because it is difficult to match meal times and places with others. In this paper, we developed the mixed reality system for co-eating with a robot. In this system, a robot and a mixed reality headset are connected and people who wear the headset can see that real robot puts computer graphics food into its mouth as if eating. In the evaluation of the developed system, people over 13 years old can enjoy eating more, feel more delicious and eat more when a robot eats together than when it only talks and does not eat.
Towards Automatic Classification of Mosquito Species Based on Wing Geometrical Features

This paper presents an initial version of a system for the automated classification of mosquitoes species, based on relevant features extracted from their wing's morphology. The algorithm developed allows identifying the mosquito's species by using key reference points of the wing, such as the radio of the circular geometries of spots presents within the wing. The aim was to develop an initial version of a system for improving the standard manual method in which mosquitoes are classified, as a proof of concept. For testing the system, two particular species: Limatus durhamii and Wyeomyia sp. were used for classification using a simple perceptron. The model reached an accuracy value of 95.46% in predicting new wing samples. Initial results indicate that with future refinements, an automated classification system is feasible.
Textual Clustering: Towards a More Efficient Descriptors of Texts

In this paper, we present how itemsets can be used as a discriminating descriptor in a textual clustering process. We implemented a platform named "IDETEX" capable of extracting itemsets from textual data and using them for the experimentation in different types of clustering methods, such as K-Medoids, Hierarchical clustering and Self-Organizing Map (Kohenon). To some extent the experimentations performed reveal promising results with different classifiers either "Hierarchical", "Non-hierarchical" or "Neural network".
Enhancing performance of the back-propagation algorithm based on a novel regularization method of preserving inter-object-distance of data

Artificial neural networks, consisting of many levels of nonlinearities, have been widely used to deal with various supervised learning tasks. At present, the most popular and effective training method is back-propagation algorithm (BP). Inspired by manifold regularization framework, we introduce a novel regularization framework, which aims at preserving the inter-object-distance of the data. Then a refined BP algorithm (IOD-BP) is proposed by imposing the proposed regularization framework into the objective function of BP algorithm. Comparative experiments on various benchmark classification tasks show that the new regularization BP method significantly improves the performance of BP algorithm in terms of classification accuracy.
Fuzzy Target Reaching Control of a Differential Drive Mobile Robot Subject to Friction Torques

In this study, the aim is to control a differential drive mobile robot (DDMR) to reach a target position despite the presence of disturbances. The study established a comprehensive model of a DDMR including its kinematics, dynamics and actuator dynamics. Based on this comprehensive model, a fuzzy target reaching control (FTRC) is proposed to achieve the target-reaching. By means of the designed FTRC, the distance and orientation between a target and the DDMR are quickly converged. Finally, the simulations are conducted to validate the effectiveness of the proposed FTRC. The corresponding results show that the proposed control strategy possesses a high level of robustness.
Using Digital Image as a Cover of Information Hiding: Review Paper

Abstract One of the most important topics that concern many researchers is how to make information that has a high degree of confidentiality hidden and not accessible except by the people who have the authority to view it, images are one of the most important multimedia that can be used as a cover to include the data, because the image contains how much A huge amount of data that can include confidential information in it, as well as the image is one of the most multimedia that is sent and received thus that the process of messaging with pictures does not arouse suspicion, this research provided a detailed study of a group of articles that used the image as a cover in the inclusion of information Confidentiality, and focus was on the most important domains of strength in each article. Articles were studied from 2013 to 2020 that were published at the containers of Scopes, and Google Scholar.
A Comprehensive Perspective on Mobile Forensics

The modern-day smartphones are the result of the technological progression that is happening in this digital world. This technological advancement has brought an incremental augmentation where these were not perceived as critical by the smartphone users. Also, the computational capability and networking competence has been dragooned constantly to maintain the momentum with the ever-expanding workload demands. This scenario has endorsed the smart gadgets such as smartphones and tablets to accomplish the growing complex challenges. In this digital era, the next generation users are substituting the conventional way of preference such as the personal computers and laptops with smartphone for the social connectedness, e-commerce, financial transaction, market updates, latest news, or even editing images. Users willingly install various mobile apps on to their smartphone and consequently providing their valuable and sensitive personal information to their service providers without thinking and knowing the security lapses and repercussions. Considering the fact, the smartphones' size and its portability, these devices are much more susceptible of being stolen, becoming jeopardized, or being exploited for various cyber-attacks and other malevolent activities. Essentially, the hackers look forward to the new mobile vulnerabilities so that they exploit the revealed vulnerability once a newer edition of the respective mobile operating system is released. In view of the fact that the smartphones are too vulnerable to various exploits, the necessity for a digital investigation entrained to establish a separate domain named mobile forensics. This established forensic domain is specialized in acquiring, extracting, analyzing, and reporting the evidence that is obtained from the smartphone devices so that the exploiting artifacts and its respective actions are determined and located. This chapter puts forward the various processes involved with the mobile forensics that can be employed for examining the evidences of various cyber incidents. Furthermore, it discusses the various vulnerabilities with the iOS and Android mobile operating systems and how they are being exploited in detail. The chapter also discusses the various approaches of data extraction and the respective industry standard for the tools that are being utilized for the same.
Proposal of an interest word presentation system when browsing the web using eye movements

In recent years, eye tracking technology has received a lot of attention. This time, as a basic study of interaction that can extract user's interest information, we tried to develop a user interaction system using a low-cost, non-contact eye-gaze input device. Our system uses the user's gaze point and gaze time when browsing a web page to extract words that the user may be interested in and display the information on the screen.
An improved hash function inspired by the fly hashing for near duplicate detections

This paper addresses the problem of improving the fly hashing [1] that is a high-dimensional hash function based on the fruit fly olfactory circuit. The encoding of fly hashing only uses sparsely addition operations instead of the usual costly dense multiplications, and thus results in efficient computations which is important for near duplicate detection tasks in large-scale search system. However, the firing rate based winner-take-all (WTA) circuit of it is neither biologically plausible nor energy saving, and if this circuit is taken into consideration, theoretical results of locality-sensitive are no longer strong. To improve the fly hashing, we proposed a locality-sensitive hash function based on random projection and threshold based spike-threshold-surface (STS) circuit, and both of them are biologically plausible and can be computed very efficiently in hardware. We also presented a strong theoretical analysis of the proposed hash function, and the experimental result supports our proofs. In addition, we performed experiments on datasets SIFT, GloVe and MNIST, and obtained high search precisions as well as fly hashing with less time to consume.
Design and Implementation of a Hand Gesture Based Digital Lock Demonstrator

The security is a vital apprehension. An omnipresent part of all security systems is a key protected admittance control. It allows only the authorized people to enter into a restricted zone. This paper focuses on designing and implementing hand gesture recognition based digital lock system. The objective is to design a functional lock system using Human-Computer Interaction (HCI) with display for the education purpose. Keeping in mind the demonstration purpose, the system modules are kept easily accessible. The solution is based on a web camera, which is used as the main component to captures a live-stream video with the help of a specifically designed algorithm in the MATLAB. The system is able to identify the hand-gesture. This algorithm is designed to enhance a specific frame from the video to get better results. Later, this frame is converted into a binary image so it can be easily subtracted from a background image that has been provided in the database. A system prototype is implemented and its functionality is tested. The results are presented.
Automated Bilingual Linking of Wordnet Senses

Wordnets for different languages are linked through synsets - sets of synonymous word senses. We present a method of automated transforming synset mapping to sense mapping to build a network of translational equivalents. Two heuristics based on a cross-lingual distributional similarity model are compared with several variants of machine learning based approach exploring different descriptive features. The features are extracted from bilingual and monolingual dictionaries and distributional semantics models, interpreted against wordnets.
Secure Code Recommendation Based on Code Review Result Using OWASP Code Review Guide

Sistem Informasi Akademik dan Pengasuhan (SIAP) is an application web that facilitates the academic community in accessing information about academics and student care. However, in its development, if the code system is poorly written, a web application might have vulnerabilities. This study aims to determine the vulnerabilities found in SIAP using OWASP guide and how to fix these vulnerabilities. OWASP is the best choice because it can be obtained free of charge and freely on the internet and periodically up to date. Before vulnerability testing, analysis about security level of SIAP is done using OWASP ASVS. Then, OWASP Testing Guide is used for vulnerability testing. In the tests conducted, it was found that SIAP is vulnerable to injection, broken authentication, and broken access control. Injection is one of the vulnerabilities that has the highest risk value. After testing, a code review using OWASP Code Review Guide is performed to find the location of the vulnerability in the source code. For the last, a secure code recommendation will be given that can overcome the vulnerabilities found from the used guidelines.
Application of Optimized Partitioning Around Medoid Algorithm in Image Retrieval

In image retrieval, the major challenge is that the number of images in the gallery is large and irregular, which results in low retrieval accuracy. This paper analyzes the disadvantages of the PAM (partitioning around medoid) clustering algorithm in image data classification and the excessive consumption of time in the computation process of searching clustering representative objects using the PAM clustering algorithm. Fireworks particle swarm algorithm is utilized in the optimization process. PF-PAM algorithm, which is an improved PAM algorithm, is proposed and applied in image retrieval. First, extract the feature vector of the image in the gallery for the first clustering. Next, according to the clustering results, the most optimal cluster center is searched through the firework particle swarm algorithm to obtain the final clustering result. Finally, according to the incoming query image, determine the related image category and get similar images. The experimental comparison with other approaches shows that this method can effectively improve retrieval accuracy.
Fuzzy Network Based Evaluation Model for Semi-Physical Virtual Training

This research focuses on evaluation index of the semi-physical throwing virtual training system, and the evaluation model of the system is constructed from the perspective of training students. Based on these the subjective and objective evaluation index system, the fuzzy network based reasoning technology is used to establish the training completion rate prediction model.
Short-term Photovoltaic Power Forecast Based on Greenhouse Energy Supply Requirements

Abstract In order to solve the problem of “disposal of light” in distributed photovoltaic power plants,this paper proposed that the energy supply of photovoltaic power plants could achieve local consumption in agricultural greenhouses, and predict the photovoltaic power generation in advance according to the load demand of greenhouses. The historical power data and meteorological data of the input variables were normalized, and the PV power prediction was performed at a time interval of 1 h for a time comparison. The prediction method based on the iterative improved BP neural network model was proposed. The prediction results showed that the prediction error was not More than 2%,could meet the energy supply stability requirements of greenhouse loads.
Design and Implementation of Object Tracking System Based Mean-Shift with Locust Search Optimization on Raspberry Pi

Having an optimal object tracking system is an advantage especially in everyday use. One of the example is use in victim localization. The optimal object tracking system as mentioned earlier means a system that capable of reaching the convergence point of region of interest (ROI) with small number of iterations. The smaller the number of iterations needed for reaching the convergence point, the faster the system follows the object's movement. A system that is able to follow the movement of objects quickly, especially in accordance with case studies of victim localization, is certainly needed. In this paper, the object tracking system is built on Mean-Shift algorithm. Mean-Shift has a convergent search technique that requires a large number of iteration processes. In order to achieve an optimal object tracking system as the purpose of this paper, an optimized algorithm is carried out. Instead of shift the ROI point sequentially until reach the optimum point as done in Mean-Shift, the optimization algorithm will search the ROI optimum point randomly with a movement that pays attention to the previous optimum point. Optimization algorithm used in this paper is Locust Search algorithm. Object interest being tracked in this paper is human face. Object tracking system will be deployed in Raspberry Pi 3 Model B+ because of its characteristics that are suitable to be implemented in this paper's case, in form of prototype.
A SoLiD App to Participate in a Scalable Semantic Supply Chain Network on the Blockchain (Demo).

To allow for inter-organisational processes in networks withlow trust, Supply Chains and corresponding information are moving tothe blockchain. On the blockchain, this information poses a scalabilitychallenge. To tackle this challenge, we propose a solution that minimisesthe data stored on the blockchain, which we base on semantic datamodelling in knowledge graphs, decentralised management of interlinkeddata, and a light-weight Smart Contract. In this demo, we focus onthe web agent to participate in Supply Chain networks built using ourapproach, and our corresponding data modelling
Multi-Agent Reinforcement Learning based on Value Distribution

Abstract Reinforcement learning in a multi-agent setting is very important for real-world applications, but it brings more challenges than those in a single-agent environment. In the multi-agent setting, the agent generally has a bias of overestimation on the value function. In our work, we pay attention to the issue of overestimation bias with continuous actions in the multi-agent learning environment. We propose a method to reduce this bias by adopting the distributional perspective on reinforcement learning. We combine it within the framework of off-policy learning Actor-Critic and propose a novel approach Multi-Agent Deep Distributional Deterministic Policy Gradient (MAD3PG). We empirically evaluate it in three competitive and cooperative multi-agent settings. Our results show that in a series of difficult motor tasks the agents trained by MAD3PG significantly outperforms existing benchmark.
Quasi-optimal Trajectories yielded by Metaheuristic based Algorithms used in Closed-loop

A stand-alone metaheuristic based algorithm can solve a specific optimal control problem. The result is a quasi-optimal solution that is a sequence of values assigned to control inputs over the control horizon. However, this solution can be used only by an open-loop system. This paper proposes a method to achieve a closed-loop control structure able to control the dynamic system optimally, using the quasi-optimal state trajectory constructed through the metaheuristic based algorithm.
Vecsim: Carrier-based, Privacy-Preserving Cellphone Contact Tracing

In this paper, Vecsim, a novel contact tracing method, is proposed. Vecsim determines the user proximity based on the existing log data already collected by the cellphone network carrier for network management purposes, and is transparent to the users. Compared to the existing methods that require user involvement, such as downloading an application and turning on Bluetooth, Vecsim is easier to deploy and may cover a larger population. Vecsim protects user privacy by focusing on user proximity detection, which is different from localization. In addition, proximity detection is a much easier problem than localization and can achieve higher accuracy. The key novelties of Vecsim include a simple method for distance estimation based on the similarity of two data records, as well as exploiting the massive log data to learn the discontinuity of the signal field. Vecsim has been tested with the signal field generated by a commercial ray tracing software program in a 2 km by 2 km urban area. The results show that Vecsim alerts over 96% of cellphones within 50 m to each other, while alerting less than 4.5% of cellphones beyond 150 meters.
Design and Implementation of a Mobile Context-aware Model of Traditional Chinese Medical Health Preserving Based on Ontology

Constructing a context-aware model of traditional Chinese medical (TCM) health preserving in a mobile environment can take advantage of information technology to promote the ideas of TCM health. Through analysis, the context information includes the traditional Chinese time and space expressions such as heavenly stems and earthly branches, solar terms, twelve-hour and positions. Converting the original context information like acquired Gregorian calendar dates and sensor values is the primary task of modeling. Based on the basic table, this paper proposes a time conversion algorithm of the calendar by heavenly stems and earthly branches, and calculates the conversion from time to twelve-hour, date to solar terms, and GPS data to positions, and then builds a multi-dimensional context-aware model of TCM health preserving based on ontology.
Programmable Photonic Circuits: a flexible way of manipulating light on chips

Programmable Photonic integrated circuits are optical chips where the flow of light is configured through electronics. This provides a flexible way to explore new photonic functions, and a lower threshold for deployment in new applications.
Instance segmentation model CP-Condinst

Abstract With the rapid development of deep learning image recognition technology, people have higher and higher requirements on the accuracy and speed of segmentation recognition. So instance segmentation becomes more and more important. The accuracy and speed of segmentation recognition are increasingly demanded. Recently, Shen Chunhua et al. proposed a very quantized instance segmentation model CondInst. In the paper, which used CSPResNet instead of ResNet, and added a Prediction module method to the head to improve model accuracy. Finally, on the MS COCO2017 data set, the simulation results show that CondInst’s AP, AP50, AP75, APS, APM and APL were increased by 0.2%, 0.4%, 0.3%, 0.1% and 0.4% respectively in the 50 layer residual model of our model, compared with CondInst’s AP, AP50, AP75, APS, APM and APL.The experimental results show that the method has certain practical value.