Reviewerly/exHarmony huggingface dataset:
Traffic Control Enhancement with Video Camera Images Using AI

Traffic congestion has been an emerging issue when it comes to problems faced by commuters on road on a daily basis. It leads to loss of time, money, and fuel when one is stuck in a traffic jam. This has led to the need of more path-breaking technologies in the field of intelligent transport systems (ITS). Today, a lot of data are available which can be used to extract important information and perform the desired analysis. With CCTV surveillance cameras at almost every traffic pole, information like count of vehicles can be used to analyze the traffic patterns at a particular location. In this paper, different methods have been used to get the accurate count of vehicles and their performances have been analyzed. Popular image processing method background subtraction and deep learning algorithms: R-CNN, Fast R-CNN and Faster R-CNN have been implemented.
Spatial Domain Steganography Techniques and Neural Network based Steganalysis with Differential Storage

Steganography techniques have been used since ancient time. Today when all the confidential data are being sent digitally then the need for steganography to be applied to digital media has increased all the more. This paper aims to achieve a thorough understanding of the existing spatial domain steganography techniques. It focuses on understanding the advantages and limitations of existing algorithms so that a more effective technique combing best features of one or more techniques can be proposed. Then an algorithm is proposed which focuses on steganalysis of images classifying them as stego or cover using neural network. It compares the performance of neural network based on the different inputs to the neural network. The experimental implementation show that LSB is best suited for steganography and feature extraction as an input, gives the best steganalysis results. To reduce the storage space, differential storage is used.
Feasibility of container orchestration for adaptive performance isolation in multi-tenant SaaS applications

SaaS application instances typically serve multiple tenants to improve cost-efficiency. This results in the need for adaptive performance isolation between tenants in order to guarantee custom service level objectives (SLOs) about request latency or throughput. Current solutions, which are based on request scheduling algorithms, suffer from SLO instability under globally varying workloads. This means that the configuration for an SLO has to be recalibrated when total workload patterns change such as an increase or decrease in the number of subscribed tenants, or the application becomes co-located with other types of resource-intensive applications. Lately container technology such as Docker and container orchestration frameworks like Kubernetes have been used to increase cost-efficiency, multi-tenancy and elasticity. This paper investigates if the problem of adaptive performance isolation can be mapped to resource management concepts of Kubernetes through a series of experiments. These experiments show that Kubernetes provides good support for QoS differentiation and adaptive resource allocation by grouping tenants according to their SLO class (e.g gold vs bronze) in different containers. Moreover, SLO instability does not occur when co-locating these containers with other container-based applications provided that a few interferences between CPU-, memory- and disk-io intensive applications are taken into account. However SLO instability does occur when the number of subscribed tenants changes. This latter problem is not caused by the replication and auto-scaling concepts of Kubernetes, but by a non-linear resource scaling phenomenon that is inherent when the goal is to meet multiple custom SLOs in a cost-optimal way.
Redefining the Approach to Cybersecurity

One of the most critical issues in cybersecurity is represented by social engineering attacks. These threats have been known for years, but it is very difficult to handle them effectively, because they are strictly related to human nature. Social engineering is not just a phishing email; indeed, it is possible to distinguish several forms of attack which combine different elements, from human to social to physical and technological. According to a psychological point of view, social engineering is a powerful means of gaining information exploiting individuals’ weaknesses. Moreover, due to the mechanisms of persuasion, widely studied in literature, it is easy to imagine how complicated the management of this threat is. Appropriate training of employees, especially of key roles of the company, can be an effective antidote to social engineering. Given the current scenario and the future perspective in cybersecurity, it is clear that the approach used to manage cybersecurity requires a radical change. Currently, the preferred cybersecurity strategy is still based on technological solutions, without brilliant results, since cyberthreats keep growing. Many are convinced that Artificial Intelligence (AI) will be an opportunity for managing cybersecurity; whether true or not, it is however evident that AI has also the power to generate new threats and to strengthen the existing ones. Therefore, we should be very prudent when technologies are presented as a miracle solution for cybersecurity problems. The starting point is that technology has to be deployed under full human control. Then, critical thinking is needed to develop alternatives to improve the current approach to cybersecurity. In short, we need to develop a multidisciplinary vision of cybersecurity, involving other disciplines and assuming different perspectives.
Some Contemporary Approaches for Human Activity Recognition: A Survey

Human activity recognition(HAR) in video analysis has been an prominent area of research in the field of computer vision due to its large span of applications like intelligent surveillance systems, robot learning, Human Computer Interaction(HCI), health care. An activity recognition in video involve a large amount of data to be processed making it a challenging task. In the recent years, researchers have proposed several model using deep learning with different input modalities. This paper presents an analytical survey of recent deep learning models on the basis of input modality, feature extraction, classification, dataset used and accuracy.
Influence of virtual reality on engineering design creativity

This study explored the influence of Virtual Reality (VR) on Engineering Design (ED) creativity. This study adopted a quasi-experimental design with a pre-test and post-test. A teaching experiment was conducted in a high school in Taipei City, Taiwan. The main findings were: 1) the use of VR in ED instruction moderately and positively affected ED competence among students in identifying constraints, 2) VR positively affected the cognitive and affective domains in the ED process, but the VR effects on the psychomotor domain were nonsignificant, and 3) the use of VR-based instruction positively affected the students' ED results with a medium effect regarding functionality and uniqueness.
Experimental Investigation to Emphasize on True Potential of Solar PV Module

In the current era of increasing applications of solar energy, it is most crucial to emphasize on true performance.Also world community is focusing on use of artificial intelligent methods for forecasting the performance of systems. In this paper an attempt is made to emphasize on application of soft computing for performance measurement and also true potential determination. For this, experiment is conducted in laboratory and in outdoor conditions in GLA University, Mathura, India. In outdoor conditions results are enthusiastic because Q indoor value of MSE, RMSE, MBE and SDE is 84%, 9.6%, 6.3 % and 6.65% while the Q outdoor value of MSE, RMSE, and MBE is and SDE is 29%, 5.38%, 5.25 % and 1.19%. Here Q is error between simulated and experimental energy.
A Survey of Blockchain-Based Solutions for Energy Industry

The energy industry needs to shift to a new paradigm from its classical model of energy generation, distribution, and management. This shift is necessary to handle digitization, increased renewable energy generation, and to achieve goals of environmental sustainability. This shift has several challenges on its way and has been seen through research and development that blockchain which is one of the budding technology in this era could be suitable for addressing those challenges. This paper is aimed at the survey of all the research and development related to blockchain in the energy industry and uses a software engineering approach to categories all the existing work in several clusters such as challenges addressed, quality attribute promoted, the maturity level of the solutions, etc. This survey provides researchers in this field a well-defined categorization and insight into the existing work in this field from 3 different perspectives (challenges, quality attributes, maturity).
An Efficient Two-Server Ranked Dynamic Searchable Encryption Scheme

Searchable encryption (SE) allows users to search over encrypted data without decrypting.In most existing SE schemes, server returns all matched files without relevance ranking, and the update mechanism are suffering with high communication and computation cost, which are not efficient enough to satisfy the real-life dynamic scenario.Addressing the above issues, we proposed TS-RDSE-a Two-Server Ranked Dynamic Searchable Encryption scheme.We integrate orthogonal vector and efficient homomorphic encryption cryptosystems to build a vector-level dynamic secure index, which simultaneously supports efficient dynamic update operations like deletion and insertion of files flexibly.Moreover, in order to rank the search results by relevance without decryption, we build a secure sorting protocol based on the widely-used tf-idf weighting formula and addition property of partial homomorphic encryption, which achieves accurate sorting for the search results while protecting the privacy of relevance scores.We give a comprehensive analysis of the correctness of TS-RDSE in the aspect of searching and sorting with mathematical proofs.The security analysis shows that TS-RDSE is secure against adaptive dynamic chosen-keyword attacks (CKA2) by honest-but-curious adversaries in random oracle model.The performance analysis shows that TS-RDSE has both a very light user workload and a moderate server workload, and it is superior to the existing approaches in terms of functionalities and expansibility.Extensive experiments on the real-world dataset validate our analysis and show that TS-RDSE is suitable for the real world cloud storage environment.
WAGNN: A Weighted Aggregation Graph Neural Network for robot skill learning

Robotic skill learning suffers from the diversity and complexity of robotic tasks in continuous domains, making the learning of transferable skills one of the most challenging issues in this area, especially for the case where robots differ in terms of structure. Aiming at making the policy easier to be generalized or transferred, the graph neural networks (GNN) was previously employed to incorporate explicitly the robot structure into the policy network. In this paper, with the help of graph neural networks, we further investigate the problem of efficient learning transferable policies for robots with serial structure, which commonly appears in various robot bodies, such as robotic arms and the leg of centipede. Based on a kinematics analysis on the serial robotic structure, the policy network is improved by proposing a weighted information aggregation strategy. It is experimentally shown on different robotics structures that in a few-shot policy learning setting, the new aggregation strategy significantly improves the performance not only on the learning speed, but also on the control accuracy.
Understanding the Transit Market: A Persona-Based Approach for Preferences Quantification

The study aims at utilizing a persona-based approach in understanding, and further quantifying, the preferences of the key transit market groups and estimating their willingness to pay (WTP) for service improvements. The study adopted an Error Component (EC) interaction choice model to investigate personas’ preferences in a bus service desired quality choice experiment. Seven personas were developed based on four primary characteristics: travel behaviour, employment status, geographical distribution, and Perceived Behavioural Control (PBC). The study utilized a dataset of 5238 participants elicited from the Hamilton Street Railway Public Engagement Survey, Ontario, Canada. The results show that all personas, albeit significantly different in magnitude, are negatively affected by longer journey times, higher trip fares, longer service headways, while positively affected by reducing the number of transfers per trip and real-time information provision. The WTP estimates show that, in general, potential users are more likely to have higher WTP values compared to current users except for at-stop real-time information provision. Also, there is no consensus within current users nor potential users on the WTP estimates for service improvements. Finally, shared and unique preferences for service attributes among personas were identified to help transit agencies tailor their marketing/improvement plans based on the targeted segments.
Advancing and Validating Models of Cognitive Architecture

We present a methodology for proposing and evaluating components of cognitive architectures comprising complementary approaches: hypothesizing and behavioral mechanisms, evaluating the aptness of those mechanisms in providing accounts of human behaviors; embed models of hypothesized mechanisms within simulation systems and observe synthesized model behaviors. We illustrate these theoretical approaches with examples.
Improving Matrix Factorization Based Expert Recommendation for Manuscript Editing Services by Refining User Opinions with Binary Ratings

As language editing became an essential process for enhancing the quality of a research manuscript, there are several companies providing manuscript editing services. In such companies, a manuscript submitted for proofreading is matched with an editing expert through a manual process, which is costly and often subjective. The major drawback of the manual process is that it is almost impossible to consider the inherent characteristics of a manuscript such as writing style and paragraph composition. To this end, we propose an expert recommendation method for manuscript editing services based on matrix factorization, a well-known collaborative filtering approach for learning latent information in ordinal ratings given by users. Specifically, binary ratings are utilized to substitute ordinal ratings when negative opinions are expressed by users since negative opinions are more accurately expressed by binary ratings than ordinal ratings. From the experiments using a real-world dataset, the proposed method outperformed the rest of the compared methods with an RMSE (root mean squared error) of 0.1. Moreover, the effectiveness of substituting ordinal ratings with binary ratings was validated by conducting sentiment analysis on text reviews.
Embodied Realistic Avatar System with Body Motions and Facial Expressions for Communication in Virtual Reality Applications

Providing effective communication in virtual reality (VR) applications requires body language and facial expressions to transmit users’ thoughts and behavior. Embodied VR is an effective way to realistically render users’ movements onto a virtual avatar in a virtual environment in real time. There are many VR applications that utilize this technique; however, most of them have limitations in photorealistic realism, body motion, and facial expressions. In this work, we introduce a novel VR communication system that mimics users’ movements, facial expressions, and speech in order to render these capture data into different types of avatar representations in real-time. This system could be beneficial for any type of VR application that requires avatar-based communication in a 3D immersive virtual environment.
Cultural coalitions detection approach using GPU based on hybrid Bat and Cultural Algorithms

Currently, robot technology plays a crucial role in our modern life. Its great favor over humanity was not limited to the industrial domain, but also on the social one. Social robots become equipped with an artificial culture that allows them to interact with humans. Although the great importance of this technology, some misdeeds can create a real catastrophe. Coalition creation is one of its most dangerous troubles, where a subset of robots cooperates to impose pernicious decisions. There are efficient coalitions detection methods, but their exponential computation time makes their use limited only to small data. This paper is an extended version of Kechid and Drias (2019) presented at the IEA/AIE 2019 conference. In this paper, we propose a Cultural coalitions detection approach using GPU based on hybrid Bat and Cultural Algorithms. Unlike the existing literature, We view the problem of finding coalitions as an optimization problem to get relevant solutions in a significantly reduced amount of time. The proposed approach can increase the population diversity and improve the searching ability for an optimal exploration–exploitation balance. Also, it can launch several cultural bats in GPU to make real parallelism. Experimental results on several datasets show that the proposed method will considerably reduce the runtime. These datasets represent the result of artificial cultural agents playing the colored trails (CT) game. Concerning the creation of profiles, we use real datasets generated based on the World Values survey.
On the use of ”Active Breaks” to perform Eye Exercises for more Comfortable VR Experiences

Prolonged use of virtual reality (VR) head-mounted display (HMD) systems without removing the HMD remains a challenge, due to many factors. One is the fact that the eyes of the user suffer visual fatigue caused by stereoscopic HMDs. In clinics, people with eye-fatigue symptoms or who have difficulty seeing in daily life, are recommended to perform specific exercises, such as ”Thumb-moving” for focal changes and making a ”Figure-eight” by rolling the eyeballs. These practices efficiently help the eyes rapidly recover back to a normal state. In VR, existing solutions for visual comfort include adaptation practices, or taking breaks at different times: at the beginning (prior to exposure), in between by simply removing the HMD, and post-experiment with adaptation practices or natural decay. In this work, we mainly focus on the introduction of a recovery form called ”Active Breaks”, which briefly take place in between VR sessions, and are very different from passive methods. Our first motivation for this work is to bring eye exercises into VR experiences to enhance well-being. The second objective is to virtualize and deploy those exercises into the virtual environment for the user to practice without having to remove their HMD as in other approaches. These correspond to real-world exercises (RWE) after removing the HMD, and VR exercises (VRE) while still wearing the HMD, along with a Baseline (no exercises) condition. After an evaluation of user preference on the helpfulness of the introduced exercises, a majority of subjects rated both approaches as ”Helpful” or ”Very much helpful”, and rated RWE as more preferred over VRE. In terms of support for the superiority of VRE over RWE, we did not find significant differences in terms of eye-blink rate, eye tiredness, sense of presence, or VR sickness. However, we found one significant difference for a sub-component of IPQ: the RWE condition showed significantly higher levels of General Presence compared to VRE.
An answer to an open problem of Mesnager on bent functions

In 2014, Mesnager proposed two open problems in [4] on the construction of bent functions. One problem has been settled by Tang et al. in 2017. However, the other is still outstanding, which is solved in this letter by considering a class of PS− vectorial bent functions.
Coin Counting and Sorting Machine

Counting and Interpret currency coins with accuracy and high speed is a challenging problem for banks and stores, and even consumers. The systems of the machine that are available in the market are not well enough because sometimes it makes mistake when detect the value of coin and cause the error calculation in the result. In this proposed work, a coin operated machine which can differentiate between coins accurately and automatically is implemented. An automatic coin counting and sorting machine prototype is developed by using ARDUINO-UNO as the main controller. This work included design the display panel to show total quantity of each coin and total amount. The coin acceptor has been used as it acts as coin selector to determine the denomination of coins in sorting system is designed by using DC motor to hold and distributes coin to the corresponding slot.
Observation of Presence in an Ecologically Valid Ethnographic Study Using an lmmersive Augmented Reality Virtual Diorama Application

The main research question is centered on the usefulness of immersive Augmented Reality (AR) applications for informal learning activities. Can users experience presence – or a sense of being there - when using AR apps? To begin to approach this question an ethnographic study was conducted in July, 2019 in a museum with 56 volunteer participants to document behavior and measure learning, attitudes, and emotional outcomes of an AR application. Reported are the preliminary results of behavior indicative of presence observed in the study, and insights gained that are useful in understanding future designs.
Assessment of the Practices for Early Mathematics Thinking in Preschools of Pasaje City, Ecuador

&lt;p style="text-align:justify"&gt;Preschool education is fundamental to shape children’s aptitudes and skills in early life. Ecuador is following a global education trend for starting-up mathematical thinking at earlier development ages, but this is only reflected in vehement curricular changes that are not supported properly. As a result, the safeguarding of a good children’s education appears to be lessened. This work aimed to evaluate the mathematics thinking practices in preschools of Pasaje city, Ecuador. The investigation employed a descriptive approach, hence data were collected from 65 teachers and 810 parents from public and private preschools by means of interviews and questionnaire in an attempt define some causes (teacher’s education, children’s socio-economical and family circumstances) that impair the initiation of the numerical, spatial and metric and geometric aptitudes in children. The results showed that not only preschool practices but also the home environment linked to socio-economic status appear to have positive or unfavorable influences on their children’s education.&lt;/p&gt;