Reviewerly/exHarmony huggingface dataset:
Decentralized Approach to Secure IoT based Networks using Blockchain Technology

Emerging Technologies of Fourth Industrial Revolution such as Internet of Things has the potential to change the way we are living today and interact with information systems and devices.From a small device like a simple glucose monitor of healthcare sector to Autonomous cars from transportation industry, IoT plays a vital role in connected information, human interaction and data.At the same instance, IoT deals with personalized human and quite important data from various types of devices, a small loophole can be a reason to bring disastrous impact on human lives, a minor vulnerability in IoT networks may challenge the complete cycle of IoT network.It may generate calamity type of event, not only in information systems but also on the physical human lives as well, because of a single point of failure as IoT based networks usually deployed on centralized systems.In this paper, we are proposing a decentralized approach to remove single point of failure with the help of new layer of security based on Blockchain technology as advancement in securing IoT networks.
Multi-view Multi-modality Priors Residual Network of Depth Video Enhancement for Bandwidth Limited Asymmetric Coding Framework

Asymmetric coding methodology for multi-view video plus depth is a promising technique for future three-dimensional and multi-view driven visual applications for its superior coding performance in bandwidth limited conditions. Since the depth video suffers from asymmetric distortions corresponding to viewpoint, it's a challenge in smooth and quality consistent content based interaction. To solve this challenge, we propose a residual learning framework to enhance the quality of compression distorted multi-view depth video. In this work, we exploit the correlation between viewpoints to restore the target viewpoint depth maps by using multi-modality priors, which are depth maps from adjacent viewpoints with better quality and color frames in the same viewpoint. A residual network is designed to fully exploit the contribution from these priors. Experimental results show the superiority of our framework in the quality improvement on both decoded depth video and synthesized virtual viewpoint images.
MCTS-Based Automated Negotiation Agent

This paper introduces a new negotiating agent model for automated negotiation. We focus on applications without time pressure with multidimensional negotiation on both continuous and discrete domains. The agent bidding strategy relies on Monte Carlo Tree Search, which is a trendy method since it has been used with success on games with high branching factor such as Go. It also exploits opponent modeling techniques thanks to Gaussian process regression and Bayesian learning. Evaluation is done by confronting the existing agents that are able to negotiate in such context: Random Walker, Tit-for-tat and Nice Tit-for-Tat. None of those agents succeeds in beating our agent. Also, the modular and adaptive nature of our approach is a huge advantage when it comes to optimize it in specific applicative contexts.
Optimal Path Planning for Urban Vehicles Using Internet of Things: A New Navigation Perspective

One of the important applications in intelligent transportationsystem is path planning for vehiclesat urban areas. A driver can reach a destination in a short time with a pleasant ride using less fueland reach his or her destination safely. However, the destination status may change, and no morebecomes a desired destination. In this paper we highlighted, discussed and analyzed related worksfor vehicle’s path planning using Internet of Things (IoT). A new perspective for the destination wasalso proposed which defines how important considering the updated information of the destinationin real-time using IoT that brings driver’s attention when planning for a trip. A mathematical modelfor an optimal path planning was introduced which composes of two cost functions; path planningfunction and destination function. A gas station scenario was introduced as an example to demonstrate the proposed model. Futuristic look at integration design for the proposedsystem was alsohighlighted.
An Improved Particle Swarm Optimization Algorithm

Based on a brief introduction to the principle of particle swarm optimization (PSO) and different improved methods of PSO at home and abroad, the particle swarm optimization algorithm is improved for the shortcomings of particle swarm optimization. This paper selects the particle velocity formula with a learning model, which has good exploration ability and can effectively avoid the algorithm falling into local optimum, and recon figure some parameters of the algorithm. A PSO with two crossover operations (PSOCO) is put forward, and the next research direction of PSOCO algorithm is given.
Mindset for Software Architecture Students

Software architecture students need to believe that they can change their abilities in order to become proficient with software architecture design. Addressing students' beliefs about their capabilities introduces the realm of mindset. This paper reports about a survey that we conducted in a large university to study a set of factors associated with the students' mindset. The study found that the students' mindsets weakly correlates with their cognitive levels and are associated with their expectations from the course. In addition, it found that the students who prefer practicing software architecture have more open mindset than the ones who prefer quizzes. The findings provide new knowledge about the connections between the mindsets of the students, their perception of software architecture, and their approach to learning software architecture practices. The results could be used to design intervention strategies to improve the ability of the students to learn software architecture.
Neural Message Passing on Hybrid Spatio-Temporal Visual and Symbolic Graphs for Video Understanding

Many problems in video understanding require labeling multiple activities occurring concurrently in different parts of a video, including the objects and actors participating in such activities. However, state-of-the-art methods in computer vision focus primarily on tasks such as action classification, action detection, or action segmentation, where typically only one action label needs to be predicted. In this work, we propose a generic approach to classifying one or more nodes of a spatio-temporal graph grounded on spatially localized semantic entities in a video, such as actors and objects. In particular, we combine an attributed spatio-temporal visual graph, which captures visual context and interactions, with an attributed symbolic graph grounded on the semantic label space, which captures relationships between multiple labels. We further propose a neural message passing framework for jointly refining the representations of the nodes and edges of the hybrid visual-symbolic graph. Our framework features a) node-type and edge-type conditioned filters and adaptive graph connectivity, b) a soft-assignment module for connecting visual nodes to symbolic nodes and vice versa, c) a symbolic graph reasoning module that enforces semantic coherence and d) a pooling module for aggregating the refined node and edge representations for downstream classification tasks. We demonstrate the generality of our approach on a variety of tasks, such as temporal subactivity classification and object affordance classification on the CAD-120 dataset and multilabel temporal action localization on the large scale Charades dataset, where we outperform existing deep learning approaches, using only raw RGB frames.
A NOVEL SIMPLE AND HIGHLY SECURE METHOD FOR DATA ENCRYPTION-DECRYPTION

In the course of the past 30 years, data has become pivotal to all aspects of human life. Data generated, captured, and replicated are increasing in size and expanding applications. The proliferation of fast wireless networks has encouraged data storage within the cloud. So, protecting data from attackers has become urgent to maintain its security and confidentiality, need for security and privacy technologies, systems, and processes to address it. This research paper proposes a simple and highly secure encryption decryption (SHSED) algorithm that can be used for cloud computing-based applications. It achieves the Shannon’s concept of diffusion and confusion by the involvement of logical operations, such as XORing, addition, and subtraction in addition to byte shifting. It is also characterized by the flexibility in the secret key length and the number of rounds. Experimental results have demonstrated powerful security level and a clear improvement in the encryption execution time measurements and security strength as compared with cryptosystems widely used in cloud computing.
Development of a CT image case database and content-based image retrieval system for non-cancerous respiratory diseases: Method and preliminary assessment

To evaluate the benefits of using a CT image case database (DB) with content-based image retrieval system for the diagnosis of typical non-cancerous respiratory diseases. Using this DB, which comprised data on 191 cases covering 69 diseases, 933 imaging findings that contributed to differential diagnoses were annotated. Ten test cases were selected. Image similarity between each marked test case lesion and the lesions of the top 10 retrieved cases were assessed and classified as similar, somewhat similar, or dissimilar by two physicians in consensus. Additionally, the accuracy of five internal medicine residents’ abilities to interpret CT findings and provide disease diagnoses with and without the proposed system was evaluated by image interpretation experiments involving five test cases. The rates of concordance between the subjects’ interpretations and the correct answers prepared in advance by two specialists in consensus were converted into scores. The mean (± SD) of image similarity among the 10 test cases was as follows: 5.1 ± 2.7 (similar), 2.9 ± 1.0 (somewhat similar), and 2.0 ± 2.4 (dissimilar). Using the proposed system, the subjects’ mean score for the correct interpretation of CT findings improved from 15.1 to 28.2 points (p = 0.131) and for the correct disease diagnoses, from 9.3 to 28.2 points (p = 0.034). Although this was a preliminary small-scale assessment, the results suggest that this system may contribute to an improved interpretation of CT findings and differential diagnosis of non-cancerous respiratory diseases, which are difficult to diagnose for inexperienced physicians.
Reverse engineering approach for improving the quality of mobile applications

Background Portable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and continuously evolved to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality. Methods We proposed a general method to detect mobile applications’ anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and ontology by using a UML modeling environment, an OWL ontology-based platform and ontology-driven conceptual modeling. We present and test a new method that generates the OWL ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design’s semantic and structural anti-patterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of anti-patterns using the ontology merging on mobile applications. Results The proposed method detected 15 semantic and structural design anti-patterns which have appeared 1,262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. “The anti-patterns in the class group” is the most group that has the maximum occurrences of anti-patterns and “The anti-patterns in the operation group” is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, the Protégé platform, and the OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.
Teaching an Oscilloscope through Progressive Onboarding in an Augmented Reality Based Virtual Laboratory

The first impression is important in many aspects of human decision-making. In mobile apps, this impression can be influenced by an onboarding process. In addition, not only the user experience (UX) can be improved via onboarding, but also the user can get a very good didactical introduction to a new topic or different functionalities of an app. Therefore, this study examines different onboarding types and develops an onboarding process into an Augmented Reality (AR) based mobile application (app) that teaches students how to use and operate electro-technical laboratory equipment. This onboarding process is then assessed by students through a subsequent questionnaire in terms of attractiveness, functionality, and novelty. The results of this field study serve to examine this first prototype for possible optimizations and to further develop the app accordingly.
A Practical Guide to Support Change-proneness Prediction

During the development and maintenance of a system of software, changes can occur due to new features, bug fix, code refactoring or technological advancements. In this context, software change prediction can be very useful in guiding the maintenance team to identify change-prone classes in early phases of software development to improve their quality and make them more flexible for future changes. A myriad of related works use machine learning techniques to lead with this problem based on different kinds of metrics. However, inadequate description of data source or modeling process makes research results reported in many works hard to interpret or reproduce. In this paper, we firstly propose a practical guideline to support change-proneness prediction for optimal use of predictive models. Then, we apply the proposed guideline over a case study using a large imbalanced data set extracted from a wide commercial software. Moreover, we analyze some papers which deal with change-proneness prediction and discuss them about missing points.
Live Software Development Environment for Java using Virtual Reality

Any software system that has a considerable growing number of features will suffer from essential complexity, which makes the understanding of the software artifacts increasingly costly and time-consuming.A common approach for reducing the software understanding complexity is to use software visualizations techniques.There are already several approaches for visualizing software, as well as for extracting the information needed for those visualizations.This paper presents a novel approach to tackle the software complexity, delving into the common approaches for extracting information about software artifacts and common software visualization metaphors, allowing users to dive into the software system in a live way using virtual reality (VR).Experiments were carried out in order to validate the correct extraction of metadata from the software artifact and the corresponding VR visualization.With this work, we intend to present a starting point towards a Live Software Development approach.
Anonylitics: From a Small Data to a Big Data Anonymization System for Analytical Projects

When a company requires analytical capabilities using data that might include sensitive information, it is important to use a solution that protects those sensitive portions, while maintaining its usefulness. An analysis of existing anonymization approaches found out that some of them only permit to disclose aggregated information about large groups or require to know in advance the type of analysis to be performed, which is not viable in Big Data projects; others have low scalability which is not feasible with large data sets. Another group of works are only presented theoretically, without any evidence on evaluations or tests in real environments. To fill this gap this paper presents Anonylitics, an implementation of the k-anonymity principle for small and Big Data settings that is intended for contexts where it is necessary to disclose small or large data sets for applying supervised or non-supervised techniques. Anonylitics improves available implementations of k-anonymity using a hybrid approach during the creation of the anonymized blocks, maintaining the data types of the original attributes, and guaranteeing scalability when used with large data sets. Considering the diverse infrastructure and data volumes managed by current companies, Anonylitics was implemented in two versions, the first one uses a centralized approach, for companies that have small data sets, or large data sets, but good vertical infrastructure capabilities, and a Big Data version, for companies with large data sets and horizontal infrastructure capabilities. Evaluation on different data sets with diverse protection requirements demonstrates that our solution maintains the utility of the data, guarantees its privacy and has a good time-complexity performance.
Hybrid Solution for Motion Capture with Kinect v2 to Different Biotypes Recognition

Motion capture (MOCAP) systems are powerful tools for the health field; they enable kinematic analysis applicable to various rehabilitation purposes. Within the MOCAP technologies, Microsoft Kinect stands out for its advantages as cost, portability, and markerless characteristic. However, its tracking algorithm is modeled from machine learning, so Kinect is conditioned to identify the human body based on a certain pattern and bodies that depart from this pattern are not properly recognized. Thus, Kinect is not feasible for the kinematic analysis of individuals with different biotypes, such as member discrepancy or amputees. Based on the exposed, this work aims to create a tool which uses Kinect as a hybrid optical MOCAP system in which color markers will be positioned on the joints of interest to add extra information through colored passive markers to the standard Kinect skeleton tracking. A case study was conducted with an individual with lower limb discrepancy. The tests of the tool occurred with the numerical analysis of a sequence of movements made by the same. The analysis was made in MATLAB to quantify the angular variation of limbs and length of limb sections. The data was treated by comparing the hybrid method with the standard Kinect method and indicated the viability of the tool to capture movement of the tested biotype even when light variation conditions common to RGB systems are present.
A SEMANTIC RETRIEVAL SYSTEM IN REMOTE SENSING WEB PLATFORMS

Abstract. This paper proposes a solution to reduce the semantic gap between final users and data/processing providers in a web market place dedicated to remote sensing products. Nowadays, search engine are common tools on the Internet. Users are accustomed to use them and used to get tabular classification of provided answers. These smart agents are set up to answer basic questions using automatic pages redirection or chitchat. In this research, to ensure coherence between user’s requests and platform answers, natural language processing algorithms and knowledge graphs are integrated within a web platform thanks to a NoSQL graph database connected to open thesauri and Geographic Information Systems (GIS). Therefore, the most pertinent services can be proposed based on input sentences including non-technical vocabulary but also geographical components (the user interface includes a text area and an interactive map). While processing chains and remote sensing ontologies were presented in one of our previous studies, this article focuses on natural languages algorithms and knowledge mining.
Feasibility of Digital Applications for Toilet Locating and Monitoring Services in Urban Area

This study focuses on Swachh Bharat Toilet Locator (SBTL) and Google Maps Toilet Locator (GTL) comparing their feasibility, usability and review system. The study includes feasibility and content analysis of Google Toilet Locator (GTL) in comparison with other toilet locator applications available on the basis of reviews, ratings, comments and downloads with graphical comparison and analysis of data. The sample size includes 5,030 toilets located under 8 ULBs (Urban Local Bodies) i.e. NDMC, EDMC, SDMC, North DMC, Delhi Cantonmentunder Delhi Municipal Corporation (DMC) and GMC, MCG &amp; Faridabad MC in NCR Region. The study concluded that feasibility on the basis of number of toilets available are more in Delhi as compared to NCR but when the ratio of application usage or toilet vs review ratio is taken into consideration it was seen that out of 11 toilets in NCR 1 review was recorded which is better than Delhi which is 16 toilet visits – 1 person reviewed. Hence it had been concluded the review system introduced by app is poorly used by user. Lack of awareness among users is an important factor that government had to take strong note and action. Most google app users use apps for various other navigation tools like to know real time traffic, locating travel route, location near restaurants, hotels etc. None of the users have used google app or any other app to locate public toilet locations. The users may be interested in using such app if mapping of toilet is done on periphery basis of at least 100 meters to 200meters. Most of the apps developed by private or public platform are lacking on this feature. Also, the rating criteria and app language should be in vernacular language so that people can do rating easily. Since most of the users of public toilets on regular basis belong to aspiring class or destitute class, awareness should be created for rating. Usage devices are installed in public toilets in Delhi area for better monitoring of toilet services. It is required to promote toilets and their rating mechanism through display boards, audio-visual devices showing citizen engagement in monitoring cleanliness and hygiene of toilet etc.