Reviewerly/exHarmony huggingface dataset:
Research on Target-Driven Navigation of Mobile Robot Based on Deep Reinforcement Learning and Preprocessing Layer

Abstract Recently, with the rise of deep reinforcement learning model, robot navigation based on this method has a huge advantage compared with traditional slam method, which has attracted extensive attention. However, when the navigation algorithm trained in the virtual environment is transferred to the real environment, the navigation performance of the robot will decline sharply because of the great difference between the virtual environment and the real environment. In order to improve the navigation ability of mobile robot, this paper implements a mobile robot navigation system based on deep reinforcement learning without environment map and only visual input. At the same time, in order to solve the problem of poor generalization ability of deep reinforcement learning from virtual environment to real environment, this paper proposes a preprocessing layer with knowledge and combines it with deep reinforcement learning module. The combined algorithm model alleviates the performance fault problem caused by the migration algorithm and the performance difference between virtual sensor and real sensor. At the end of this paper, a navigation experiment based on the turtlebot is designed, which proves that the deep reinforcement learning algorithm with the preprocessing layer can alleviate the performance fault problem caused by the migration algorithm, and have a certain ability of obstacle avoidance and avoidance without the environment map.
Autoregressive density modeling with the Gaussian process mixture transition distribution

We develop a mixture model for transition density approximation, together with soft model selection, in the presence of noisy and heterogeneous nonlinear dynamics. Our model builds on the Gaussian mixture transition distribution (MTD) model for continuous state spaces, extending component means with nonlinear functions that are modeled using Gaussian process (GP) priors. The resulting model flexibly captures nonlinear and heterogeneous lag dependence when several mixture components are active, identifies low-order nonlinear dependence while inferring relevant lags when few components are active, and averages over multiple and competing single-lag models to quantify/propagate uncertainty. Sparsity-inducing priors on the mixture weights aid in selecting a subset of active lags. The hierarchical model specification follows conventions for both GP regression and MTD models, admitting a convenient Gibbs sampling scheme for posterior inference. We demonstrate properties of the proposed model with two simulated and two real time series, emphasizing approximation of lag-dependent transition densities and model selection. In most cases, the model decisively recovers important features. The proposed model provides a simple, yet flexible framework that preserves useful and distinguishing characteristics of the MTD model class.
Agility in Telecommunication Product Development, Challenges and The Way Forward

On time delivery of products with industry expected suite of features is one of the most common challenge for any product development organization. In order to address this, most of the software product development organizations have transformed their processes to agile models in recent years. Though most of them have tried to implement available flavors of agile models such as Scrum, Extreme programming, Test Driven Development etc., there is a wide variation on methods they use, challenges they face, and the level of their adoption. Adoption level varies among different sectors in the industry. The level is high for web development, pure software & cloud-based product companies, wherein it is low for organizations developing systems with special hardware and embedded software such as communication products. This is basically due to the complexity of the processes they use in their product development lifecycles. At least some phases of their development lifecycle find it difficult to meet requirements expected by the regular agile models such as quick turnaround time, light weight viable deliverables with business value etc. This paper discusses some of the prominent process models used by telecommunication product industry, reasons for low adoption of Agile models, challenges faced while implementing related practices; suggests how to overcome those challenges by making use of traditional methods like Kanban and practices from recent innovations such as DevOps to get prepared for the way forward.
Continual Learning of Object Instances

We propose continual instance learning - a method that applies the concept of continual learning to the task of distinguishing instances of the same object category. We specifically focus on the car object, and incrementally learn to distinguish car instances from each other with metric learning. We begin our paper by evaluating current techniques. Establishing that catastrophic forgetting is evident in existing methods, we then propose two remedies. Firstly, we regularise metric learning via Normalised Cross-Entropy. Secondly, we augment existing models with synthetic data transfer. Our extensive experiments on three large-scale datasets, using two different architectures for five different continual learning methods, reveal that Normalised cross-entropy and synthetic transfer leads to less forgetting in existing techniques.
Influence of the Optical Aspects of Photographic Composition on the User Experience in the Issues Related to Decision Making, Choices and Level of Visual Comfort

This article investigates the level of influence of the optical aspects of image composition on the issues and consequent problems of the user experience. This work is part of the research project n. 461710-JOR-013-2019/1 (registered in the Unicap Research Database and approved by the CCP at an ordinary meeting on 09/October/2018) “Virtual environments: influence of the optical aspects of image composition on the quality of user experience” coordinated by Dr. Marcia Campos. The objective was to collect data relative to the perception/reaction of the user versus levels of optical contrasts of the image composition. Therefore, to investigate this level of influence, an experiment was carried out in two stages: the first step consisted of producing 17 images with specific characteristics of visual representation covering various levels of contrasts of the optical aspects for each image. The second step was the application of questionnaires with users, with questions based on the images produced in the first stage. The results indicate evidences of the correlation of the optical aspects of image composition with the reactions choices from the users; such as, time spent for decision making and choices about the images regarding elements of the scene and level of visual and physical discomfort. The evidence point to the results that optical aspects of image composition, mainly the representation of its levels of contrasts, are relevant for the quality of the user experience concerning both physical and psychological perception issues.
Developing Autonomous Adaptive Behavior for Human Behavior Simulation of an Atypical Architectural Space

Atypical buildings are growing in number daily, and the social demands on them are increasing. The atypical building design process is very different from existing building design processes because the generation and transformation of shapes occupies a large part of that process. In the process of designing atypical buildings, it is possible to overlook this safety and convenience while being immersed in form values. Ultimately, this study aims to develop a technology for architects to grasp the dwelling performance in a design in real time and to reflect it in the design. To do this, we developed a simulation module embedded in a commercial atypical design tool (Rhino and Grasshopper), named ActoViz. It transforms the designed plan into a 3D game-based virtual environment in real time and allows intelligent human figures to freely flow through the plan to evaluate it as including module. This study seeks to advance the development of ActoViz. There is a limit to the accuracy of user behavior simulations in which predetermined behavior models are simply repeated, because the physical forms of atypical buildings are so diverse. Since the realism of human-figured agents has a positive effect on the simulation, advanced and precise interactions between agent and space are very important. In this study, ragdoll physics and model predictive control techniques are applied to achieve more adaptive behavior for the virtual users. Through the use of such technologies, inconsistencies in the virtual users’ behaviors and the physical space can be minimized, and more natural reactions can be achieved.
Open IoT technologies in the classroom — a case study on the student’s perception

The Internet of Things (IoT) has been perceived by several authors as a technology with a potential impact in educational contexts, highlighting the concept of hypersituation and its didactic potential. This paper presents the main results relating the student's perception about an IoT based learning environment supported on an IoT device and a set of didactic guides. The IoT device was developed with low cost and open technology. Complementarily to the device development, a visual guidebook was created to facilitate the device construction by teachers and students, even if they do not have prior knowledge about the use of IoT technologies. Along with the device assembly guide, interdisciplinary didactic guides were also developed to be used in learning tasks. The results obtained demonstrate that teens involved in the study expressed a desire to continue to use the device in their classes and that it was a stimulating and facilitating factor for learning.
Service-Oriented Satellite Mobile Communication System Research on Effectiveness Evaluation

Starting from the future service-oriented communication needs, according to the present situation of satellite mobile communication system research and the current practice of mobile communication, the hierarchical structure model of service-oriented satellite mobile communication system effectiveness evaluation is established according to the steps of analytic hierarchy process (AHP). Through in-depth analysis of all levels of indicators, the judgment matrix of each layer is constructed, and through consistency test, a reasonable and feasible index system is finally established. Comprehensive evaluation of the LEO/GEO satellite mobile communication system effectiveness, and further put forward suggestions on the optimal configuration of the system.
Research on the Influence of Perspective Angle on Document Image Correction Results

Because of the obliquity and rotation between the camera imaging plane and the paper plane, the document image taken by mobile phone presents the phenomenon of irregular quadrilateral perspective distortion, which affects the accuracy of optical character recognition. In order to eliminate this kind of distortion, based on the original image, a perspective correction algorithm with high resolution is studied by using the principle of spatial mapping transformation and bilinear interpolation. The experimental results show that the proposed method still has a good correction ability for images with large deformation angles, allowing the maximum perspective angle to be 75 degrees. And the high quality image correction results can be obtained by using this method, the recovery rate of the perspective image can reach 98. The practical application shows that the method is simple, practical, convenient and reliable.
Iraqi Colloquial Dialect Dictionary Corpus Creation, Analyzing and Translation

Abstract The Iraqi dialect is one of the most beautiful dialects in the Arabic world. It contains a wide variety of vocabulary as well as phrases spoken in Iraqi population. The aim of this paper is to create, analysis, and translate of Iraqi Colloquial dialect corpus. The hardest step is to create the Iraqi corpus that were generated by writing of Iraqi films or stories and published them in GitHub. There are two phases: analyzing, which implement pre-processing includes: tokenization, remove stop words, remove punctuation, and remove duplicated words. The purpose of analyzing process is to collect the words to make Iraqi-Arabic-English dictionary. In the second phase is: testing, which involve searching about Iraqi word, translating it to Standard Arabic and English, adding it if not exist, predicting Iraqi words that depends on similar in spell, getting Iraqi synonyms words, and display the whole dictionary. New method was suggested to cover the problem of unofficially written for spoken dialects, as well as new algorithm to find the Iraqi words synonyms.
TEXT2FSL

Education nowadays is progressively becoming advance, all the things that are occurring in the technology world has a direct impact on education and E-learning systems. Computer-Assisted Instruction (CAI) provides text and questions, or problems to learners. It also summarizes students' performance and generates flashcard. CAI offers numerous advantages for education and training when properly designed and implemented. Students understandably find the resulting programs dull and informative. CAI applications should be designed where the learners have control over the pace of their learning. The use of sign language furthers the reading competence of deaf people and enhances their acceptance and understanding of learning content presented to them. This paper focuses on determining on how e-Filipino Sign Language (FSL) can aid the deaf and mute students by implementing daily learning materials and assessments. Results show that the developed system produced positive feedback from the intended end-users' learning capability.
CoCon: A Conference Management System with Formally Verified Document Confidentiality

Abstract We present a case study in formally verified security for realistic systems: the information flow security verification of the functional kernel of a web application, the CoCon conference management system. We use the Isabelle theorem prover to specify and verify fine-grained confidentiality properties, as well as complementary safety and “traceback” properties. The challenges posed by this development in terms of expressiveness have led to bounded-deducibility security , a novel security model and verification method generally applicable to systems describable as input/output automata.
Towards End-to-End Face Recognition Method through Spatial Transformer

Abstract Face recognition technology is widely applied in daily life, but in most methods, similarity or affine transformation is employed to align face images according to five facial landmarks. The face alignment module is implemented independently, thus it’s difficult in end-to-end training. In this paper, the main purpose is to design a towards end-to-end trainable face recognition method based on indoor scenes. Due to that spatial transformer can implement any parametrizable transformation, we joint it with recognition network, making end-to-end training possible. Simultaneously, any prior knowledge on facial landmarks isn’t required. The model jointly with spatial transformer can achieve 0.3% higher accuracy than similarity transformation. Most downsampling methods ignore the sampling theorem, making convolutional networks not shift-invariant. We replace max-pooling by MaxBlurPool in spatial transformer network, and the accuracy is improved by 0.25%.
An information security risk assessment method for cloud systems based on risk contagion

The rise of cloud computing and cloud storage brings huge benefits to the information industry, but at the same time, related applications also face greater information security challenges. Therefore, this paper proposes a risk assessment method of cloud system information security based on risk contagion, which mainly evaluates the risk value of the whole system caused by a module being attacked as a risk source. In this method, the calculation of asset value is improved, and the permissions relationship is introduced into the calculation of risk contagion coefficient.
Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments

Abstract This paper describes results from an observational, exploratory study of visual data exploration in a large, multi‐view, flexible canvas environment. Participants were provided with a set of data exploration sub‐tasks associated with a local crime dataset and were instructed to pose questions to a remote mediator who would respond by generating and organizing visualizations on the large display. We observed that participants frequently posed requests to cast a net around one or several subsets of the data or a set of data attributes. They accomplished this directly and by utilizing existing views in unique ways, including by requesting to copy and pivot a group of views collectively and posing a set of parallel requests on target views expressed in one command. These observed actions depart from multi‐view flexible canvas environments that typically provide interfaces in support of generating one view at a time or actions that operate on one view at a time. We describe how participants used these ‘cast‐a‐net’ requests for tasks that spanned more than one view and describe design considerations for multi‐view environments that would support the observed multi‐view generation actions.
PPTL model checking for blockchains

How to model and verify the actions with true concurrency in a blockchain? This is an open issue. To this end, Propositional Projection Temporal Logic (PPTL) is employed to model a blockchain. And then, the existing PPTL model checking algorithm is employed to check some temporal properties in the blockchain. As a result, a new method for modeling and verification of blockchains is formed. Compared with the existing approaches, the new one has ability of deal with true concurrency (synchronization mechanism) in a blockchain, in the process of formal verification.
Application of Visual Cryptography Scheme in Software Watermarking

Software watermark is a unique identifier that is embedded in software that can be extracted later to prove the identity of the owner. Software is prone to threats like piracy, modification, and illicit use. In this technologically sophisticated period, software watermarking is necessary to authenticate ownership of the software. In the proposed scheme, collatz conjecture to embed the watermark is used and the input to collatz function is generated using a visual cryptographic scheme. The watermark value is split into different shares by the visual cryptographic secret sharing scheme. Instrumentation is done using sbt-instrumentation to extract the watermark. The advantage of this scheme is that it is flexible for any number of embedding points. This watermarking scheme is also resistant to model checking and watermarking attacks.
Exploiting Gabor Feature Extraction Method for Chinese Character Writing Quality Evaluation

Abstract The automatic evaluation of Chinese character writing quality has a wide application prospect. Most of the existing evaluation methods of Chinese character writing quality are based on radical segmentation and feature judgment, which require the high accuracy of Chinese character segmentation. However, there are many problems in the real handwriting, such as continuous writing, uneven strength of writing, personalized writing style and so on, which lead to the difficulty of segmentation in the ordinary handwriting. To solve the above problems, we propose an effective method based on image texture where the uniformity of writing lines and writing style is taken as an effective criterion. In our method, Gabor transform is used to extract the image features of writing samples, and finally the statistical learning method of support vector machine is used to effectively evaluate the writing quality. Experiments on multiple real datasets including CHAED show that our method is effective and accurate. The advantage of this method is that it does not need to segment fonts, and the cost of global feature extraction is small.
SVM Based Approach to Text Description from Video Sceneries

Human uses communication language either by written or spoken to describe visual world around them. The study of text description for any video goes increasing. This paper presents a system which produce English descriptions from the complex video samples. Here system produces text description from complex video, where it represents a framework that gives output as description for any long length video with multiple objects. This paper is broadly classified into two modules training and testing modules. Where the training module perform extracting of its unique features a with its description found in that video and is stored in database. In testing module consider the video sample which under goes frame extraction, preprocessing, segmentation, feature extraction and the extracted features are compared with features which are computed in training module then identify the video action, classify it and finally generate the text description using langauge model. The sentences are generated from objects for this assessment, a preferred database from youtube are accumulated in which 250 samples from 50 domain names. The performance of the system can be calculated and gives the accuracy of 90% with minimum processing time for object 2.
REDEMON: Resilient Decentralized Monitoring System for Edge Infrastructures

The Guifi.net community network has evolved during the past 15 years into a telecommunications infrastructure that offers Internet access to more than 80.000 people. The monitoring system currently in place for this network is lagging behind the growth of the infrastructure, requiring manual intervention and counting several single points of failure. In this paper we present REDEMON, a resilient decentralized monitoring system, hosted on distributed and interconnected edge devices, for a reliable, eventually-consistent monitoring of the Guifi.net network, leveraging CRDT-based data structures implemented on AntidoteDB. We developed the REDEMON system as a prototype featuring resilience, decentralization and automation, in order to replace the legacy monitoring system. To assess the system, this prototype was deployed on resource-constraint edge nodes in the Guifi.net production network and evaluated under realistic conditions. The decentralized assignment mechanism successfully achieves setting the minimum number of monitoring servers per network device that satisfies the established system requirements. Besides, by concentrating the workload on the minimum required number of servers running at their maximum capacity, the remaining devices can idle away, reducing the consumption footprint of the system. With regard to computing resources, we measure a moderate CPU and RAM usage by the monitoring system on low-capacity devices, while we observe that a considerable network traffic is required for achieving a resilient and consistent data storage layer. This resilient and decentralized architecture could lay the basis for other edge applications in the cloud computing domain that need to coordinate over distributed and consistent shared data.
CAN ANTITRUST TRUST BLOCKCHAIN?

Governments must anticipate today’s fast moving technologies to be effective. Blockchain potentially is the ideal tool to assist antitrust in enforcing regulation and fully exploiting its core principles—competition and consumer welfare. Blockchain offers antitrust an enormous opportunity and as any powerful tool it also has the capacity to harm as well as benefit if abused and left totally uncontrolled. Blockchain is not immune from the economic principle of trust. This chapter explores the delicate balance between regulation of and for blockchain.