Reviewerly/exHarmony huggingface dataset:
Hierarchical Motion Planning of AUVs in Three Typical Marine Environments

Broad waters, harbor waters, and waterway waters make up more than 90% of autonomous underwater vehicles (AUV) navigation area, and each of them has its typical environmental characteristics. In this paper, a three-layer AUV motion planning architecture was designed to improve the planning logic of an AUV when completing complex underwater tasks. The AUV motion planning ability was trained by the improved deep deterministic policy gradient (DDPG) combined with the experience pool of classification. Compared with the traditional DDPG algorithm, the proposed algorithm is more efficient. Using the strategy obtained from the training and the motion planning architecture proposed in the paper, the tasks of AUVs searching in broad waters, crossing in waterway waters and patrolling in harbor waters were realized in the simulation experiment. The reliability of the planning system was verified in field tests.
Semantic structures of the american blog discourse

In this article the semantic structures of constructing American blog discourse are analyzed. Those structures are based upon semantic relations between propositions in the text of the blogs and aim to enhance coherence in this discourse. The results show that particular types of interpropositional relations are used on macro-semantic level for different subgenres of blogs (the semantic relations of equivalence, comparison, contrast, ‘part-whole’/’whole-part’, implication/causality, chronological order and spatial order relations). In particular, these interpropositional relations differ in the subgenres, such as political, business, socio-cultural, technology, and sports. In addition, the analysis brings out the different percentage of their usage.Manuscript received 23.04.2020
Using i*-Based Organizational Modeling to Support Blockchain-Oriented Software Engineering: Case Study in Supply Chain Management

Popularized by the rise of cryptocurrencies,Hamadi, Yasmine Ben blockchain technology has been gaining an increasing exposure. The potential of the distributed ledger lies in decentralization and immutable information.Heng, Samedi It promises complete transparency and a unique source of truth. In this article, a blockchain project is considered from a business and social perspective rather than from a technical one. More precisely, the focus is on understanding, from a functional analyst perspective, how the technology can fit the adopting organization’s supply chain. More specifically,Wautelet, Yves the i* organizational modeling framework is proposed to depict the interactions between involved actors and the roles of the multiple stakeholders of a blockchain. i* allows to identify the relations and goals within the actors of a complex network at early and late stages of the blockchain’s adoption. The applicability and advantages/limitations of i* to the organization of blockchain projects is tested through a case study of a food retail group who is adopting the distributed ledger for Supply Chain Management (SCM).
Energy-Efficient Systems for Smart Sensor Communications

A wireless sensor network (WSN) is a communication network with ad hoc configuration consisting of a number of tiny, low-power, low-cost sensors which are normally distributed in a decentralized fashion and have limited processing capability. WSNs have found a wide range of applications such as industrial process control, healthcare monitoring, surveillance, forest fire detection, natural disaster detection, target tracking, among many other applications. It is known that WSNs are resource-constrained, hence, energy efficiency is crucial for all applications of WSNs to extend the life span of the sensors' batteries. The most energy consuming operation in WSN is data communication, hence, it is important to reduce amount of data transmission through WSNs without significantly affecting the transferred information. In this paper we will focus on two directions of data-efficient signal representations that are expected to provide WSNs with sufficient energy control. The first direction is the use of intelligent short word-length (SWL) systems via embedded sigma-delta modulation, and the second direction is to use compressive sensing (CS) with chaotic sequences. If security is a factor, then CS via chaos can support secure communication in addition to its main function as a technique for data compression.
WITHDRAWN: Comparing performance of multiple classifiers for regression and classification machine learning problems using structured datasets

Abstract Machine learning aims at improving performance through repeated trials using different classifiers. Given the large number of classification algorithms and difference in their output on account of differing underlying models, it’s important to formulate guidelines on which classification algorithm to use under specific conditions. This study examines the performance of different classifiers on datasets in the public domain and presents a comparison of the results obtained. Based on results found for multiple datasets XG Boost AS is seen to produce superior or comparable results when measured in terms of linear regression, standard deviation and mean absolute error found in the test data making it a candidate for consideration for regression as well as classification machine learning problems especially for structured data.
Deep Learning Based Dog Behavioural Monitoring System

Dogs are one of the most popular pets in the world. It is usual that pet owners are always concerned about the health and the wellbeing of their pets. The activity levels of the dogs vary from each other based on breed and age. Tracking the behavioral changes using image processing and machine learning concepts and notifying the pet owners via a mobile application is the main objective of this research. Breed recognition has been done applying deep learning concepts to the user-uploaded video or the photograph of the dog. This research mainly focuses on walking, running, resting, and barking activity patterns of the dog. A surveillance camera and sensors were the main equipment for data collection. The audio feature of the surveillance camera is used to identity the barking behavior of the dog. Dogs from different ages belonging to Pomeranian and German Shepherd breeds have been selected for this experiment. Transfer learning with ResNet50, Inception V3, and support vector machines have been used to recognize and classify the activities of the dogs. The research study was able to achieve the accuracy levels as follows: - breed recognition - 89%+, walking pattern recognition - 99.5%, resting pattern recognition - 97% and barking pattern recognition - 60%. With the above accuracy levels, the research was able to identify the unusual behaviour of the dogs.
The Models of Data and Knowledge Representation in Educational System of Mathematical Training of IT-specialists

The question of the choice of methods of data and knowledge representation in the systems with elements of artificial intelligence is considered. The scheme of the knowledge base of educational system, which contains pedagogical, methodical, substantive and personal knowledge, is demonstrated. The author built the system of database and knowledge for mathematical training of IT specialists. The subject and methodological knowledge are explained on the basis of condition-action rules. The use of relational databases, hierarchical network models and production systems has been justified. A mathematical model of subject knowledge, which describes their composition and structure, has been constructed. A mathematical model of personalized knowledge has been constructed using an oriented graph.
Distant Reading Sentiments and Emotions in Historic German Plays

Sentiment and emotions are important parts of the analysis and interpretation of literary texts, especially of plays. Therefore, the computational method to analyze sentiments and emotions in written text, sentiment analysis, has found its way into computational literary studies. However, recent research in computational literary studies is focused on annotation and the evaluation of different approaches. We present a tool to investigate the possibilities of Distant Reading the sentiments and emotions expressed in the plays of Lessing. Researchers can explore polarity and emotion distributions and progression on concerning structural and character based levels but also character relations. We present various use cases to highlight the visualizations and functionalities of our tool and discuss how Distant Reading of sentiments can add value to research in literary studies.
Session Key based Image Cryptographic Algorithm using Logistic-Sine Map and Crossover Operator for IoT

Due to the increasing demand for IoT applications in various fields such as healthcare, smart city, smart grids, industrial internet, etc.The privacy and security become a major issue in front of various researchers working in this field.This work proposed a novel image encryption algorithm based on a logisticsine map and crossover operator of a genetic algorithm.Various 1-D chaotic maps are discussed in the literature review, but in some cases, hybrid 1-D chaotic maps have higher performance than simple 1-D chaotic maps.So 1-D chaotic map along with a crossover operator is used in this work.Here logistic-sine maps and crossover are used to generate the random session key for each image encryption.Also, a crossover operator is used in encryption rounds for increasing confusion and diffusion.Here in this work, for each image encryption, a new session session key is generated.The proposed algorithm is tested on various parameters for effective randomness.Experimental results show that the proposed algorithm performance is better than existing algorithms in terms of randomness and secure enough to resist all the existing cryptanalytic attacks.
Cloud Computing Using AWS: An Analysis

Cloud computing has become a driving innovation, it has become so popular and successful that everyone is stepping ahead towards Cloud. Today Cloud computing is the foremost demand of companies as it benefits their growth. With Cloud computing, new businesses witnessed immense success by locating their services and data on the internet without depending on any of physical operations. This independency and trend have inspired many eminent companies such as Netflix, Salesforce, and Amazon to move towards Cloud-based infrastructure. Cloud computing also refers to a set of policies, technologies and controls used to protect data, applications, and infrastructure. There is a wide range of Cloud service providing companies and platforms in which Amazon Web Services (AWS) and Microsoft Azure are regarded as the leading Infrastructure as a Service (IaaS) Cloud. In this paper, AWS and Microsoft Azure are compared and points why AWS is at peak or better than others are highlighted. The aim of this paper is to present an insightful description of Cloud computing, its characteristics, and Cloud computing models. This paper also describes various AWS services and shows its uniqueness by comparing it with other Clouds. It also explores certain benefits of Cloud computing using AWS over traditional IT service environments including how it is helping education industry to evolve and how it is transforming our lifestyle for better and easy access to things. This article gives an overview of the benefits of AWS.
Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval

Sketch as an image search query is an ideal alternative to text in capturing the finegrainedvisual details. Prior successes on fine-grained sketch-based image retrieval (FGSBIR)have demonstrated the importance of tackling the unique traits of sketches asopposed to photos, e.g., temporal vs. static, strokes vs. pixels, and abstract vs. pixelperfect.In this paper, we study a further trait of sketches that has been overlooked todate, that is, they are hierarchical in terms of the levels of detail – a person typicallysketches up to various extents of detail to depict an object. This hierarchical structureis often visually distinct. In this paper, we design a novel network that is capable ofcultivating sketch-specific hierarchies and exploiting them to match sketch with photo atcorresponding hierarchical levels. In particular, features from a sketch and a photo areenriched using cross-modal co-attention, coupled with hierarchical node fusion at everylevel to form a better embedding space to conduct retrieval. Experiments on commonbenchmarks show our method to outperform state-of-the-arts by a significant margin.
Self-Supervised Learning of Compressed Video Representations

Self-supervised learning of video representations has received great attention. Existing methods typically require frames to be decoded before being processed, which increases compute and storage requirements and ultimately hinders large-scale training. In this work, we propose an efficient self-supervised approach to learn video representations by eliminating the expensive decoding step. We use a three-stream video architecture that encodes I-frames and P-frames of a compressed video. Unlike existing approaches that encode I-frames and P-frames individually, we propose to jointly encode them by establishing bidirectional dynamic connections across streams. To enable self-supervised learning, we propose two pretext tasks that leverage the multimodal nature (RGB, motion vector, residuals) and the internal GOP structure of compressed videos. The first task asks our network to predict zeroth-order motion statistics in a spatio-temporal pyramid; the second task asks correspondence types between I-frames and P-frames after applying temporal transformations. We show that our approach achieves competitive performance on compressed video recognition both in supervised and self-supervised regimes.
Auxiliary Learning by Implicit Differentiation

Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss. Here, we propose a novel framework, AuxiLearn, that targets both challenges based on implicit differentiation. First, when useful auxiliaries are known, we propose learning a network that combines all losses into a single coherent objective function. This network can learn non-linear interactions between auxiliary tasks. Second, when no useful auxiliary task is known, we describe how to learn a network that generates a meaningful, novel auxiliary task. Evaluation of AuxiLearn in a series of tasks and domains, including image segmentation and learning with attributes in the low data regime, shows consistent improvement in accuracy compared to competing methods.
A Novel Machine Learning-Based Ship Detection for Pre-annotated Ship Database

Right now, present a novel machine learning-based ship detection for pre-explained transport database, which is intended for preparing and assessing transport object recognition calculations. Programmed object identification in the oceanic condition has gotten significant, with a wide cluster of utilizations in regions, for example, maritime fighting, vessel traffic administrations and fishery the board. The manual detection of items is not extremely effective due to the intense climatic conditions, for example, mist, downpour, storm and so forth. It might prompt flawed expectations and it is absolutely reliant on the ability of the individual. This adds to the usage of programmed object recognition component. The acknowledgment of inshore and toward the ocean ships is a major task for an enormous combination of employments in both military and non-military faculty fields.
Renewable Generation Electric System

A stable and sustainable energy industry contributes indubitably to the business development, as well as to guarantee the quality of life of a population. However, the economic and social development of insular regions is very vulnerable, due to the high external energetic dependency. Therefore, it is necessary to change the current paradigm of those regions, to take advantage of their endogenous resources to generate electrical energy. The developed work analyses the potential renewable energies (wind and solar) present on the island of Brava, Cape Verde, and assesses its capacity to combine them with an energy storage system that can meet the energetic requirements of the island. The first step consists in the analysis and profiling of the data (energy consumption, solar radiation, wind speed) observed in the island for one year. Simultaneously, several types of storing technologies are analyzed to find the best fitting solution, given the characteristics of the electrical system of the island. The next step consists in the development of a mathematical model, based on LCOE calculus and on the renewable penetration level that determines the optimal mix of power to install of each technology. The results allow to conclude that, despite the inferior solar power generation when compared to the wind power, the most economical solution to the implementation of a 100% renewable system consists of an electricity production park primarily composed by solar powers.
Application of digital twins of equipment for managing complex renewable energy

Abstract The paper describes a control system for renewable energy complexes with optimization of operating modes based on digital twins of equipment. The application of digital twins allowed us to develop a simulator that simulates the main processes occurring in the ground heat pump system. The simulator allows: to perform computer experiments to study different modes of Polygon operation; to demonstrate the physical essence of the ongoing processes to form the qualifications and skills of students in the field of non-traditional and renewable sources of electric and thermal energy. The use of digital twins also allows the implementation of a renewable energy combination management system to improve the energy efficiency of hybrid power complexes. The algorithm developed by the authors for selecting combinations of renewable energy sources to increase the energy efficiency of the complexes takes into account the current values of the operating parameters of the equipment, the forecast of energy consumption, as well as the forecast of power generation by each of the components of the complex.
LIRNet: Local Integral Regression Network for Both Strongly and Weakly Supervised Nuclei Detection

Nuclei detection is one of the most important issues in the field of histopathology image analysis. Recent studies exploit convolutional neural networks to either locate the centroids of nuclei (counting-based approaches) or isolate them with sophisticated boundaries (segmentation-based algorithms). However, the training of these two methods demands considerable and laborious pixel-wise annotations labeled by pathology experts. To reduce the annotation cost, we propose a novel local integral regression network (LIRNet) that allows both strongly and weakly supervised learning (SSL/WSL) frameworks on nuclei detection. In addition, the LIRNet is able to predict a refined density map of nuclei, in which locating each nucleus is barely affected by the post-processing algorithms. Quantitative experimental results demonstrate that the SSL version of the LIRNet achieves state-of-the-art performance compared with counterparts. Besides, the WSL version possesses a competitive detection performance and an effortless data annotation that dramatically reduces annotation cost by 74.3%.
Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-sided Label Shifts

In many real-world pattern recognition scenarios, such as in medical applications, the corresponding classification tasks can be of an imbalanced nature. In the current study, we focus on binary, imbalanced classification tasks, i.e.~binary classification tasks in which one of the two classes is under-represented (minority class) in comparison to the other class (majority class). In the literature, many different approaches have been proposed, such as under- or oversampling, to counter class imbalance. In the current work, we introduce a novel method, which addresses the issues of class imbalance. To this end, we first transfer the binary classification task to an equivalent regression task. Subsequently, we generate a set of negative and positive target labels, such that the corresponding regression task becomes balanced, with respect to the redefined target label set. We evaluate our approach on a number of publicly available data sets in combination with Support Vector Machines. Moreover, we compare our proposed method to one of the most popular oversampling techniques (SMOTE). Based on the detailed discussion of the presented outcomes of our experimental evaluation, we provide promising ideas for future research directions.
Investigating the Effect of Multidimensional Review Text and Anonymity on Review Helpfulness: An Empirical Investigation in the Context of Employer Review Sites

Employer review sites have grown popular over the last few years, with 86 percent of job seekers referring to reviews in these sites before applying to job positions. Though review helpfulness is studied in various contexts, it has received limited attention in the employee review context. In an attempt to solicit unbiased reviews, these sites allow an option of keeping reviewer information anonymous. Besides, these sites provide review text in multiple dimensions. We investigate review helpfulness focusing on the anonymity of the reviewers, and the role of review text in multiple dimensions. We use a publicly available Glassdoor dataset to model review helpfulness using a Tobit regression. The results show that anonymity and review length in multiple dimensions of review text positively impact review helpfulness. Moreover, anonymity positively moderates the review length in the cons section.
Digital experience design of qinhuai lantern based on user journey map

This research extends the design concepts and research methods of user journey maps to the field of intangible cultural heritage, and selects the typical lotus lantern material package in Qinhuai lanterns as an example to explore the digital experience design of lanterns. On-site research methods are used to understand users, define target users and build personas. AEIOU recording framework is used to record user behavior, analyze and reorganize user behavior with card classification, and summarize user behavior flow. The Likert Smiley Scale is used to ask children to evaluate specific behaviors, get emotional curves, and summarize design opportunities from the perspective of valley filling. Given the pain points in the experience of the target user children and the indirect user parents, the lotus lantern material package is digitally redesigned from four perspectives of materials, connection methods between materials, functions and storage to meet the users' emotional experience. This research method and process have certain reference significance for experience design research on the same type of intangible cultural heritages.