Reviewerly/exHarmony huggingface dataset:
Strategic Alliances for Technology Adoption: Alliances and Partnerships for Blockchain Adoption

This paper aims to study the relevance and importance of strategic alliances for emerging technology adoption. The case researched and discussed here is Blockchain adoption in the semiconductor industry. As a technology, Blockchain has been around for over a decade and is known to provide tremendous value in business transactions. However, the adoption has not gained traction mainly due to the fact that it takes a network to adopt an industrial Blockchain and cannot work in silos. Most companies are shying away from it as they haven't explored what makes a successful strategy for adoption. A literature review was done on the similar technology adoption in the past. The nature of Blockchain and its network dependency were considered. It was clear that strategic alliances are the way to move forward. The various aspects to be considered while forming an alliance, such as understanding the core competencies, finding the right partners, and form of alliances was studied. The research converged in understanding the fact that companies should move away from the transactional business model and have the right expectations and scoping on all sides, standardization, digital integration, and a steady focus on security and privacy.
Superlinear Speedup of Parallel Population-Based Metaheuristics: A Microservices and Container Virtualization Approach

Population-based metaheuristics such as Evolutionary Algorithms (EAs) can require massive computational power for solving complex and large scale optimization problems. Hence, the parallel execution of EAs attracted the attention of researchers as a feasible solution in order to reduce the computation time. Several distributed frameworks and approaches utilizing different hardware and software technologies have been introduced in the literatures. Among them, the parallelization of EAs in cluster and cloud environments exploiting modern parallel computing techniques seems to be a promising approach. In the present paper, the parallel performance in terms of speedup using microservices, container virtualization and the publish/subscribe messaging paradigm to parallelize EAs based on the Coarse-Grained Model (so-called Island Model) is introduced. Four different communication topologies with scalable number of islands ranges between 1 and 120 are analyzed in order to show that a partial linear/superlinear speedup is achievable for the proposed approach.
Risk of modern contamination of river waters by pesticide DDT and HCH

The problem of modern contamination of river waters by DDT and HCH persistence organochlorine pesticides from point sources — the sludge collectors, warehouses and burial grounds which are in an unsatisfactory state is investigated. Examples of contamination of river waters by DDT and HCH in various regions of the our country are given. The factors enhancing risk of contamination of river waters by DDT and HCH, and also a consequences of their intake in a human body through food chains are considered. Preventive and remediation measures for decrease in risk of contamination of river waters by DDT and HCH are listed.
Semi-supervised Deep Neural Networks for Object Detection in Video Surveillance Systems

Moving object detection under video surveillance systems is a critical task for many computer vision applications. That being said, extracting object from real-world surveillance video is still a challenging task, since various appearances and shapes, and light condition are all unsolved problems. Exploiting contexts from adjacent frames is believed to be valuable for tackling these challenges in surveillance video and is far from development. In this paper, we introduce a novel one-stage approach, named SDNN, to detect objects with multiple successive frames in videos. Specifically, the network fuses the context information of multiple frames, and combines predictions from multi-scale feature maps at different layers. The multi-frame feature fusion scheme enable the training process follows an end-to-end fashion. Experimental results conducted on surveillance video dataset show that the proposed SDNN achieved state-of-the-art results. The source code is available at https://github.com/jmuyjl/SDNN .
One-Shot Video Object Segmentation Using Attention Transfer

In this paper, we consider the problem of one-shot object segmentation in videos. Given an input video where the object mask of the first frame is provided, our goal is to segment each remaining frame in the video into foreground and background. We propose an attention based knowledge transfer mechanism that transfers the object knowledge from the first frame to other frames in a video. Our model is a Siamese network with two streams. The first stream will process the first frame in a video, and the second stream will produce segmentation mask of any other frame in a video. Each stream is a convolutional neural network (CNN) that produces attention maps in certain layers. Our proposed approach is based on the observation that the attention maps in CNN contain valuable information that can boost the performance of CNN architectures. In our work, we propose a method for transferring the attention maps from the first stream to the second stream in the Siamese architecture. This will allow our model to transfer the knowledge from the first frame (with ground-truth segmentation mask) to other frames in the video. Our experimental results on two benchmark datasets demonstrate that our proposed model outperforms other state-of-the-art approaches.
Science as Gift, or Knowledge as the Offer That Cannot be Refused: Introducing Russian Science and Technology Studies

This article introduces a set of articles written by Russian social epistemologists and science and technology studies scholars based on research conducted in the first major Russian Academy funded project on science and technology studies. Most of the articles take off from Peter Galison’s concept of scientific ‘trading zones’. However, the author develops a theme found in Ilya Kasavin’s article on ‘science as gift’, which is designed to transcend both ‘capitalistic’ and ‘communistic’ conceptions of science. However, the resulting political economy of science as gift – albeit quite recognizable – resembles the practice of extortion in mutual protection rackets; hence, the ‘offer too good to refuse’.﻿
Keyword Extraction using Graph Based Supervised Term Weighting

Text classification of massive textual data has become an integral part and necessity of most computer applications. Eventually, term weighting becomes an important aspect as it directly affects the classification quality of document classification. The challenge of effective term weighting followed by the deficiency of traditional methods such as Term Frequency-Inverse Document Frequency (TF-IDF) method has encouraged numerous researchers to propose different algorithms. Term Frequency-Inverse Gravity Moment (TF-IGM) in vector space model is one such example which precisely measure the class distinguishing power of terms. In graph based model TextRank has become quite popular for term weighting. However, no algorithm has been proposed yet in graph based model which directly influences the class distinguishing power of a term. Hence, we propose a novel Graph Based Supervised Term Weighting (GBSTW) model which modifies the TextRank algorithm to incorporate class label information. The model has been validated using three mobile review data sets and has been compared with the pre-existing modified TextRank model. Results show that GBSTW model outperforms the pre-existing modified TextRank algorithm in terms of precision, recall and F-measure.
Prediction of Solar Energy Potential with Artificial Neural Networks

The energy requirements have been met from fossil fuels since the early 1800s. Considering the environmental awareness and limited fossil resources, using renewable energy resources are compulsory to meet the increasing energy demand. Solar and wind energy, biofuels, and natural gas are leading ones. Solar energy is an effective and clean energy source compared in terms of sustainability, reliability, and economy. In the maritime sector, eco-friendly and sustainable qualities are sought in all of the efforts to reduce costs. Therefore, in many maritime fields, solar energy is used as an alternative energy source. The purpose of this study is achieving maximum efficiency from solar panels by using optimization technique. The energy estimation was performed by artificial neural networks method on solar panels based on weather changes in Izmir Gulf. The results are compared with the “Renewable Energy General Administration” data of Turkey. As a result, the obtained data will be informative to the researcher who will study solar energy’s maritime applications. Besides, this study will be a possible source to make comparisons with similar solar energy studies.
The Role of Information and Communication Technologies in Family Dynamics

ICT influence in various activities in the family is a periodical topic to study as the technological developments grow day by day. This research attempts to study the influence of ICT in various family activities with special reference to parent and children. 88 families from Chennai city were surveyed and interviewed to find the influence of ICT in usage, purpose, preference, leisure activities, depression time activities, and communication time. Findings found the changes occurred in families and their opinion on ICT usage in a contemporary family structure. It is found that parents and children use the ICT devices in a similar way except for preference of activity. Parents prefer ICT more for entertainment whereas children preference is for education, developing social networks, etc. Except for communication facilitation, family members feel that ICT has changed most of the family activities, but it is considered as modern and acceptable in today's digital world. Family members have both positive and negative opinions regarding ICT usage in their family.
A Study of Deterioration in Classification Models in Real-Time Big Data Environment

Big Data (BD) is participating in the current computing revolutions. Industries and organizations are utilizing their insights for Business Intelligence (BI). BD and Artificial Intelligence are one of the fundamental pillars of Industrial Revolution (IR) 4.0. IR 4.0 demands real time BD analytic for prediction and classification. Due to complex characteristics of BD (5 V’s), BD analytics is considered a difficult task in offline mood. However, in real time or online mood, BD analytic become more challenging and requires Online Classification Models. In real time mood, the nature of input streams (input data) and target classes (output class) are dependent and non-identically distributed, which cause deterioration in OCM. Therefore, it is necessary to identify and mitigate the causes of this deterioration in OCM and improve OCM performance in RTBDE. This study investigates some fundamental causes of deterioration of Online Classification Models and discusses some possible mitigation approaches. This study also presents some experimental results to show the deterioration in OCM due to real time big data environment. In the future, this study will propose a method to mitigate deterioration in Online Classification Models.
Effects of Tactile Perception on Emotion and Immersion to Film Viewing in a Virtual Environment

In this research, we compared three tactile conditions (No vs. Soft vs. Spiky) in both positive and negative scenes to explore whether tactile perception could influence emotional responses and immersive experience in a virtual environment (VE). The results showed that, when viewing positive scenes with soft stimuli, participants experienced an increase in both positive emotions and their level of immersion compared to those in the No and Spiky tactile conditions. We also found that participants in the No and Spiky tactile conditions reported no significant differences in either emotion or immersion when viewing positive scenes. During the viewing of negative scenes, spiky stimuli did not intensify negative feelings, while soft stimuli decreased negative emotions. In terms of immersion, there was no meaningful difference between the three tactile conditions for negative scenes. Overall, this study has demonstrated the important association between tactile perception, emotion, and immersion in a VE.
Change Management Practices for Continuous Delivery - A Systematic Literature Mapping

The agility proposed by new software development practices such as Continuous Delivery and Continuous Deployment may introduce challenges to the teams who adopt such practices. One of these challenges is the change management process. The goal of this systematic literature mapping is to understand which change management practices are used in environments that adopted CDE or CD, focused on practices that could benefit application support teams, an important stakeholder in the software development life-cycle. Our study indicates the change management practices, in environments where CDE and CD have been adopted, have not been deeply explored and documented, an opportunity for future researches.
Outlier Detection from Non-Smooth Sensor Data

Outlier detection is usually based on smooth assumption of the data. Most existing approaches for outlier detection from spatial sensor data assume the data to be a smooth function of the location. Spatial discontinuities in the data, such as arising from shadows in photovoltaic (PV) systems, may cause outlier detection methods based on the spatial smoothness assumption to fail. In this paper, we propose novel approaches for outlier detection of non-smooth spatial data. The methods are evaluated by numerical experiments involving PV panel measurements as well as synthetic data.
A Fast Page Outline Detection and Dewarping Method Based on Iterative Cut and Adaptive Coordinate Transform

In camera-based document capture systems, page outline detection and dewarping are used to improve the readability and optical character recognition (OCR) accuracy of documents. This paper presents a robust book page outline detection and dewarping method with low hardware resource requirements that can handle pages with various layouts. We propose an iterative method to detect page outlines in the presence of occlusion and clutter. Further, we propose a method based on an adaptive coordinate transform model for page dewarping. This method can dewarp documents with complex layouts that contain short text lines, graphical contents, etc., while avoiding the unpleasant image distortion induced by dewarping.
ShowRunner: A Tool for Storyline Execution/Visualization in 3D Game Environments

We introduce ShowRunner, a tool for visualizing story world execution within a 3D game environment. ShowRunner takes as input an abstract, declarative specification of a story script and a set of mappings between terms in the story and data elements in the game engine and executes the story’s actions, using virtual cameras to film and present the action to a user. The implementation details on the working of the tool, as well as instructions on how users with various design and API constraints can utilize the tool are discussed in this paper.
Experience of automated formation of educational and methodological documentation at university

The article discusses the process of automation of the formation of educational and methodological documents in a higher educational institution. The main problems arising in the development of educational and methodological documents that are faced by teachers of higher education are highlighted, and options for solving them are proposed. The stages, principles and advantages of automation are discussed. The components of the proposed approach are considered, in particular: the model of educational and methodological support in terms of essence-relationship; templates of basic teaching documents; integration with the curriculum; the need to reuse educational content; the possibilities of batch formation of training documents; quality control of educational and methodological documentation; experience of implementation and use, as well as problems and possible directions of development. The functionality of the UMKA software system is given. The directions of automation of educational and methodological activities in general are considered.
Application on Semantic Web Technology in Automatic Assessment of English Compositions

In recent decades, automatic composition evaluation has made great progress in foreign countries, and the system is widely used in practice. In recent years, automatic evaluation system has gradually become a hot and important research object in natural language processing. Automatic evaluation is non-manual, and the composition is graded by computer. This paper first introduces the related concepts and characteristics of Semantic Web. And then it introduces the significance and main systems of automatic evaluation system. Finally, this paper puts forward some suggestions for the problems faced.
LSTM Network for Carrier Module Detection Data Classification

At any time the popularity of the Internet, the city's smart grid has been developing rapidly. At the same time, the number of smart meters has increased year by year. The effective mining of the power module carrier module data has also been upgraded to a new level. However, due to the fact that the existing carrier module data analysis scheme is not mature enough, a complete set of data analysis schemes for carrier module is urgently needed in the market. Through the most advanced detection technology and data analysis method, we can realize the data mining of carrier module and improve the use value of data. In the past, traditional statistical learning methods are used, such as naive Bayes and support vector machines, but with the progress of the times, these technologies can not meet the needs of people. Different from the past, this paper proposes a deep learning network based algorithm for data mining of carrier module detection. The carrier module detection data is modeled by LSTM deep neural network to automatically identify the different noise environment, and then the carrier module is dynamically adjusted according to the recognition results. Experiments show that the model has very good results. In addition, the technology solution will not affect the existing business processes. As far as we know, this is the first time to apply the deep learning method to the analysis of the carrier module detection data, which has a great inspiration for the future power system data processing.
A Tool for Making Segmented Speech Corpus for ASR and TTS Modeling

To develop models of Natural Language Processing (NLP), such as speech recognition and speech synthesis, require the provision of a speech corpus that has been segmented and useful as training data. On the other hand, making a speech corpus costs a lot because it can include studio rent and payment of recorded speaker fees. Therefore, in this paper, we develop a tool for making speech corpus equipped with a function that guides the speaker to record their speech in sentences. This tool can be run independently on a personal computer so that we can do recording anytime and anywhere. To produce a better speech corpus, the recording results of this tool still require to be checked because they could potentially have a signal clip or low amplitudes.
Relative Hand Movement and Voice Control Based Home Automation and PC

Hand gesture recognition is one of the significant research domains in the computer science field to which people have paid attention in the past decade. It has been considered as a highly successful technology, where it saves time to unlock any device and provide high stability with an increased accuracy rate. It provides a simple interface for humans to communicate with machines. This paper describes an external hardware-independent system which can control electrical appliances using hand gesture and voice control. We will be using a subpart of this field which is measuring the distance of relative hand movement with respect to the sensors used. The system uses a distance-measuring technique to provide certain functionalities to external hardware. The relative hand movement is recognized using ultrasonic sensors and voice pattern is recognized using analog to digital converter. The hardware part of the ultrasonic sensor is interfaced with Arduino. After getting the sound speed (sonic speed) and time between emission and reception, the distance can be calculated and provide necessary functionalities. In Voice Control Google Speech Recognition is used to convert speech to text and provide the desired functionalities. The workflow of the entire system is controlled using Arduino and Python code.
Examples of Uses of Artificial Intelligence in Video Archives

This demo paper presents different applications of artificial intelligence techniques applied to video archives such as face recognition, visual search and classification based either on faces, objects or landmarks. All algorithms are based on features that are extracted once at the beginning of the pipeline, this shared first stage allows flexibility and reduced computing time. The algorithms have been put into production early in the development process allowing early feedback and resulting in a user-focused development.
Adaptive Service Selection for Enabling the Mobility of Autonomous Vehicles

Recent advances in the Internet of Things (IoT) provides rich opportunities to future mobility services for the development of more flexible solutions. Instead of using a fixed set of data sources or services, applications can benefit from those flexible mechanisms by adapting to change in the sensing environment such as sensor disappearance/degradation or service unavailability. In this paper, we contribute with an approach that enables dynamic selection of the services for mobility to meet requirements of autonomous driving use-cases. Our approach is focused on different mobility services using available data sources and data processing methods with their related quality parameters. Those services are inspired by the standards and the dynamics of real-test road. We present a prototypical implementation of the mechanism for optimal service selection in an autonomous driving test environment and evaluated our testing results with respect to correctness and performance.
Constraining the Implementation Through Architectural Security Rules: An Expert Study

Today, security is still considered to late in the process of software engineering. Architectural rules for security can support software architects and developers in consciously taking security into account during design and implementation phase. They allow to monitor a software system’s security level. As a step towards monitoring and controlling the erosion of an architecture’s security specifications we present a set of rules derived from well-known security building blocks such as patterns along with our identification process. Through these rules we aim to support architects in monitoring the implementation’s conformance with security measures and, hence, in building secure software systems. The architectural security rules we identified are evaluated through expert interviews with industrial software engineers.