Reviewerly/exHarmony huggingface dataset:
Mining the Contributions Along the Lifecycles of Open-Source Projects

Recently the impact of developers’ behavior on the evolution of open-source software (OSS) has become a hot topic. When does the developer commit his/her code? Is there any regularity of the time distribution of commit along the lifecycles of open-source project? Will the change of the core member in a development team has an impact on software evolution process? We are quite interested in these above questions so we conducted an empirical study in this paper. We collect more than 50,000 commits from 6 open-source software in Github and design a formula to measure the contributor’s contribution value. We then take four major experiments to analyze some issues about inert intervals and the impact of the change of main contributors on software evolution. To make the result visible, we also design an automatic mining tool which can automatically mine the metadata from specified repository and make it graphically presented. Through the experiments we gained some interesting findings such as there is no inevitable statistical connection between a contributor’s inert interval and his contribution value, and main contributors’ change has a huge impact on the software evolution. We believe that these findings will have deeper research significance in the future.
Digitalization of Education in Russia: Problems and Prospects

The article is devoted to the problem of modernization of higher education in the digital economy. The directions of improving the quality of the educational process based on the use of the opportunities of the digital economy are determined. The main attention is paid to solving the problem of implementing the main stages of knowledge transfer and the integration of participants in this process.
Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling

Recent years have seen a surge of interest in natural language processing (NLP) for social media because the massive unstructured data from social media provide valuable information. However, natural language processing in this domain often suffers from the lack of large scale labeled data used for building models. In this paper, we focus specifically on the task of named entity recognition (NER) for Chinese social media. We propose a neural network model for domain adaptation which builds on domain-adversarial training and language modeling. The model is capable of learning from multiple sources of training data: labeled in-domain data, labeled out-of-domain data, as well as (large-scale) unlabeled in-domain data. To demonstrate the effectiveness of our approach, we experiment on an enlarged Chinese social media corpus. Results show that the approach outperforms baselines significantly.
A Study on Topic Modeling for Feature Space Reduction in Text Classification

We examine two topic modeling approaches as feature space reduction techniques for text classification and compare their performance with two standard feature selection techniques, namely Information Gain (IG) and and Document Frequency (DF). Feature selection techniques are commonly applied in order to avoid the well-known “curse of dimensionality” in machine learning. Regarding text classification, traditional techniques achieve this by selecting words from the training vocabulary. In contrast, topic models compute topics as multinomial distributions over words and reduce each document to a distribution over such topics. Corresponding topic-to-document distributions may act as input data to train a document classifier. Our comparison includes two topic modeling approaches – Latent Dirichlet Allocation (LDA) and Topic Grouper. Our results are based on classification accuracy and suggest that topic modeling is far superior to IG and DF at a very low number of reduced features. However, if the number of reduced features is still large, IG becomes competitive and the cost of computing topic models is considerable. We conclude by giving basic recommendations on when to consider which type of method.
Cross-Cluster Projects in Development of Innovations and New Industries

In modern conditions, innovations appear not so much as a result of successive linear processes, but as a result of cross-functional interactions between different industries. Modern innovative projects are the results of cross-cultural, cross-information, cross-disciplinary collaborations. Their results are not only the development of perspective projects, but also the emergence of new industries in the economy. In the Chapter, the model of implementation of cross-innovative projects in the cluster as an EFFECTIVE modern production system is considered. Prerequisites are investigated and the model of realization of cross-cluster projects is offered. Considered are examples of successful implementation of collaborative projects within the clusters of countries in Western Europe and the USA. The perspective directions of cross-cluster projects for the development of the economy of Russian regions are determined. Dedicated perspective purposes for the management companies of clusters to promote cross-cluster innovation.
Recommender systems for contextually-aware, versioned items

While existing Recommender systems assume items are fixed entities, this research considers situations where there can be different versions of an item. We propose a process that is a type of contextually-aware post filtering for recommending items, and illustrate the system with real data from a newspaper. The novel framework decides whether or not to recommend particular news articles based on news trend and incorporates user states as additional contextual information and recommends versioned items based on user preferences.
An Algorithm for Detecting the Expressive Musical Gestures of Violinists Based on IMU Signals

The article presents an algorithm for classifying the style of expression of violin playing based on IMU sensor, located on the violinists forearm. In the initial phase of research, the original set of measured signals was extended by transferring them to new coordinate systems. Additional motion dynamics signals, including estimated linear velocity, have been obtained using transformations typical for inertial navigation systems (INS). In the next part of the work, universal features as well as indicators typical for IMU signals were extracted. The final experiment concerned the comparative effectiveness of data classification, using features selected by mutual information and random forest algorithms. The evaluation of the performance of the proposed algorithm has been carried out using a publicly available database. The obtained level of classification accuracy exceeded 90%.
Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes

The imbalance problem is receiving an increasing attention in the literature. Studies on binary cases are recurrent but limited when considering the multiple classes approach. Solutions to imbalance domains may be divided into two groups, data level approaches, and algorithmic approaches. The first approach is more common and focuses on changing the training data aiming to balance the data set, oversampling the smallest classes, undersampling the biggest ones or using a combination of both. Instance reduction is another approach to the problem. It tries to find the best-reduced set of instances that represent the original training set. In this work, we propose a new Prototype Generation method called DCIA. It dynamically inserts new prototypes for each class and then adjusts their positions with a search algorithm. The set of generated prototypes may be used to train any classifier. Experiments showed its potentiality by enabling an 1NN classifier to perform sometimes as well or even better than some ensemble classifiers created for different multiclass imbalanced domains.
THE WORLD IN PIECES: AARON EINBOND

Abstract Aaron Einbond was born in New York in 1978. He received his compositional education in the US (Harvard, University of California, Berkeley), the UK (Cambridge, Royal College of Music) and France (IRCAM), and his teachers have included Mario Davidovsky, Julian Anderson, Edmund Campion and Philippe Leroux. He currently teaches music composition, sound and technology at City University, London. He is interested in applications of technology within instrumental music, and almost all of his works combine electronics and acoustic instruments. Since 2007 – beginning with his piece Beside Oneself for viola and electronics (first performed by Ellen Ruth Rose), composed while studying at the University of California, Berkeley – he has also used audio analysis and retrieval software to transcribe recorded sounds into instrumental notation. Einbond's interest in phonographic transcription connects his work to that of other composers of his generation, including Patricia Alessandrini, Joanna Bailie, Richard Beaudoin and Cassandra Miller. (It also finds precedents in a wider musical interest in forms of transcription that one can find in the music of composers as diverse as Peter Ablinger, Luciano Berio and Michael Finnissy.) What makes Einbond's work distinctive is his focus on timbre as a musical parameter, rather than more abstract or easily quantifiable values such as pitch.
Antecedents And Outcomes Of Qwl Among Employees In Banks

Quality of work life is the major factor to be considered in working environment of any organization. The performance of employees and the organization lies on the ability of the employees based on working environment. The QWL leads to better working environment which improves the performance of organization. The present study has made an attempt to find the level of factors causing QWL and the impact of outcome of QWL in banks. Since the profile of the banks may be associated with the level of outcomes of QWL, the present study has made an attempt to examine it with the help of one way analysis of variance and t-test. The included outcomes of QWL are job satisfaction, job stress, organizational climate, organizational commitment, employees retention behaviour, service quality employees and service productivity of employees. The highly associated determinants of QWL and the significant difference among the PUSBs and PRSBs have been noticed. The significantly associating important profiles of the banks regarding the existence of outcome of QWL are identified.
Flow2Seg: Motion-Aided Semantic Segmentation

Motion is an important clue for segmentation. In this paper, we leverage motion information densely represented by optical flow to assist the semantic segmentation task. Specifically, our framework takes both image and optical flow as input, where image goes through a state-of-the-art deep network and optical flow goes through a relatively shallow network, and results from both paths are fused together in a residual manner. Unlike image, optical flow is weakly related to semantics but can separate different objects according motion consistency, which motivates us to use relatively shallow network to process optical flow to avoid overfitting and keep spatial information. In our experiment on Cityscapes, we find that optical flow improves image-based segmentation on object boundaries especially on small thin objects. Aided by motion, we achieve comparable results with state-of-the-art methods.
Overview on the PHRESCO Project: PHotonic REServoir COmputing

PHRESCO is an EU-H2020 funded project that was running for four years and will be ending in September 2019. PHRESCO focused on the development of efficient cognitive computing into a specific silicon-based technology by co-designing a new reservoir computing chip, including innovative electronic and photonic components that will enable major breakthrough in the field. So far, a first-generation reservoir with 18 nodes and integrated readout was designed, fabricated, characterized and a training method has been developed. Additionally, large efforts of the consortium were dedicated to the design of the second-generation chip consisting of larger networks (60 nodes), with an on-chip readout and novel training approaches. This short abstract provides key information on the status of the work achieved and discuss further the potential exploitation routes and the key barriers that still need to be removed to bring the technology to a higher maturity level. A part of the exit strategy of PHRESCO is to identify potential future cooperation with interested stakeholders who are willing to co-develop the PHRESCO technology together with the PHRESCO partners for bringing it to an exploitable or marketable system. This abstract lays down the foundations for potential exploitation activities with interested stakeholders.
iCAT: An Interactive Customizable Anonymization Tool

Today’s data owners usually resort to data anonymization tools to ease their privacy and confidentiality concerns. However, those tools are typically ready-made and inflexible, leaving a gap both between the data owner and data users’ requirements, and between those requirements and a tool’s anonymization capabilities. In this paper, we propose an interactive customizable anonymization tool, namely iCAT, to bridge the aforementioned gaps. To this end, we first define the novel concept of anonymization space to model all combinations of per-attribute anonymization primitives based on their levels of privacy and utility. Second, we leverage NLP and ontology modeling to provide an automated way to translate data owners and data users’ textual requirements into appropriate anonymization primitives. Finally, we implement iCAT and evaluate its efficiency and effectiveness with both real and synthetic network data, and we assess the usability through a user-based study involving participants from industry and research laboratories. Our experiments show an effectiveness of about 96.5% for data owners and 92.6% for data users.
Blockchain Awareness Among Computer Science Students: A Preliminary Study

The main goal of research was to investigate to what extent computer science students are familiar with blockchain technology (BC). The survey was conducted among the students of Cracow universities. In order to analyze deeply the results of the survey, indicators of the knowledge factor and the usage factor were developed to assess knowledge and usage of BC respectively. The main findings point out to the popular internet portals and conversations with other people as the main sources of information on BC for students. Students are less likely to obtain their information from university classes and lectures, television, radio, and other media. The percentage of active BC users almost doubles the percentage of respondents having specialized knowledge, therefore the relationship between knowledge and usage of BC is rather weak. Traditional banking and trade are the most frequently indicated areas of BC applications by the respondents. The conducted research also shows that BC may be perceived as a tool for social/technology evolution and revolution. The survey results have important implications for universities as they confirm that university curricula are not updated sufficiently quickly to keep up with the emerging technologies.
Real Time Key Frame Extraction Through Parallel Computation of Entropy Difference

The advancement of image processing in the field of Artificial Intelligence has created various research prospects in the area of object detection, pattern recognition etc. Capturing real time video stream for multiple cameras within a region of interest has become a common phenomenon for an Intelligent Situation Awareness System. Video processing is an important application which is rapidly developing nowadays as an area of extensive research. Content retrieval as well as information collection from a video requires both syntactic and semantic analysis. For a large video data, some set of frames are used to represent the video content. These are identified as key frames. Several algorithms have been defined to extract key frames from a stored video file. The existing algorithms that have been defined for key frame extraction are based on sequential mode. This paper looks into the extraction of key frames for any real time video stream. The experimental results show that there is an effective reduction in the execution time to a huge extent in the case of distributed processing as compared to the sequential processing of frames. In this paper, we propose a distributed framework to regulate the speed of key frame generation for heterogeneous speed of incoming video.
Designing the internet of tangible things for outdoors environments with university students

The Internet of Things and, specifically, the Internet of Tangible Things (IoTT) create several opportunities, lately also for outdoors environments. However, people are often consumers of IoTT solutions and not protagonists of their creation. This paper presents research related to the idea of involving people as protagonists in the design of IoTT for outdoors environments. It explains how an innovative course of IoTT design was organised within a traditional university context, and how its workshop organisation enabled different students to become protagonists of IoTT design. It concludes by drawing general lessons for future editions of IoTT design workshops for outdoors environments.
Human Pose Detection From a Digital Image With Machine Learning Technique

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Bayesian Non-Parametric Mixture Model with Application to Modeling Biological Markers

The effect of treatment on patient's outcome can easily be determined through the impact of the treatment on biological events.Observing the treatment for patients for a certain period of time can help in determining whether there is any change in the biomarker of the patient.It is important to study how the biomarker changes due to treatment and whether for different individuals located in separate centers can be clustered together since they might have different distributions.The study is motivated by a Bayesian non-parametric mixture model, which is more flexible when compared to the Bayesian Parametric models and is capable of borrowing information across different centers allowing them to be grouped together.To this end, this research modeled Biological markers taking into consideration the Surrogate markers.The study employed the nested Dirichlet process prior, which is easily peaceable on different distributions for several centers, with centers from the same Dirichlet process component clustered automatically together.The study sampled from the posterior by use of Markov chain Monte carol algorithm.The model is illustrated using a simulation study to see how it performs on simulated data.Clearly, from the simulation study it was clear that, the model was capable of clustering data into different clusters.
Annotating a Driving Experience Corpus with Behavior and Subjectivity

To communicate with humans in a human-like manner, systems need to understand behavior and psychological states in situations of human-machine interactions, such as in the cases of autonomous driving and nursing robots. We focus on driving situations as they are part of our daily lives and concern safety. To develop such systems, a corpus annotated with behavior and subjectivity in driving situations is necessary. In this study, subjectivity includes emotions, polarity, sentiments, human judgments, perceptions, and cognitions. We construct a driving experience corpus (DEC) (261 blog articles, 8,080 sentences) with four manually annotated tags. First, we annotate spans with driving experience tags (DE). Then, three tags, other’s behavior (OB), self-behavior (SB), and subjectivity (SJ), are annotated within DE spans. In addition to describing the guidelines, we present corpus specifications, agreement between annotators, and three major difficulties during the development: the extended self, important information, and voice in mind. Automatic annotation experiments were conducted on the DEC using Conditional Random Fields-based methods. On the test set, the F-scores were about .55 for both OB and SB and approximately. 75 for SJ, respectively. We provide error analysis that reveals difficulties in interpreting nominatives and differentiating behavior from subjectivity.
Potential of blockchain technology for trade integration of developing countries

Blockchain technology (BT), famous due to its use in digital currencies, also offers new opportunities in other fields, one of which is trade integration. Developing countries especially could benefit from greater trade integration with BT, as the technology can, for example, remedy deficiencies with regard to financial system access, intellectual property protection and tax administration. BT allows virtually tamper-proof storage of transactions and other data on decentralised computer networks. In fact, it is possible to store not only data, but also entire programmes this securely: Smart contracts enable the automation of private transactions and administrative processes. This article summarises the latest research on the use of BT in trade integration by examining in more detail five key and, in some cases, linked fields of application. The first is trade finance, where BT could deliver direct cost savings for exporters and importers by removing the need for credit-lending intermediaries. Second, tamper-proof storage of information on the origin and composition of goods could enhance supply chain documentation. This makes it possible to more reliably verify compliance with sustainability standards, particularly for globally produced goods. However, for the information in blockchains to be truthful, it must be entered correctly (it is then tamperproof), a process that therefore requires monitoring. Third, BT could deliver improvements in the field of trade facilitation by making it easier for border authorities to access information on goods and thus easing reporting requirements for exporting firms. By reducing dependence on central database operators, BT could help bring about a breakthrough with existing digital technology in the area of trade. Fourth, facilitating access to information on goods could also simplify customs and taxation procedures and make them less vulnerable to corruption and fraud. This goes hand in hand with cost reductions for exporters and better mobilisation of domestic resources for public budgets. Fifth, in the field of digital trade, BT also facilitates management of digital file rights in environments where, for institutional reasons, there is little intellectual property protection. This could help to promote digital industries in developing countries. However, when it comes to using BT in border and customs systems in particular, it is essential to involve the relevant authorities at an early stage. At the same time, it is necessary to promote uniform technical standards for supply chain documentation in order to safeguard interoperability between the different systems across actors and national borders and thus fully leverage the cost advantages. If these guidelines are taken into account, then BT could effectively support sustainable trade integration of developing countries.