Reviewerly/exHarmony huggingface dataset:
Predictive Technique To Improve Classification On Continuous System Deployment

We introduce Last in First Focus (LIFF) approach for any system deployment. As per current market need most of the system deployment follow continues integration (CI) and continues deployment (CD) approach. In CI/CD, regular system release comes for deployment and it requires lot of post system deployment focus on last deployed or updated system. Continuous development, delivery and deployment is one of the essential parts of a product lifecycle. With addition of new features or major redesign with latest technology e.g. containerization, there may be sudden increase in tickets, which need to be handled effectively. When system does not perform expected behavior then system monitory module generates tickets. These tickets need to be analyzed and forward to corresponding department for resolution. With increase in number of tickets, more human resources may be required to perform this task. Here machine learning can provide helping hand in this job via analyzing the ticket and predicting the impacted system component for the resolution of ticket. The dataset collected after deployment of redesigned system component may be imbalanced due to more tickets on one system component only. This impacts the accuracy of classification metrics. This article resolved this problem by using Algorithmic level solution or Data level solution. Data level solution has achieved by oversampling of minority classes or under sampling of majority classes. Synthetic Minority Oversampling Technique, or SMOTE is used to address imbalanced dataset problem.
Enactive methods towards situational learning - engaging people with intellectual and developmental disability in design

In this paper we explore how enactive methods may support and enhance the design of mobile solutions for people with intellectual and developmental disabilities. Our project deals with supporting independent public transport and applies enactive methods in order to account for the interdependent nature of technology, disability, and environment. We staged three iterations of a bus workshop and one theatre workshop where real, everyday scenarios were acted out to gain tacit yet crucial insights. These enactive workshops and the prototype testing showed that transport activities are context dependent and unique to each individual. In our case, enactive methods revealed that independent public transport goes beyond a need for reminders, time management and communication, towards the management of unforeseen events. Our work shows how a closer realistic setting may provide more nuanced, personal, and detailed design insights that support emotional and situational understandings of user experiences.
Automatic Key Phrase Extraction

The ever-increasing number of text articles on the internet necessitates automated key phrase tagging. Information retrieval is aided by automatic key phrase generation in resources. An automated approach that extracts the key ideas directly from the text is needed to produce key phrases for texts from all possible domains. We present a graph based unsupervised framework for summarization that extracts keywords from a single document automatically. In this paper, an improved page rank algorithm is proposed for directed graph. Initially the document is taken as an input, then the input document is converted into a graph. The sentences are represented by the nodes of the graph and the similarity scores between the sentences are represented by the edges. To calculate the similarity score between the sentences we combine the result of cosine similarity and semantic similarity. We set a threshold value as an average of similarity scores, if the edge weight is more than or equal to the threshold value, then only edge will be included into a graph. The graph is converted into a directed graph then apply the proposed page rank algorithm on it to calculate the rank of the sentences. Our model is executed on the following data sets: Inspec (Hulth, 2003) data set and NUS (Nguyen and Kan, 2007) data set for keyphrase extraction and DUC 2002 data set and BBC News Articles for text summarization.
OWL Language Conversion Based on the Legal Structure of Wed Database

The manual description of legal framework network knowledge ontology is a time-consuming and laborious project. In order to improve the efficiency of ontology construction, this paper discusses the transformation from legal framework network database to OWL ontology, aiming to help users select some or all of the frameworks in the legal framework network database to realize the automatic description from legal framework network database to ontology language 0wl. Based on the definition of class and attribute in the network ontology of legal framework, this method uses the function of Jena’s ontology subsystem to create ontology template including class, attribute, restriction, and other information. By using SQL language to call the database, all kinds of information of the data table are automatically filled into the ontology template according to the ontology class and attribute. It also evaluates the generated owl documents and discusses the information that needs to be corrected manually.
An Introduction to Graph Neural Networks from a Distributed Computing Perspective

The paper provides an introduction into the theoretical expressiveness of graph neural networks. We discuss the basic properties and main applications of standard GNN models, and we show how these constructions are both upper and lower bounded in expressive power by the Weisfeiler-Lehman test. We then outline a wide variety of approaches to increase the expressiveness of GNNs above this theoretical limit, and discuss the strengths and weaknesses of these methods.
The multiplicity of left-to-right maxima in geometrically distributed words

For fixed m ≥ 1, we study the number of weak left-to-right maxima which occur exactly m times in words whose letters satisfy a geometric distribution. First, we find the generating function and two exact expressions for the mean. Thereafter we use Rice’s integrals to derive an asymptotic formula as n tends to infinity for the average in random geometric words of length n for each fixed value of m.
SSM: A Semantic Metasearch Platform for Scientific Data retrieval

Scientific research in all fields has advanced in complexity and in the amount of data generated. The heterogeneity of data repositories, data meaning and their metadata standards makes this problem even more significant. In spite of several proposals to find and retrieve research data from public repositories, there is still need for more comprehensive retrieval solutions. In this article, we specify and develop a mechanism to search for scientific data that takes advantage of metadata records and semantic methods. We present the conception of our architecture and how we have implemented it in a use case in the agriculture domain.
TextCut: A Multi-region Replacement Data Augmentation Approach for Text Imbalance Classification

In the practical applications of text classification, data imbalance problems occur frequently, which typically leads to prejudice of a classifier against the majority group. Therefore, how to handle imbalanced text datasets to alleviate the skew distribution is a crucial task. Existing mainstream methods tackle it by utilizing interpolation-based augmentation strategies to synthesize new texts according to minority class texts. However, it may mess up the syntactic and semantic information of the original texts, which makes it challenging to model the new texts. We propose a novel data augmentation method based on paired samples, called TextCut, to overcome the above problem. For a minority class text and its paired text, TextCut samples multiple small square regions of the minority text in the hidden space and replaces them with corresponding regions cutout from the paired text. We build TextCut upon the BERT model to better capture the features of minority class texts. We verify that TextCut can further improve the classification performance of the minority and entire categories, and effectively alleviate the imbalanced problem on three benchmark imbalanced text datasets.
Dataset Characteristics for Reliable Code Authorship Attribution

Code authorship attribution aims to identify the author of software source code according to the author’s unique coding style characteristics. The lack of benchmark data in the field, forced researchers to employ various resources that often did not reflect real programming practices. Throughout the years, research studies have used textbook examples, students’ programming assignments, faculty code samples, code from programming competitions and files retrieved from open-source repositories as research objects. The diversity of the data raised concerns about the feasibility of capturing the appropriate data characteristics to reliably evaluate code attribution. In this paper, we investigate these concerns and analyze the effect of the dataset characteristics and feature elimination techniques on the accuracy of code attribution. Unlike the majority of the work done in this field, which mainly concentrates on designing new features, we explore the nature of the data used in previous studies and assess the factors that influence the attribution task. Within this analysis, we investigate the robustness of three feature sets regarded as reliable benchmarks in the attribution research. Based on our findings, we define a process for deriving a reduced set of features for accurate and predictable attribution and make recommendations on the dataset characteristics.
Research on the Classification of Argument Components Based on BiLSTM-ATT-CNN-CRF

Argument Components Classification is an essential subtask in argumentation mining. Most machine learning methods improve the accuracy of the argument components classification by artificially constructing features. In this paper, the deep learning model of BILSTM-ATT-CNN-CRF is proposed. Firstly, the features can be learned automatically through the convolutional neural network, saving the cost of manual construction. Secondly, the combination of bilstm and attention network can better capture the contextual information between sentences. Finally, the Argument Components are classified by conditional random field. BiLSTM, BiRNN, and BiGRU network layers were used in experiments. It was found that bilstm had the best experimental performance. On the Persuasive Essays dataset published in Germany, this method is better than the baseline method.
A Stochastic Process Based Modular Tool-Box for Simulating COVID-19 Infection Spread

The novel coronavirus disease (COVID-19) culminated in a pandemic with many countries affected in varying stages. We aimed to develop a simulation environment for COVID-19 spread, taking environmental and social factors into account.The program was written in R language. A stochastic point process simulation model for simulating epidemics, a maximum-likelihood estimation model, an exponential growth rate model for calculating the basic reproduction number (R0), and functions for generating graphical representations of the simulations were utilized. Geographical area definition, population size, the number of initial infected individuals, period of simulation, parameters accounting for the radius of spread like masks usage, mobility level, intrinsic viral virulence, average infectious period, fraction of population vaccinated, time of vaccination, the efficacy of the vaccine, presence or absence of quarantine centers, time of establishment of quarantine centers, the efficacy of case detection and average time to quarantine from the detection of the infection were considered.When the defined parameters were input, the model performed successfully producing the epidemic curve, R0 and an animation of infection spread. When parameters of known epidemics such as COVID-19 in California, Texas and, Florida, the epidemic curve generated was comparable to the epidemic curve in reality.This model can be utilized by many countries to visualize the effects of various mitigation strategies applied in their stage of disease and for policy makers to make informed decisions. It is applicable to many infectious diseases and hence can be used for research and educational purposes.
AHA: Adaptive Hadoop in Ad-hoc Cloud Environments

Cloud computing has become a highly efficacious and popular method when dealing with large computational demands. To better utilize cloud computing resources, an interesting proposal involves using the available machines for distributed computing in an ad-hoc manner during regular and off-peak hours. Our proposed framework, named AHA, implements resource availability considering (RAC) task speculation within the latest version of Apache Hadoop (3.3.0). The resource availability history of each worker node is stored locally and considered during scheduling of MapReduce (MR) workloads. In addition, a fuzzy-rule based self-tuning solution is also proposed to alleviate the need for manual tuning regarding resource availability consideration. Our preliminary evaluations indicate that AHA is able to decrease execution time by up to 20.2 % for certain MR workloads in ad-hoc cloud settings. Overall, the approach shows potential in addressing this real-world issue as our results are also on average upper-bounded by Apache Hadoop with respect to workload execution time in a simulated ad-hoc cloud environment.
A Method to Recognize Eyeball Movement Gesture using Infrared Distance Sensor Array on Eyewear

Sensing technology for eyeball movement (i.e., gaze movement) has enabled various applications, e.g., hands-free input interfaces and provided information that helps us understand human beings in various fields. However, the existing method, which senses eyeball movement constantly using a wearable device has limitations that should be addressed, e.g., cost. Therefore, we propose a method to recognize eyeball movements using eyewear equipped with multiple infrared distance sensors. The proposed method recognizes eyeball movement by sensing time-series data of eyelid skin movement, which accompanies eyeball movement, using an infrared distance sensor inside the eyewear. Evaluation results of five subjects demonstrate that the proposed method can recognize five types of movement with an F-value of 1.0 and 20 types of movement with an F-value of 0.94. Our study showed the feasibility of the proposed method for gaze input interface and for inexpensive technology that constantly senses the eyeball movement.
Multi-Directional Weighted Interpolation for Color Imaging System

In this paper, we proposed a new color filter array interpolation, which has two contributions to demosaicking: different from conventional interpolation methods based on two directions or four directions, the proposed method exploits to a greater degree correlations among more neighboring pixels along eight directions to improve the interpolation performance. As compared with the latest demosaicking algorithms, experiments show that the proposed algorithm provides superior performance in terms of both objective and subjective image qualities.
Segmented Region based Feature Extraction for Image Classification

Reliability and accuracy is the key concern of an automated image classification process. However, the impact of background or surrounding area is very less in compared to object features, which create ambiguity while assigning the appropriate class label and reduce the classification accuracy. This paper presents a new model to address this issue which select the relevant features from the segmented images based on the inner and outer regions. The key idea of this model is that the texture features within the objects are more relevant than the outside area of the objects. The proposed model applying a segmentation method for automated segment the image. The segmented images are then subdivided into two parts (i.e. inner and outer). The 463 shape and texture features are extracted from the inner, outer parts of the segmented images and also from the whole image. Next, these extracted features are used to train the classifier using support vector machine (SVM). A database of 644 images that consisting of 8 classes is used to verify the efficacy of the proposed model. The result proves the efficacy of the proposed model which achieves classification accuracy up to 97.79 % from the inner part of the image. The classification accuracy of inner features is increased by 9.58% from surroundings features.
DFT: A deep feature‐based semi‐supervised collaborative training for vehicle recognition in smart cities

Abstract Intelligent transport systems (ITS) is a popular field of research in smart city. Image‐based vehicle recognition is one of the most promising new techniques. The traditional appearance‐based method has some limitations in feature view. The machine learning algorithm can satisfy multiple feature views, but it mainly adopts an improved supervised learning method, which needs manual annotation and has low efficiency. The semi‐supervised collaborative training method is widely used in the image recognition process to improve machine learning algorithms' accuracy and generalization performance. This paper proposes DFT (deep feature‐based training) method for vehicle recognition in smart cities. DFT is also a semi‐supervised collaborative training method on basis of two base learners. DFT adjusts data pre‐processing and training process, optimizes the constructing a disagreement encoding network, and expands the recognition disagreement of pseudo‐labelled samples‐based training set. Compared with the typical collaborative training methods, DFT greatly accelerates the model's training process by reducing the convergence time, and improves the efficiency of vehicle recognition, while remaining the recognition accuracy unchanged.
Principles of Remote Sattestation

Joshua Guttman has collaborated with others to set out principles for attestation of trust in the setting of trusted computing. I describe herein attestation of trust in authentication of web addresses via a means of binding security into the addresses themselves, and I discuss the analogues of such attestation principles in this setting.
Influencing Factors on the Adoption of Face Recognition Technology on Campus Based on SEM

Nowadays, the facial recognition technology (FRT) is gradually being applied in our life. In order to better serve the campus life, this paper tries to understand and study the influencing factors on the adoption of FRT on campus. A structural equation model (SEM) approach is designed to quantify multiple influencing variables and figure out the relationships among each other. The data collection is obtained through questionnaires, and the representativeness of data is analyzed using SPSS software. After that, the data were modeled using the Amos toolkit. The results reveal that the core factors about the adoption of FRT on campus are privacy and promotion.
“Russian” scientometrics: national adoption strategies

Scientometrics as an applied discipline is becoming increasingly popular in the field of organization of scientific research and higher education. At the same time, it is increasingly opposed to “real” science, there is always an active dialogue between scientists and managers in order to find a compromise. Therefore, the purpose of this study is to model the cognitive image of scientometrics in the Russian scientific discourse, with the help of which it will be possible to identify cognitive scenario failures and basic mental conflict points. The cognitive method of metaphorical modeling is the main tool of our research. As a result of the conducted research, the high productivity of the morbial metaphor was revealed, which, in turn, allowed to formulate the theory of the boundary state of acceptance of applied scientometrics in the Russian scientific discourse. The theoretical significance of the results obtained consists in the expansion of scientific research in the field of scientometrics in Russia. The practical significance of the results received through our research consists in the possibility of their implementation in the development of scientomentric awareness and improvement of scientometric literacy on various levels.
Culture, Education and Science of the Far Eastern Region

The authors characterize the state, structure and dynamics of changes in the system of primary, secondary and higher education in the Far East from the appearance of the first schools to the end of the 1990s. It is noted that the Far Eastern education system, which developed in the context of all-Russian tendencies, had its own characteristics associated not only with the nature and rate of settlement of the region, its geopolitical status, but also with the specifics of economic development. The main historical stages of the development of science in the Far East, from the emergence of the first scientific institutions at the end of the 19th century to the present day are highlighted, the names of outstanding scientists, organizers of science are named, achievements in various fields of knowledge are shown, taking into account the specifics of the tasks in the development of fundamental and applied research. The main development directions of the artistic culture of the region from the second half of the 19th to the beginning of the 21st centuries, the most important events of artistic life are outlined, prominent cultural figures of the Far East are presented.
Hitting the Moving Target: What Information Systems Competencies Are Required from Mid-Level Information Systems Managers by Organisations in Sub-Saharan Africa?

The rapidly changing nature of digital innovations demands changing digital competencies which often lag behind the pace of digital innovations. This phenomenon has been described as aiming ‘the moving target’. At the same time, these competencies differ or may have different emphasis or importance to different geographic and economic regions. Consequently, extant literature has suggested a periodic identification of information systems (IS) competencies relevant to organisations beyond the UK and USA where such research is dominant. However, extant literature focuses on current competencies of entry-level graduates with less emphasis on middle to senior managers. Again, traditional methods of identifying IS competencies by analysing job postings or through surveys have been found, not to be able to uncover certain difficult to observe competencies. Furthermore, there is less theorisation of IS competency studies in the literature. This study, therefore, explores current IS competencies, which are relevant to mid-level management IS professionals of organisations in sub-Saharan Africa (SSA), through the lens of the iceberg competency model, and using a diversified Delphi approach. The results painted a view of a strong emphasis on individual foundational competencies as compared to other competencies and contradict a stronger emphasis on IS specific competencies required for entry-level IS jobs. The implications for practice, IS education in SSA, and suggestions for future research are presented.