Reviewerly/exHarmony huggingface dataset:
Effects of the Use of a Widescreen Display on Information Retrieval

In recent years, PC screens have become wider and larger. The nature of our visual field is that visual acuity is at its highest when someone is looking at the center of something, making the central part look the clearest. This study used a widescreen display and a traditional display depicting a character to be retrieved at the center of the screen. Ten characters were displayed at 10 locations at the right or left edge of the screen. The study subjects were then asked to retrieve a particular character from among the 10 characters. Subsequently, this study analyzed the relationship between retrieval time and the display location of the character in the widescreen display and the traditional display. The effects of the use of the widescreen display on information retrieval were examined. Focused attention at the center of a screen can delay or even prevent the user’s awareness of information displayed just above the center of the right edge, at the center of the right edge, and just below the center of the right edge. Information should be displayed on wide screens while considering these unique human visual characteristics.
W-core Transformer Model for Chinese Word Segmentation

Chinese word segmentation is an important research content in the field of Natural Language Processing (NLP). In this paper, we combine the Transformer model to propose the Window Core (W-core) Transformer for the tasks. In this model, W-core can preprocess sentence information according to the characteristics of Chinese and incorporate features extracted by the Transformer model. Experimental results show that the W-core Transformer model can improve the effect of the original Transformer model on Chinese word segmentation. Finally, we improve the performance of W-core Transformer by increasing the number of encoder layers and oversampling.
An analysis of Monte Carlo simulations for forecasting software projects

Forecasts of the effort or delivery date can play an important role in managing software projects, but the estimates provided by development teams are often inaccurate and time-consuming to produce. This is not surprising given the uncertainty that underlies this activity. This work studies the use of Monte Carlo simulations for generating forecasts based on project historical data. We have designed and run experiments comparing these forecasts against what happened in practice and to estimates provided by developers, when available. Comparisons were made based on the mean magnitude of relative error (MMRE). We did also analyze how the forecasting accuracy varies with the amount of work to be forecasted and the amount of historical data used. To minimize the requirements on input data, delivery date forecasts for a set of user stories were computed based on takt time of past stories (time elapsed between the completion of consecutive stories); effort forecasts were computed based on full-time equivalent (FTE) hours allocated to the implementation of past stories. The MMRE of delivery date forecasting was 32% in a set of 10 runs (for different projects) of Monte Carlo simulation based on takt time. The MMRE of effort forecasting was 20% in a set of 5 runs of Monte Carlo simulation based on FTE allocation, much smaller than the MMRE of 134% of developers' estimates. A better forecasting accuracy was obtained when the number of historical data points was 20 or higher. These results suggest that Monte Carlo simulations may be used in practice for delivery date and effort forecasting in agile projects, after a few initial sprints.
Thermal Comfort in East Campus Center of ITB Bandung

Abstract The campus center is the building regarded as the center of student activities. It’s the typical building commonly found on campuses. The building is specifically intended for the students to entertain and socialize themselves. The building can also function as the center for small-scale conference conferences; the meeting is leased to student communities and local organizations. By various activities taking place in the building, it’s demanded to meet the need for the users’ thermal comfort to realize all of their programs following their objectives respectively. The study on ITB Campus Center Building aims to encourage students to be familiar with the influence of the building orientation, dimensions, shape, material, configuration, and the opening type that affect the space’s air circulation. This research used a quantitative descriptive method approach by measuring the temperature and humidity through the gauge for temperature and humidity and an anemometer for wind speed. It identifies the study object’s thermal comfort based on the PMV Index in the CBE Thermal Comfort Tools. And from the results of our research, it can be concluded that the building has fulfilled most aspects of thermal comfort.
The process of humanitarian education of students

The purpose of the present article is to substantiate approaches to the formation of the humanitarian vector of training students as future professionals in various fields of activity. The humanitarian vector sets the direction of the students’ professional training. In terms of substantive aspect, it is considered as a worldview and praxeological phenomenon associated with the awareness, comprehension, and assimilation of the value foundations of Russian society and the individual, as well as with the further practical implementation of the internalized value attitudes of the individual. As a form and a way of existence of this content, the humanitarian vector represents a consolidating, centripetal force of social medium, acting as a spiritual synergistic attractor. Based on the sociological data, the authors consider the contradictions existing in the axiological space of the country, which have significant impact on the worldview maturity of young people. The key concept in the work is spirituality, which is considered as an integral phenomenon. The novelty of the work is associated with the substantiation of the thesis about two projections of spirituality: extrovert (objective-rational) and introvert (subjective-intuitive). The conclusion of the study indicates that the main functional purpose of the humanitarian vector is the worldview maturity of the individual as the goal and result of the students’ humanitarian education process. A qualitative indicator of worldview maturity is bridging the gap between what should be and what is in the subject’s moral consciousness and behavior.
Using PPP Method in the Process of Online Training and Strengthening EFL Teachers’ Pedagogic Competence

Abstract English at the elementary school level serves as local content by the policies of each school; as a result, it makes the time allocation of English subject reduced and likewise, the teachers’ pedagogical competence. The study aims to describe the process of online training and strengthening in fostering EFL teachers’ pedagogic competence. It used a qualitative descriptive method and involved three teachers from Rejodadi Elementary School, which observation and interview are the research instruments. The results showed that the process of training and strengthening in fostering teachers’ English pedagogic competence used Presentation, Practice, and Production (PPP) method. “Presentation” was applied to explain the materials related to teaching English at an elementary level such as the students’ characteristics, teaching methods, strategies and media, “Practice” was implemented to drill the teachers’ skills about the materials presented, and in “Production”, teachers implemented what they had got from the previous steps by conducting a micro-teaching activity. Therefore, the process of online training and strengthening in fostering EFL teachers’ pedagogic competence used PPP method in achieving its goal.
Promotion of Educational Services of Higher Educational Institutions Using Innovative Technologies

The article discusses the specifics of promoting educational services using innovative technologies for higher education institutions. The aim of the study is to develop an algorithm for promoting higher education institutions using innovative technologies. The authors considered the most common problems faced by institutions of higher education when integrating promotion tools with the use of innovative technologies into their activities. An algorithm for promotion with the use of innovations, adapted for institutions of higher education, has been developed the practical application of which is currently possible by classical universities in the Russian Federation.
Formation of professional competencies of bachelors of the “Applied informatics” direction in the implementation of distance learning using software on the 1C:Enterprise platform

The objectives of the implementation of the National Project “Digital Economy” are the modernization of the education system and the introduction of modern technologies and teaching aids. The article discusses the achievement of these goals in the modern conditions of the organization of the educational process, which require the introduction of models of distance and blended learning using practice oriented methods. To form the competencies of bachelors in the “Applied informatics” direction, a practice oriented learning model has been developed based on 1C software products, based on the matrix of correspondence between curriculum disciplines and information systems and services offered by the developer. When implementing such a model, it is necessary to use the methodology of a continuous system for the formation of professional competencies, which consists of several stages, which also include all types of planned practice curricula. Successful achievement of the set goals is possible due to the integration of practical examples and software solutions, which allows solving professional problems without the departure of students to enterprises. The experience of organizing student training on practice oriented cases within the framework of the formation of professional competencies of graduates of the direction of training bachelors “Applied informatics” is presented. The features of the proposed solutions in the implementation of interdisciplinary links of IT disciplines using the 1C:Enterprise platform are considered.
Objective object segmentation visual quality evaluation based on pixel-level and region-level characteristics

Objective object segmentation visual quality evaluation is an emergent member of the visual quality assessment family. It aims at developing an objective measure instead of a subjective survey to evaluate the object segmentation quality in agreement with human visual perception. It is an important benchmark to assess and compare performances of object segmentation methods in terms of the visual quality. In spite of its essential role, it still lacks of sufficient studying compared with other visual quality evaluation researches. In this paper, we propose a novel full-reference objective measure including a pixel-level sub-measure and a region-level sub-measure. For the pixel-level sub-measure, it assigns proper weights to not only false positive pixels and false negative pixels but also true positive pixels according to their certainty degrees. For the region-level sub-measure, it considers location distribution of the false negative errors and correlations among neighboring pixels. Thus, by combining these two sub-measures, our measure can evaluate similarity of area, shape and object completeness between one segmentation result and its ground truth in terms of human visual perception. In order to evaluate the performance of our proposed measure, we tested it on an object segmentation subjective visual quality assessment database. The experimental results demonstrate that our proposed measure with good robustness performs better in matching subjective assessments compared with other state-of-the-art objective measures.
Business E-mail Compromise — Techniques and Countermeasures

Business Email Compromise (BEC) is a method through which the attacker defrauds the organization and all its stake holders by the use of the business email that the employees carelessly use in public domain. This paper discusses about Business Email Compromise (BEC) that can be identified and categorized into five broad categories which are, CEO Fraud, Bogus Invoice Scheme, Account Compromise, Lawyer/Attorney Impersonation, and Data Theft. The research majorly focuses on finding the techniques used for BEC, detection techniques that could be used for taking the corrective measures against the attack and finding the possible countermeasures for preventing the BEC attack. The major techniques used by attackers and criminals for performing a BEC attack are usually Credential- grabbing and Email-only Method. Credential-grabbing techniques include techniques like phishing-related techniques and malware-related techniques. Phishing-related techniques can include compromise using various methods like from Direct Links, PDF Files, HTML or File-hosting services. Detection of such attacks can be done by various methods, such as Impersonation classifier, content classifier, text classifier, link classifier, classifier algorithm. There is BEC-Guard that can be installed in order to track these methods. Name and nickname matching is a method where in order to detect name spoofing, the impersonation classifier needs to match the sender name with a name of an employee. Countermeasures are the best possible way to prevent the BEC attack in the first place and the best defense for countermeasure is a well-informed workforce. Majorly, the countermeasures used includes, training and awareness programs, Phishing attack trainings, using of SPF, DKIM, DMARC anti-spoofing and email authentication techniques.
Towards an Efficient Hardware Implementation of CNN-Based Object Trackers

Convolution Neural Network (CNN) has achieved a performance boost to the visual tracking field. However, CNN-based trackers feature slow computational speed and large memory size. These issues challenge the embedded implementation of the CNN-based trackers. In this paper, we show how interpolation schemes can significantly reduce the memory requirements. In addition, we present a design-space exploration of the fixed-point representation of the CNN-based trackers aiming for a cost-efficient hardware implementation. Moreover, we present a hardware accelerator of the online training stage of the tracker. Our proposed accelerator can train the whole fully connected layers at a rate of 45.9 frames-per-second.
Detection of Visual Similarity Snooping Attacks in Emails using an Extended Client Based Technique

This paper provides an Extended Client Based Technique (ECBT) that performs classification on emails using the Bayessian classifier that attain in-depth defense by performing textual analysis on email messages and attachment extensions to detect and flag snooping emails. The technique was implemented using python 3.6 in a jupyter notebook. An experimental research method on a personal computer was used to validate the developed technique using different metrics. The validation results produced a high acceptable percentage rate based on the four calculated validation metrics indicating that the technique was valid. The cosine of similarity showed a high percentage rate of similarity between the validation labels indicating that there is a high rate of similarity between the known and output message labels. The direction for further study on this paper is to conduct a replica experiments, which enhances the classification and flagging of the snooped emails using an advanced classification method.
State-witnessed virtual transactions for atomic execution of complexity-unconstrained smart contracts

The current model of execution of persistent blockchain scripts hasn't radically changed since its introduction in Ethereum. One of the drawbacks of this model is the computational limit set per one transaction (e.g. gas limit). In this paper, we introduce a concept of Virtual Transaction and its special case, Continuation Transaction, allowing for execution of scripts of unlimited computational complexity, as well as opening up possibilities for patterns like the asynchronous execution model, routines and triggers, enabling more complex decentralised applications and a more flexible interaction model without compromising security and stability of the blockchain environment.
Employee Performance Analytics Approach Based on Anomaly Detection in User Activity

In this paper, we highlight the distinctive features and critical areas of analytical tool application for the employee performance analytics of user activity. We describe problems of applying data analytics methods and technologies to ensure employee performance analytics. We also discuss the use of user activity time-series data analysis methods and techniques to provide employee performance analytics and describe approaches for processing unstructured data from different sources of user activity for further analytics using anomaly detection methods. Finally, we introduce a new strategy of building features from hybrid data streams from different sources and compare it with current practices.
Blockchain Medical Asset Model Supporting Analysis of Transfer Path Across Medical Institutions

The complete transfer path pays attention to the synergy among medical institutions, the result of treatment and the temporal sequence. However, the patient’s visiting behavior usually spans many medical institutions. It is not only difficult for inter-agency medical records, examination results and treatment process to be transmitted comprehensively and efficiently, but also difficult to trace the origin of the complete transfer path. This paper proposes a blockchain medical asset model to support the analysis of transfer paths across medical institutions. Firstly, this method establishes a sharing mechanism based on blockchain across medical institutions, and proposes a mapping algorithm between visiting data and blockchain assets. To solve the problem of lack of traceability and reduce the cost of using medical assets, the blockchain is used to transfer the status and inspect structure of diagnosis and treatment process among institutions. Then, aiming at the problem of lack of referral for patient transfer paths, a blockchain based full-chain transfer path analysis method is designed to find the optimal transfer paths for local medical institutions and overcome the bottleneck of the lack of referral for medical institutions transfer paths. Experiments show that the blockchain medical asset model proposed in this paper can cover the whole chain data of transfer, and can meet the needs of cross-medical institutions tracing the complete transfer path. The prediction algorithm used in this model has better performance than other prediction algorithms in mining the optimal path.
Reinforcement Learning for Clue Selection in Web-Based Entity Translation Mining

Web-based mining is a promising approach for entity translation. Traditional web-based mining methods construct spotting queries in a heuristic, one-step paradigm, which cannot resolve the diversity of entity names and is incapable of leveraging the information in the returned bilingual pages for iterative spotting query refinement. To resolve the above drawbacks, this paper proposes a reinforcement learning-based method, which models web-based entity translation mining as a Markov Decision Process (MDP). Specifically, we regard the query construction as a multi-turn, state-to-action mapping procedure, and learn a Dueling Deep Q-network based clue selection agent which can adaptively select spotting clues based on both short-term and long-term benefits. Experiments verified the effectiveness of our approach.
Search and evaluation methods for class level information retrieval

The task of expertise retrieval can be extended and generalized to a class search over collections; in this paper we describe the search for classes in the form of journals over a huge collection of articles. Given a textual query we aim to find thematically related and most appropriate classes (i.e. journals) for this request. To gain these suitable journals we utilize existing, new, and varied aggregation techniques introduced in expert search in which every article acts as a potential voter for its class - i.e. the journal it is published in.
Effect of Persona Stereotyping on Design Solutions: Observations from an Empirical Study with Novice Designers

Persona is widely used in the design industry as a tool to understand and empathize with the users. Personas are intended to help designers by providing them useful user information and more importantly helping them remove the personal bias toward their users. However, without proper training and education, the information given in the persona could be misleading resulting in designers not appreciating the benefits of the tool. Such behavior could not only fail the actual purpose of creating a persona but can also be misleading to designers while taking crucial design decisions. With the increase in the number of young designers joining design academics and industry in India, it becomes important to investigate design behavior of novice designers while working with such tools in the design process. This paper presents observations from an empirical study done with 80 novice designers. The study aimed to investigate how user information presented in the form of a persona affects the conceptual design solutions produced by a novice designer.
The mineral chemistry and geothermobarometry of gabbroic rocks crystallization of Dehsard area (Southeast of Iran)

The mineral chemistry and geothermobarometry of gabbroic rocks crystallization of Dehsard area (Southeast of Iran)
Research on Nearby Consumption of Renewable Energy and Electric Power User Matching Method Based on Energy Blockchain Technology

The research on renewable energy nearby consumption and optimal matching among power users has gotten more and more attention today. The characteristics of energy blockchain and its role in electric power trading are analyzed. The comprehensive index system is researched based on the energy attributes with the goal of achieving the optimal energy attributes and path selection. From the perspectives of the energy consumers and producers, a differentiated optimal matching strategy is designed. The corresponding operating strategy is also proposed from the perspective of the grid. The details such as contents in energy blockchain and process of negotiation of transactions are analyzed. The results show that the matching strategy can help the energy users match the optimal trading objects and promote the nearby consumption of renewable energy. After analyzing the relevant factors which affect the transactions, the results indicate that each user optimizes its own attributes and guarantees its credit can improve competitiveness in the blockchain network with lower cost and higher proportion of time-shiftable loads.
An Efficient Empirical Solver for Localized Multiple Kernel Learning via DNNs

In this paper we propose solving localized multiple kernel learning (LMKL) using LMKL-Net, a feedforward deep neural network (DNN). In contrast to previous works, as a learning principle we propose parameterizing the gating function for learning kernel combination weights and the multiclass classifier using an attentional network (AN) and a multilayer perceptron (MLP), respectively. Such interpretability helps us better understand how the network solves the problem. Thanks to stochastic gradient descent (SGD), our approach has linear computational complexity in training. Empirically on benchmark datasets we demonstrate that with comparable or better accuracy than the state-of-the-art, our LMKL-Net can be trained about two orders of magnitude faster with about two orders of magnitude smaller memory footprint for large-scale learning.