Reviewerly/exHarmony huggingface dataset:
Face Recognition Algorithm Based On Convolutional Neural Network

Abstract In response to the needs of modern enterprises for intelligent security of face authentication, a face recognition algorithm based on deep convolutional neural network is introduced in this paper. In order to improve the accuracy of face recognition, the design of 11 convolutional layers and 4 pooling layers. The network uses the standard face data set CASIA-WebFace for training, and the face recognition accuracy on the LTW database can reach more than 97.8%. Based on this deep learning network, a face recognition management system was designed. The system realized 1: 1 face authentication and 1: N face recognition. After applying the convolutional neural network algorithm, the face search time is significantly reduced, indicating that the algorithm has higher efficiency and practicability.
Instrument for Testing Innovation on the Sustainable Growth of Manufacturing SMEs in Nigeria

This paper is based on a pilot study which was conducted to test innovation on the sustainable growth of SMEs by focusing on the methodological viewpoints. The objective of the pilot study was to assess the viability, length of time, cost, and adverse effects of innovation on the sustainable growth of SMEs in Nigeria so as to enhance the design of the questionnaire before its full implementation. A review of the research instrument was performed by four multi-disciplinary academics who specialised in management, accounting, and strategic management, respectively. The aim was to ensure consistency in the questionnaire so that respondents would have no trouble completing them. The statistical package for social sciences (SPSS) Version 23 was used to assist in the exploratory factor analysis (EFA) which tests for reliability and the Cronbach alpha values were used to examine the content and face validity of the instrument. All the constructs and items used in the model were adapted from previous literature. The sample size used for the study comprised 100 respondents recruited from the SMEs operating in Nigeria. The findings showed that all the constructs in the model carried a high Cronbach alpha value of above 0.7. Therefore, all items used in the instrument were retained. This study is expected to contribute to the literature on methodological multivariate studies, the quantitative approach research on innovation, and the sustainable growth of SMEs.
The harm that judges do – misunderstanding computer evidence: Mr Castleton’s story

The reliability of computer systems, and the reliability of documents produced by computers, is important. There is a widely held perception that computers are fundamentally reliable. Further, it is commonly assumed that most computer errors are readily detectable or otherwise the result of user ‘input’ error. Since 1997 the courts have applied the presumption of the proper functioning of machines to computers. This article considered the case of Lee Castleton to illustrate that this presumption can have a devastating effect when applied.&#x0D; Index words: software, electronic evidence, computer evidence, presumption of reliability
CPARR: Category-based Proposal Analysis for Referring Relationships

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of <; subject, predicate, object>. This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.
Research on Open System of Economic and Management Laboratory Center Based on Cloud Platform

Abstract The traditional open management mode of economic and management experimental teaching center can no longer meet the needs of personalization, mobility and digitalization of college students in the new era. Through the questionnaire research of 56 experimental centers, we get the valid data of 56 experimental project managers and 372 college students’ questionnaire, and use SPSS19.0 software to analyze the data. Based on the analysis results and the design of laboratory teaching management, college entrepreneurship contest, open laboratory center and project management, this paper proposes that the traditional laboratory management mode should be transformed to the modern management mode supported by mobile, digital and We Chat public number technologies.
Measurement of Employee Awareness Levels for Information Security at the Center of Analysis and Information Services Judicial Commission Republic of Indonesia

The Center for Analysis and Information Services (Palinfo) at the Judicial Commission closely related to the management of information systems which are used to process organizational internal data and information systems on public services.Data processing and network management have an information system security risk.The Judicial Commission seeks to reduce risk and improve the quality of information security.This study aims to measure employee awareness of information security at the Center of Analysis and Information Services at the Judicial Commission, which also includes the Data/IT department.The study was conducted through an arranged interview with three experts and the dissemination of information security awareness questionnaires to all Palinfo employees, amounting to 25 persons.The results of the questionnaire were evaluated using The Human Aspects of Information Security Questionnaire (HAIS-Q) and the Analytic Hierarchy Process (AHP) method.The results showed that the level of information security awareness in Palinfo and the Data/IT section was at the "average" level.There is one focus area that shows a "good" level.While in the Data/IT department, several sections that show a "good" level.Based on these results, we recommend being used in maintaining information security, namely seven policies, ten information technology approaches, and socialization/training conducted in various ways.
A New Framework for Multi-Agent Reinforcement Learning Centralized Training and Exploration with Decentralized Execution via Policy Distillation

Multi-agent deep reinforcement learning demands for highly coordinated environment exploration among all the participating agents. Previous research attempted to address this challenge through learning centralized value functions. However, the common strategy for every agent to learn their local policies directly may fail to nurture inter-agent collaboration and can be sample inefficient whenever agents alter their communication channels. To address these issues, we propose a new framework known as centralized training and exploration with decentralized execution via policy distillation. Guided by this framework, we will first train agents' policies with shared global component to foster coordinated and effective learning. Locally executable policies will be derived subsequently from the trained global policies via policy distillation.
On the Fine-Grained Complexity of Parity Problems

We consider the parity variants of basic problems studied in fine-grained complexity. We show that finding the exact solution is just as hard as finding its parity (i.e. if the solution is even or odd) for a large number of classical problems, including All-Pairs Shortest Paths (APSP), Diameter, Radius, Median, Second Shortest Path, Maximum Consecutive Subsums, Min-Plus Convolution, and 0/1-Knapsack.A direct reduction from a problem to its parity version is often difficult to design. Instead, we revisit the existing hardness reductions and tailor them in a problem-specific way to the parity version. Nearly all reductions from APSP in the literature proceed via the (subcubic-equivalent but simpler) Negative Weight Triangle (NWT) problem. Our new modified reductions also start from NWT or a non-standard parity variant of it. We are not able to establish a subcubic-equivalence with the more natural parity counting variant of NWT, where we ask if the number of negative triangles is even or odd. Perhaps surprisingly, we justify this by designing a reduction from the seemingly-harder Zero Weight Triangle problem, showing that parity is (conditionally) strictly harder than decision for NWT.
Can Eruptions Be Predicted? Short-Term Prediction of Volcanic Eruptions via Attention-Based Long Short-Term Memory

Short-term prediction of volcanic eruptions is one of the ultimate objectives of volcanology. At Sakurajima volcano, an active volcano in Japan, experts monitor the volcanic sensor data and analyze the prior signal to predict the eruptions. Even though experts derived some patterns, it is hard to make a good prediction due to handcrafted features. To address this issue, we propose to predict eruptions using machine learning. In this paper, we attempt to predict the eruptions hourly by adapting several machine learning methods including traditional and deep learning approaches. As recurrent neural network is well-known for extracting the time-sensitive features, we propose the model especially for volcanic eruption prediction named VepNet. The assumption is based on domain knowledge that some specific triggers are the main causes of future eruptions. To take this advantage, VepNet deploys an attention layer to locate and prioritize these triggers in decision making. The extensive experiments ever conducted using data from Sakurajima volcano showed the effectiveness of deep learning approach over the traditional approach. On top of that, VepNet showed its effectiveness on prediction with AUC-score up to 0.8665. Moreover, an attempt has been made to explain the mechanism of the eruptions by analyzing the attention layer of VepNet. Lastly, to support volcano expert in issuing warnings and the safety of living people around Sakurajima, a warning system named 3LWS is proposed. The system predicted the eruptions hourly with high accuracy and reliability with the eruption rate up to 68.97% in the High-Risk level.
On the comparability of pre-trained language models

Recent developments in unsupervised representation learning have successfully established the concept of transfer learning in NLP. Mainly three forces are driving the improvements in this area of research: More elaborated architectures are making better use of contextual information. Instead of simply plugging in static pre-trained representations, these are learned based on surrounding context in end-to-end trainable models with more intelligently designed language modelling objectives. Along with this, larger corpora are used as resources for pre-training large language models in a self-supervised fashion which are afterwards fine-tuned on supervised tasks. Advances in parallel computing as well as in cloud computing, made it possible to train these models with growing capacities in the same or even in shorter time than previously established models. These three developments agglomerate in new state-of-the-art (SOTA) results being revealed in a higher and higher frequency. It is not always obvious where these improvements originate from, as it is not possible to completely disentangle the contributions of the three driving forces. We set ourselves to providing a clear and concise overview on several large pre-trained language models, which achieved SOTA results in the last two years, with respect to their use of new architectures and resources. We want to clarify for the reader where the differences between the models are and we furthermore attempt to gain some insight into the single contributions of lexical/computational improvements as well as of architectural changes. We explicitly do not intend to quantify these contributions, but rather see our work as an overview in order to identify potential starting points for benchmark comparisons. Furthermore, we tentatively want to point at potential possibilities for improvement in the field of open-sourcing and reproducible research.
Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.
A Multimode Network Steganography for Covert Wireless Communication Based on BitTorrent

Network steganography is a hidden communication technique, which utilizes the legitimate traffic as the vehicle to transfer the secret information covertly over the untrusted network. BitTorrent (BT) is one of the most prevalent P2P services for transmitting video files over wireless networks. An enormous amount of video data is transmitted over BitTorrent traffic continuously, to make it potentially available for confidential information transfer. Hence, in this paper, the BitTorrent file-sharing service of P2P is chosen as the host for information hiding, and a multimode steganographic method based on Bitfield message is proposed. Taking advantage of BitTorrent cooperative transmission and the non-content-authentication mechanism of Bitfield message, the secret information is delivered during the exchange process of BitMapInfo between two peers. The steganographic mode is dynamically selected in view of the secret size, achieving adaptive bandwidth. The experimental results show that our scheme can resist statistical-based detection effectively and outperform the existing method by obtaining a lower degree of detection rate under machine learning-based steganalysis.
Conditions of digitalization of education in aspect of the problem of forming ICT competence of students of pedagogical college as future teachers

The article substantiates the significance of the problem of the formation of future teachers’ ICT competence. The author’s definition of the concept “ICT competence of a student of a pedagogical college” is presented, its components (motivational-value, user-wide, general pedagogical, subject-pedagogical) are highlighted and described. Based on the analysis of normative documents, recommendations, studies in the field of digitalization of the economy and education, the conditions for the digitalization of education are highlighted and justified: the legislative framework for the digitalization of education; digital generation of students; digital pedagogical technologies and educationally significant digital technologies; training the human potential of the digital economy; digital educational environment of educational organization. The characteristics of the conditions for digitalization of education are described. The experience of taking into account the conditions of digitalization of education in the aspect of the problem of the formation of ICT competence of students as future teachers is described on the example of an educational organization — GAPOU “Chita Pedagogical College”, specialty 44.02.01 Preschool education. An example of the organization of a laboratory and practical lesson using various digital technologies in the framework of the author’s interdisciplinary course “Theory and Methods of Using ICT in a Preschool Educational Organization” is given. Based on the analysis of the results of the pedagogical experiment, the conclusion is substantiated that the application of the digitalization of education in the educational process of the pedagogical college in the aspect of the formation of ICT competence of future teachers is one of the effective ways of forming the ICT competence of future educators of preschool children.
Blockchain and Organizational Characteristics: Towards Business Model Innovation

Blockchain seems to challenge the current business models by providing opportunities for new value creation. However, several research gaps remain in literature in evaluating how firms can leverage new approaches to innovation management and opportunities created by blockchain. Supporting organizational characteristics affecting digital innovation management process also need attention in order to challenge the traditional theories while developing unique fundamental assumptions between innovation processes and outcomes. Thus, blockchain and organizational characteristics need to be understood as an encompassing, overarching and interrelated ecosystem in digital innovation management. Grounding on digitalization and innovation management, this research conceptualizes how blockchain technology and supporting organizational characteristics (i.e., R&D investment, strategic alignment, cultural support, top management knowledge and involvement, insights from customers and end-users) can be integrated for business model innovation. This research develops a conceptual framework involving multi-disciplinary collaborative actions that strengthen and empower business model innovation.
Point cloud human posture estimation using single RGB image

Point cloud estimation of human posture has consistently been a challenging tasks that holds extraordinary consideration. It has broad range of uses from the classification to recognition of operation. The fundamental key challenge is the location and confinement of the key focuses in the variety of several body poses. In our work we utilizes a solitary RGB picture and gauges the point cloud human posture to gain proficiency with the diverse component data of human body parts. The entire procedure is isolated into two steps namely human pose feature extraction and point cloud estimation of posture. Extricating the special features of the data and train them by using Convolutional Neural Network (CNN). CNN can learn both location and feature data to identify human pose. Further, with the acquired special features and estimated human pose, point cloud of the human posture is evaluated. With the reduced feature ambiguities, point cloud of the data outperforms state-of-the-art approaches.
Making the Home Accessible - Experiments with an Infrared Handheld Gesture-Based Remote Control

A universal remote control for many different technical devices in the living environment - which should be very easy, almost intuitively to use - would be most desirable, especially for elderly and mobility-impaired persons. For this purpose, a flashlight-like handheld infrared gesture-controlled remote we call SmartPointer is being developed and evaluated. Carrying out a mid-air gesture with the SmartPointer moves the irradiated beam of structured IR light across a receiver box near the device, which detects the light pattern’s trajectory and converts the identified gesture into device-specific commands. In our laboratory study, the user experience of the SmartPointer system and its gesture recognition capabilities were examined with a group of 29 elderly volunteers who were asked to repeatedly carry out quasi-intuitive gestures with the SmartPointer to operate four typical home appliances: light switch (on/off), heating device (warmer/colder), blinds (up/down) and door (lock/unlock). Applying adaptive rule-based signal processing and pattern recognition, an overall recognition rate of 94.3% could be achieved so far.
ROLE STRUCTURE OF HIGHER SCHOOL TEACHER’S PROFESSIONAL ACTIVITY

The article raises topical issues of the implementation of the competency approach to the professional activity of a higher education teacher, differentiation of pedagogic work, the introduction of individual and subjective approaches to the organization of the educational process at a university. The changes occurring in higher education, and the requirements for the teacher in the modern educational environment, which are associated with the multifunctionality of the role of the teacher in modern conditions, are described – be able to educate students and evaluate knowledge and competences, organise scientifi c activities, update the content and forms of taught disciplines taking into account state standards of education of the Russian Federation, involve students in project activities, apply distance learning technologies, cooperate with employers. The possible role positions of the modern teacher of higher education («methodologist-subject», «mentor», «tutor», «scientist», «educator», «coordinator»), as well as the competences, knowledge necessary for the successful implementation of these positions, have been identifi ed and described. skills, pedagogic actions and means. The results of the study of the role structure of the professional activity of teachers of Kostroma State University are presented, the main points of growth of professional competences of the university teaching staff are identified.
Poster: APIs for IPAs? Towards End-User Tailoring of Intelligent Personal Assistants

Integrated into smartphones or smart speakers, Intelligent Personal Assistants (IPAs) have grown into the most prevalent example of speech-based interfaces today. Enabling hands-free completion of tasks such as checking the weather, playing music, or controlling smart devices, IPAs have become a central feature of millions of homes. Yet, end-users are faced with barriers to understanding their `black box' devices, and a lack of opportunity to engage in end-user development (EUD) activities. Our current work considers the state of existing tools and platforms for development of IPAs, the key challenges to be overcome, and the potential benefits of doing so.
Dual Stage Bayesian Network with Dual-Tree Complex Wavelet Transformation for Image Denoising

Image de-noising always plays a vital role in various engineering bids. Moreover, in image processing technology, image de-noising statistics is persisted as a substantial dispute. Over the past decades, certain de-noising methods have been exposed incredible accomplishments. This paper intends to develop a de-noising algorithm for multimodal and heterogeneous images, while the conventional de-noising algorithms handle a specific image type. The filtered information is reversed to spatial domain to recover the de-noised image. Dual tree Complex Wavelet Transform (DT-CWT) is exploited for image transformation for which the wavelet coefficients are estimated using Bayesian Regularization (BR). To ensure the de-noising performance for heterogeneous images, the statistical and wavelet features are extracted. Subsequently, the image characteristics are combined with noise spectrum to develop BR model, which estimates the wavelet coefficients for effective de-noising. Hence, the proposed de-noising algorithm exploits two stages of BR. The first stage predicts the image type, whereas the second stage estimates appropriate wavelet coefficients to DT-CWT for de-noising. The performance of the proposed model is analysed in terms of Peak Signal to Noise Ratio (PSNR), Second derivative Measure of Enhancement (SDME), Structural Similarity (SSIM), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson Coefficient (PC), and Symmetric Mean Absolute Percentage Error (SMAPE) respectively. The proposed model is compared to the conventional models, and the significance of the developed model is clearly described.