Reviewerly/exHarmony huggingface dataset:
Multi-method Simulation Approach for Software Project Management Training in-process Decision Support System: a Conceptual-operative Framework

Software project management (SPM) is a discipline that comprises of different topics, practice and theories. There are two dimensions in the knowledge of SPM, theoretical or concepts of SPM and; practical or experience of SPM. These two dimensions although grow separately but come across at one point that is experiential knowledge of SPM. To present these dimensions through a proper training, a practitioner needs to have a proper view on SPM process. In this paper we present a new framework for practicing SPM through a distinct simulation technique to bring on an in-process decision support system, supporting the proper training approach. The framework developed based on three different methods of simulation technique, discrete event simulation (DES), system dynamics (SD) and partially observable Markov decision process (POMDP).1
Cloud Service for Multidimensional Processing of Quantitative Data for Solving Geological

The study describes the developed cloud web service for multidimensional processing of quantitative data for solving a wide class of scientific geological tasks. The computing node “Multidimensional methods of data analysis” provides processing of tabular data using various methods of modern data analysis and allows to set their parameters and visualize the results. The node includes wide range of methods such as data preprocessing, descriptive statistics, cluster analysis, factor analysis, correlation analysis, regression analysis. Computing node “Multidimensional methods of data analysis” is a part of Computational analytical geological environment of State Geological Museum of RAS and is integrated with its services. At the same time, the computing node is an independent cloud web service which implements REST API for interaction with it. This allows a wide range of users to access multidimensional data analysis methods located on a computing node and provides capabilities of its integration into information systems as a thirdparty application for processing tabular data.
A Simulation Learning in Communication Technique Skill by Virtual Reality - Certification of Telecommunication Line Plant

In this paper, a simulated learning strategy in communication skill is implemented by virtual reality technology and is applied to the course of telecommunication line plant certification which is the important skill training course in communication engineering. The proposed system is implemented as a full automatic evaluation system to demonstrate the effect of the brand new learning strategy for field test in telecommunication skills to validate the learning performance and efficiency. It is observed, from the experimental results, that the learning strategy can not only improves the student’s learning performance and interest in skill training but also provide a stress-free and unlimited learning environment than the traditional teaching methods. That is, due to the strong extension to variety applications, it is very suitable for complementing physical training courses to achieve the best learning effect.
The (1+1)-ES Reliably Overcomes Saddle Points

It is known that step size adaptive evolution strategies (ES) do not converge (prematurely) to regular points of continuously differentiable objective functions. Among critical points, convergence to minima is desired, and convergence to maxima is easy to exclude. However, surprisingly little is known on whether ES can get stuck at a saddle point. In this work we establish that even the simple (1+1)-ES reliably overcomes most saddle points under quite mild regularity conditions. Our analysis is based on drift with tail bounds. It is non-standard in that we do not even aim to estimate hitting times based on drift. Rather, in our case it suffices to show that the relevant time is finite with full probability.
Leveraging various Transformers Based Architectures for Truecasing

Truecasing is the process of finding the proper capitalization of words in a text where the correct case information of the word is not given. The case of letters is often lost while performing language translation from non-capitalized languages (eg: Japanese, Arabic, Hindi) to capitalized languages (eg: Greek, Latin, English). In Optical Character Recognition (OCR) where the different cases for letters like ‘p’ and ‘P’ are similar, the case as well as the context and proper meaning of the text is lost. A similar situation is observed for text generated from Automatic Speech Recognition. This paper proposes to solve the problem of truecasing while focusing on three different architectures, all based on transformers. Transformers have generally proved to outperform recurrent and convolutional models. A proposal for a novel light-weight transformer architecture has been made which performs competitively with existing models that are computationally expensive. To provide a basis for comparison, two existing transformer architectures were fine-tuned for truecasing - BERT and GPT-2. These architectures are analyzed and compared on various metrics. The proposed architecture shows an accuracy of 98.25%, which is also an improvement of 0.41 against the current state-of-art.
A Vertical Semantic Search Engine In Electric Power Metering Domain

With the explosive growth of data on the Internet, the mining of vertical knowledge domain material has become more complex. In order to efficiently collect, manage and exploit the huge knowledge corpus, this paper proposes a vertical semantic search engine for the domain of electric power metering. On the one hand the engine overcomes the shortcomings of traditional general-purpose search engines in terms of lack of targeting and specialization, and on the other hand it has higher accuracy and more stable recall than general keyword-based search engines, enabling semantic understanding and relational analysis. The main innovation of the proposed engine is the use of knowledge units extracted from knowledge graphs for semantic search. Experimental results prove that this engine can improve the accuracy and timeliness of power metering knowledge retrieval, and has certain application value.
Kijksluiters: kijken naar gesprekken tussen apotheker en patiënt

Kijksluiters: watching conversations between pharmacists and patients As of 1975, Dutch patients receive a package insert with their medication. Due to extensive national and EU legal regulation efforts, the insert developed into a mandatory genre par excellence. However, its communicative functionality remained doubtful: the patient information leaflet has always been regarded as lengthy, complex and user-unfriendly. Recently, the Dutch Medicine Board has introduced a new, audiovisual medication instruction, the so-called Kijksluiter, that shows a video animation of a conversation about the medicine between a pharmacist and a patient. After a historical introduction, the second section of the paper surveys empirical studies that shed light on the main design parameters of the new genre: spoken instead of written information, animated speakers, dialogue instead of monologue. In the third part, we report on an observation study in which 16 users answer 9 scenario questions using a Kijksluiter video. The results indicate that Kijksluiters are not without user problems. Overall, two-thirds of the answers are more or less correct. Half of the participants first watched the video in its entirety before attending to the questions. The main problem this group encountered is: insufficient recollection of the relevant information. The other half of the participants navigated the Kijksluiter for each question, using the menu offering twelve small chapters. The main problems in this group was not finding the question-relevant chapter; but even after listening to the relevant information, some answers are incorrect. We conclude that, although Kijksluiter does not immediately solve all medication communication problems, its concise audiovisual format broadens the range of media available for medicine users.
Public Risk Deterrence and Security Alert Systems Using Artificial Intelligence

With the increase in threat to public safety, in the last decade, different observation cameras have been introduced in various areas to guarantee the well-being of individuals. Security is a tedious, complicated and tough job in today’s digitized world. Various anomaly detection techniques have been brought forward to design a smart control system. Observation information is produced through the camera, and then this information is transmitted over the system to the storage. Examination of the data caught utilizing these cameras can assume dynamic jobs in occasion forecast, Internet checking and objective-driven investigation applications, including oddities and interruption location. Various AI methods are being used today that can detect abnormalities with ease, in particular, the convolutional neural networks that make use of deep learning techniques have proven to increase the accuracy of crime detection significantly. The objective of this paper is to propose another strategy dependent on profound learning methods for wrongdoing recognition in video reconnaissance cameras. The proposed strategy has been assessed in the UCSD dataset and indicated an expansion in the exactness of public risk deterrence or crime identification.
Organic Integration of Information Technology and College English Teaching in English Information Teaching System

With the more and more extensive use of network information technology in English teaching, the English teaching in colleges and universities is also constantly reformed, the organic integration of network electronic technology and school English teaching has become a key trend in the future English teaching, the English teaching mode has gradually changed from the on-campus English teaching mode to the electronic English teaching mode. On this basis, English teachers need to keep up with the pace of the times, the direction of organic integration of artificial intelligence education technology and English course teaching should be clarified, and the reform of English course education should be further carried out. According to the questionnaire results of students of different majors in a university, 42.42% of university students often use this system to study, 37.37% of university students sometimes use this system to study, and no university students do not use this system to study, indicating this system used by more students to learn English; 51% of students learn English to find a satisfactory job after graduation, and only 7% of college students learn English because they are interested in learning English, indicating that students recognize the importance of English but lack confidence in English learning.
Homomorphic Denoising Filtering for 3D Face Recognition

Image denoising is a crucial method in image processing in many sectors including Medicine, Engineering, Statistics, Physics, Chemistry, and Information Science that require a clear image for examination. Image denoising is a procedure to remove noise that shows up on images caused by interference. Each image denoising method has its own advantages and constraints in its respective algorithms. The principal objective of this work is to develop algorithm for denoising of non additive noise and to be applied in facial recognition for future reconnaissance frameworks. The homomorphic denoising filter based on Wiener algorithm produces a superior outcome in image denoising and also creates a better quality of 3D images in terms of MSE, PSNR, and SNR measures.
DVul-WLG: Graph Embedding Network Based on Code Similarity for Cross-Architecture Firmware Vulnerability Detection

Vulnerabilities in the firmware of embedded devices have led to many IoT security incidents. Embedded devices have multiple architectures and the firmware source code of embedded devices is difficult to obtain, which makes it difficult to detect firmware vulnerabilities. In this paper, we propose a neural network model called DVul-WLG for cross-architecture firmware vulnerability detection. This model analyzes the similarity between the binary function of the vulnerability and the binary function of the firmware to determine whether the firmware contains the vulnerability. The similarity between functions is calculated by comparing the features of the attribute control flow graph (ACFG) of the functions. DVul-WLG uses Word2vec, LSTM (Long Short-Term Memory) and an improved graph convolutional neural network (GCN) to extract the features of ACFG. This model embeds instructions of different architectures into the same space through canonical correlation analysis (CCA), and expresses instructions of different architectures in the form of intermediate vectors. In this way, the heterogeneity of architectures can be ignored when comparing cross-architecture similarity. We compared DVul-WLG with the advanced method FIT and the basic method Gemini through experiments. Experiments show that DVul-WLG has a higher AUC (Area Under the Curve) value. We also detected vulnerabilities in the real firmware. The accuracy of DVul-WLG is 89%, while FIT and Gemini are 78% and 73%, respectively.
A Weld Line Detection Method Based on 3D Point Cloud for Automatic NDT

Abstract The quality of welding is often checked by ultrasonic waves. Manual testing is costly and inefficient, and manual testing is not possible in some extreme environments. Automatic non-destructive testing (NDT) technology uses robots to carry ultrasonic devices for automatic detection. Machine vision is one of the important methods to achieve navigation, that is, capturing the weld line through the camera, planning the optimal path through visual analysis and processing, and also based on structured light. Although the navigation method can solve the problem of rust and stain to a large extent, it is less robust in dealing with problems such as rust, light interference and stain. This paper proposes a navigation method based on 3D point cloud, which can effectively improve its robustness.
Model-based Local Distortion Flow Estimation for Wide-angle Image Rectification

Wide-angle cameras are important for large-scale surveillance because of the larger field of view. However, due to lens design limitations, it distorts the captured image to enlarge the camera view. The degree of distortion is usually varied according to the position of the object. Nevertheless, a regular and perspective image view is preferred for viewers; thus, image rectification becomes essential. This paper proposed a learning network to estimate the distortion flows coupled with locally-adaptive model fitting to correct the distortion of wide-angle lens images. Unlike some data-driven methods that directly learn the mapping between an input image and its image distortion parameters, we firstly estimated the motion flow between the distorted and rectified images. Next, by fitting a model to locally infer the model parameters, we generated a model-regularized flow map for rectification. Our experimental results show the barrel distortion can be robustly corrected.
What do students need during Covid-19? A need analysis of augmented reality with STEAM worksheet (AR-STEAM) in electromagnetic induction

Abstract This study aims to collect initial information in designing Augmented Reality-based Physics worksheet with a Science, Technology, Engineering, Art, and Mathematical (AR-STEAM) approach. The research method used descriptive qualitative by collecting some field research information was carried out by sharing the viewpoints of 26 students (male N=3 and female N=23) who had studied the material of Electromagnetic Induction. Based on field studies using questionnaires, it is known that 69.2% of respondents enjoy learning by using visual media, 66.3% consider the concept of type induction to be very abstract and difficult to apply in real life, 80.8% of respondents say they are happy if physics learning is explained in the form of three-dimensional animation. Therefore, it can be said that the STEAM-AR worksheet is needed in learning physics on the topic of Electromagnetic Induction.
An Approach to Development of Virtual Reality Training Systems

The publication describes our approach to development of virtual reality training systems (VRTS). First, we go through the motivation for this research. Next, we give definitions of different types of virtual reality and related mediums. Then, we provide a brief classification and a comparative analysis of existing VR systems. Finally, based on our experience in creating training systems in virtual reality, we analyze and systematize the most important issues in development of such innovative applications, which includes: system requirements; expected benefits; specific development tasks; stages in development; selection of a specific virtual reality technology and development environment; a model of VRTS; logic of a training system; and analysis of outcomes and effects.
The Role and Importance of Digitalization in Hospitality Sector to Ease Function

The study aims to determine the factors to propel digitalisation in the hospitality sector of an Aerospace research and development organization. The researchers explain how digitalisation improves employees' performance and support in time management. “Digitalisation has left no segment of the travel ecosystem untouched”. A technical team was constituted with experts to study and analyze the implications of implementation. A Research gap was identified due to the lack of information available especially in the hospitality sector of government organizations. The primary data is collected through observations, field surveys, and secondary data is collected through web sources, government reports, published journals, etc. The Government of India is promoting cashless transactions and digitalization in all sectors. The implication of the study will help the policymakers and managements to apply, adopt such digitalization in various fields of hospitality, public relations, information counters, the food industry, and the tourism sector.
Areas of Application for Nonlinear Shift Registers in PRS Generators

One of the common basic elements of a modern streaming encryption generator is feedback shift registers. In practice, linear bias shift registers (LFSR) are most commonly used. Such cryptographic primitives have been well proven in use for many years. The main advantages of LFSR are: high speed cryptographic algorithms; application of only the simplest operations of addition and multiplication, hardware implemented in almost all computing devices; good cryptographic properties (generated sequences have a long period and good statistical characteristics); are well suited for systems with low energy consumption; ease of analysis using algebraic methods due to linear structure. However, despite the prospects for the use of NLFSR as one of the main elements of the PRS generator, many fundamental problems associated with NLFSR remain insufficiently studied. To date, even such a simple characteristic as the sequence period formed by shift registers, in the feedback of which nonlinearity is introduced, is difficult to determine. In view of the above, the design of cryptographically stable PRS generation systems based on shift registers, which are resistant to various types of attacks and have good technical characteristics, is currently an urgent and necessary task.
Towards Federated Learning: A Case Study in the Telecommunication Domain

Federated Learning, as a distributed learning technique, has emerged with the improvement of the performance of IoT and edge devices. The emergence of this learning method alters the situation in which data must be centrally uploaded to the cloud for processing and maximizes the utilization of edge devices' computing and storage capabilities. The learning approach eliminates the need to upload large amounts of local data and reduces data transfer latency with local data processing. Since the Federated Learning technique does not require centralized data for model training, it is better suited to edge learning scenarios in which nodes have limited data. However, despite the fact that Federated Learning has significant benefits, we discovered that companies struggle with integrating Federated Learning components into their systems. In this paper, we present case study research that describes reasons why companies anticipate Federated Learning as an applicable technique. Secondly, we summarize the services that a complete Federated Learning system needs to support in industrial scenarios and then identify the key challenges for industries to adopt and transition to Federated Learning. Finally, based on our empirical findings, we suggest five criteria for companies implementing reliable Federated Learning systems.
A Research of PPM in Digital Communication : The Influence of SNR

Automatic modulation identification and parameter estimation of digital communication signals are the key technologies in communication systems. In recent years, with the rapid development of modern communication and signal processing technology, the communication signal system and modulation style are becoming more complex and diverse. In addition, the signal transmission environment has become increasingly bad, all of these changes make the communication signal identification, and parameters estimation requirements are getting higher and higher. In this paper, we study some modulation and demodulation methods in digital communication. According to the published literature at home and abroad and our simulations, the theory of automatic modulation recognition for communication signals is increasingly rich and perfect, especially in the low SNR of modulation recognition and parameter estimation problem under the condition of more concern.
Recent Advances in Socially Relevant Computing: Status and Perspectives

Socially relevant computational techniques emphasize the utilization of computational technology for providing solutions for personal and societal problems. It provides opportunities to show that computer science is a modern discipline that provides intellectual and technical resources for solving meaningful, global issues. SRC discloses a new framework that assists to identify, design, model tasks, and implementing computer-based solutions that have a deep understanding and align with real-time problems. The SRC, with the best intentions, is expected to produce an insightful entrepreneurial, and more versatile trained computer scientist, by focusing on problem-solving and by providing practice to individuals in identifying vital resources and engineering solutions through computation. In this survey, we present recent advances in Socially Relevant Computing (SRC). Furthermore, we present major milestones of socially relevant computing efforts.
Efficient Traffic Sign Recognition with Approximate Similarity Measure

As an important part of driverless system and automatic driving assistant system, the detection and recognition of traffic signs have a direct impact on the driving safety. To address the issue of slow detection speed and low recognition accuracy for small objects, this paper proposes a similarity measure model to quickly detect and recognize 261 types of traffic signs in Chinese Tsinghua-Tencent 100K benchmark. Firstly, Siamese network is used to generate representation space for homogeneous samples. After the training of Siamese network, a feature index table is generated. Secondly, the recognition of traffic signs is completed by a two-stage model. The detected traffic sign regions are roughly classified into three categories, namely “Indication”, “Warning”, and “Prohibition”, and then the three categories of traffic sign are fine-grained classified by using similarity measure model. Experimental results show that our proposed model improves the accuracy of small traffic signs in the wild, and realizes the fast detection and recognition of traffic signs.