Reviewerly/exHarmony huggingface dataset:
Technologies for Creating a Positive Image of Educational Organizations (On the Example of the Federal Penitentiary Service of Russia)

Modern realities of educational activities in most countries are characterized by the expansion of cross-cultural communications, increasing the role of intersubjective interaction and voluntary decision-making by subjects in all spheres of life, including education. In the context of competition in the educational services market, such parameters as brand, image and reputation have become intangible assets of educational organizations that help them simultaneously achieve their goals and optimize their development. In connection with the above, there is no doubt about the relevance of activities to create the image of educational organizations as a phenomenon of interaction with civil society institutions and individual citizens, as well as targeted interaction with the media. In order to conduct an empirical study of technologies for the formation of a positive image of educational organizations in the example of the Federal penitentiary service of Russia, the authors used the method of questioning cadets of 1st, 3rd and 5th courses, the teaching staff and employees of other subdivisions, including employees of press services of educational organizations of the Federal penitentiary service of Russia, concerning their attitude to the image of their educational institution, as well as their personal contribution to its formation.
Chapter 8. Differing design decisions – comparing Swedish FrameNet to FrameNet

Creation of framenets for languages other than English based on Berkeley FrameNet has tested the hypothesis that semantic frames, to a certain extent, are language independent. This working hypothesis facilitated reuse of frames for new framenets, defining language specific frame evoking lemmas and annotating language specific sentences. The caveat is the bias towards creating what is possible, rather than typical, in a language. The reuse of frames allowed developing SweFN in a relatively short period of time. However, the goal to build a typical, not a possible Swedish framenet, necessitated some frame modifications.This chapter provides a comparison between the English and Swedish framenets regarding semantic annotation and representation, and socio-cultural factors, including how differences forced modification of the original structure.
Determination of the level of seismic hazard in Yogyakarta using fuzzy simple additive weighting method

Abstract This study aims to obtain a mapping of seismic hazard vulnerability of Special Region of Yogyakarta (DIY) using the Fuzzy Simple Additive Weighting (FSAW) method. This mapping is serves as an act of mitigation for earthquake disasters to reduce the risk of earthquake disasters and minimize fatalities or losses in earthquake-prone areas. The factors used construct the mapping of earthquake-prone areas in Yogyakarta are social, physical, and economic vulnerabilities. Each factor has several variables. The data of this research were taken from the Central Bureau of Statistics (BPS) and the National Disaster Management Agency (BNPB) of the Special Region of Yogyakarta from 2014 to 2017. The FSAW method was used to determine the level of seismic hazard for each sub-district in DIY with a high, medium, or low vulnerability category. The steps taken in the FSAW method are (1) performing fuzzification for each variable, (2) normalizing, (3) determining the weigh of each variable, (4) determining the classification of the result; whether it is categorized under high, medium, or low level. The result from this study is a map that shows areas in each district of Special Region of Yogyakarta with high, medium, and low seismic vulnerability. Based on the social, economic and physical vulnerability factors, it was found that every sub-district in Yogyakarta City were categorized under the high-level vulnerability, while the sub-districts in Bantul, Gunungkidul, Kulonprogo and Sleman Regencies had the average level of medium.
Evaluating the application of a computer vision model in the customer service chatbot of an electric utility

With the increasingly application of Deep Learning models in computer vision, data quality and systematic metric assessment have assumed a preponderant role in the model performance. In this paper we present the evaluation approach of a model built to provide a computer vision solution, aiming to visually identify the energy meter box in households able to be connected to the electricity grid. It is provided a discussion on the real need for a careful analysis of the metrics adopted to evaluate the assertiveness of the results obtained, highlighting the evaluation framework, the importance of image quality and the main issues that must be followed to meet such a purpose. The approach herein described turned out to be on a good methodological path, presenting prediction results able to add value to the chosen application and which can be continuously improved, how is it intrinsic to the machine learning process.
Speech Recognition and Text Summarization Using Textrank Algorithm

Human beings use speech has basic form of communication, extending this concept to the world of computers will create a milestone in the field of technology. Speech recognition is the process of converting the speech into text format. This paper presents uses speech recognition in education sector along with text summarization module, Text summarization refers to process of taking long pieces of text and shortening it, by outlining the main points thereby creating the coherent summary of the document or text. This paper describes the method of using speech recognition system and text summarization model for a professor or teacher by recording the lecture delivered during the class and pass the recorded lecture to the text summarization model.
Sequence-to-Sequence Acoustic Modeling with Semi-Stepwise Monotonic Attention for Speech Synthesis

An encoder–decoder with attention has become a popular method to achieve sequence-to-sequence (Seq2Seq) acoustic modeling for speech synthesis. To improve the robustness of the attention mechanism, methods utilizing the monotonic alignment between phone sequences and acoustic feature sequences have been proposed, such as stepwise monotonic attention (SMA). However, the phone sequences derived by grapheme-to-phoneme (G2P) conversion may not contain the pauses at the phrase boundaries in utterances, which challenges the assumption of strictly stepwise alignment in SMA. Therefore, this paper proposes to insert hidden states into phone sequences to deal with the situation that pauses are not provided explicitly, and designs a semi-stepwise monotonic attention (SSMA) to model these inserted hidden states. In this method, hidden states are introduced that absorb the pause segments in utterances in an unsupervised way. Thus, the attention at each decoding frame has three options, moving forward to the next phone, staying at the same phone, or jumping to a hidden state. Experimental results show that SSMA can achieve better naturalness of synthetic speech than SMA when phrase boundaries are not available. Moreover, the pause positions derived from the alignment paths of SSMA matched the manually labeled phrase boundaries quite well.
A Fuzzy Logic Approach for Image Restoration and Content Preserving

Image filtering, which removes or reduces noises from the contaminated images, is an important task in image processing. This paper presents a novel approach to the problem of noise reduction for gray-scale images. The proposed technique is able to remove the noise component, while adapting itself to the local noise intensity. In this way, the proposed algorithm can be considered as a modification of the median filter driven by fuzzy membership functions. Experimental results are compared to static median filter by numerical measures and visual inspection. As was expected, the new filter shows better performances.
Construction of a pseudo-random number generator whose output is a normal sequence

Pseudo-random number generators (PRNGs) are widely used in information technologies, including data protection systems, and are intensively researched in modern cryptography. In this report, we describe a PRNG class, which, firstly, has been successfully tested using the most powerful modern test batteries, and secondly, it is proved the generators from these classes generate normal sequences, that is, for any generated infinite sequence X and all binary words of the same length, their limit frequencies in X are equal.
MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems

Large-scale computational problems that need to be addressed in modern computers, such as deep learning or big data analysis, cannot be solved in a single computer, but can be solved with distributed computer systems. Since most distributed computing systems, consisting of a large number of networked computers, should propagate their computational results to each other, they can suffer the problem of an increasing overhead, resulting in lower computational efficiencies. To solve these problems, we proposed an architecture of a distributed system that used a shared memory that is simultaneously accessible by multiple computers. Our architecture aimed to be implemented in FPGA or ASIC. Using an FPGA board that implemented our architecture, we configured the actual distributed system and showed the feasibility of our system. We compared the results of the deep learning application test using our architecture with that using Google Tensorflow’s parameter server mechanism. We showed improvements in our architecture beyond Google Tensorflow’s parameter server mechanism and we determined the future direction of research by deriving the expected problems.
Design of Voice-Controlled Wheelchair Model

People with lower limb disabilities have many struggles while living a normal life, these people with locomotive problems usually are supported by a variety of high-tech wheelchairs that help them with comfortable movement. Such people move their wheelchairs using joysticks or manually moving the wheels, but many people suffer from upper limb and lower limb disabilities, hence to ease their difficulty of manually operated movement, many ideas and implementations are being employed. This research paper focuses on one such idea.This project implements a voice-operated wheelchair using a speech recognition module inbuilt in our smartphones. A person in a wheelchair can move easily by giving simple voice commands like ‘forward’, ‘backward’, etc, and in this way, a person with disabilities can live a hassle-free life. This implementation even detects obstacles in the wheelchair’s path and thereby avoids unforeseen accidents. Along with it, a simple buzzer alarm is fixed on the wheelchair which can come handy in case of emergencies.This simple solution is going to go a long way in helping the disabled and is a great implementation of speech recognition technology.
Polarity Sentiment-Based Intelligent Chat Bot For Judicious Customer Service Escalation

Most of modern day Information Processing applications require Natural Language Processing. Most of the applications need extracting, processing of text to be done in their surface forms. Applications like document classification, text-extraction and machine translation comes under Natural Language Processing. Knowledge based approach of Natural Language Processing deals with representing and acquiring of knowledge to solve problems like ambiguity resolution. Ambiguities in Customer service conversation requires high level processing in order to generate the appropriate automated replies. Sentiment analysis is the interpretation of emotions and attitude of the user. Emotions can depend on mood of the user. In this work negative/positive probability is used to show the sentiment of a user which in turn can help the chat bot to generate a suitable reply to the user and escalate the conversation to a real representative when it sense that user is frustrated beyond a cutoff.
Invited Paper: The Case for Small-Scale, Mobile-Enhanced COVID-19 Epidemiology

Our understanding of COVID-19 pandemic epidemiology has many gaps, with many challenges arising on a global scale.This paper looks at the problem at a smaller geographical scale, the extent of the campus of a large organization.Equipped with an asymptomatic testing program and rough location data from the campus wireless network, we make the case that epidemiological models may be informed from this new source of data, which offers fidelity at the temporal resolution of seconds and spatial resolution of a Wi-Fi cell size, in particular for the tasks of pinpointing clusters of cases and contexts of infection transmission.We sketch the design of a system that fuses the two foregoing information streams and explain how the result can be incorporated into standard epidemiological models of communicable disease, both for better parameter estimation in elementary models, as well as for providing spatial inputs into more sophisticated models.We conclude with logistical and privacy considerations we have encountered in an associated ongoing study, to inform similar efforts at other organizations.
Attractive Area Detection of Telemetry corresponding to Command

The spacecraft is a mass of sensors, and there are a vast number of operational commands. In order to apply anomaly detection to multiple spacecraft across the missions, an event detection method is required using the only telemetry that does not use prior design information. This report has verified a simple method to detect the enhanced area in the telemetry using only command log and telemetry. It can be a powerful technique for testing that the command was executed correctly without design information.
Robust and Imperceptible Region Based Watermarking on Medical Images

Telemedicine is the remote delivery of health care services to evaluate, diagnose and treat patients using common technology, such as video conferencing and smart phones, without the need for an in-person visit. There is likelihood in altering the medical images purposely or unintentionally while transmitting over covert channel. The Physician confirms the diagnosis region obtained from the medical image as region of interest (ROI), prior to interpreting any report on evaluation. Watermarking scheme for medical images exploring DCT domain is conferred in this proposal. Fuzzy c algorithm is utilized in segmenting the assessment region (ROI) and non-interest region (RONI), further the watermark is inserted through modulation scheme termed as M-ary. The scheme efficacy is determined for MRI medical images through simulation by computation of quality metrics such as PSNR and NCC.
The Impact of Gaze Cues in Mixed Reality Collaborations

Gaze is one of the most important communication cues in performing physical tasks in both face-to-face and remote collaboration. Dynamic gaze information can indicate the user’s intention, focus, and current attention while visualising this information can often compensate for other communication channels that are not always readily available. Previous studies have shown that sharing and understanding another person’s gaze cues can benefit mutual awareness and task coordination in traditional 2D displays. However, researchers have not fully explored the impact of the virtual representations of gaze cues using Mixed Reality technologies. In this doctoral consortium presentation, I will present eyemR-Vis, a 360 panoramic Mixed Reality (MR) remote collaboration system that shares gaze behavioural visualisations between a local worker and a remote collaborator. In the paper I discuss the PhD research motivation, background material, recently published study results, and plans for future work.
Research on Evaluation Method of Maintenance Supporting Capability of the New Phased Array Radar

Based on the support characteristics of new phased array radar, the evaluation index system of maintenance support capability of new phased array radar is constructed. And combining AHP with GRG, the index weight in the index system is calculated. Combined with the theory of center of gravity of cloud, the cloud model of the evaluation of maintenance supporting capability for the new phased array radar is constructed. The cloud model is used to evaluate the maintenance support capability of a typical new phased array radar, and the effectiveness of the method is verified by an example.
Cartoon Face to Human Face Translation using Contour Loss based CycleGAN

Cartoon to Human Translation transforms a 2D vector cartoon face to a Real Human Face. The mapping is based on semantic similarity of both the input domains. This is an image$\rightarrow$mage translation problem that finds its applications in the entertainment and animation industry. Cartoon movies evolved from 2D animations in 1930 and became more lifelike with timeline. In image synthesis, audio, and other sorts of data, Generative Adversarial Networks have demonstrated promising outcomes. They also produce excellent results when translating images to images. In this research, a CycleGAN based methodology for generating target Human Faces from source Cartoon Faces is proposed, preserving the facial characteristics i.e. face shape, eyebrow alignment and hair style. In order to improve the mapping we have used contour loss along with cycle consistency loss in our model and patch discriminator is used with L2 norm.
The Sparse Learning of The Support Vector Machine

Abstract The sparse model plays an important role in many aeras, such as in the machine learning, image processing and signal processing. The sparse model has the ability of variable selection, so they can solve the over-fitting problem. The sparse model can be introduced into the field of support vector machine in order to get classification of the labels and sparsity of the variables simultaneously. This paper summarizes various sparse support vector machines. Finally, we revealed the research directions of the sparse support vector machines in the future.
Migration Privacy Protection Based on Scheduling Algorithm for Online Car-Hailing

In the era of rapid development of mobile Internet technology and big data technology, people enjoy more convenient travel because of Online Car-hailing. At the same time, the location privacy of drivers and passengers has also been leaked due to the use of Online Car-hailing. The traditional privacy preserving encryption technology for Online Car-hailing has a large amount of computation and low efficiency. In order to solve these problems, the Migration Privacy Protection based on Scheduling Algorithm (MPSA) for Online car-hailing is proposed in this paper. Firstly, the road network model is established through the road network matching technology. Secondly, the shortest path between the passenger and the car is calculated through A* algorithm and Dijkstra algorithm. Finally, the location privacy protection between the driver and the passenger is realized through the offset technology. In this paper, a scientific experiment is carried out on real data to verify the superiority of MPSA in privacy protection effect and operation efficiency.
Realization of Sea Surveillance and Imaging Radar Signal Processing System Based on Domestic Digital Signal Processor

In this paper, a radar signal processing system, which combines pulse doppler mode and stepped frequency mode, is proposed. The pulse doppler mode is used to detect targets and measure their velocity accurately, and then the stepped frequency mode is used to image the targets. The signal processing system has been implemented on the domestic Huarui No. 2 digital signal processor.
Recognition of symbols and topology in the image of a DC circuit diagram based on contours and skeletons

Scope of research presented in this paper refers to the off-line recognition and interpretation of the content that conveys images of printed or hand-drawn direct current circuit diagrams. Photographing learning materials with different quality imprints as well as under different lighting exposures may introduce a lot of noise in the image. Besides the standard noise caused by shadows and unwanted spots, common noises that introduce ambiguities into the process of symbol and circuit topology recognition are breaks on connecting lines and symbols. That is why we have designed algorithm in such a way to ensure correct recognition even in conditions of noise. Due to the mostly regular geometric shapes and relationships that define symbols and the interconnection of symbols in an electrical circuit, and the clear topology related to the existence of nodes and branches, we decided to develop a recognition method based mostly on the structural relations. To recognize symbols and topology, we use suitable geometric primitives, contours, and skeletons that can be detected in the image. The recognition method based on the structural patterns that can handle unwanted breaks and disconnections in the image of electrical circuit diagram represents the contribution of this paper.
A New RGB-D Gesture Video Dataset and Its Benchmark Evaluations on Light-Weighted Networks

Video-based gesture recognition plays an important role in the field of human-computer interaction (HCI), and most of the existing video-based gesture recognition works are based on traditional RGB gesture videos. Compared with RGB gesture videos, RGB-D gesture videos contain additional depth information along with each data frame. Such depth information is considered effective to help overcome the impact of illumination and background variations. So far as we know, there are few RGB-D gesture video datasets, which fully consider sufficient illumination and background variations. We believe this missing factor is quite common in daily usage scenarios and will bring non-necessary obstacles to the development of gesture recognition algorithms. Inspired by this observation, this paper uses embedded devices to collect and classify a set of RGB-D gesture videos which retain both color information and depth information, and proposes a new RGB-D gesture video data set named DG-20. Specifically, DG-20 fully considers the changes of illumination and background when capturing data, and provides more realistic RGB-D gesture video data for the future research of RGB-D gesture recognition algorithms. Furthermore, we give out the benchmark evaluations about DG-20 on two representative light-weighted 3D CNN networks. Experimental results show that the depth information encoded within RGB-D gesture videos could effectively improve the classification accuracy when dramatic changes in illumination and background exist.
Different Meta-Heuristic Optimization Techniques and Their Application in Solar Photovoltaic Field

A method of optimization is used to resolve issues smartly by selecting the better option from various existing possibilities. Many optimization problems are possessing characteristics, namely nonlinearity, complexity, multimodal approach, and incompatible objective functions. Sometimes even for individual simple and linear type objective functions, a solution that is optimal and does not exist, there is uncertainness of obtaining the best solution. The aim of finding methods that can resolve various issues in a defined manner potentially has found the concentration of different researchers responsible for performing the advancement of a new “intelligent” technique called meta-heuristics technique. In the last few years, there is an advancement of various meta-heuristics techniques in different areas or various fields. Meta-heuristics are a demanded thrust stream of research that showed important advancement in finding the answer to problems that are optimized. The chapter gives the guidance for enhancing research more meaningfully.