Reviewerly/exHarmony huggingface dataset:
Performance of C++ Language Code in Quantum Computing Environment

This paper considers the execution of different C++ language program codes based on simple program, functions, class inheritance, recursion and file handling under Quantum Computing Environment using the Quantum++ simulator and the execution of similar C++ language program codes on the traditional computer. The comparison of the results has been presented, analyzed and concluded with. The need, advantages and disadvantages of a Quantum Computing Environment have also been discussed briefly in this paper.
A CORPUS FOR THE STUDY ON THE ASSESSMENT OF MANDARIN PRONUNCIATION OF TIBETAN SPEAKERS

Tibetan speakers always have some types of fixed pronunciation errors when they speak Mandarin, which are affected by their native language pronunciation habits. Therefore, a system assessment that can detect the mispronunciation and overall similarity measurement of syllables or phonemes in Tibetan Mandarin to help learners improve their Mandarin level needs to be studied. A unique corpus is required in order to study on the assessment of Mandarin pronunciation of Tibetan speakers. Unfortunately, there is no such a corpus in this field for the research task. We create a particular corpus by integrating the linguistic theory of Tibetan and Chinese with speech signal processing and machine learning. In this work, we record the non-standard Mandarin audio of Tibetan students and the standard Mandarin audio. These audio recordings share the same text designed by analyzing and comparing the pronunciation characteristics of Tibetan and Chinese. Audio recordings total 5.5 hours that contain 1000 paragraphs, covering 377 syllables without tones and all phonemes in standard Chinese. Then we introduce the recording environment and recording equipments. Futhermore, we set the rules for the annotation of the audio recordings in hierarchical format through praat software: the first layer is the phrase layer, marked with Chinese characters; the second layer is the syllable layer, marked with pinyin; the third layer is the phoneme layer, labeled with Speech Assessment Methods Phonetic Alphabet-Tibetan Standard Chinese(SAMPA-TSC), which is designed by ourselves. Finally, we evaluate the corpus creation in four aspects--coverage, completeness, quality, reusability--and describe the potential of the dataset application.
Efficient Parameterized Algorithms for Computing All-Pairs Shortest Paths.

Computing all-pairs shortest paths is a fundamental and much-studied problem with many applications. Unfortunately, despite intense study, there are still no significantly faster algorithms for it than the $\mathcal{O}(n^3)$ time algorithm due to Floyd and Warshall (1962). Somewhat faster algorithms exist for the vertex-weighted version if fast matrix multiplication may be used. Yuster (SODA 2009) gave an algorithm running in time $\mathcal{O}(n^{2.842})$, but no combinatorial, truly subcubic algorithm is known. Motivated by the recent framework of efficient parameterized algorithms (or FPT in P), we investigate the influence of the graph parameters clique-width ($cw$) and modular-width ($mw$) on the running times of algorithms for solving All-Pairs Shortest Paths. We obtain efficient (and combinatorial) parameterized algorithms on non-negative vertex-weighted graphs of times $\mathcal{O}(cw^2n^2)$, resp. $\mathcal{O}(mw^2n + n^2)$. If fast matrix multiplication is allowed then the latter can be improved to $\mathcal{O}(mw^{1.842}n + n^2)$ using the algorithm of Yuster as a black box. The algorithm relative to modular-width is adaptive, meaning that the running time matches the best unparameterized algorithm for parameter value $mw$ equal to $n$, and they outperform them already for $mw \in \mathcal{O}(n^{1 - \varepsilon})$ for any $\varepsilon > 0$.
Simplified VGG based Super Resolution Restoration for Face Recognition

The resolution level of face images is one of the key factors affecting the performance of face recognition algorithms. Face recognition under low resolution conditions has always been a challenging research topic in related fields. Using super-resolution restoration technology to improve the spatial resolution of the face to be recognized and reconstruct its high-resolution information is an effective way to improve the performance of the algorithm. However, the traditional image super-resolution restoration algorithm generally has problems such as high computational complexity and difficulty in training, which restricts its application in the actual face recognition system. Therefore, this paper proposes a l ow-resolution face image super-resolution restoration algorithm based on simplified VGG network. Firstly, based on the degradation process of low-resolution face images, a set of face image samples corresponding to high and low resolution based on prior knowledge is constructed, and a streamlined 6-layer VGG network is designed to learn between high and low resolution images. The mapping relationship is finally achieved by deconvolution amplification to achieve the super-resolution restoration process of the image. The common LFW and ORL data sets are used to test and analyze the super-resolution restoration effect of the algorithm and its impact on the face recognition algorithm. The experimental results show that the proposed algorithm is superior to the classical SRCNN algorithm in super-resolution performance. When applied to the face recognition algorithm based on Lenet-5, its recognition performance is significantly improved.
Theoretical Evaluation of Software Coupling Metrics

Software module coupling is an important design parameter that can be used in various studies including fault prediction, impact analysis, re-modularization assessment, software vulnerabilities assessment, etc. However, two couplings can vary in important coupling factors' coverage. This paper aims to evaluate the coupling metrics to their coverage of important coupling factors'. We perform a thorough survey of coupling metrics, that ends up with the collection of numerous coupling metrics. After that, we evaluate these metrics by their coverage of important coupling factors. The mapping of coupling metrics with the coupling factors shows that the coupling levels been considered by many metrics. Yet, the difference between these levels has been ignored by most of the metrics. Moreover, the broadness, hiddenness, and rigidness of data flow are ignored by most coupling metrics. Apart from that, the combined effect of these aspects is ignored by all the metrics.
Cross-Domain Document Object Detection: Benchmark Suite and Method

Decomposing images of document pages into high-level semantic regions (e.g., figures, tables, paragraphs), document object detection (DOD) is fundamental for downstream tasks like intelligent document editing and understanding. DOD remains a challenging problem as document objects vary significantly in layout, size, aspect ratio, texture, etc. An additional challenge arises in practice because large labeled training datasets are only available for domains that differ from the target domain. We investigate cross-domain DOD, where the goal is to learn a detector for the target domain using labeled data from the source domain and only unlabeled data from the target domain. Documents from the two domains may vary significantly in layout, language, and genre. We establish a benchmark suite consisting of different types of PDF document datasets that can be utilized for cross-domain DOD model training and evaluation. For each dataset, we provide the page images, bounding box annotations, PDF files, and the rendering layers extracted from the PDF files. Moreover, we propose a novel cross-domain DOD model which builds upon the standard detection model and addresses domain shifts by incorporating three novel alignment modules: Feature Pyramid Alignment (FPA) module, Region Alignment (RA) module and Rendering Layer alignment (RLA) module. Extensive experiments on the benchmark suite substantiate the efficacy of the three proposed modules and the proposed method significantly outperforms the baseline methods. The project page is at \url{https://github.com/kailigo/cddod}.
An Efficient Multi Secret Image Sharing Scheme Using Hill Cipher

This paper presents a multi secret sharing scheme using Hill cipher and Blakley secret sharing. The proposed scheme can reconstruct secret images with only k or more of shadow images. It is compared with the scheme proposed by another work, which produced a visual secret sharing scheme for gray scale images with a $$ \left( {n,n} \right) $$-threshold. The proposed scheme decreases the threshold to $$ \left( {n,n} \right) $$ and it’s compared with other work. Besides, the proposed scheme shares more than one secret key using the Hill Cipher algorithm without depending on a random grid as a shared image. Using Hill Cipher and Blakley to share more than secret image increases the security of secret images with lossless image recovery and no pixel expansion. Experimental results demonstrate the efficiency of the scheme.
Minimising Intercultural Communication Difficulties when Teaching International Students with the Help of an Electronic Educational Complex

This study describes the use of an electronic educational complex for international engineering students. While doing so, it analyses whether the studied complex can reduce intercultural interaction difficulties and thus become a motivating factor in the educational process as a whole.
Correspondence Networks with Adaptive Neighbourhood Consensus

In this paper, we tackle the task of establishing dense visual correspondences between images containing objects of the same category. This is a challenging task due to large intra-class variations and a lack of dense pixel level annotations. We propose a convolutional neural network architecture, called adaptive neighbourhood consensus network (ANC-Net), that can be trained end-to-end with sparse key-point annotations, to handle this challenge. At the core of ANC-Net is our proposed non-isotropic 4D convolution kernel, which forms the building block for the adaptive neighbourhood consensus module for robust matching. We also introduce a simple and efficient multi-scale self-similarity module in ANC-Net to make the learned feature robust to intra-class variations. Furthermore, we propose a novel orthogonal loss that can enforce the one-to-one matching constraint. We thoroughly evaluate the effectiveness of our method on various benchmarks, where it substantially outperforms state-of-the-art methods.
Kelime Gömmelerini Kullanarak Türkçe Dili İçin Sözlük Metodu ile Yazım Düzeltme

Today, a massive amount of data is being produced, which is referred to as “big data.” A significant part of big data is composed of text data, which has made text processing all the more important. However, when text processing studies are examined, it can be seen that while there are many world language-oriented studies, especially the English language, there has been an insufficient level of studies published specific to the Turkish language. Therefore, Turkish was chosen as the target language for the study. A Turkish corpus of approximately 10.5 billion words was created, consisting of unlabeled data containing no spelling errors. Word vectors were trained using the Word2Vec method on this corpus. Based on this corpus, a new method was proposed called the “dictionary method,” with a dictionary created covering almost all known Turkish words. Then, text classification was applied to a multi-class Turkish dataset. This dataset contains 10 classes and approximately 1.5 million samples. Vector values of the token words in this dataset were transferred from the dictionary by transfer learning. However, words not found in the created dictionary were considered as incorrect; then, using LSTM (Long Short-Term Memory), which is a deep neural network (DNN) architecture, the proposed method attempts to predict correct or similar words as replacement words. Following this process, it was seen that the accuracy rate improved by 8.68%. Turkish dataset that is created, corpus and dictionary will be shared with researchers in order to contribute to Turkish text processing studies.
HEMISPHERIC DOMINANCE IN VISUAL ATTENTION MAY INFLUENCE THE DEVELOPMENT OF READING SKILL IN SCHOOL BEGINNERS

M. Makashvili1, T. Taliashvili1, N. Maglakelidze1, T. Kopadze2, E. Maglakelidze3 1Ilia State University (GEORGIA) 2European University (GEORGIA) 3Gogebashvili Telavi State University (GEORGIA)
Specialized Mobile Applications as Means of Optimizing the Power Supply System of the Republic of Belarus

The active development of the mobile platforms sector such as smartphones, smart watches, fitness bracelets and other wearable electronic devices in the last 4 years has caused a huge growth of the market of mobile applications-services. Western countries are actively implementing mobile applications-services for household energy users. In Belarus, there is a positive dynamics in the number of active users of applications-services, every year it is possible to attract 7 % of unique customers. In the present paper the experience of implementation and operation of the mobile application for power systems is analyzed. The dynamics of the market of mobile applications-services has been traced: the amount of time spent by the person in interaction with the device was determined as well as the total expenditure of funds in the application at the moment; also, the growth of expenses within the applications was forecasted. The potential benefits for business and end-users of services from the implementation of mobile applications-services were demonstrated. Their properties, main qualities and advantages over traditional methods of “business – client” interaction are considered. A number of features needed for the successful functioning of the mobile application in the power system of Belarus are identified. The process of creating software for smart devices has been studied and the functionality needed by the mobile application for the power system has been proposed for attracting new users. A special algorithm for the launch and successful “life cycle” of a mobile application has been proposed. It allows to operate and develop mobile applications-services as efficiently as possible, as well as to avoid the most common errors at the stages of their development and launch. The implementation of a mobile application in the power system of the Republic of Belarus, in the long-term, will significantly increase customer loyalty, simplify interaction with users, make it possible to build new models of distribution of electricity, and, consequently, regulate the demand for energy resources.
Finnish Language Modeling with Deep Transformer Models.

Transformers have recently taken the center stage in language modeling after LSTM's were considered the dominant model architecture for a long time. In this project, we investigate the performance of the Transformer architectures-BERT and Transformer-XL for the language modeling task. We use a sub-word model setting with the Finnish language and compare it to the previous State of the art (SOTA) LSTM model. BERT achieves a pseudo-perplexity score of 14.5, which is the first such measure achieved as far as we know. Transformer-XL improves upon the perplexity score to 73.58 which is 27\% better than the LSTM model.
Stabilization of Digitized Processes

Stabile production processes are oriented on flexible production process; they enable implementation of digitized concepts in real production technologies.The presented chapter describes research results oriented on actual state mapping in selected industrial companies, with core orientation of people engagement in core processes connected with production digitization.Quantitative and qualitative research by selected industrial companies was realized in combination with hypothesis testing and verification.Core research problem of the presented chapter is the compatibility between employee and cobot by workplace from the ethical point of view.Based on analyze of selected workplaces in industrial companies were identified crucial decisive parameters of effective cooperation between human and cobot with subsequent formulation of conclusions for setting the rules of ethical mutual cooperation "human-robot" by production workplace.Radical innovations in industrial companies connected with the integration of cobot technologies in the production processes are strongly connected with the aim, to give the stability in the mutual connection between employee and cobot.A model of standardized co-operation between human and cobot will be presented; it is based on the key elements of the standardization process.This is a new point of view on the ethics of the workplace, where the "human-machine" cooperation replaces the cooperation model "human-cobot."
Towards Performance Improvement of User Plane Traffic in Wireless System: Security Aspects

Security practices are one of the sources of power consumption and introduces latency. In this short paper, we present the identified overhead in terms of power consumption and latency, introduced by applying security at multiple layers in emerging wireless networks, particularly for the 5G user data communication. Novel security mechanism is proposed for 5G systems for power efficient and low latency user data communication. The proposed mechanism enables the battery charge to last around 5mins longer and reduces latency by 4%, for 10GB data transfer.
Multi-frame and Multi-scale Conditional Generative Adversarial Networks for Efficient Foreground Extraction

Alongside autonomous submissions, foreground extraction is considered to be the foundation for various video content analysis technologies, like moving object tracking, video surveillance and video summarization. This paper proposes an efficient foreground extraction methodology based on conditional Generative Adversarial Network. The proposed generator, which is made up of two networks working in series- Foreground Extractor and Segmentation Network, maps the video frames to corresponding foreground masks. The discriminator aids the learning of generator by learning to differentiate between seemingly real and fake foreground maps. The method used a multi-scale approach in order to capture robust features across multiple scales of input using the Feature Extractor Network, which are then used by the successive Segmentation Network to produce the final foreground map. In addition, a multi-frame approach is also used to facilitate capturing of appropriate temporal features. The performance of the proposed model is evaluated on CDnet 2014 Dataset and outperforms existing methods.
NEW TRENDS FOR EDUCATION: BLOCKCHAIN TECHNOLOGY

Since the invention of technology such as the Internet, mobile technologies, etc., education has tended more towards technology-based learning and made the most of the services it provides. However, there are certain educational technology aspects that need more profound and comprehensive investigation with regard to their effectiveness and reliability. Today, there is significant debate on the subject of Blockchain. This new technology is basically an encrypted decentralized database. It has gained a reputation as a reliable technology since it offers a new approach to information security for all sectors. There is no data tampering allowed because all nodes on the decentralized peer-to-peer (P2P) network have a copy of the entire ledger. This technology that has swept various industry fields such as government, banks, healthcare and many successful startups worldwide has been launched recently. The education sector is also one of these sectors that will be impacted by this technology. Universities can utilize this cutting-edge technology in both academic and administerial domains. Blockchain has shown its efficiency when applied in the academic institutions for educational purposes and many universities have already started to adopt and take advantage of its potential outcomes. The literature review has been used to conduct this study. However, analysis of the literature has revealed that there are insufficient studies and researches on how blockchain has been used in educational institutions. In this research, the most important features of blockchain technology and its application for enhancing education will be exposed. In addition, the importance of the blockchain technology and the challenges related to its implementation will be discussed. This study will be a beneficial guideline for administrators, instructors as well as students by offering broader insight into the advantages and features of blockchain technology, and how it can be implemented more effectively.
Preferential Batch Bayesian Optimization

Most research in Bayesian optimization (BO) has focused on \emph{direct feedback} scenarios, where one has access to exact values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems. However, in domains such as modelling human preferences, A/B tests, or recommender systems, there is a need for methods that can replace direct feedback with \emph{preferential feedback}, obtained via rankings or pairwise comparisons. In this work, we present preferential batch Bayesian optimization (PBBO), a new framework that allows finding the optimum of a latent function of interest, given any type of parallel preferential feedback for a group of two or more points. We do so by using a Gaussian process model with a likelihood specially designed to enable parallel and efficient data collection mechanisms, which are key in modern machine learning. We show how the acquisitions developed under this framework generalize and augment previous approaches in Bayesian optimization, expanding the use of these techniques to a wider range of domains. An extensive simulation study shows the benefits of this approach, both with simulated functions and four real data sets.
Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference

Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This paper presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving the backend's inference accuracy.