Reviewerly/exHarmony huggingface dataset:
Anomaly Detection and Classification in Time Series with Kervolutional Neural Networks

Recently, with the development of deep learning, end-to-end neural network architectures have been increasingly applied to condition monitoring signals. They have demonstrated superior performance for fault detection and classification, in particular using convolutional neural networks. Even more recently, an extension of the concept of convolution to the concept of kervolution has been proposed with some promising results in image classification tasks. In this paper, we explore the potential of kervolutional neural networks applied to time series data. We demonstrate that using a mixture of convolutional and kervolutional layers improves the model performance. The mixed model is first applied to a classification task in time series, as a benchmark dataset. Subsequently, the proposed mixed architecture is used to detect anomalies in time series data recorded by accelerometers on helicopters. We propose a residual-based anomaly detection approach using a temporal auto-encoder. We demonstrate that mixing kervolutional with convolutional layers in the encoder is more sensitive to variations in the input data and is able to detect anomalous time series in a better way.
THE ROLE OF METAPHOR IN HISTORICAL KNOWLEDGE

The article continues the author's series of publications on the terminological apparatus of the historian. The problems of humanitarian science cannot have "final solutions". F.R.Ankersmith paid attention to the fact that the attitude to the role of metaphors knows not onli "ups" but also "downs". Exploring aspects of the conceptual arsenal associated with the explicit use of the metaphor. "Implicit use" refers to those semantic situations when a historian or another humanist enters into a kind of dialogue with other sciences, carrying out terminological borrowing. Since the semantic content of the term changes in this case, then, in essence, a metaphorical action takes place that does not require a special name. The article contains, the author's reflection on the circumstances and features of the use of metaphors. The author finds out how and why historiography becomes metaphorical. Referring to specialists who developed the cognitive basis of the theory of metaphor, the author analyzes different traditions of understanding what metaphor is. There is a search for a positive sense of metaphor usage with some ambiguity. Since the metaphor creates a new meaning of the word, its cognitive significance becomes unquestionable. By citing examples of various uses of metaphors in the texts of historians, the author shows how a metaphor helps clarify the uniqueness of historical situations.
Progressive Transfer Learning

Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch. It is challenging due to the significant variations inside the target scenario, e.g., different camera viewpoint, illumination changes, and occlusion. These variations result in a gap between the distribution of each mini-batch and the whole dataset's distribution when using mini-batch training. In this paper, we study model fine-tuning from the perspective of the aggregation and utilization of the global information of the dataset when using mini-batch training. Specifically, we introduce a novel network structure called Batch-related Convolutional Cell (BConv-Cell), which progressively collects the global information of the dataset into a latent state and uses it to rectify the extracted feature. Based on BConv-Cells, we further proposed the Progressive Transfer Learning (PTL) method to facilitate the model fine-tuning process by jointly optimizing the BConv-Cells and the pre-trained ReID model. Empirical experiments show that our proposal can improve the performance of the ReID model greatly on MSMT17, Market-1501, CUHK03 and DukeMTMC-reID datasets. Moreover, we extend our proposal to the general image classification task. The experiments in several image classification benchmark datasets demonstrate that our proposal can significantly improve the performance of baseline models. The code has been released at \url{this https URL}
MODEL FOR ASSESSING HIGHER ORDER THINKING SKILLS IN TRAINING IN GRAPH DATABASES

S. Hadzhikoleva, M. Borisova, E. Hadzhikolev, H. HristovPlovdiv University Paisii Hilendarski (BULGARIA)
Financial inclusion and economic growth 1

This chapter defines financial inclusion and provides a guide to its measurement. It discusses the determinants of and barriers to financial inclusion and assesses the link between inclusive finance and financial development. The chapter examines the effect of inclusive finance on economic growth. It then discusses the roles of institutional architecture in the financial inclusion–economic development nexus. A more succinct view of financial inclusion in three-dimensional form was provided by T. Hall. The components of Hall’s three-dimensional view of financial inclusion are financial participation, financial capability, and financial well-being. One of the important indicators of financial inclusion is savings. Determinants of and barriers to financial inclusion can be usefully categorised into demand-side factors and supply-side factors. Theoretically, financial inclusion can promote economic growth and development through the following channels: capital accumulation, innovation and entrepreneurship, income and employment, opportunities for diversification, productivity, and financial security. Financial inclusion promotes the accumulation of savings in the banking system and in other financial intermediaries.
Design of Convolutional Neural Network with Cuckoo Search Algorithm for Super-Resolution UHD Systems on FPGA

Ultra high definition (UHD) video is considered as a technology that generates a video image with a resolution 16 times as many pixels as high definition television. Extensive and complex computational difficulties are there to generate a video in super-resolution (SR). Some extensive floating-point computations are needed for the direct implementation of area-pixel scaling, which is a factor for calculating one target pixel. In this paper, a hybrid Convolutional Neural Network and Cuckoo Search (CNN-CS) algorithm are proposed, which can replace the large scale of complex training and time-consuming machine learning technique. Cuckoo Search (CS) algorithm is based on bird’s levy-flight approach that can optimize the CNN. CS forecasting model isolates the redundant or irrelevant image patches and, therefore, it reduces the required memory space. The SR is performed effectively in hardware with less hardware utilization and memory space, where LR input data are processed line-by-line using the proposed hybrid CNN-CS algorithm. To optimize the design constraints, CS forecasting model estimates the required patches. Parallel sub pipeline (PSP) technique improves the parallelism process thereby reducing the computational complexity and improving the throughput rate and speed of execution. Finally, the proposed hybrid CNN-CS SR design is coded in Verilog HDL and is implemented with the XCKU040 FPGA device. To validate the proposed CNN-CS SR algorithm, standard datasets, such as Set-5, Set-14, BS-100 and Urban-100, are used. The visual quality of the proposed CNN-CS SR design is evaluated by the standard evaluation metrics such as peak signal-to-noise ratio (PSNR) and SSIM. The proposed hybrid CNN-CS SR design is compared with the existing state-of-the-art techniques both qualitatively and quantitatively. Experimental results demonstrate that the proposed method improved the throughput of 42.83%, memory space and power consumption are reduced to 16.16%, 63.81%, respectively, over the existing techniques.
A SYSTEM FOR DESIGN AND DEVELOPMENT OF MULTIMEDIA M-LEARNING RESOURCES: CONCEPTUAL MODEL

T. Dobreva, D. Stoyanova, S. Stoyanova-Petrova, N. MilevaPlovdiv University Paisii Hilendarski (BULGARIA)
Ensemble of Hierarchical Temporal Memory for Anomaly Detection

Hierarchical Temporal Memory (HTM) is a continuously learning algorithm derived from neuroscience that models spatial and temporal streaming data. It was demonstrated that HTM produces a good performance in predicting unusual patterns or anomaly detection in univariate datasets. In this paper, we deploy the HTM algorithm for the anomaly detection problem in multivariate datasets, which are more common in practical scenarios. We first investigate the implementation of HTM using multi-encoders for multiple variables and analyze its performance in different parameter settings. Then, we introduce a new framework for ensemble learning by using single-encoder HTMs as weak learners. We carried out experiments on public datasets in different dimensions. Our experimental results show that our new approach outperforms the multi-encoder implementation of the HTM algorithm.
An Automatic Document Summarization Approach based on Fuzzy Ontology and Machine Learning

It is hard for researchers to retrieve the desired information from the huge number of academic literatures efficiently. This paper proposes an automatic summarization method based on fuzzy ontology and machine learning to solve this problem. This method uses fuzzy ontology to model the literature context, and extracts the literature related context information, including the citation, publication time, the author's research interest, the recently published literature, etc. We integrate the domain element and the time element into the term frequency-inverse document frequency (TF-IDF) model, extract words from the document set that reflect the core content of the document. Then obtain topic-related words through the Latent Dirichlet Allocation (LDA) model. Finally, the important sentences are selected as abstracts according to these words. Experiments show that our method is superior to the standard TF-IDF method and LDA method.
Qubit4Sync: a qubit-based synchronization system for quantum key distribution

Qubit4Sync is a synchronization method for quantum key distribution which uses the same qubits exchanged during the protocol; in the same spirit of classical communication protocols reling on clock recovery methods that uses the data stream itself.
Spring Stepper: A Seated VR Locomotion Controller

Consumer-level Virtual Reality (VR) locomotion has been gaining momentum with research focused on omnidirectional unlimited techniques in one place employing image processing, inertial sensors in mobile devices, redirected walking, and body tracking for hands-free locomotion while seated or standing. The affordability of current VR technologies has increased its user install base, thus requiring novel, creative and more effective forms of locomotion other than teleportation that can help users make the most of reduced spaces for walk-in-place. This paper presents the Spring Stepper, a seated VR locomotion prototype for walking in place. Finally, a preliminary study on usability and task completion was conducted comparing it against the 3D Rudder, a commercial off-the-shelf walking in place controller for Desktop VR and Playstation 4. Results indicate that the Spring Stepper was not perceived as usable with a System Usability Score of 65.26/100 in comparison with the teleportation, which obtained a score of 86.03. Interestingly, the Spring Stepper was better perceived with the 3D Rudder. In terms of time completion and task accuracy, the Spring Stepper was outperformed by the other technique. Despite these results, we believe that the stepping interactive mechanics of the Spring Stepper prototype caused slower and less accurate interactions but had the most impact on users who preferred.
Continuous and long-term stabilization of degenerate optical parametric oscillators for large-scale optical hybrid computers

The minimum requirements for an optical reservoir computer, a recent paradigm for computation using simple algorithms, are nonlinearity and internal interactions. A promising optical system satisfying these requirements is a platform based on coupled degenerate optical parametric oscillators (DOPOs) in a fiber ring cavity. We can expect advantages using DOPOs for reservoir computing with respect to scalability and reduction of excess noise; however, the continuous stabilization required for reservoir computing has not yet been demonstrated. Here, we report the continuous and long-term stabilization of an optical system by introducing periodical phase modulation patterns for DOPOs and a local oscillator. We observed that the Allan variance of the optical phase up to 100 ms was suppressed and that the homodyne measurement signal had a relative standard deviation of 1.4% over 62,500 round trips. The proposed methods represent important technical bases for realizing stable computation on large-scale optical hybrid computers.
A Discussion on Various Methods in Automatic Abstractive Text Summarization

Automatic abstractive text summarization (ATS) is important in terms of optimized data storage and transmission requirements on technical aspect and also for quick view of the information for the people with their busy lives. This perspective and applicability have triggered the thoughts of various researchers to develop the efficient models that can provide consistently good in terms of understandable, meaningful and short summaries. The main aim of this discussion is to address the literature available in the domain for understanding the researcher’s perspective of development of such models and to form the platform of thoughts for the researchers in the field to finalize new strategies of the development. Some of the papers addressed here show the use of neural network platform in ATS along with graph-based hypothetical methods which show significant difference in perspectives considered while developing the models and flexibility and complexity involved in the implementation for the sake of experimentation and evaluating the performance of models.
Positive and Negative Searches Related to the Bitcoin Ecosystem: Relationship with Bitcoin Price

In this study, we investigate whether public awareness of positive or negative possible incidents pertaining to the bitcoin ecosystem are related to bitcoin price and we model bitcoin price volatility taking into consideration public awareness. We take a middle-of-the-road approach, by using a simpler – and thus less data demanding - proxy for public awareness compared to studies that have used complex models that include many parameters to capture the relationships and factors in the ecosystem, but at the same time, a richer approach compared to approaches that simply use the volume of searches for “bitcoin” and its “price” as a proxy in their models. Specifically, we use six different Google Trends queries as proxies in our models: three searches for positive incidents, and three for negative ones. We employ a dataset with monthly price data that covers the time period from September 1st 2011 to December 31st 2019 and we use GARCH and EGARCH models to test whether public awareness of positive or negative possible incidents pertaining to the bitcoin ecosystem is related to bitcoin price and to model price volatility. Results show that majority of our proxies of public awareness are significantly related to price. Moreover, our EGARCH model has detected an asymmetry pertaining to the price volatility’s reaction to price news, specifically an “anti-leverage effect”, that is, the price volatility is more sensitive to good financial news rather to bad news. In addition, we detected a significant effect of both old and novel news.
Design and Realization of Intelligent Wheelchair Based on STM32

Abstract In response to the market demand of people who need special care, a smart wheelchair based on STM32 is designed. Through the STM32F103ZET6 development board, it uses the smart car (simulated electric wheelchair) as the carrier with the attitude calculation algorithm, GPS positioning, Lora wireless communication, PWM speed control and distance detection, the gesture control of the movement and steering of the car, automatic obstacle avoidance, GPS Real-time transmission of positioning information, one-key call for help, and video recording of unexpected situations. The gesture recognition rate of the system is stable and the safety is high, and it can meet the requirements of wheelchair intelligence.
Intelligent and Autonomous Guidance Through a Geometric Model for Conventional Vehicles

Cyber-physical systems (CPS) in the automobile industry are facing major challenges related to the use and validation of these CPS, which entails high costs in the implementation and training tests in the physical world, thus limiting research. Therefore, there is a need to shorten the validation times of these CPS with the use of 3D simulation software. This research article proposes to simulate a CPS in the simulation software Webots, with the aim of emulating the autonomous movement of conventional vehicles by integrating a GPS sensor and a compass sensor which provide information on location and orientation, these data are used for the implementation of a geometric model by vectors, the same one that is developed in a controller that allows to take actions on the vehicles in the simulation software in order to emulate an urban traffic. Finally, a series of configurations have been made to evaluate the geometric model, managing to maintain the default speed of 94.194% with curves greater than 90 degrees. In addition, the validation of this system in a real environment through the instrumentation in land vehicles is drawn as future lines.
An Unsupervised Technique to Generate Summaries from Opinionated Review Documents

In the last few years, there has been a tremendous change in the way users behave over the net. This is mainly because of the growth that has happened in the field of Web technology. In earlier times, the role a user over the net played was that of an information consumer, now it’s more of a data creator role. This role change has benefitted the world of politics, social network analysis, financial market analysis, etc., to name a few. Due to this huge creation of data, a mechanism that can automatically analyze and interpret this opinionated data is badly needed. Toward this research direction, unlike other summarization techniques, the paper proposes a novel method that is unsupervised and also domain-independent for generating opinion summaries. The final summaries that were generated are at four levels that range from being coarse to more granular ones. The proposed technique was tested on various data sets that were from nine different domains. The experimental results clearly indicated that 70–75% of the summaries generated were matching with the manually selected ones.
Hardware Accelerating the Optimization of Transaction Schedules via Quantum Annealing by Avoiding Blocking.

The isolation property of database theory guarantees to avoid problems of not synchronized parallel execution of several transactions. In this paper we propose an algorithm for an optimal transaction schedule for the different cores of a multi-core CPU with minimal execution time ensuring the isolation property. Optimizing the transaction schedule is a combinatorial problem, which is ideal to be solved by quantum annealers as special form of quantum computers. In our contribution we show how to transform an instance of the transaction schedule problem into a formula that is accepted by quantum annealers including a proof of validity and optimality of the obtained result. Furthermore, we analyze the number of required qubits and the preprocessing time, and introduce an approach for caching formulas as result of preprocessing for the purpose of reducing the preprocessing time. In an experimental evaluation, the runtime on a quantum annealer outperforms the runtime of traditional algorithms to solve combinatorial problems like simulated annealing already for small problem sizes.
The Analysis of Digital Provider Sentiment Of ‘by.u’ On Google Play Which Uses The Support Vector Machine (SVM) Method

Abstract The development of technology in the industrial revolution 4.0 is very rapid in various sectors in trade, communication and other sectors; both nationally and internationally. One of the fields, it is the telecommunications sector. Becoming one of the digital based providers of ‘by.u’ from Telkomsel which is the first digital operator in Indonesia. The algorithm testing uses Support Vector Machine (SVM) from 300 user review data applications. The ‘by.u’ review consists of 150 negative reviews; which refers to dislike or bad expressions. Next, 150 positive reviews; which refers the constructive suggestion or support the text in using Indonesian. The resulting model gets the results of testing accuracy by using Support Vector Machine with accuracy value shows that: 87.24
Domain-Focused Linked Data Crawling Driven by a Semantically Defined Frontier

We propose a method for focused crawling of linked data with a frontier based on the semantic data elements in use within a knowledge domain. This method addresses the challenges of crawling large volumes of heterogeneous linked data, aiming to achieve improvements in crawling efficiency and accuracy. We present the results obtained by our method in a case study on the cultural heritage domain, more specifically on Europeana, the European Union digital platform for cultural heritage. We have evaluated the crawling method in two Europeana data providers that are publishing linked metadata with Schema.org elements. We conclude that the proposed focused crawling method worked well in the case study, but it may need to be complemented with complementary frontier delimiting strategies when applied to other domains.
ARE WE READY FOR OPEN SCIENCE – THE ANSWER OF THE BALKAN UNIVERSITIES

S. Hasani, E. Stefanova, K. Stefanov, A. GeorgievSofia University “St. Kliment Ohridski” Sofia (BULGARIA)
Fault-Tolerant Implementation of Quantum Arithmetic and Logical Unit (QALU) Using Clifford+T-Group

The quest of efficient quantum circuit is to achieve quantum supremacy in theory as well as in practice. The foremost obstacle is to protect the cohesive time of extremely fragile quantum states from inherent noise. To address this issue, Quantum Error Correction Code (QECC) with fault-tolerant quantum circuit is most desirable. Aiming to contribute toward designing an efficient Quantum Information Processor (QIP); in this work, we have shown the design of an important QIP module, i.e., Arithmetic Logic Unit (ALU). The entire design has been made on top of quantum Clifford+T-group. In the design phase, initially, we formulate a 1-bit design and then to make a generalized representation of the ALU, multiple smaller modules have been integrated. For ensuring improved features in this component, the design has been made fault-tolerant, circuit optimization rules are executed to minimize the design metrics and parallelism in high latency T-gate is ensured. In a way to check the functional correctness of our proposed design, several logical operations have been successfully tested over it.
Natural Language Processing

Natural language processing (NLP) is an interdisciplinary subfield, which has components from major fields such as linguistics, computer science, and artificial intelligence. NLP is mainly concerned with the interaction between humans and computers. The scope of NLP ranges from computational understanding and generation of human languages to processing and analyzing large amounts of natural language data. The scope of NLP also contains text, speech, cognition, and their interactions. In this chapter, we briefly cover the history of NLP, the differences between rule-based NLP and statistical NLP, and the major NLP methods and techniques. We finally conduct a case study on NLP to prepare you for real-world problems.