Reviewerly/exHarmony huggingface dataset:
BoydCut: Bidirectional LSTM-CNN Model for Thai Sentence Segmenter

Sentence is imperative in order to build the top level of NLP (Natural Language Processing) applications such as Information retrieval, News Summarization, Knowledge graph. In Thai language, neither each word token is not separated by using just only space like English language, nor the sentence is verified its boundary by using full stop symbols. This paper proposes BoydCut, an NLP framework for identifying sentence boundaries based on Bidirectional LSTM-CNN Model. We develop this framework by utilizing the combination of character, word, and part of speech features. With Bidirectional LSTM, it can learn sequent of word-level in sentences and learn extracted features from character level. With the benefit of the combination Bidirectional LSTM-CNN Model, we do not need feature engineering for feature extraction that can be saved a lot of cost and time in order to build a sentence segmentation model. We also simply design the experiments in a different bucket of features extracted from a deep sequential model. The result empirically shows well perform in internal and external data, as well as help a lot in order to build several useful on the top level of NLP applications.
The teacher’s role in discovery, preparation, and development of gifted students in the field of informatics

The national development strategies of many countries prioritize the stronger orientation to an economy based on the knowledge, on the development of research work, and technological development and innovation. Bulgaria is one of the few countries in the world that offers young students the opportunity to study computer science from a very early age in extracurricular classes and also organizes contests in computer science for students of secondary school classes. As a result, Bulgarian students can be recognized on average as the priorities of international information competitions. The role of teachers in discovering and preparing gifted children for such competitions is an irrevocable and challenging task. Teachers need to use focused and well-planned approaches. The general didactic principles for creating a learning process are presented. Essential for the successful preparation of future medalists is the early start of learning the basic principles of programming. Some of the fundamental practical situations that show the significant role of the teacher are presented. Proof of the applicability of the proposed approach is the performance by years of students from the school in Rousse.
sVana—The Sound of Silence

When it comes to living in a society, it is important to communicate with people around us for a better living and to survive in the human race and when it comes to communication with people, the one who are hearing or speech impaired are always left behind, in other words, they have a problem communicating with other people and when it comes to video calling, they always had to use the usual text chat to communicate. Our aim is to remove this barrier and create a platform where hearing and speech impaired people can communicate even via video calls. The proposed system translates Indian sign language (ISL) into text for the hearing-impaired user while for the normal user, and it converts the speech into text. It also helps hearing-impaired people to communicate with Google Voice Assistants without having voice making it a smart assistant.
Tiny Perforations in Minute Beads from the Prehistoric Southwest: An Experimental Case for Lithic Minute Microdrills

Minute disc beads from the prehistoric American Southwest often exhibit perforations finer than 0.75 mm in diameter. Early archaeologists postulated that these tiny perforations were created with organic drills made from bone splinters or cactus needles. This article presents an experimental case for flaked-stone minute microdrills, which are refined replicas of more robust documented jewelry microdrills. With experimental lithic minute microdrills, I create perforations that match the size and appearance of tiny perforations observed in archaeological minute beads.
Experience Report on Variability Improvement in a Product Line Engineering Unaware Company

Product Line Engineering (PLE) enables strategic reuse within an organisation, thus reducing development costs, decreasing the time to market, and increasing product quality. As a core activity in PLE, variability management supports modelling of commonality and variability throughout the engineering life cycle. Given the increased complexity of modern software-intensive systems, variability management is becoming increasingly important. Transitioning to PLE approaches is a challenging task, as potential benefits must be carefully weighed against costs introduced by PLE approaches. This paper presents a collaborative approach for reverse-engineering variability and configuration knowledge with minimal domain expert involvement and provides insights into the experience we gained from our industrial collaboration.
The Linkage Between Blockchain and the Regulatory Function of Governments

The study mainly aims to understand the linkage between “blockchain” and “the regulatory function of governments”. The research questions are 1) How can be identified the blockchain as a concept and assessed the regulatory function of governments with the trust/compliance relationship? and 2) What extend the blockchain has affected the regulatory function of government, and how can be unveiled the relevance between the blockchain and the regulatory function of governments? In methodology, it is a theoretical and exploratory study. This study has constructed in three sections. In the first section, the conceptual aspects of blockchain have assessed. The second section is about the regulatory function of governments in the trust. The studies on “regulatory state” and “state-citizen relationship” have respectively scanned, and the virtues and drawbacks of blockchain have combined in terms of “trustless, privacy sensible organizational, public efficiency social impact.” Discussion and conclusion sections, the linkage between the blockchain and the regulatory function of the State is evaluated.
Review of Fuzzy Control for Path Tracking in the Robotino System

The main goal of this paper is to study works performed with Robotino, mainly by using fuzzy logic and other metaheuristics for achieving path tracking. After this, a short comparison will be made between the previously performed works. In order to help in the orientation of envisioning future works, and to analyze how efficient it is to apply fuzzy logic (including type-2 fuzzy logic), in this specific area of robotics, regardless of whether it works in conjunction with other techniques, tools, software, hardware, methodologies. Or which of the sensors of the robot are being used at the time of the works, being able to use only the infrared sensors or all the sensors that it has. The works that are considered under this study are mainly focused on the use of the Robotino, or for the path tracking by implementing omnidirectional robots.
K. Aaron Smith &amp; Dawn Nordquist (Eds.) (2018). <i>Functionalist and Usage-based Approaches to the Study of Language. In honor of Joan L. Bybee</i>

Abstract This article reviews Functionalist and Usage-based Approaches to the Study of Language. In honor of Joan L. Bybee
Implementation and Analysis of Various Visual Cryptographic Techniques for Sensitive Data Protection

With the advancement of technology, more and more personal data are digitized, leading to increasing vulnerabilities in data. Protection of this data is a paramount importance for today's era. Visual cryptography is the cryptographic technique in which various forms of visual data like pictures, texts, etc. are encrypted such that they cannot be directly read and need to be visually decrypted for use. Sensitive data like defense data involves diverse content types and different formats of data related to criminology, military, aeronautics, communications and space flights. Communicating the crucial data safely is a topic of vital concern for government. This paper intends to analyze the various cryptographic techniques and explore appropriate visual cryptographic solution for securing the sensitive data.
국내 제약기업의 R&D투자와 기업특성요인 간 관계 : 비모수적 접근

Objectives: Using data on 86 Korean pharmaceutical firms over a 5-year period from 2013 to 2017, this study empirically analyzes the relationship between research and development (R&D) investment and firm-specific factors. Methods: Investment variables are R&D expenditure and intensity. Variables for firm-specific factors include firm size, firm age, firm location, technological innovation, and export orientation. Nonparametric statistical methods are used to test the difference in R&D investment and groups are then classified according to the level of firm-specific factors for inter- or between-group comparison. Results: First, R&D intensity was related to firm size, firm age, and technological innovation. Second, R&D expenditure was related to firm size and technological innovation and this relationship was statistically significant. Conclusions: The results are expected to provide policy implications for R&D investment decisions and strategic choices made by managers as well as policy makers.
Linear Regression from Uncertain Data and its Applications to Housing Price Prediction

Abstract Most of the figures we get in our daily life are imprecise, a person’s height is an approximation. Uncertain data is usually represented by many values around it in a limited region with probability density function. This paper first defines the uncertain data, and then proposes a linear regression algorithm based on the uncertain data. In order to verify the rationality, correctness and effectiveness of the algorithm, many experiments were carried out.
Smart Cities as Panopticon: Highlighting Blockchain’s Potential for Smart Cities Through Competing Narratives

This chapter argues that the narratives of smart cities demonstrate the potential value of blockchain technologies. Drawing upon competing narratives within the cultural imaginary, both the ‘dream’ of a better city, and the ‘fear’ of an oppressive structure will highlight the need to consider both Bentham and Foucault’s Panopticon. The term ‘panopticon’ is defined and explored within the context of blockchain technology. In doing so three concepts are identified: the enabling nature of a panopticon; the use of a blockchain-enabled-panopticon to encourage human flourishing; and the ability for technology such as this to enhance standards above a basic minimum of the law. This chapter suggests that understanding smart cities, panopticon and blockchain, may allow for a better account for competing narratives of fear that can lead to a deeper understanding of how this technology can be deployed.
Collaborative Visual Analytics Using Blockchain

Blockchain, a decentralized, distributed and encrypted ledger, was created to eliminate the need for a central trusted entity. Blockchains provide users with a secure, trusted, auditable, and immutable record of transactions and are applicable to systems that require a trustworthy record of information. Our work explores the use of blockchain in collaborative visual analytics systems where users share and store a record of the visual analysis of some data. We built Share.va, a framework that allows users to store and share the states of visual analytics dashboards through a blockchain. We apply Share.va to an existing visual analytics dashboard and conduct a pilot study to understand the effectiveness and limitations of blockchain in collaborative visual analytics.
ResSCNN: A semantic segmentation method for fast processing of large-scale input

Abstract With the development of driver-assistance systems and driverless cars, vehicles are becoming more and more intelligent. However, today’s intelligent vehicles rely more on large sensors to sense the environment, so it is becoming more and more important for vehicles to be able to understand road information, and semantic segmentation has developed greatly. We find that the current mainstream semantic segmentation model is slow and inaccurate in the case of large scale input images. In this paper, we combine the large-scale input Fast-SCNN with the residual module to construct the residual semantic segmentation convolutional neural network (ResSCNN), after the experimental test, when the input scale is 1024 × 2048 px, yielding an accuracy of 68.4% mean intersection over union at 123.1 frames per second on Cityscapes dateset. Our network has achieved better results than the previous mainstream methods.
Malaria Disease Detection Based On Segmentation And Classification Of A Cell Image

Malaria is a hazardous mosquito-borne disease. It's commonly transmitted through the bite of an infected plasmodium carrying Anopheles mosquito. Nowadays medical imaging techniques are playing a predominant role in detection of various illnesses like tumours, cancer, infected bites and many more. This study is aimed to present a way to easily identify a plasmodium infected cell using computational techniques. This will be achieved through proper image processing techniques through which the spots on cell will be properly segmented using active contour segmentation and the processed image will be passed to the supervised learning model for better classifying if the cell is parasitized or uninfected. The results achieved through the proposed method will provide effective automated detection of the parasite in the blood sample
Structural Application of Medical Image Report Based on Bi-CNNs-LSTM-CRF

Medical image reporting is an important information carrier for medical staff to record medical activities for patients. It contains a large number of technical terms and medical knowledge. Extracting effective information from medical imaging reports can better serve clinical decision making and promote the development of the medical field. This paper focuses on the breast medical imaging report, analyzes the structural characteristics of the report, designs the medical record structured template, extracts the text features from the image report, and forms the structured data in the canonical form. In this paper, the machine learning model bidirectional CNNs-LSTM-CRF is used to extract the characteristic information of related lesions in the image report. The experimental data comes from an imaging inspection report provided by a medical institution to evaluate the effect of the structure by predicting the BI-RADS classification information. The data was provided by a medical institution, and the extracting result of the feature tagging is that the average accuracy is 95.71%, the average recall is 98.10%, and the average F1 values 97.29%.
An ABAC Based Policy Definement for Enriching Access Control in Cloud

Cloud Computing becomes most preferable solution for satisfying the various requirements of organizations and institutions. Different types of clouds like IaaS, PaaS, SaaS makes cloud capable to fulfills the client's different kind of needs like computer processing power, storage spaces, databases, software, application, web based solutions. Cloud computing can also be useful and worthy in providing certain customized solutions to enhance the capability of legacy systems in terms of effectiveness, reliability and optimization by replication of environment up to satisfactory extent. To provide adequate security solutions for cloud is still a challenging task and access control mechanism is one of the domain which demands significant attention on the mission towards securing clouds. In this paper, our work primarily focus on defining ABAC components, mapping functions and access control policies composed by access rules. Amazon Web Services is one of the most prominent cloud providers. Identity and Access Management (IAM) and Amazon S3 are access management and storage facilities of AWS respectively. ABAC based access policies are attached with the user and storage components for authorization.
Efficient Nested Key Equation Solver Architectures for Generalized Integrated Interleaved Codes

The Generalized Integrated Interleaved (GII) codes nest short Reed-Solomon (RS)/ BCH sub-codewords to generate codewords of stronger RS/BCH codes. They can achieve hyper-speed decoding and good error-correcting performance with low complexity, and hence are one of the best candidates for next-generation terabit/s communications and storage. The key-equation solver (KES) of the nested decoding for correcting extra errors limits the achievable clock frequency and contributes to a significant portion of the decoder area. This paper summarizes our recent work on hardware architecture design for the nested KES. The clock frequency bottleneck is first eliminated by reformulating the nested KES and exploiting architectural transformations. Then the complexity of each processing element (PE) in the nested KES architecture is reduced by a scaled nested KES algorithm. Furthermore, the number of PEs is reduced by exploiting the data dependency and analyzing the minimum number of coefficients to keep for the involved polynomials.
Informatization and personalization as tendencies in the culture of contemporary society

Informatization and personalization as tendencies in the culture of contemporary society In this article attempt is made to generalize some data about the informatization process. The author considers it in the context of such stages of development of the information culture of society as the forming of the human language, the elaboration of writing system, the development of printing and, finally, the creation of universal computers. Then the information sector of the economy of modern society is being studied and conclusion is made that taking into account the individual tastes of people leads to the development of personalization mechanisms in the industry of informational products and services.
Research on IACA of distribution network fault location

Abstract In power system, the probability of distribution network failure is the highest, which will directly affect the power consumption experience of customers. The timely and accurate fault location of distribution network is very important to improve the reliability of power supply. In this paper, firstly, the switch function and evaluation function of distribution network fault location are discussed. ACA(Ant Colony Algorithm) algorithm has some defects such as premature convergence. In order to solve this problem, the IACA(Improvement Ant Colony Algorithm) method is proposed. Finally, the feasibility and effectiveness of the new method are verified by establishing a simple distribution network model.
Developing (Almost) Free Distributed System Labs Using Container-based Technique

Developing successful labs is essential to advanced CS courses. As the fast development on novel programming models and system frameworks, our CSE education calls for a collaborative, community-sourced design effort that maximizes the benefit from effective lab practice. However, building one unified lab platform is expensive, while outsourcing lab works to students faces challenges like infrastructure requirements, heterogeneous operating environments, and the desire to incentivize individual work. To address these challenges, we present our recent efforts on developing, deploying, and analyzing up-to-date distributed system labs based on container-based techniques and open source community supports. Our work uses docker containers that provide a light and consistent environment to support fast lab exercise construction and migration. This enables our students host their own labs with a modest laptop or desktop. Further, We provide a machine learning engine that automatically crawls students' homework images and evaluated with the weighted Adaboost algorithm on elected course topics. As such, we allow to build novel and practical distributed computer labs for (almost) free. We deployed and evaluated our work in a continuous three-year course teaching. The results show that our students provide positive feedback on learning and experiencing complex computer system concepts without increasing the complexity of lab setup or assessment.
Detection of Abnormal Behavior Based on the Scene of Anti-photographing

In the era of increasingly prominent information security issues, Anti-Photographing is a subject worthy of study, which is based on the problem of camera equipment detection and camera behavior analysis. Although the current target detection algorithm is relatively mature, there are still problems in the accuracy of target detection in complex scenes. Resnet50 is a neural network framework with high learning efficiency based on the residual module, which is widely used in object recognition tasks. In this paper, we propose a new detection framework, which is based on the Gaussian skin color model combined with the Openpose method to improve the accuracy of hand region extraction. At the same time, the mobile phone recognition model based on the Resnet50 network is used to identify whether the hand holds a mobile phone. At last, the gestures of the person’s hand and face are combined to complete the evaluation of whether there is a photographing behavior. The results of testing experiments on the photographing behavior in the video frame show that the framework has a good detection effect on photographing behavior.
RiWAArch Style: An Architectural Style for Rich Web-Based Applications

The Rich Web-based Applications are complex systems with their rich Graphical User Interfaces and special communication model named Delta-Communication. Rich Web-based Applications are suffering from lack of strong architectural formalism, which can realize the abstract architectural elements of them. If we can introduce an abstract architectural style for Rich Web-based Applications, it will help in reducing the complexity by increasing the realization; and the abstraction will improve the adoptability by lowering the learning curve. This research introduces an abstract hybrid architectural style with a novel configuration towards reducing the complexity by increasing the realization. The knowledge delivered in this paper can be used to implement frameworks/libraries to assist the rapid development of the Rich Web-based Applications.
Performance Comparison of Variants of Hybrid FLANN-DE for Intelligent Nonlinear Dynamic System Identification

This work presents the performance comparison of different variants of hybrid Functional Linked Artificial Neural Network (FLANN) structures and Differential Evolution (DE) algorithm (FLANN-DE) for intelligent nonlinear dynamic system identification. FLANN is single-layer artificial neural network structure having less computational complexity and preferred for online applications and DE being a derivative-free metaheuristic algorithm is used as a global optimization tool. System identification finds its application in direct modelling, channel identification and estimation, geological exploration, instrumentation and control. Direct modelling is based on adaptive filtering concept and can be developed as an optimization problem. The goal of direct modelling is to estimate a model and a set of system parameters by minimizing the prediction error between the actual system output and the model output. The identification problem involves the construction of an estimated model which generates the output which matches that of desired system output when subjected to the same input signal. In this present work, hybrid FLANN-DE is proposed for direct modelling of nonlinear dynamic systems and comparative analysis is carried out for different variants of FLANN structures such as Chebyshev FLANN (CFLANN), Legendre FLANN (LFLANN) and Trigonometric FLANN (TFLANN) in terms of performance, the speed of computation and accuracy of results.