Reviewerly/exHarmony huggingface dataset:
Distant Supervised Relation Extraction Model for Reinforcement Learning Combined with Noise Network

The distant supervised relation extraction has received wide attention from scholars in recent years. Existing methods for distant supervised relation extraction are based on bag-level for relation prediction, but they do not correspond to sentences and relation one by one. So in 2018, some scholars have proposed distant supervised relation extraction based on the sentence-level with reinforcement learning methods. Adding adaptive noise to the parameters of the reinforcement learning algorithm can effectively improve the performance of the algorithm. In this paper, the parametric noise is added to the neural network weights to increase the exploration of reinforcement learning. Experiments show that reinforcement learning with noise effectively improves the effect of distant supervised relation extraction based on the sentence-level.
Prediction of Vocational Students Behaviour using The k-Nearest Neighbor Algorithm

Abstract This article discusses the implementation of the k-NN algorithm in predicting student behavior. The school is a management unit that has data that correlate with students. All student data is stored in an academic information system that can be processed to predict student behavior. One of the data assessing student behavior is in the database of counseling guidance. Some data that will be processed include attendance, lateness notes of problems, teacher responses, tuition payments, broken home. The sample being tested was the data of 100 vocational students from various classes and various majors and divided into two categories. From this experiment, it can be seen that the most accurate K value is the value of K = 1, 3 and 4. The accuracy of the testing data generated is 94.9%.
Representation with Index Matrices of Single Ended Primary Inductor Converter Functioning

The paper aims to represent with index matrices electronic circuit functioning. A specific example (SEPIC) illustrates the proposed methodology. The last one provides automated modelign of electronic devices containing two-terminal components. Index matrices with real number elements are used in voltage and amperage calculations in order operations with sparse usual matrixes to be avoided. In addition, the given approach is also applicable in electronics education, particulary - in fundamental training.
An Electronic Voting Scheme Based on FOO Protocol

Electronic voting has become a hotspot in cryptography application. FOO protocol is a protocol that can be applied to large-scale electronic voting. An electronic voting scheme is proposed based on the FOO protocol. The blank vote and the intermediate institution are introduced into the scheme to generate the only serial number, and the blank vote is used in each phase, introducing the supervising institution to verify the key of bit commitment. By analysis, the scheme can effectively solve the security problems such as vote collision, abstention, anonymity, receipt-freeness, and so on. Compared with other electronic voting protocols, it has better practicability and operability.
A Cordic-based Acceleration Method On FPGA For CNN Normalization layer

This paper studies the accelerated method used by FPGA to implement the Alexnet Normalization layer, a classic network of the classical network. This paper uses Cordic algorithm to implement basic operations such as division, exponential function, square root function, etc, combining with the characteristics of FPGA to consume floating-point operation resources. This paper analyzes the errors generated by the different digits of data after quantization and fixed-point input of the layer to determine the final data bit width. Vivado 2018.1 is used for the realization and simulation environment of this paper. The bit width is selected to meet the design requirements and save resources as much as possible. After reducing the bit width, the relative error is controlled below 0.02 and the Normalization layer is completed in 51.5 cycles in the calculation of the spatial position (x, y) unit. The final result relative error is controlled below 0.015 by using the method which greatly improves the calculation speed of the Norma- lization layer.
Assessing Visual Engagement for Visuomotor Skills Rehabilitation Training in Infants

Simple visual stimuli, with bright colours and dynamically evolving over time, are among the most effective mechanisms through which to engage a baby´s attention. In earlier work, we have developed a visual stimulating tool to aid rehabilitation programs, which can be used with infants of up to 2 years of age. The feedback from the early use of the device has been rather positive. Yet, until now, there was no explicit way to assess the degree of engagement of the infant’s attention, or even when the focus of said attention moved away from the stimulus. Hence, it has been difficult to understand whether the proposed specific rehabilitation procedure has failed, for a given infant, or the loss of attention led to a decrease in efficiency in the intervention. In the current work we develop and exploit a simple eye tracking tool, based on a laptop’s own webcam, to evaluate the child’s loss of attention to visual stimuli. The main differentiating criterion, set forth for this eyetracker, is that it should work without an explicit calibration stage. The use of the specific camera is motivated with the fact that the laptop can be used for visual stimuli deliver, as well as a series of data processing steps. The results attained thus far were rather encouraging, leading even to a subsequent study, replacing infants by adults undergoing a rehabilitation program, after suffering from brain stroke.
Organizational Commitment & Work Engagement Affected by Knowledge management: Evidence from banking sector of Pakistan

Current study is intended to explore the influence of knowledge management (KM) on organizational commitment and work engagement of the banking employees. Paper examines the dimensions of knowledge management that can influence the effective elements of employee performance. Five dimensions of knowledge management practices are bootstrapped on the organizational commitment and work engagement. Data collected form the different banks of Punjab, Pakistan by including 171 usable questionnaires. Data analysis was done with the help of SPSS and smart PLS. Results reveals that knowledge sharing, codification and retention helps in boosting organizational commitment of employees while knowledge creating, retention and sharing has a significant impact on work engagement. Knowledge management construct is not discussed as a complete construct as researchers opt some of its dimensions. Moreover, KM relationship with organizational commitment and work engagement is missing in the past literature. Knowledge creation, retention and sharing has a strong influence on the organizational commitment and work engagement. Managers should implement KM processes to uplift the knowledge of workers but also to boost their commitment and work engagement.
Multilingual Recognition Based on Neural Network with High Recognition Degree

Abstract Language recognition has become an important branch of natural language processing. Due to the interference of out of vocabulary words and the same words in different languages, it has been a challenging task to accurately identify language categories with high similarity. In this paper, N-gram is combined with word2vec to process the data, MLP and CNN models are selected for improvement and experimental analysis. The experimental results show that the data processing and model training of 20 kinds of highly acquainted languages are used in the above-mentioned way, which reduces the dependence of the model on the size of the data set and the length of the text, the accuracy of the model detection between the languages with high recognition degree is improved, and the robustness of the language recognition model on different data sets is enhanced, with the accuracy rate being over 97.
Developing a Reinforcement Learning Agent for the Game of Checkers

The aim of the following paper was to develop, test and evaluate a simple self-learning agent for the game of Checkers based on reinforcement learning and neural networks. The approach followed in this work is rather simple and based on a single deep neural network which is used to evaluate the board states and to choose the next best move for the agent. During the training phases the neural network is trained using a reward system based on different criteria derived from the prior and the current board state that resulted from the last action taken. The neural network takes a state-action pair, consisting of the current state and a possible move option and predicts the total reward that can be expected. This way all possible move options are evaluated and the one with the highest value is chosen as the next move.
A Survey on Anomaly Detection Techniques in Large-Scale KPI Data

For Internet-based services quality, it is very necessary for Internet companies to monitor a large number of key performance indicators (KPIs) and accurately detect anomalies. With the increasingly complex structure of the system, the changing characteristics of the performance monitoring data have gradually become a challenge for anomaly detection. Recently, in the performance management sector, there has been renewed interest in research on anomaly detection of KPI streams. There has been a lot of work in the area of clustering-based unsupervised anomaly detection. This paper presents a survey of various clustering-based anomaly detection techniques and discusses the advantages, limitations, and practical significance of different algorithms. Some practical application-related kinds of literature are summarized. At the end of the paper, we put forward some new research trends and opinions and suggestions for the research direction.
Non-Uniform Deblurring for Simple Lenses Imaging System

As a sub-topic in computational imaging, simple lenses imaging provides an alternative way to achieve a close-to-perfect imaging system design, which simplifies the complex, multiple lens elements optical system to one simple lenses, and delivers the aberration correction process to a dedicated reconstruction algorithm. In this paper, we propose an effective blind restoration algorithm for optical aberrations removal without calibrate procedure. In general, our algorithm consists of two steps: a blind non-uniform PSFs estimation and a non-blind reconstruction. The algorithm can be directly used for simple lenses imaging system, and further utilized for aberrations removal cases in traditional optical imaging equipment. We obtain results that are comparative with the pre-calibrate non-blind restoration.
Living with RITA

Despite the widespread proliferation of conversational user interfaces (CUI) through smart devices, their uptake in novel use cases is heavily contingent upon user trust. This is particularly the case in the personal communication domain, where users are especially cautious, given privacy concerns and uncertainty over data sharing and access. This paper considers the rise of CUI as an interaction paradigm, exploring how issues of trust manifest within the social context of a real estate tenancy. We present lessons from a series of semi-structured interviews, participatory design (PD) workshops and prototype evaluation of a multi-party tenancy CUI - RITA. Preliminary findings indicate that trust can be enhanced in CUIs through greater transparency of actions, enhanced control and clearer privacy settings. It also suggests that design should account for the ways in which trust manifests in sensitive user contexts, like tenancies based on preestablished relationships and power dynamics.
Efficient Parallel Graph Extraction

In this chapter, we introduce the homogeneous graph extraction task, which extracts homogeneous graphs from the heterogeneous graphs. In an extracted homogeneous graph, the relation is defined by a line pattern on the heterogeneous graph and the new attribute values of the relation are calculated by user-defined aggregate functions. When facing large-scale heterogeneous graphs, the key challenges of the extraction problem are how to efficiently enumerate paths matched by the line pattern and aggregate values for each pair of vertices from the matched paths. To address the above two challenges, we propose a parallel graph extraction framework. The framework compiles the line pattern into a path concatenation plan, which is selected by a cost model. To guarantee the performance of computing aggregate functions, we first classify the aggregate functions into distributive aggregation, algebraic aggregation, and holistic aggregation; then we speed up the distributive and algebraic aggregations by computing partial aggregate values during the path enumeration. The experimental results demonstrate the effectiveness of the proposed graph extraction.
The Development of Indonesian Language Learning Model through Puppet Creation Media

Abstract The aim of this research is to analyse the development model in learning Indonesian language through puppet creation media in fifth-grade students of elementary school NW Rekat Lauk. The research is research and development. It orients the development to produce and test a product of language learning media through puppet creation. Direct interviews conducted the research step. The development steps were product design, expert validation, and testing. The learning media by using puppet creation which developed and used based on the media expert which categorized “good enough” because it was between 26,04&lt;x&lt;33,96 with a total score was 27 and the mean was 2,7. It categorized the material expert “good” between 30,6 &lt;x&lt;16,8 with a total score was 32 and the mean was 27. The learning result of the highest score was 85, the lowest score 55, the number of minimum total criteria was 60, and the percentage was 88,23%. The students’ learning response were excellent students who answered “no” showed which no” with the mean percentage of “yes” was “97,05%. And “No” were 2,95 %. Thus, media of learning by using puppet creation can influence the students’ learning outcomes.
Design Opportunities for Digital Men's Health

Many men stop exercising as they age, engage in risky behaviours such as alcohol misuse, are reluctant to admit to mental health problems, and avoid seeking help. Men are generally hard to reach for community health interventions. However, interventions run at football clubs have successfully engaged men and have led to positive health outcomes. Mobile health technology might similarly be designed to engage and encourage men via connections with football. This technology could be used to augment and extend community programs, or be used to target global fan bases. However, it is not clear if and how what attracts men to community interventions can translate to technology. In this paper we report a design study with 18 middle-age male participants exploring what men find important in football, and connections between football, health and technology. We present five design opportunities to guide and prompt further innovation in this area.
MODERN PROBLEMATICS AND ANALYSIS OF EFFICIENCY OF APPLICATION OF DIFFERENT APPROACHES TO DESTRUCTION OF INFORMATION ON MAGNETIC MEDIA

The introduction and active use of information transmission and storage systems in the Ministry of Defense (MoD) of Ukraine form the need to develop ways of guaranteed removal of data from media after their use or long-term storage. Such a task is an essential component of the functioning of any information security system. The article analyzes the problems of guaranteed destruction of information on magnetic media. An overview of approaches to the guaranteed destruction of information on magnetic media of different types is presented, and partial estimates of the effectiveness of their application are given by some generally accepted indicators of performance evaluation. The article also describes the classification of methods of destruction of information depending on the influence on its medium. The results of the analysis revealed the main problems of application of software methods and methods of demagnetization of the information carrier. The issue of guaranteed destruction of information from modern SSD devices, which are actively used in the formation of new systems of information accumulation and processing, became particularly relevant in the article. In today's conditions of development of the Armed Forces of Ukraine, methods of mechanical and thermal destruction are more commonly used today. In the medium term, the vector of the use of information elimination methods will change towards the methods of physical impact by the pulsed magnetic field and the software methods that allow to store the information storage device, but this today requires specialists to develop new ways of protecting information in order to avoid its leakage.
Pollutant Absorber Design to Minimize Air Pollution Emissions from Industry

Abstract Pollutants emitted from industries vary depending on the type of fuel and the height of the chimney used. The higher the chimney used, the further the pollutant distance emitted. In this research, Pollutant Absorber was made with a condensation system using activated coconut shell charcoal. The research method uses Research &amp; Development with the Design for Production (DFP) approach. In developing the model, pollutant emission analysis is carried out: before entering the chimney, during the chimney, and after exiting the chimney. Then, in designing tools to design absorbent devices, chimney designs with coconut shells are made as absorbent. In creating and making tools, it is done in two ways, namely simulation and testing. The results of the research are models of the distribution of air pollutants from industry, air pollutant absorbent designs emitted from industry, and prototype devices capable of absorbing SO 2 of pollutants emitted through the chimney.
An Optimized Scheme of Information Hiding-Based Visual Secret Sharing

Information hiding-based visual secret sharing (IH-VSS) uses the information hiding techniques to embed secret data into images. In the IH-VSS scheme, hidden images are divided into n shadows under the guidance and constraints of some predetermined methods, and only a certain number or all of the credible shadows work together to recover hidden information. Based on (2, 2)-threshold IH-VSS scheme, this paper proposes an optimized IH-VSS scheme, it can randomly locate the hidden position of the secret data. We set a random number and use it as the initial hiding position, then combined with the length of the secret data, assigns the address, and completes the hiding of the secret data. The experimental results show that under the same embedding capacity, the peak signal-to-noise ratio (PSNR) of the optimized scheme is higher than other similar schemes, which is high to 55.41 dB. Moreover, the difference between the two shadows is very small.
Introducing Intelligent Interior Design Framework (IIDF) and the Overlap with Human Building Interaction (HBI)

Increasingly our interiors will have technology embedded throughout the space or used to morph or augment the space. The increased ubiquity of technology will result in humans living inside structures that not only provide for their occupant’s needs but anticipates those needs beforehand. The Intelligent Interior Design Framework (IIDF) goes beyond “smart homes” connected to the internet of things (IoT). We seek to introduce the Intelligent Interior Design Framework (IIDF) as an interdisciplinary strategy that can be used to design experience inside our interiors. The IIDF is a theoretical framework for emerging intelligent interiors, divided into three domains. These categories are Smart Geometry (SG), Ambient Intelligence (AmI), and Information Modeling (InfoMod). The discipline of Human-Computer Interaction (HCI) and Human Building Interaction (HBI) by their very nature, are interdisciplinary and collaborative. These different disciplines work in some way with the three domains in intelligent design: Smart Geometry, Ambient Intelligence, and Information Modeling.
Application of Source-to-Sink System in the Prospect Analysis in Early Stage of Sandstone Uranium Exploration

Abstract In-situ leaching sandstone uranium deposit has the advantages of low mining cost and environmentally friendly. The research ideas from “source” to “sink” are applied to the study of the mineralization of sandstone-hosted uranium deposits. Prospect areas are predicted by studying the source-sink system of sandstone uranium deposits. Soil radon survey is carried out in prospect areas to delineate the abnormal range of soil radon and to reduce the scope of the favorable exploration area. Accurately determining the exploration area is conducive to reduce the damage to vegetation and environment in the exploration area, and to achieve the goal of green exploration.
A kernel machine for hidden object-ranking problems (HORPs)

Abstract Hidden Object-Ranking Problems (HORPs) are object-ranking problems stated as classification or instance-ranking problems. There exists so far no dedicated algorithm for solving them properly and HORPs are usually solved as if they were classification (multi-class or ordinal) or instance-ranking problems. In the former case, item-related ordinal information is negated and only class-related information is retained; in the latter case, item-related ordinal information is considered, but in a way that emphasizes class-related information, so that the items are not only sorted but also clustered. We propose a kernel machine that allows retaining item-related ordinal information while avoiding emphasizing class-related information. We show how this kernel machine can be implemented with standard optimization libraries provided slight modifications on the original kernel. The proposed approach is tested on Tahitian pearls quality assessment and compared with four other classical methods. It yields better results (93.6 % ± 3.9 % of correct predictions without feature selection, 94.3 % ± 3.4 % with feature selection) than the best of the other tested methods (91.3 % ± 3.4 % and 92.6 % ± 4.3 % without and with feature selection for the instance-ranking approach), this improvement being significant (p-value &lt; 0.05). Moreover, this method exhibits no significant difference in the results with and without feature selection (p-value = 0.33), which may be a hint that its learning bias fits the problem well and can thus alleviate the data preprocessing workload.
Comparison of Probability Distributions for Evolving Artificial Neural Networks Using Bat Algorithm

In the field of continuous engineering, Predictive models have been broadly emerged with Artificial Neural Networks (ANN) for data classification and prediction. Commonly, Neural Networks (NN) have been trained through the backpropagation algorithm, which is considered as a traditional approach. In optimization problems, Bat Algorithm (BA) has been extensively carried out with ANN to tackle different barriers. One of the prominent issues is the worst population initialization in terms of retrieving initial weights for each neuron in ANN. The advantage of a strong pattern of initiating the search vectors may lead to improve overall algorithm performance. In this article, we have proposed the novel variant of NN structure with BA probability distributions. The proposed initialization methods composed of Gamma distribution (G-BAT-NN), an Exponential distribution (E-BAT-NN), Beta distribution (B-BAT-NN), and finally the Weibull distribution (W-BAT-NN). We implemented the proposed techniques for the ANN classification of feed forward neural networks using BA and suggest the enhanced versions of BA for ANN classification. We verified the results on 8 real-world data sets taken from the UCI repository for the classification problem.