Reviewerly/exHarmony huggingface dataset:
A Research on the Evolution of Chinese Semantics Based on Distributed Representation

The diachronic evolution of word meaning has always been an important topic in linguistics, with new achievements in traditional linguistics. However, traditional “field work” can only be analyzed qualitatively, which needs accurate data collection and consumes a lot of manpower and material resources. In recent years, with the gradual rise of deep learning technology, more and more researchers have used distributed semantic representation to address semantic related problems. This new method not only opens up a new way of studying the semantic evolution of ancient Chinese, but also breaks the limitations of traditional linguistics. Under this background, this paper uses the BERT model, which is trained with the “Siku Quanshu” (Imperial Collection of Four) to get the distributed representation, (1) to practise the automatic annotation in ancient Chinese, with an accuracy of 92%; (2) to practise the automatic detection of semantic changes, including extension of meaning, synchronic distribution of meaning and diachronic evolution of meaning.
Grid Resilience Enhancement by Reference Shaping based on Gray-Box Model

We propose a new method for enhancing resiliency of power systems integrated with renewable power resources. In the proposed method, we design a resilience enhancer that shapes reference values, generated by existing any power flow computation scheme such as EDC, so that the influence of faults on power system dynamics can be appropriately mitigated. The design of the resilience enhancer is based on so-called gray-box modeling, which is a fusion of a few prior knowledge on the grid and data acquired in its healthy operation. The effectiveness of the proposed method is investigated through a three-machine system integrated with three solar farms.
Using Matrix Factorization and Evolutionary Strategy to Develop a Latent Factor Recommendation System for an Offline Retailer

Recommendation systems have been developed for online services with a simple product mix. This study extended its application to an offline retailer with a more complex product mix. Purchasing records of two thousand members within one year from an offline retailer in Taiwan were used as the dataset for the study. Datasets of the first 9 months were used for training and models were tested by the records of the last 3 months. This study developed a recommendation system by integrating a matrix factorization to uncover latent factors from both customers and items, and an evolutionary program to optimize the parameter settings of duration adjustment functions that were applied to assign weights so that past purchasing records closer to the testing period would receive higher weights. The objective of the system is to predict the likelihood of customers’ purchasing behavior toward items they never purchased during the training period. By measuring the average percentage-ranking of items for two thousand members, the recommendation system developed in this study outperformed two other approaches, popularity and item-based nearest neighborhood systems. In addition, academic and practical contributions were also discussed.
An Efficient Proactive Secret Sharing Scheme for Cloud Storage

Aiming at the security problems of cloud storage industry, this paper proposes an efficient proactive secret sharing (PSS) scheme for cloud storage. The scheme divides the secret of large amount of data into multi-secrets, and uses secret sharing to make multi-secrets meet the security of the $$(k,\,n)$$ threshold scheme. The scheme periodically updates and verifies the shadow shares held by cloud service providers. In this way, an adversary can recover the secret if and only if it captures at least $$k$$ shares during a period of update rather than any time. The scheme only calculates a few of multi-secrets at a time, which reduces the computational cost of the system. The scheme takes into account the long-term security issues and processing performance of the system, which can effectively protect the confidentiality and integrity of data, and can also be used to prove the integrity of remote data. Compared with the previous PSS scheme, the scheme has higher execution efficiency, less interaction between shareholders, and lower requirements for communication channels, which can better to meet the needs of cloud storage in the current era.
Digital Media Impact on Reading

Introduction: This research observes whether there is a difference in the way information is memorized based on the means it is obtained: printed or digital.Objectives: To observe if the memory of information is affected by the media it is obtained from-printed or digital-and also to observe if there is any response time difference, considering participants' gender and academic area in the information apprehension process.Method: To evaluate these observations, a memory test has been developed and applied to Technology and Psychology graduate students at BandTec Digital School and at Pontifícia Universidade Católica de São Paulo (PUC-SP), respectively.The total of 195 students was interviewed, 62 (31.8%) from Psychology area (PUC-SP) and 133 (68.2%) from Technology area (BandTec Digital School).There were 103 (52.8%) interviews with digital media and 92 (47.2%) with printed content, 117 (60%) tests were answered by men and 76 (39%) by women, and the age range was 16 -43.Conclusion: Based on quantitative research information, expected hypothesis was achieved: information acquired through printed material is better apprehended than when acquired through digital media.This conclusion provides significant information to educators, giving support to the creation of new teaching strategies for future generations.
Robust Tracking via Feature Enrichment and Overlap Maximization

Recently, Convolutional Neural Networks (CNNs) based approaches have demonstrated an impressive gain over conventional approaches which resulted in rapid development of various visual object tracker. However, these advancements are limited in terms of accuracy due to the distractors available in the videos. Moreover, most of the deep trackers operate on low-resolution features, such as template matching, which are semantically reliable but are spatially less accurate. We propose an efficient feature enrichment module within tracking framework to learn the contextual reliable information and spatially accurate feature representation. Proposed feature enrichment combines enriched feature sets by exploiting contextual information from multiple scales as well as preserving the spatial information details. We integrate proposed feature enrichment module within baseline ATOM which solves the tracking problem by target estimation and classification components. The former component estimates the target based on IoU-predictor, while the later component is trained online to enforce high discrimination power. Experimental study over three benchmarks including VOT2015, VOT2016, and VOT2017 revealed that proposed feature enrichment module boosts the tracker accuracy.
Development of a Model of the Development Level of Computer Literacy of Employees of Agro-industrial Complex of Kazakhstan

The level of computer literacy of workers in the agro-industrial complex of Kazakhstan is considered.The author emphasizes that in modern conditions, one of the main tasks of the priority development of the agro-industrial complex of the country and regions to address food issues and the need to increase competitiveness is the intensification of agroindustrial production.Automation, complex mechanization and the development of information technologies that allow each unit of resources used to get a greater number and variety of high-quality food products-this is the most effective way to develop the agro-industrial complex.The global trend in the development of a socio-economic indicator that determines the prospects for digitalization of the agro-industrial complex is being studied.The direction of research of this indicator is aimed at developing a competitive agricultural economy and improving the quality of agricultural products.The results obtained represent a mathematically sound scientific judgment that expresses the future state of the studied indicator.Calculated calculations are based on the algorithm for finding estimates of the coefficients of the regression equation.Methods of statistical analysis of the obtained regression equation are applied.The mathematical analysis of calculations of possible trajectories of development of the factor attribute for the midterm period of the lead is carried out.The overall quality of the multiple regression equation is checked.
An Evolutionary Approach to Combinatorial Gameplaying Using Extended Classifier Systems

Extended classifier system (XCS) is an extension of a popular online rule-based machine learning technique, learning classifier system (LCS), in which a classifier’s fitness is based on its accuracy instead of the prediction itself, and a genetic algorithm (GA) and reinforcement learning (RL) component is utilized for exploratory and learning purposes, respectively. With the emergence of increasingly intricate rule-based learning techniques, there is a need to examine feasible methods of learning that can overcome the challenges posed by complex scenarios while supporting online performance. Checkers is a strategic, combinatorial game having a high branching factor and a complex state space that provides a promising avenue for scrutinizing novel approaches. This paper presents a preliminary investigation into feasibility of XCS in such complex avenues by taking 6 × 6 checkers as a specific case of study. The XCS agent was adapted to this problem, trained with random agent and was able to perform well against the alpha–beta pruning algorithm of various depths as well as human agents of different skill levels (beginner, intermediate and advanced).
An internal supplemental action measurement to increase the gap of action values and reduce the sensitivity to overestimation error

Reinforcement learning holds considerable promise to help address sequential decision-making problems, in which Q-learning is one of the most used algorithms. However, Q-learning suffers from overestimation errors, especially when action values in the same state are similar. To reduce such damages, we introduce an internal supplemental action measurement based on the variation of the expected state values between a state transition, which can measure the action causing the state transition. For the reason that the internal supplemental action measurement can increase or decrease the action values according to the action performance, it can increase the gap of the action values, thus reducing the sensitivity to the overestimation error. The experimental results in the Markov chain and the video games demonstrate the performance advantage of applying the internal supplemental action measurement, in which the mean evaluating scores with the internal supplemental action measurement are 131.6% in SpaceInvaders, 187.9% in Seaquest, and 176.6% in Asterix respectively of that without the internal supplemental action measurement.
Secure Resource Sharing on Hyperledger Fabric based on CP-ABE

Nowadays, Web services are becoming more and more common in people's lives. However, resources and services are coupled on the Web. That's to say, users have to expose their data to service providers in order to trade for Web services. Consequently, users lose control to their personal data, and undertake the hazard of data abuse and privacy leakage. Distributed resource sharing systems are dedicated to solving this problem, which typically face huge challenges in terms of access control mechanisms and data availability. Blockchain-based methods provide an effective solution, but mining consumes large computing power overhead and the accomplishment of access control mechanism is incomplete. In view of the above-mentioned challenges encountered in distributed resource sharing systems, we design a secure resource sharing architecture based on Hyperledger Fabric, and established a dedicated network and consensus mechanism to avoid unnecessary energy costs. We developed a fine-grained access control implementation plan for both acquisition and modification operations to flexibly realize the users’ unconditional control over personal data. We implemented a software prototype to verify the effectiveness of the proposed architecture.
An Approach to Extracting Ontology Concepts from Requirements

The paper proposes an approach to extracting ontology concepts from the results of automatic processing the software requirements written in natural Russian-language. Relevance of developing an approach to automating the process of extraction of the ontology concepts from the requirements written in natural language is due to the necessity to maintain the requirements specification in a consistent state under conditions of business environment variability. First we discuss the advantages of the ontology-based approach to the requirements engineering as a stage of the software engineering process. The main attention is focused on the possibilities of presenting knowledge about the software application domain and requirements specification in the form of ontologies. We analyzed possibilities of automatic Russian text processing tools for extracting ontology concepts and consider such tools as ETAP-4, MaltParser and UDPipe. The choice of UDPipe as a tool for automatic processing the requirements texts is explained by the best results that it showed when analyzing texts such as software requirements. Then we describe the main classes, object properties and data properties of the proposed requirements ontology and the application domain ontology. An additional advantage of the proposed approach, increasing its practical utility, is building the system of production rules by which the analysis of the results of the automatic processing of the requirements texts obtained using the UDPipe tool.
Study of Linear-Correlation Based Solar Irradiance Measurement Device Photovoltaic Application

Solar irradiance is the most fundamental element for energy generation from the photovoltaic system. Huge number of measurement instruments are produced commercially for better reading of this parameter. Nevertheless, there are still no detail studies that have ever been reported regarding the critical relationship between solar irradiance and other electrical parameters such as voltage or current in the literature. This study is essential to determine the correct parameters to be considered in developing a precise measurement of solar irradiance. In this study, a linear-correlation based solar irradiance measurement device has been designed to investigate this relationship. The obtained solar irradiance values from the proposed device exhibit an excellent agreement with those measured by the commercial solar meters/sensors which highly suggest that this study should become a trigger for better improvement of the solar measurement system in the future.
Chinese Named Entity Recognition in Medicine

Entity Recognition (NER) of Chinese material bank names such as text and medical history of social media is a challenging challenge. We describe and appraise character series display methods containing Chinese characters contained in Chinese NER, which develops richer features and improves the performance of the model using multi-task learning, self-interest, and efficient self-awareness training methods. The proposed model is currently the best single model with a rigorous F1 of 90.70% in the electronic history dataset (CCKS-NER 2017).
A novel MapReduce-based deep convolutional neural network algorithm

Deep convolutional neural networks (DCNNs), with their complex network structure and powerful feature learning and feature expression capabilities, have been remarkable successes in many large-scale recognition tasks. However, with the expectation of memory overhead and response time, along with the increasing scale of data, DCNN faces three non-rival challenges in a big data environment: excessive network parameters, slow convergence, and inefficient parallelism. To tackle these three problems, this paper develops a deep convolutional neural networks optimization algorithm (PDCNNO) in the MapReduce framework. The proposed method first pruned the network to obtain a compressed network in order to effectively reduce redundant parameters. Next, a conjugate gradient method based on modified secant equation (CGMSE) is developed in the Map phase to further accelerate the convergence of the network. Finally, a load balancing strategy based on regulate load rate (LBRLA) is proposed in the Reduce phase to quickly achieve equal grouping of data and thus improving the parallel performance of the system. We compared the PDCNNO algorithm with other algorithms on three datasets, including SVHN, EMNIST Digits, and ISLVRC2012. The experimental results show that our algorithm not only reduces the space and time overhead of network training but also obtains a well-performing speed-up ratio in a big data environment.
Viterbi Decoder with Configurable Constraint Length with Bit Error Correction for Satellite Communication

The Viterbi algorithm is an efficient method to decode convolution encoded data using the concept of maximum likelihood estimation. This paper presents the design and FPGA implementation of a Viterbi decoder for satellite communication. The design is coded in Verilog HDL using the Vivado 2017.4 tool and QuestaSim for behavioral and post-layout simulations. The decoder is designed to decode the output of rate half convolution encoder. It successfully decodes data for any constraint length and corrects error up to 4 bits. The implementation is done on the Zynq-7000 development board. The maximum operating frequency achieved is 221.9 MHz with a power consumption of 37.62 mW.
Qualitative Reasoning for Service Interactions

The realization of human-centered intelligent services requires a conceptualization of space and time that is intuitive for the human and employed in a concrete way by the services. In this position paper, we argue that qualitative temporal and spatial representation and reasoning must play a role in service interactions and service compositions in particular. We describe an initial prototype dealing with qualitative temporal relations and illustrate the added value of these kind of approaches in the tourism domain, as an exemplification.
The Sync-Up Process to Assist Multiple Stakeholder Communication of Requirement Analysis in Embedded Medical Software Development

The development of embedded medical software development involves multiple stakeholders, such as software developers, firmware, and hardware engineers. Such diverse members require effective communication to deliver the embedded project in time. In order to assist multiple stakeholder communication, the Sync-Up process has been developed. The process was reviewed by leading experts in the embedded safety-critical domain, and suggestions and recommendations were addressed to evolve the process. The validation of the Sync-Up process was conducted through case studies that were conducted in an embedded company and academic setting. In this paper, the result of the case studies and the future plan to evaluate the implementation part of the process will be presented.
Systems Engineering Roadmap for Dependable Autonomous Cyber-Physical Systems

Autonomous cyber-physical systems have enormous potential to make our lives more sustainable, more comfortable, and more economical. Artificial Intelligence and connectivity enable autonomous behavior, but often stand in the way of market launch. Traditional engineering techniques are no longer sufficient to achieve the desired dependability; current legal and normative regulations are inappropriate or insufficient. This paper discusses these issues, proposes advanced systems engineering to overcome these issues, and provides a roadmap by structuring fields of action.
FFHIApp: An Application for Flash Flood Hotspots Identification Using Real-Time Images

Extreme climate changes have become the new norm in today’s world. As a result, flash flood disasters continue to increase. It has become imperative to devise a quick disaster response system for minimizing the magnitude of damage and reducing the difficulties of human life. In this paper, we propose an application using android technology that provides real-time updates and prompt flow of authentic information of flood-ravaged areas to the rescue personnel or common people. The application accepts images belonging to flood-affected regions from rescue personnel, volunteers, etc. It authenticates and then filters those images using deep learning techniques. The severity of floods is estimated and plotted on a map using the associated location information of the images. The data is analyzed by using clustering techniques and visualized on the map. Subsequently, the affected areas of the flash flood are identified. Their peripheries are mapped so that these hotspots can be targeted for immediate relief operations.
MFECN: Multi-level Feature Enhanced Cumulative Network for Scene Text Detection

Recently, many scene text detection algorithms have achieved impressive performance by using convolutional neural networks. However, most of them do not make full use of the context among the hierarchical multi-level features to improve the performance of scene text detection. In this article, we present an efficient multi-level features enhanced cumulative framework based on instance segmentation for scene text detection. At first, we adopt a Multi-Level Features Enhanced Cumulative ( MFEC ) module to capture features of cumulative enhancement of representational ability. Then, a Multi-Level Features Fusion ( MFF ) module is designed to fully integrate both high-level and low-level MFEC features, which can adaptively encode scene text information. To verify the effectiveness of the proposed method, we perform experiments on six public datasets (namely, CTW1500, Total-text, MSRA-TD500, ICDAR2013, ICDAR2015, and MLT2017), and make comparisons with other state-of-the-art methods. Experimental results demonstrate that the proposed Multi-Level Features Enhanced Cumulative Network (MFECN) detector can well handle scene text instances with irregular shapes (i.e., curved, oriented, and horizontal) and achieves better or comparable results.
Pupils’ Summative Assessments in Mathematics as Dependent on Selected Factors

School assessment is a determining factor for a pupil in all functions. The aim of the research was to determine correlation between pupils’ logical thinking and their school assessment and between pupils’ mathematics skills and their school assessment. The sample size was created by 252 high school students (117 boys and 135 girls). As research tools were used reasoning mathematical, logical thinking and mathematical skills tests. Kruskal-Wallis test was used as statistical method. A significant effect was found at the 1% significance level between these factors and school assessment, where the greatest differences are caused mainly by evaluations in mathematics that were perceived either as the best or the worst. The research also monitored the use of the newly-developed GTOLT (Group Test of Logical Thinking), which is based on the GALT (Group Assessment of Logical Thinking) and TOLT (Test of Logical Thinking) tests, rooted in standardized tools.