Reviewerly/exHarmony huggingface dataset:
A Development of Instructional Package Using Problem-Based Learning for Power System Transients

This paper purposes to develop instructional package by using Problem-Based Learning for power system transients in case study of Metropolitan Electricity Authority (MEA) which has developed the instructional package of problem-based learning for power system transients from the real problems in the power system by designing and creating instructional package such as teacher manuals which consist of activity plans, content paper, activities, tests, instruction media and program manuals, etc.. They are used for teaching and learning 4 units: basic of electrical power system, power system simulation, simulation program and case studies of power system transients which directed to 5 experts in order to assess the quality of the instructional package. It was found that the developed instructional package was appropriate extremely. After that, the experiment was conducted with a sample group of 30 undergraduate students enrolled in the power system analysis course, graduate technical education curriculum, electrical engineering, King Mongkut’s University of Technology North Bangkok. The achievement of learners through the learning process with the instructional package by performance testing according to the Meguigans’s standard criteria is equal to 1.09. In addition, the learners were satisfied at a high level.
Construction and Exploration of Intelligent Manufacturing Training Rooms Integrated with Production

The Made in China 2025 strategy puts forward the goal of building China a manufacturing power. To meet the demand for high-quality technical and skilled talents, in this paper, an intelligent manufacturing training room for the cultivation of intelligent manufacturing professionals in higher vocational colleges is constructed. It is proposed that the intelligent manufacturing training room should have the characteristics of exhibition, teaching and scientific research. Taking these characteristics as the basic construction concept, the layout of the training room is designed; the construction scheme and the construction mode are elaborated in detail. Practice has proved that the intelligent manufacturing training rooms can serve the teaching, researching and talent training in higher vocational colleges, and plays a positive role in promoting the school development, improving teachers’ scientific research ability and enhancing students’ practical ability. It can provide reference for the construction of similar training rooms in other colleges and universities.
Clustering RDF data using K-medoids

Semantic web is a knowledge graph formed around semantic languages to enable computers and software to understand contents on the web. The content is explicitly annotated with semantic metadata using Resource Description Framework (RDF) language. However, the main issue is how to efficiently retrieve the RDF data taking into account a wide variety semantic and syntax nature and large-scale of such data. This paper aims to introduce a novel mechanism based on K-medoids algorithm for narrowing down the contents of the Web to clusters pertaining subset of information. We integrated sequence alignment algorithms with linguistic similarity measures to build a distance matrix which is used later in K-medoids clustering algorithm. The experimental outcomes showed a promised result for accuracy and quality of clustering.
Prospects for the use of modern technologies to increase student involvement

The article discusses the prospects of role-playing games in the educational process to increase students’ motivation and involvement, as well as the possibility of modern information technologies (primarily social networks) for constructing innovative forms of such games. The author’s format of the live-action role-playing game is presented. It was developed and tested at the Saratov State University. It involves the integration of numerous educational tasks into a single plot, within which each participant plays a role and communicates with other participants to complete tasks. The main interaction, plot development and group activities occur in dialogs, conversations and groups of social networks. This approach has shown its viability in teaching legal disciplines, and also has prospects for use in IT education.
The Information Security Risk Assessment Model Based on Improved Electre Method

Information security is a global problem and has attracted worldwide attention. Information security risk assessment is of great significance to understand the information security situation clearly and put forward pertinent measures. Information security risk assessment is system engineering, involving standards, technology, management and other factors. Owing to the complexity and uncertainty of information system, information system security assessment inevitably involves a certain degree of fuzziness. This paper brings forward the information system security assessment model based on improved Electre method with linguistic variables. First, a two-level index system for information system security is constructed based on the feasibility and maneuverability condition. Second, linguistic variables are adopted to represent evaluation values of second-level indicators and WAA operator is adopted to calculate the evaluation values for first level indicators. Then the improved Electre method is adopted to evaluate and rank the alternatives. The Results show that the improved Electre method is more effective and more practical for security risk assessment of information system.
Word Segmentation in Document Images using Deep Convolutional Encoder Decoder Network

Tremendous amount of data is present in the form of scanned document ScD or in digital image format. To extract information present in documents Optical Character Recognition OCR of these documents is unavoidable. OCR software [2], [3] internally segments pieces of word and OCR that specific parts. So to the great extent accuracy of OCR and quality of text recognition is reliant on the quality of segmentation achieved. In this study, we propose a novel technique for Word Level Segmentation WLS using Deep Learning DL. To handle unavailability of annotated data artificial data generation technique is also proposed.
Design Information Security Management System Based on Cryptography

Encryption technology is the cornerstone of networked security technology. The data encryption process refers to the conversion of information into meaningless information through the encryption key and the encryption function, and the receiver restores the cipher text to the plaintext through the decryption function and decryption key. Designing an information security management system is an important information support platform for networked design. This article will focus on how to use cryptographic techniques to ensure network design information security.
Multiview Panorama Alignment and Optical Flow Refinement

Current techniques for “RealVR” experiences are usually limited to a small area around the capture setup. Simple linear blending between several viewpoints will disrupt the virtual reality (VR) experience and cause a loss of immersion for the user. To obtain smoother transitions, optical flow based warping between viewpoints can be utilized. Therefore, the panorama images of these viewpoints do not only need to be upright adjusted but also their viewing direction need to be aligned first. As panoramas for VR are usually of high resolution for high quality results in every direction, the optical flow in a high resolution is also indispensable. This chapter gives an overview of how to align several viewpoints to a common viewing direction and obtain high resolution optical flow in between.
Creating/Deleting Objects Fast Forword Planning

Fast forward programming for creating/deleting objects is a method to deal with the problem of creating/deleting objects. This paper puts forward the concept of object element, which can be divided into ordinary object and object element. A new algorithm, RCDO algorithm, is designed. The partial relaxed RCDO algorithm is used to expand the planning diagram. In the process of expanding the planning diagram, mutual exclusion between actions, propositions and objects is considered. According to the newly constructed heuristic function, the heuristic value is calculated. In order to avoid the appearance of plain state, the enhanced mountain climbing method was used as the search strategy. The planning process for CDOFF is described in detail below.
Distributional memory explainable word embeddings in continuous space

Natural Language Processing (NLP) is a key processing step in fusion systems that need to process unstructured -and possibly human generated- text in natural language. Recent developments in Deep Learning have greatly increased the performance of NLP tasks. In particular, learned word representations have the form of high dimensional real valued vectors, called word embeddings, that have a number of amenable properties such as representing similar words with similar values of their vectorial representation, and capturing semantic regularities that correspond to geometric properties in the continuous high dimensional space. However, word embeddings have the drawback of being non interpretable. That is, their dimensions cannot be clearly associated to linguistic features. In this work, we propose real valued explicit linguistic word vectors that enjoy the properties of learned word embeddings while being human understandable.
Overview of Lattice-Based Methods in Cryptanalysis

In this paper, we give a short introduction to the hard lattice problems and application of corresponding methods in cryptanalysis. We provide several examples of specialized lattices we may use to solve certain instances of the related tasks, such as the Knapsack Problem or the Learning with Errors Problem. In this study, we pay special attention to using lattice-based cryptanalysis with asymmetric Knapsack-based cipher systems.
OA18: A New Office Actions Benchmark

Action recognition is one of the current hot topics in the literature due to its important applications. To better evaluate the existing models, several datasets are publicly available and range in scale from synthetic datasets to complex realistic datasets. However, it seems that very few datasets are domain specific. This paper publishes a new recorded dataset that studies 18 domain specific human actions, specifically, actions performed by employees inside an office. This is the first published dataset in the literature for the office domain. Moreover, a python-based software is developed and used for labeling the recoded videos and presented for future usages. The recorded dataset is designed not only to study several existing challenges in the literature which include the variation of viewpoints for the same class and the issue of shaky videos, but we also cover the usage of deep learning techniques on this small dataset by performing data augmentation and transfer learning.
Electric Machine Control Technics

In this chapter, a review of the control theory focused on the control of electrical machines is shown. The most control structures for electrical machine control are described, as well as the regulation based on classic controllersController such as the proportional–integral–differential (PID). The structures of the PID controller are also described in order to know how to choose the most suitable for the design. The digital control is introduced in detail as digitalization methods for integrators and derivators, PIDsProportional-Integral-Differential (PID), aliasing, zero-order hold, quantifiersQuantifier, and time delays. The digital PID is implemented based on models of Simulink®, m-function, and Stateflow®, which will serve as an introduction to the subsequent chapters. As an alternative to classical PID controllersController, non-linear control based on fuzzy logic is introduced, ending the chapter with a comparison between classical PI control, adaptive PI control, and PI control plus fuzzy control.
A Survey on Linguistic-Based and User-Based Recommending Posts Using Two-Level Clustering Methods

Online social networks have produced bunch of online social groups where individuals can collaborate and exchange their ideas. The real problems that conflicts with the user security and convenience are confidentiality break, groups without inception, confusion created from various groups’ difficulty in managing groups. This can be overcome to an extent by an automated filtering method required to categorizing group members based on their responses. This paper proposes clustering of group posts on thematic, emotional, stylistic, sentimental and psycholinguistic methods and group members are categorized based on their responses to the posts belonging to different clustering methods. This categorization helps to stop irrelevant notifications to the users which are received from more than one groups, via recommending the posts which might interest the users. It also helps to identify the group members those violate group policies by posting negative posts. The post categorization increases the performance where there are large numbers of members in a social group by performing linguistic clustering. The contribution work is to implement location-aware personalized posts recommendation using user’s behavioral patterns and their geographic location. Another, important work is to implement text-to-speech system converting English text into speech using speech synthesis technique. The system gives user rating to the users who share posts depending on the cluster they belong to. The system provides read later post functionality to the users. The system also provides the users with hash-tag recommendations. The system also provides user with posts based on long - term and short - term analysis. The system has been tested on Twitter API group data where a significant solution to an unaddressed problem associated with social networking groups is offered.
Developing Mindful Collection Decision Making: A Case Study on Analyzing Acquisition E-book Projects Using Financial Concepts

Traditionally, the College of Staten Island (CSI) library has employed cost per use in determining its collection development strategies but is attempting to broaden these assessments by using cost-benefit financial concepts. Due to significant budgetary cuts, the library has begun reviewing different assessment strategies that could maximize the development of its collections with the least amount of cost incurred. At the core of the financial mindful evaluations chosen, the CSI library intends to mindfully demonstrate fiduciary responsibility and accountability for e-book project expenditures, while determining some potential beneficial outcomes in monetary terms. The projects that are analyzed using financial concepts are two long-standing Demand-Driven Acquisition (DDA) e-book projects and the CSI library Open Educational Resources (OER) project involving the utilization of Open Access (OA) textbooks in comparison with its print textbook reserve collection. The strengths and weaknesses of using quantitative financial analysis to document the success of these projects are reviewed within the context of the historical usage of financial valuations in academic libraries.
Delta compression optimisation for UAV‐enabled mobile edge caching

Unmanned aerial vehicles (UAVs)-based sensor network is an effective mechanism for recognising and tracking of manoeuvring targets as well as expanding the monitoring coverage in the battlefield. However, there exists redundancy among the spectrum data collected by a UAV monitor within a data collection period, which may waste storage space and reduce the speed of data uploaded to the control centre. The authors assume that each UAV is equipped with an edge computing server and propose a delta compression method, which can save cache space and transfer time. First, they present a cost model and evaluation model for delta compression of the COPY/ADD class. Then an optimisation problem is formulated aiming to obtain the optimal delta encoding. Additionally, a maximal total length of copied fragments (MTLC) algorithm is proposed to find more mutually separated L-grams common fragments between the data collected at two adjacent moments. Theoretical analysis proves that the MTLC algorithm can generate a good delta encoding with the maximum total length and then the minimum total number of the COPYs. Moreover, numerical results show that MTLC has better performance in constructing a good delta encoding than the simple greedy and hash suffix array delta algorithms.
Implementation of Handoff System to Improve the Performance of a Network by Using Type-2 Fuzzy Inference System

Wireless networks comprise of different networks such as cellular, WiFi, WIMAX, etc. Mobility-based networks are increasing at a rapid rate and similarly, the demands of the users to access Internet are also increasing at any time and at any place. Thus, to fulfill the demands of the users, an effective handover is required. However, with the advancement in technology, the methods used earlier were not seemingly more effective. In the traditional approaches, the limited numbers of parameters are used. This paper consists of a scheme of advancement in the fuzzy system of inference, i.e. fuzzy type-2, as it provides an additional design for systems used in situations with large amount of uncertainties. It is concluded with the simulation results that the proposed system outperformed the traditional approach by increasing the number of parameters to be utilized in achieving the handoff.
Self-Elastic Real Time File(s) and Database Configuration Replenish Using FADBUPD in Time Critical Applications

In the challenging IT sector, corporates across the globe are advancing to automation processes to minimize the manual interventions for modifications in production systems. The changes in production environments can be relating to cronjobs, MIS, alters concerning the dismissals of running binaries or system hygiene and, update file or database based configuration being used in time critical applications. Any of said requirements in live environment require operations team to do the modifications to bring the change into effect. Mistakes committed in manual modifications of file(s) or database based configurations update reload can lead to huge penalties. Post modifications, binaries or applications are required to restart to bring the change to work. Considerable downtime is required to restart applications and client running huge traffic may not allow in peak hours. Also, incorrect manual changes may cause huge loss especially to financial organisations. To counter the issue, the paper postulates a real time FADBUPD algorithm. Self-elastic file / database based configuration replenish algorithm dynamically reloads database or file based configurations into the binaries or applications running on production systems. Thus it reduces the risk of manual failures as well as the downtime needed to restart time critical applications. It ensures high availability. This algorithm can be easily incorporated into any time critical application running in heterogeneous environment that runs on a specific file(s) or database based configuration. Any change being done to file(s) or database will be real time detected in a running process or application. This will eliminate the downtime required to restart application and will provide consistency in live incoming traffic at high throughput.
Digital Design of Radial Basis Function Neural Network and Recurrent Neural Network

Artificial Neural Network (ANN) are significantly used for fast and highly accurate computation in various fields. This paper addresses digital design for two Machine learning Algorithms, Radial Basis Function Neural Network (RBFNN) and the Long Short Term Memory Recurrent Neural Network (LSTM-RNN). The stochastic gradient descent (SGD) method is used as a learning algorithm for the former and Simultaneous Perturbation Stochastic Approximation (SPSA) method is used for the latter. The design are implemented in MATLAB simulink and further field tested for speech recognition.
CIEA: A Corpus for Chinese Implicit Emotion Analysis

The traditional cultural euphemism of the Han nationality has profound ideological roots. China has always advocated Confucianism, which has led to the implicit expression of Chinese people's emotions. There are almost no obvious emotional words in spoken language, which poses a challenge to Chinese sentiment analysis. It is very interesting to exploit a corpus that does not contain emotional words, but instead uses detailed description in text to determine the category of the emotional expressed. In this study, we propose a corpus for Chinese implicit sentiment analysis. To do this, we have crawled millions of microblogs. After data cleaning and processing, we obtained the corpus. Based on this corpus, we introduced conventional models and neural networks for implicit sentiment analysis, and achieve promising results. A comparative experiment with a well-known corpus showed the importance of implicit emotions to emotional classification. This not only shows the usefulness of the proposed corpus for implicit sentiment analysis research, but also provides a baseline for further research on this topic.
Impossible Differential Cryptanalysis of Surge

Abstract In 2018, Li Lang et al. proposed a new lightweight block cipher which is called Surge. Its block size is 64-bit, and the length of key size is 64/80/128-bit, respectively. They also proved that the cipher has good performance on security and effectively resists on differential attack, linear attack and algebraic attack. In this paper, some 4-round impossible differentials are constructed with the miss-in-the-middle method. Furthermore, 6-round impossible differential attacks are proposed on Surge- 64/Surge-80/Surge-128 algorithms based on the 4-round impossible differentials. The data complexity of the attack is 2 38 /2 39 /2 39.5 chosen plaintexts, the time complexity is 2 51.4 /2 51.7 /2 60.9 6-round encryptions, and the storage complexity is 2 40 /2 40.1 /2 49 storage units. This is the first time to make the impossible differential cryptanalysis for Surge.
An approach to secure multi-tier websites through SQL-Injection detection and prevention

The vulnerability of SQL injection allows attackers to directly flow commands into the database of the underlying web application and destroy their functions or confidentiality. In recent times it has gone from threatening the front ends of the web application to exploiting vulnerabilities in the back-end scheme of this database. In this study, we proposed a model to prevent different SQL injections for web-based multi-tier architecture. The model exhibits its efficiency by the tracking of different SQL injections and the performance result illustrates its applicability.
Modeling the Behaviors of Participants in Meetings for Decision Making Using OpenPose

This paper discusses the modeling for interactions between the behaviors of participants in a meeting for decision making. First, we adopt the behaviors (i.e., eye, facial, and hand movements) detected by OpenPose [9] as a skeleton detection algorithm using a single camera. Second, we propose a modeling method for the participant behaviors based on neural networks. Furthermore, we discuss the relationships between the participant behaviors and the model parameters in multi-layered neural networks based on the experimental results. Notably, we show that the participant characteristics are represented by the indices based on the parameters of the abovementioned models.