Reviewerly/exHarmony huggingface dataset:
Screening out All Valid Aristotelian Modal Syllogisms

It is easy to understand that whether a classical syllogism is valid.That whether a modal syllogism is valid is not so transparent.The prevailing view on Aristotelian modal syllogistic is that the syllogistic is incomprehensible due to its many faults and inconsistencies.Although adequate semantic analysis or reconstruction of the syllogistic have be given by many authors, it is far from obvious how to extend these results so as to consistently cover the whole modal syllogistic developed.The major aim of this paper is to overcome these difficulties, and screen out 384 Aristotelian valid modal syllogisms from 6656 Aristotelian modal syllogisms in natural language.They can be formalized by means of set theory and generalized quantifier theory, and their validity can be proved by possible world semantics and the truth definition of Aristotelian quantifiers defined in generalized quantifier theory.The basic steps of screening out all valid Aristotelian modal syllogisms are as follows: firstly one can get all possible modal syllogisms obtained by adding modal operators to 24 valid classical syllogisms, and secondly eliminate invalid modal syllogisms by characteristic rules of modal syllogisms.It is hoped that these innovative achievements will make contributions to promote the development of Aristotelian and generalized modal syllogistic, natural language information processing, and further research on knowledge representation and knowledge reasoning in computer science.
A universal designated multi verifiers content extraction signature scheme

A notion combined the content extraction signature and the universal designated verifier signature was put forth by Lin in 2012. Specifically, it allows an extracted signature holder to designate the signature to a prospective verifier. However, existing designs become inefficient when multi verifiers are involved. To improve the efficiency, in this paper, we extend the notion to the universal designated multi verifiers content extraction signature (UDMVCES). Implementing our new scheme, the extracted signature holder can efficiently designate the signature to multi verifiers. Additionally, we provide the security notions and prove the security of the proposed scheme in the random oracle model. To illustrate the efficiency of our UDMVCES scheme, we analyse the performance of it. The analysis shows that the computation costs and signature lengths of the new scheme are independent of the number of verifiers.
A Framework of Social Robot for Elderly individuals

Developing and studying robots as an assistive tool for health-care professionals is a growing area of research due to the rapid growth in the number of elderly people and the demand for specialized caregivers. This paper presents our preliminary work of perceptive social robot for improving the quality of life of elderly individuals.
An Academic Achievement Prediction Model Enhanced by Stacking Network

This article focuses on the use of data mining and machine learning in AI education to achieve better prediction accuracy of students’ academic achievement. So far, there are already many well-built gradient boosting machines for small data sets prediction, such as lightGBM, XGBoost, etc. Based on this, we presented and experimented a new method in a regression prediction. Our Stacking Network combines the traditional ensemble models with the idea of deep neural network. Compared with the original Stacking method, Stacking Network can infinitely increase the number of layers, making the effect of Stacking Network much higher than that of traditional Stacking. Simultaneously, compared with deep neural network, this Stacking Network inherits the advantages of the Boosting machines. We have applied this approach to achieve higher accuracy and better speed than the conventional Deep neural network. And also, we achieved a highest rank on the Middle School Grade Dataset provided by Shanghai Telecom Corporation.
Multiple Random Empirical Kernel Learning with Margin Reinforcement for imbalance problems

Imbalance problems arise in real-world applications when the number of negative samples far exceeds the number of positive samples, such as medical data. When solving the classification of imbalance problems, the samples located near the decision hyperplane contribute more to the decision hyperplane, and the samples far from the decision hyperplane contribute less to the decision hyperplane. So we can consider giving higher weights to the samples near the decision hyperplane, but they are sensitive to noise, and too much emphasis on them may lead to unstable performance. This paper proposes a Margin Reinforcement (MR) method to overcome the above dilemma. Because the imbalance problem is a cost-sensitive problem, MR gives positive samples a uniform high weight to improve the misclassification cost of the positive sample. For negative samples, according to their entropy, samples away from the decision surface and noise samples mixed in the positive samples are given a smaller weight, in order to improve the efficiency and robustness of the algorithm. Therefore, MR can emphasize the importance of samples located in overlapping regions of positive and negative classes and ignore the effects of noise samples to produce superior performance. Multiple Random Empirical Kernel Learning (MREKL) has proven to be effective and efficient in dealing with balance problems. In order to improve the performance of MREKL on imbalanced datasets, MR is introduced into MREKL to propose a novel Multiple Random Empirical Kernel Learning with Margin Reinforcement (MREKL-MR). MREKL-MR efficiently map the samples into low-dimensional feature spaces, then utilizes the MR approach to reenforce the importance of margin samples and decrease the effects of noise samples. Experimental results on 28 imbalanced datasets indicate that MREKL-MR is superior to comparison algorithms. Finally, the effectiveness of MREKL-MR in dealing with imbalance problems is verified on the Heart Failure dataset.
A Novel Secure Video Steganography Technique using Temporal Lifted Wavelet Transform and Human Vision Properties

Steganography is a term that refers to the process of concealing secret data inside a cover media which can be audio, image and video. A new video steganography scheme in the wavelet domain is presented in this paper. Since the convolutional discrete wavelet transform produces float numbers, a lifted wavelet transform is used to conceal data. The method embeds secret data in the detail coefficients of each temporal array of the cover video at spatial localization using a unique embedding via YCbCr color space and complementing the secret data to minimize error in the stego video before embedding. Three secret keys are used in the scheme. Method’s performance matrices such as peak signal to noise ratio and Normalized Cross Correlation (NCC) expresses good imperceptibility for the stego-video. The value of Peak Signal to Noise Ratio (PSNR) is in range of 34-40dB, and high embedding capacity
Implementation K-nearest neighbour for student expertise recommendation system

Abstract The ability of students to determine their chosen field of expertise is still subjective, many students choose the field of expertise because their classmates choose the field of expertise not by considering their abilities and interests. This research uses the KNN classification method to determine areas of expertise that are in accordance with student expertise. The KNN method was chosen because it is a method that uses supervised algorithms where the results of new query instances are classified based on the majority of the categories in the KNN whose purpose is to classify test data based on training data. This system was tested using the confusion matrix method and the results were 98.30% of the total student data sample of 30 people.
A method for improving the detection accuracy of subpixel edge

In the process of workpiece dimension measurement, edge detection has great influence on the precision of dimension measurement. The Zernike moment edge detection method can detect the edge information completely. However, the algorithm is greatly affected by noise and the running time is too long, which leads to the insufficient precision of the workpiece size finally. Therefore, this paper proposes a subpixel edge detection method combining the improved Harris corner detection and Zernike moment, and conducts experimental verification. The experimental results show that the proposed algorithm can better reduce the impact of noise on the image of the workpiece, locate the image edge of the workpiece quickly and accurately, and detect the image edge of the workpiece completely, improve the accuracy of workpiece dimension measurement.
Path Planning of Uninhabited Aerial Vehicle Added the Guiding Factor

Path planning is vital to the application of Uninhabited Aerial Vehicle (UAV). A good path planning can be obtained by searching out a path between the start location and end location in time, along which UAV is able to accomplish the flight mission and avoid the hostile threats well. Due to the big search space, the original ant algorithm system (ACS) can easily converge to the local optimization and calculates efficiency slowly. The guiding factor is added to modify ACS and to solve the problems mentioned above. It can reduce the blindness of local search and ensure ants to reach the destination node. Both operations of the simple obstacle and the mixture obstacle are using to prove the method mentioned above. From the results of simulation, we can conclude: (1) The guiding factor can help to avoid both the simple obstacle and the mixture obstacles accurately and to obtain the optimal flight path. (2) Compared with conventional ACS, the calculate efficiency is higher and the local optimization is inexistence under modified ACS.
An edge computing‐based predictive evaluation scheme toward geological drilling data using long short‐term memory network

Abstract Edge computing has been identified as one of the new computing paradigms, while improving service response performance and reliability in cloud computing environment. Meanwhile, there has been a tremendous surge of interest in the study of analyzing geological data. Given the success of edging computing in some industrial applications, it is also expected that it can be applied in this field, with the ever‐increasing demand for effectively achieving mineral exploration. Then, a novel application is accordingly developed in this article, through the combination of edge computing devices and geological data analysis model. Specifically, on the basis of multiple drilling data in the mine, a predictive evaluation scheme is proposed. After preprocessing the complex original data and analyzing the relationship between mineral composition content change and depth, the long short‐term memory (LSTM) as a recurrent neural network in the field of deep learning, is used to model certain mineral components, due to the satisfactory performance of LSTM in addressing time series data. Moreover, an evolutionary computing method, that is, the particle swarm optimization algorithm, is also incorporated into our scheme to optimize some key parameters, in an effort to further improve the computational performance. Finally, in edge computing environment our scheme is deployed on a typical edge device, that is, the Raspberry Pi, to achieve mineral exploration in a more efficient manner, while avoiding the limitation of resource concentration. The experimental results show that our edge computing‐based predictive evaluation scheme can effectively improve the performance in analyzing geological drilling data.
Students’ preference for electronic and printed academic reading texts

Students’ preference for the medium of academic reading texts has been diversified in the modern era. Some students’ preference for attaining information and knowledge has been confined only to print media, some are found to be attracted to electronic media and some prefer to use both electronic and print media for their academic purposes. This article is based on the cross sectional survey carried out at nine campuses across Makawanpur district, Nepal in the Academic Year 2018-2019.The purpose of this study is to investigate the Bachelor of Business Studies students ‘preference regarding electronic and print media of reading texts. The researcher adopted simple random sampling to select 526 students from the population of 798students. To collect data, the researcher used a questionnaire regarding the students ‘preference for electronic and print medium of the reading texts. Frequency and percent statistics of students who preferred print medium of reading texts; and the frequency and percent statistics of students who preferred electronic medium of reading show that more students in the research study preferred the print medium of reading texts versus the electronic medium. The chi-square test of independenceÇ2 (1) = 0.631, critical value = 3.841 and p &gt; .05 show that statistically, there was no significant association between gender and the preferred medium of the academic reading texts.
Design and Realization of Eye Control System for Small Ground Unmanned Platform

Among human-computer interaction (HCI) technologies, eye tracking technology has the characteristics of direct, natural and sensitive. It can replace traditional interactive devices to improve the naturalness and efficiency of HCI. In this paper, an eye control system for small ground unmanned platform is designed and realized. Firstly, eye gesture and conscious blinking behavior models are constructed, and real-time recognition algorithms of eye behaviors are designed. Then, to avoid “Midas touch” problem, an area-based eye movement interactive mechanism combining eye gesture and blinking behaviors is proposed. Finally, eye control system of unmanned platform is realized. Experimental results demonstrate the effectiveness of the proposed mechanism.
The Analysis on the Implementation of Mobile-Assisted Language Learning Strategy Through Quizizz Application to Improve Student’s Reading Comprehension at Undiksha Singaraja

This research aimed at improving student's reading comprehension by applying MALL-based learning strategy through Quizizz application in literal reading class.Subjects of the research were the second-semester students of D class at Undiksha Singaraja who took the literal reading subject.The research was a classroom action research conducted in two cycles.The data were collected by giving reading comprehension test to measure the student's reading comprehension; observing the teaching-learning process; and giving questionnaire for collecting the data of student's perception toward the implementation of MALL-based learning strategy.The results showed that (1) the score of student's reading comprehensions test improved significantly at the end of each cycle; (2) the students had a generally positive perceptions toward the implementation of MALL-based learning strategy.In short, the student's reading comprehension in II D class could be improved by applying MALL-based learning strategy through Quizizz application.
Behavior Analysis using Serverless Machine Learning

This paper supplies a route for using the Watson Machine Learning API on IBM Cloud to carry out serverless data analytics using machine learning as a service. Transforming the large amount of data produced by an organization into intelligence can be done using advanced analytics methods such as using a modified Mahalanobis Distance algorithm for synthesis of correlation data under the purview of machine learning. Further refinement of correlation data is done using a Multivariate Reliability Classifier model. The consumption of this advanced analytics service can be done in a serverless manner where the developer only must be concerned with how the data is analyzed, i.e., scoring, batch or stream models with a continuous learning system without the outlay of hardware upon which to train those models. This paper examines the usage of such serverless AI systems in the scope of user behavior analysis over varied demographics.
Intrusion Detection of Industrial Control System Based on Stacked Auto-Encoder

With the deep integration of industrial control systems and Internet technologies, how to effectively detect whether industrial control systems are threatened by intrusion is a difficult problem in industrial security research. Aiming at the difficulty of high dimensionality and non-linearity of industrial control system network data, the stacked auto-encoder is used to extract the network data features, and the multi-classification support vector machine is used for classification. The research results show that the accuracy of the intrusion detection model reaches 95.8%.
Multi-agent Negotiation in Real-time Bidding

In this paper, we study the problem of multi-agent negotiation in real-time bidding scenario. We present a new solution, Extended Q-learning Network (EQN), which iteratively assigns the state transition probability and finally converges to a unique optimum effectively; Importantly, we propose a naive edge computing framework between mobile devices and cloud servers to handle the data preprocessing and transmission simultaneously to reduce the load of cloud servers; Finally, extensive experiment on real dataset demonstrates EQN's soundness (fast convergence), properties and effectiveness (achieve state-of-the-art in real-time bidding task). Our experimental results manifest that our proposed approach outperforms the comparable baselines.
Innovative Management Approach: Credential Digitalization

This article presents the results of a scientific study on the innovative approach to management of the means of digitalization of credentials in the practice of financial management of an enterprise.Development of digitalization of credentials is due to a new type of economic relations formed under the influence of an innovative directionthe digital economy.Today, digital technologies are becoming an integral part of financial management, which is caused by the general trend of the Industry 4.0, which sets new strategic directions, based on the flow of innovations in various areas of production.Application of strategic financial instruments within digital economy is used to evaluate the quantitative and qualitative parameters of performance.Digitalization in financial management contributes to transparency of accounting and reporting information, the speed of systematic assessment in order to make decisions aimed at achieving the financial goals of the activity taking into account the interests of various users (government, business owners, financial management).
Generation of Non-Coordinate Navigation Knowledge from a Flow of Input Views

An approach to automatic generation of a network of reference 'views' specifically structured for non-coordinate robot navigation is presented.The reference views are selected from the input flow while the robot moves along a random continuous trajectory in its environment.The network is organized as an atlas that represents inner model of the robot environment.It accumulates a 'rich' navigation knowledge extracted from the flow of 'poor' views.No coordinate transformation is involved, but fundamental topological concepts: the continuity of a robot trajectory and discontinuities ('heavy changes') detected in the input flow.Avoiding any explicit coordinate transformation becomes possible using a local inversion of the knowledge hidden in the relation 'robot control -change of view'.Both techniques -the inversion and detection of heavy changes -are specific for a particular robot architecture.The approach was verified on a virtual static 2D-workspace.
Social Adaptation of Foreign Students in Higher Educational Institutions (Case of Samara University)

was inspired by the objective of the Education National Project: to increase the number of foreign citizens studying in Russian universities.The aim of this study is theoretical
Usability Evaluation of an Augmented Reality Children's Book

This paper aims to contribute to the Augmented Reality area by evaluating a mobile application in books designed for children. Two sets of usability evaluation criteria for Augmented Reality were compiled from two systematic reviews. The first one served as base for a heuristic evaluation, and the second one for a usability evaluation. We also present the entire process of evaluation, as well as the results and discussion.
Out with the Old, In with the New: Revising ERM Workflows in a Time of Change

The E-Resources and Metadata team of the University of Guelph Library have recently been motivated by multiple factors to undertake a massive review and revision of their workflows and accompanying documentation. This presentation explored their motivations for the task, as well as the principles and goals they used to guide their efforts. The presenters gave updates on the progress made so far and outlined future plans.
Web-app realization of Shor’s quantum factoring algorithm and Grover’s quantum search algorithm

Quantum algorithms are well-known for their quadratic if not exponential speedup over their classical counterparts. The two widely-known quantum algorithms are Shor’s quantum factoring algorithm and Grover’s quantum search algorithm. Shor’s quantum factoring algorithm could perform integer factorization in O(logN). Grover’s quantum search algorithm could solve the unsorted search problem in O(√N). However, both algorithms are introduced as theoretical concepts in the original papers due to the limitations of quantum technology at that time. In this paper, an improved way is presented to realize the two algorithms into a web application using state-of-the-art quantum technology. The web-app is designed and built considering the uses of a quantum simulator and libraries provided by ProjectQ and Rigetti Forest. The result shows that both algorithms are realizable into web-applications.
Dynamic Graph Algorithms with Batch Updates in the Massively Parallel Computation Model

We study dynamic graph algorithms in the Massively Parallel Computation model, which was inspired by practical data processing systems. Our goal is to provide algorithms that can efficiently handle large batches of edge insertions and deletions.We show algorithms that require fewer rounds to update a solution to problems such as Minimum Spanning Forest, 2-Edge Connected Components, and Maximal Matching than would be required by their static counterparts to compute it from scratch. They work in the most restrictive memory regime, in which local memory per machine is strongly sublinear in the number of graph vertices. Improving on the size of the batch they can handle efficiently would improve on the round complexity of known static algorithms on sparse graphs.Our algorithms can process batches of updates of size Θ(S), for Minimum Spanning Forest and 2-Edge Connected Components, and Θ(S1–∊), for Maximal Matching, in O(1) rounds, where S is the local memory of a single machine.