Reviewerly/exHarmony huggingface dataset:
Laboratory manual based on smartphone application in increasing students’ psychomotoric learning outcome

Abstract This study aims to determine the differences in psychomotor learning outcomes between students who are use a smartphone-based laboratory manual with students who use laboratory manual based on printed module. This type of research is quasi experimental research. The sample consisted of 40 students 11 th grade high school with 20 experimental group students and 20 control group students. Data collection uses observation sheets of student lab work skills. The results showed that there were significant differences between student learning outcomes using the lab manual on smartphone with students using printed modules so that it could be said that laboratory manual based on smartphone application could improve students’ psychomotor learning outcomes.
Cathoristic Logic

Cathoristic logic is a multi-modal logic where negation is replaced by a novel operator allowing the expression of incompatible sentences. We present the syntax and semantics of the logic including complete proof rules, and establish a number of results such as compactness, a semantic characterisation of elementary equivalence, the existence of a quadratic-time decision procedure, and Brandom’s incompatibility semantics property. We demonstrate the usefulness of the logic as a language for knowledge representation.
Heterogeneous features extraction based on deep learning for drug-related webpages classification

In this paper, heterogeneous features extraction is conducted by deep learning for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and effective images are chosen by the FOCARSS algorithm. Second, text-based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Webpages representation is generated by concatenating representations of text and images. Heterogeneous feature extraction are conducted by deep learning and classical methods, such as PCA, respectively. Feature selection is also conducted using information theory. Last, extracted features and selected features are classified. Experimental results demonstrate that the classification accuracy of features extracted by deep learning is higher than those of features extracted or selected by classical methods, and also higher than the accuracy of single modal classification.
A Text Classification Model to Identify Performance Bonds Requirement in Public Bidding Notices

A performance bond is a means of guaranteeing that a product that will be delivered by the seller in a timely and workmanlike manner. To verify whether a performance bond has been laid down in a bidding notice, an assessment through an internal auditing process is made periodically, but it can be costly and take longer than the available time to conclude an audit engagement. We propose to explore and apply algorithms to create a model able to identify bidding notices that demand performance bonds, to make the assessment process more efficient. We applied Four different classification algorithms (SVM, kNN, Random Forest, and Naive Bayes) on two different vector space representations (term frequency and term frequency inverse-document frequency). Random Forest with term-frequency produced the best model, which achieved $$F_{1}$$-score of 0.933 on the test set. The promising results show the model created could help decrease the time and effort spent on verifying the performance bonds requirement on bidding notices.
Feature Selection Algorithm based on Double-hook Function and TWSVM

Twin support vector machine (TWSVM) produces a pair of non-parallel hyperplanes in the progress of classification, which makes each feature correspond to two weights. While using the two weight vectors to select features directly is not possible. In this paper, a feature selection algorithm based on double-hook function and twin support vector machine is proposed, namely double-hook TWSVM algorithm. In this algorithm, combined with the characteristics of the double-hook function, the two weight vectors generated in the process of solving the linear TWSVM are merged into one single weight vector, which represents the corresponding feature’s degree of importance, so that this weight vector can be used to select the characteristics. Finally, the algorithm is analyzed by UCI dataset. To further prove the performance of our algorithm in the aspect of feature selection, it is then used to select color features from a real dataset of woodiness board and make classification. The experimental results show that the feature selection algorithm based on the double-hook twin support vector machine can remove the redundant features, reduce the dimension of features, and ensure a high classification accuracy, which means the algorithm has played a crucial role in the selection of features.
Linear Rotate Subspaee based Visual Tracking Methods with Application to UAV Stand-off Target Tracking

Using airborne cameras and guiding UAVs to track moving ground targets is an important aspect of the applications of UAVs. In this paper, a visual target tracking algorithm for airborne cameras is designed based on such application scenarios. During the dynamic changes in the relative positions of the target and UAV, the algorithm continuously uses the current target image to simulate and generate the possible pseudo-image of the target after rotation, which is a linear rotate subspace, and generate pseudo-filters. In the process of tracking it determines whether to update the target image and the corresponding filter by tracking performance, and thus improves the stability of visual tracking. The proposed algorithm is tested on the data set from the UAV tracking perspective, and results show that the tracking accuracy and robustness of the proposed algorithm are higher than those of the current mainstream tracking algorithms.
Swarm Filter - A Simple Deep Learning Component Inspired by Swarm Concept

Swarm is a research topic not only of biologists but also for computer scientists for years. With the idea of swarm intelligence in nature, optimal algorithms are proposed to solve different problems. In addition to the proactive aspect, a swarm can provide useful hints for identification problems. There are features that only exist when an individual belongs to a swarm. An idea came to us, deep learning networks have the ability to automatically select features, so they can extract the characteristics of a swarm for identification problems. This is a new idea in the combination of swarm characteristic with deep learning model. The previous studies combined swarm intelligence with neural networks to find the optimal parameters and architecture for the model. When performing our experiments, we were surprised that this simple architecture got a state-of-the-art result. This interesting discovery can be applied to other tasks using deep learning.
Classification of Benign or Malignant Cell Nuclei using Nucleolus

Cytology directly examining cells in the early detection of cancer plays an important role in the medical diagnosis, but this diagnosis depends on the experience and technology of a pathologist. Problem is that it costs time for the examination and its objectivity is poor in the less experienced pathologist. Although there are some previous researches to detect nuclei, this paper proposes the automatic classification of benign or malignant of cell nuclei based on the characteristics that nucleolus has the features of malignant cell nuclei and appears frequently in the malignant cell with cancer. Classification of benign or malignant cell nuclei is performed by detecting nucleoli and counting the number of nucleolus detected.
Using Synthetic Images with Deep Convolutional Neural Networks for Racial Face Recognition

In the past, people usually employ the facial feature extraction and shallow learners such as decision trees, SVM, Naive Bayes, etc. to classify faces of different races. Deep learning usually takes lots of time to train. But with the advances in hardware and new algorithm proposed, the training time problem is gradually alleviated. The deep convolutional neural networks have good effect on images classification. In this paper, we use the deep convolutional neural networks to try to solve the problem of classification faces of different racial origin. Because the convolutional neural networks usually require a huge amount of data for training for good performance, such training set of real racial faces is not available to us. As a result of small set of real racial faces, this study proposes to incorporate synthetic facial images in our training set to sufficiently increase the size of the training set. To the best of our knowledge, this study is the first to propose to incorporate synthetic racial faces to train a deep convolutional neural network to classify real racial faces. We compare the performance of only employ synthetic facial images and mixtures of synthetic and real facial images in the training set. Our experiments show that training with only the real facial images (2,500 images) can achieve 91.25% accuracy in classifying faces of three different race origins. However, the classification when training with a mixture of 2,500 real facial images and 15,000 synthetic facial images can be further improved to 98.5% in accuracy.
Invisible-Guardian: Using Ensemble Learning to Classify Sound Events in Healthcare Scenes

The dramatic increase of senior population worldwide is challenging the existing healthcare and support systems. Whereas traditional home health monitoring systems (like video-based method) often cause users’ discomfort with severe feeling of privacy intrusion, which impedes the popularity of home health monitoring systems. Then Invisible-Guardian is proposed, an ensemble learning (EL)-based high-efficiency auditory information centered healthcare system for the elderly at home. The system collects everyday sound, like door open and close through a sound sensor array arranged indoors. Information such as the current event type is obtained after preprocessing, extracting features, classifying with the EL and supplemented by the inertial sensing units. If an emergencies event occurs, such as fall, the information will be sent to the doctor and relatives immediately, which greatly protect the daily life safety of the elderly living alone. The feasibility of the system is verified by human activity recognition experiments, and the recognition accuracy reaches 94.17%. Compared with the traditional home health monitoring method, it not only reduces the cost burden of hardware equipment, but also monitoring elders’ daily life with less feeling of privacy intrusion. what’s more, it minimizes the amount of data and ease of calculation, which improves the efficiency of home health monitoring for the elders and more favorable to the safety of the elderly living alone.
Simulated website login to improve network data crawling efficiency

With the increasing demand for content from news websites, Web crawler technology has been widely used in the automatic crawling of news website data. However, data crawling behavior is often found and blocked by websites, resulting in inefficient crawling. This paper analyzes the structure of news websites, designs and implements a highly efficient simulated login model for crawling, which avoid the human authorized step during the log-in process. The code testing shows that this simulated login method effectively improves the efficiency and data integrity of the crawler.
Fuzzy Inference Model for Determining the Question Difficulty Level within Adaptive e-Assessment Instructional Systems

The e-assessment is a crucial process that allows to validate the previous state of knowledge acquired by the student, as well as to monitor his/her progress and/or validate the level of knowledge obtained by him/her at the end of the learning process. The aim of this paper is to propose a fuzzy inference model for determining the difficulty level of e-assessment questions within adaptive instructional systems, thus allowing to integrate and analyze key features of the questions properly matching with the student's cognitive profile for an appropriate question selection. In order to validate the model a prototype was built and tested through a case study. Results obtained demonstrate the effectiveness of the proposed fuzzy inference model for adaptive e-assessment instructional systems.
Enhancement of Reflected Faces on Semi-reflecting Surfaces

Face recognition is interesting research area in computer vision. This paper proposes to enhance faces reflected on semi reflecting surfaces such as glass window, glass screens or any other mirror like surfaces. Visibility or clarity of reflected image is depending on the reflecting ability of material surface on which reflection occurs. Other than mirror surfaces, majority of reflected images are less visibility. So recognition of reflected face is a challenge in the proposed method. This paper addresses enhancement of reflected face image. Estimating atmospheric light and medium transmission map, recover haze free image. Apply CLAHE i.e., adaptive histogram equalization by limiting contrast to obtain enhanced reflected face image.
Debiased Converted Measurements based Target Tracking Algorithm in Boost -phase

The nonlinearity of measurements makes a direct impact on the accuracy and timeliness of target tracking. Considering that, a method of debiased converted measurements is presented. Firstly, the nonlinear measurement equations are pseudo-linearized and then an unbiased least squared estimator for target location in three dimensional space is derived, by which detector's bearings-only measurements can be converted to target's spatial position in Cartesian coordinates, and so the above-mentioned nonlinearity is improved greatly. In addition, the statistics characteristic of position error is given. The 8-state gravity turn model for target in boost-phase is introduced and target status estimated by EKF based on debiased converted measurements, the traditional EKF and UKF are compared by simulation. It is shown that the proposed algorithm exhibits the similar accuracy as UKF, while its computation efficiency approximates to the traditional EKF, consequently, its feasibility and efficiency is born out.
Lower Limbs Gait Rehabilitation System Based on Regression Analysis

Based on online machine learning, the automatic control system for lower limbs gait rehabilitation is designed. The system structure design, motion signal acquisition and rehabilitation analysis processing technology are introduced. The software architecture of the distributed control system, kinematical modeling of the lower limbs exoskeleton, implementation of fundamental motion control algorithm and the design of gait rehabilitation system are expounded. This paper illustrates the software architecture of distributed control system, and the design of motion sensing and controlling algorithm. The system utilizes polynomial regression and online gradient descent to analyze customers' lower limbs gait, these results are used by motion control system to generate more accurate motion parameter; and has the function of perceiving, recording, and remotely monitoring customers' motion. The system has friendly interactive interface for staffs of monitor center or relatives of patients to supervise state of patients' motion and information of environment in real time. Experimental results prove that the system could reliably help customers' lower limbs gait rehabilitation, possessing some practical value.
Mitigating Rogue Node Attacks in Edge Computing

In this work, we present a secure authentication protocol for edge computing using the Software-Defined Networking platform and wireless network virtualization. The main contribution of the presented protocol lies in: (1) designing and implementing a practical solution to the security problem of rogue Fog nodes, and (2) providing the cloud service provider with exclusive control over the security configuration and specification of its leased virtual networks independent of the security mechanisms implemented by the underlying infrastructure provider. The cloud provider dynamically enforces its security policies on the communication at the network edge, therefore circumventing any possible security vulnerabilities in the underlying physical wireless infrastructure. The authentication protocol manages the trusted communication among the mobile client, the infrastructure provider, and the cloud service provider, yet confines the Public-Key Infrastructure (PKI) deployment to solely the infrastructure and cloud providers. This enhances the scalability of the system and reduces the complexity of its security management and configuration. A system testbed is simulated using the Mininet emulator and the Amazon EC2 Cloud.
Health Geographic Information System as Monitoring Tool Using Regular Monthly Report in District Level Health Office: Study Case in Bandung District Level Health Office, Indonesia

In Indonesia, there is a Sub-district Level Health Center, named PUSKESMAS which stands for Pusat Kesehatan Masyarakat. Sub-district Health Centers provide certain public health services to the community directly. Regarding health reporting, District Level Health Centers must report various types of forms to the District Head Office. One type of reporting is spreadsheet-based monthly reports, special templates have been provided by the District Head Office. The Subdistrict Health Center must fill in the data in a numerical value according to the template provided. Because data is only presented in numerical values, it is still difficult to monitor and analyze what health conditions are in the city. Visualization of data grouped based on the coverage area of the District Level Health Center is needed at the district level to monitor health conditions every month. The proposed system can read and extract data from a spreadsheet then transform it into a specific database management system. The web-based interface combined with map applications such as google maps, is used as a user display that can be accessed from any type of PC, laptops, tables, etc. The entire system and application are called web-based health geographic information systems. To monitor and analyze health conditions, users can use the features provided for example: the top ten diagnoses in all District Level Health Centers, etc.
Analysis of Kapferer Mine Network using Graph Energy Ranking

Vertex centrality is one of the procedures to evaluate complex networks, and it can disclose current patterns of networks. By evaluating their structural characteristics, it enables us to understand networks and their elements. One of the complex networks of nodes and interactions is the social network. It is increasing very greatly every day owing to the addition of fresh nodes. In such a vast network, therefore, not all nodes are equally essential, identifying influential nodes becomes a practical issue. To quantify the significance of nodes in networks, centrality measures were implemented. The multiple criteria are used to select critical nodes in the network. Various centrality measures such as Betweenness Centrality, Degree Centrality, Closeness Centrality, and some well-known centrality measures are therefore used to define vital nodes. In this article, we suggested a centrality to rank the nodes using a graph invariant called graph energy named as Graph-Energy-Ranking (GER). GER provides a better knowledge of the current network by evaluating the effect of node deletion on graph connectivity and thus enables us to better understand and maintain the network. In the current paper GER is applied on well-known social network called Kapferer mine network and results have been discussed.
Research on Standard Influence Model Based on Neural Network

Standard impact is a qualitative measure of the relationship between standards adopted by standardization agencies. This paper uses GB standardized database metrics, by referring to the five elements of Chen Yunpeng's article, the radial basis neural network is established to study the impact index of each standardization institution on GB. The results show that IEC, NF, DIN and BS have a greater value of influence index on GB than other organizations. The equivalent adoption of IEC and its domain coverage quantity are far greater than other standardization agencies. The conversion time and average effective adoption time of NF are much higher than other standardization organizations, it indicates that different international standardization organizations have great differences in the influence on GB, GB needs to learn from other institutions in different dimensions to improve itself.
Collaborative filtering algorithm based on user interest change

The collaborative filtering algorithm can provide personalized service recommendations based on the user's personal interests. In view of the shortcomings of traditional similarity measurement methods for user interest changes, this paper proposes a collaborative filtering algorithm based on user interest changes. When calculating the user similarity, the time penalty function is added to the traditional Pearson correlation coefficient, and then the score prediction is used to obtain the target user's rating data to make recommendations for the user. The improved algorithm can adapt to the change of user interest under the change of time. The experimental results on the MovieLens dataset show that compared with the traditional algorithm, the proposed algorithm can make recommendations for users more accurately.
Sales Price Prediction of Used Clothes Considering the Market Price of Flea Market Application

This study is about sales price prediction of used clothes in EC site. Since used clothes differ in size, color, etc. depending on each product, its sales price changes greatly depending on the needs of customers. The market price of the flea market is related to sales price in EC site, and a sales price prediction model is constructed. As a result, it is found that the market price in flea market application contributes to the improvement of the accuracy of the sales price prediction, and it is possible confirm the change of the influence of the market price on the sales price prediction by the distribution volume in flea market application.
Recognizing sets of generators in finite polar spaces

We characterize non-singular polar spaces embedded in non-singular polar spaces of the same rank using subsets of generators satisfying a natural intersection condition. By [7] and [8] this result has applications to the theory of Cameron-Liebler sets. In fact, our result is a significant improvement of the main result in [7].
Static Output Feedback Control of Discrete-time T-S Fuzzy Systems with Immeasurable Premise Variables

It is proverbial that T-S fuzzy systems are powerful tools to study nonlinear systems, which are praised as universal approximator. In the most of the existing study, the premise variables (PVs) are always presumed to be exactly known. While, in the practical situations, the PVs are not always measurable, that is, immeasurable. In this case, how to make the most of the information provided by the measurable premise variables (MPVs) and alleviate the negative effect caused by immeasurable ones becomes necessary. This paper explores the issue of static output feedback control design for T-S systems in discrete-time form with immeasurable premise variables (IPVs), which is more challenging than the continuous case. The feedback gain matrix in the controller is derived using two different transformation techniques based on linear matrix inequalities (LMIs). And a numerical example in simulation is utilized to confirm the validity of our results.