Reviewerly/exHarmony huggingface dataset:
Modified RNP Privacy Protection Data Mining Method as Big Data Security

Privacy-Preserving Data Mining (PPDM) has become an exciting topic to discuss in recent decades due to the growing interest in big data and data mining. A technique of securing data but still preserving the privacy that is in it. This paper provides an alternative perturbation-based PPDM technique which is carried out by modifying the RNP algorithm. The novelty given in this paper are modifications of some steps method with a specific purpose. The modifications made are in the form of first narrowing the selection of the disturbance value. With the aim that the number of attributes that are replaced in each record line is only as many as the attributes in the original data, no more and no need to repeat; secondly, derive the perturbation function from the cumulative distribution function and use it to find the probability distribution function so that the selection of replacement data has a clear basis. The experiment results on twenty-five perturbed data show that the modified RNP algorithm balances data utility and security level by selecting the appropriate disturbance value and perturbation value. The level of security is measured using privacy metrics in the form of value difference, average transformation of data, and percentage of retains. The method presented in this paper is fascinating to be applied to actual data that requires privacy preservation.
An Artificial Intelligence and Internet of Things Platform for Healthcare and Industrial Applications

In this chapter, we present an artificial intelligence (AI) and Internet of Things (IoT) platform. The core functions of this platform are an AI data processing pipeline and an IoT data processing pipeline. In the pipelines, all different types of application-specific data are processed. For applications where an AI is needed, e.g., face/object/scene detection/classification/recognition, an AI engine is presented. For applications where large-scale searching is needed, a search engine is presented. For applications where most data are sensor data, the IoT pipeline is used. These two pipelines are parallel to each other with data communication mechanism. In the data processing core part, they work independently processing different types of data. But on the boundary and interface, they share many supporting functions including the web/mobile app API, user management, and device management.
Obstacle Analysis of Application of Blockchain Technology in Power Data Trading Based on Improved DEMATEL-ISM Method Under Fuzzy Environment

Power data transaction is significant to the benefit increment of power enterprises and the revitalization of social resources. Blockchain has wide application prospects in data trading, but its concrete implementation faces many difficulties. The article proposes an obstacle analysis framework to understand the barriers of application of blockchain technology in power data trading, aiming to provide decision-making guidance and management suggestions for relevant personnel. Firstly, an obstacle index system with 11 barriers is determined from three aspects of application, technology and legal policy. Secondly, a quantitative analysis model is constructed with (IT2TrFN), improved hesitant fuzzy linguistic term set (HFLTS), decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM). And the K-means method is employed to optimize the discrimination of influence relations among indicators. Then, the model is applied in a case study to testify its applicability. The result shows that difficulty in data maintenance is fundamental obstacle, the barriers of smart contract and supervision in transaction are important factors. Finally, a series of optimization measures and suggestions are put forward for different obstacles.
An Improved Siamese Network Model for Handwritten Signature Verification

Handwritten signature verification is one of the most prominent and prevalent biometric methods in many real applications. A siamese neural network, which can extract stylistic features of handwriting writers, proves to be efficient in verifying handwritten signature. However, a traditional siamese neural network fails to fully represent an author&#x2019;s writing style and suffers from low performance when the distribution of positive and negative handwritten samples is extremely unbalanced. To address this issue, this paper proposes an improved siamese network model with two main ideas: a) adopting a two-stage convolutional neural network to verify original and enhanced handwriting images simultaneously, and b) utilizing the Focal loss to handle the extreme imbalance between positive and negative handwritten samples. Experimental results on three challenging signature datasets of different languages demonstrate that compared with state-of-the-art models, the proposed model achieves a higher prediction accuracy.
Comparing Image Similarity Methods for Face Images

In order to realize real-time computer vision projects we need to avoid time consuming operations such as more inference for deep learning. Our current application uses face images to decide whether there is a mask on the face so as to prevent unhealthy situations in view of epidemic. Since frames are sequentially coming it is necessary to eliminate similar frames to avoid more inference. We show how to measure a similarity between two frames by comparing traditional and deep learning based methods in this study. This study shows that deep learning based method is more efficient than traditional methods when comparing images.
Implementation of a Flexible and Lightweight Depth-Based Visual Servoing Solution for Feature Detection and Tracing of Large, Spatially-Varying Manufacturing Workpieces

This work proposes a novel solution for detecting and tracing spatially varying edges of large manufacturing workpieces, using a consumer grade RGB depth camera, with only a partial view of the workpiece and without prior knowledge. The proposed system can visually detect and trace various edges, with a wide array of degrees, to an accuracy of 15 mm or less, without the need for any previous information, setup or planning. A combination of physical experiments on the setup and more complex simulated experiments were conducted. The effectiveness of the system is demonstrated via simulated and physical experiments carried out on both acute and obtuse edges, as well as typical aerospace structures, made from a variety of materials, with dimensions ranging from 400 mm to 600 mm. Simulated results show that, with artificial noise added, the solution presented can detect aerospace structures to an accuracy of 40 mm or less, depending on the amount of noise present, while physical aerospace inspired structures can be traced with a consistent accuracy of 5 mm regardless of the cardinal direction. Compared to current industrial solutions, the lack of required planning and robustness of edge detection means it should be able to complete tasks more quickly and easily than the current standard, with a lower financial and computational cost than the current techniques being used within.
Topic Classification of Islamic Consultation Question and Answer Using Supervised Learning

Islamic question-and-answer (Q&A) websites are available as platforms for sharing and learning about Islam. Different Islamic Q&A websites usually shares similar Q&A topics that have been frequently asked by Islamic learners. However, due to a large number of Q&A entries in such websites, manual topic classification would be costly and time consuming. The objectives of this research are to develop a classification system for Islamic Q&A topics and analyze the vocabulary words that affect the classification results. To achieve these objectives, well-known supervised learning methods that have been previously implemented to classify Islamic texts are utilized, namely K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), and Multinomial Logistic Regression (MLR). In this research, these classifiers are evaluated in classifying Islamic Q&A entries. The evaluation finds that the SVM achieves the best accuracy and Hamming loss at 79.8 percent and 0.202, respectively. This research also finds that the relevant or specific vocabulary from a class can improve the classification system’s ability to predict correctly and vice versa.
Span-based Model for Chinese Event Detection

Current Chinese Event Detection (ED) models often incorporate character-level features to improve model performance. In this paper, to incorporate span-level features, we propose a span-based Chinese event detection model (BiLSTM-span, Bidirectional Long Short-Term Memory-span) and its extension (BiLSTM-span-ext). The BiLSTM-span model regards Chinese ED as a span classification problem rather than the sequence labeling problem, and the BiLSTM-span-ext, based on the BiLSTM-span, adds a component that could incorporate span-level information. The experimental results show that our model could achieve the F1 score, which is approximate to the current state-of-the-art Chinese ED model. Besides, we conduct extensive experiments to study span representation methods and show that span representation methods influence the BiLSTM-span model&#x0027;s performance dramatically. Last, we show that BiLSTM-span-ext&#x0027;s ability to incorporate dictionary information at the span-level.
Information System Security Analysis to Determine Server Security Vulnerability with Penetration Testing Execution Standard (PTES) Method at VWX University

Server security is indispensable as part of the protection and prevention of information theft measures. This aspect ignored by some agencies that already feel safe with the security conditions of the server network owned by installing antivirus or firewall on the server. There are still considerable agencies such as universities that constantly have not tightened the security of information on the server. Therefore, penetration testing required to determine vulnerabilities in the server by using Penetration Testing Execution Standard (PTES) method to find security gaps in an agency in this case, namely employee data servers at VWX University were in this research found vulnerabilities involves Cross-Site Tracing (CST) attacks, Sensitive Data Exposure, Password Guessing, DDoS Attack, and Sniffing activities that inflict access to the server system.
Finger Vision for Visually Impaired

In the present world visually impaired people use a state-of-art called Braille to read and interpret the text. But these books are available in only certain places and not in a great numbers. This makes visually impaired people to come out and enjoy the real world everyone is enjoying. To overcome this and to help visually impaired people to read the text on the go anywhere and under any circumstances we have proposed a system which is a wearable finger device that is worn on the pointing finger that can read aloud the text pointed by the pointing finger. This ensures the visually impaired people enjoy reading on their own and get a real life reading experience wearing this device. In the proposed system, a finger wearable device is put on the pointing finger and the device is mounted with camera that captures the word pointed by the finger. This image processed from TesserOCR is pre processed by image binarization. This image is then processed with TesseractOCR. Finger from the image is found and the word that&#x0027;s pointed is tracked down and is read aloud for the visually impaired. This helps the visually impaired person to get a real time reading experience. The goal of the project is to provide aid to blind and visually impaired with a portable device to read any text on the go, whether in the digital realm or physically, on any surface.
Sentiment Analysis on Social Security Administrator for Health Using Recurrent Neural Network

Twitter is a social media used to convey opinions, exchange information, upload videos and photos. On social media Twitter, the exchange of information is fast becoming an advantage, so it is often used in delivering news and opinions in the form of criticism and suggestions such as to government agencies, for example, every time there is an issue of increasing dues to Social Security Administrator for Health, it is always a battle of opinion between the public. Social Security Administrator for Health is a government agency that guarantees the health of the Indonesian people; in this case, civil servants and private workers are required to register for this insurance and insurance for the poor. Opinion wars related to the issue of increasing insurance contributions between the public in the form of positive and negative opinions, a sentiment analysis system will be created using the Recurrent Neural Network classification method. This system can help analyze opinions based on people's perspectives on Twitter social media, from the research results in the sentiment analysis of Twitter users, with an average accuracy of 86.67%.
Comparative Evaluation of Machine Learning Algorithms with Parameter Optimization and Feature Elimination for Fraud Detection

Machine Learning (ML) algorithms could be performed efficiently for lots of areas range from education, medicine, defense industry to consumer applications and finance. Especially data classification in finance area has become a striking part of literature recently. Especially, data classification in finance area has become a striking part of literature recently. On this issue,5 well known ML algorithms which are Logistic Regression Classifier, Decision Tree Classifier, AdaBoost Classifier, K Nearest Neighbor (KNN) Classifier and Support Vector Classifier (SVC) are evaluated by using &#x201C;Credit Card Fraud Detection&#x201D; dataset to deal with Fraud and Non-Fraud classification. After all algorithms are performed, it is seen that SVC has the best f1 and precision-recall scores. Moreover, ANOVA is more useful strategy to eliminate irrelevant features compared to Mutual Information method for the dataset. Optimization of model parameters are also critical factor improving classification performance.
Vein Visualization Based on Deep Learning with a Smartphone

Intravenous infusion is a method frequently used in medical treatment. Locating veins is the prerequisite for intravenous infusion. At present, vein localization in many medical scenarios is still performed manually. However, when encountering infectious diseases, the difficulty of manually locating vein will increase sharply due to the wearing of corresponding protective measures. Inspired by the successful application of deep learning in the field of semantic segmentation, a lightweight neural network is proposed to realize vein visualization from color skin images. The feature loss is included in the training loss to reduce the noise of the visualization result and enhance the smoothness of the image. The network is deployed to a smartphone, which greatly improves the convenience of vein visualization. Experiments were conducted to evaluate the proposed method and its mobile terminal performance. The experimental results show that the proposed method can clearly locate veins in skin areas, and thus can be used to assist medical treatment.
Improved K-Means Clustering for Initial Center Selection in Training Radial Basis Function Networks

Radial Basis Function networks accuracies mainly affected by its center selection from dataset. K-means (KM) clustering is a widely in numerous field for data classification and centers selection. However, initial centers selection poses high impact on KM clustering outcome. It suffers from its immense reliance on the initial centers selection algorithm from the dataset. KM algorithm has been enhanced for its performance from diverse perspectives over the years. Nonetheless, a good balance between quality and efficiency of the centers selected by the algorithm is not attained. To overcome this issue, this paper proposed an improvement on KM clustering algorithm in getting initial centers and reduce its sensitivity to initial centers. This paper introduce the use of improved K-means (KM) clustering that consider the each point distance as probability for selecting the initial centers with radial basis function network (RBFN) training algorithm. The proposed approach uses improved KM for centers selection in RBFN training algorithm shows accuracy improvement in predictions and with simpler network architecture compared to the conventional RBFN. The proposed network called IKM-RBFN was tested against the conventional RBFN, KM-RBFN, back-propagation neural network and long short-term memory neural network in FOREX EURUSD pair price predictions. The results are compared to proposed method on its root mean square error (RMSE) and mean absolute error (MAE) results. The proposed method shows promising results in improving RMSE accuracy over 20% in compared to other tested networks.
Relation Extraction - A Study on Kernel Functions

This paper presents a study on one of the important methods for the relation extraction problem. Roughly, the problem of relation extraction is the problem of finding all (some) facts in a given natural language sentence. The method is based on kernel functions for the support vector machines (SVM) algorithm. A kernel function is a function that represents the similarity of semantics between sentences. The kernel functions from the literature considered, in this paper, to be the most important ones are: bag-of-words kernel (BOWK), global context kernel (GCK), and sub-tree kernel (STK). The SVM algorithm is utilized to learn a candidate sentence classification model for a target relation <tex>$r$</tex>. The classification model can then be used to determine whether a new candidate sentence (with two labeled object descriptions) is a positive or a negative candidate sentence with respect to the given relation <tex>$r$</tex>. The paper includes a systematic comparison of the various kernel functions proposed in the literature. Based on the strength and weaknesses of the various kernel functions, new kernel functions are proposed, namely, directed global context kernel (DGCK), global context synonyms kernel (GCSK), directed global context synonyms kernel (DGCSK), and sub-tree synonyms kernel (STSK). Lastly, the paper contains a comparison of the evaluation results of the new kernel functions with the results of the kernel functions proposed in the literature.
Density peak clustering algorithm based on relative density and improved assignment strategy

The density peak clustering algorithm (DPC) has poor accuracy when clustering datasets with large density differences among clusters, and the assignment strategy may cause cluster label error propagation. To solve these problems, we proposed a density peak clustering algorithm based on relative density and improved assignment strategy, called DPC-RDIA. In this algorithm, the calculation of the relative density combines the natural nearest neighbors (NNN) and K-nearest neighbor (KNN), which overcomes the effect of density differences among clusters. The improved assignment strategy is divided into two steps. The first step is to calculate the average distance within the NNN, and the obtained value is adjusted as a parameter for judging whether the sample can be assigned; The second step sets a parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d_{m}$</tex> , calculate the label distribution in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d_{m}$</tex> range of the sample, and set the sample's label as the label with the largest proportion. Finally, we propose a cluster spacing method to judge whether unassign samples are noise or should be assigned to existing clusters. The experiments use four clustering algorithms for comparison, and the results show that the DPC-RDIA algorithm has better clustering effects and has a certain research value.
From Smart Phones to Smart Students: Distraction versus Learning with Mobile Devices in the Classroom

We collaborated with a vocational school in China to examine the effect of using smartphones in the classroom on students' academic performance. We randomly allocated students taking one lecture in Chinese verbal into three experimental conditions: (i) smartphone banned; (ii) smartphone allowed and used at will by students, and (iii) smartphone allowed, used at will by students and relied upon by teachers to assist instruction. We measure the performance gain of students by the change in the scores they obtained in identical tests taken at the beginning and the end of the lecture. We find that allowing students to use smartphones during the lecture at will reduced performance by 12% (6.3 points on a 100-point scale) compared to when students could not take the smartphones into the classroom. However, allowing smartphones into the classroom and asking teachers to actively use them to assist instruction increased their performance by 20% (10.6 points). These results are driven mainly by already strong students and students in IT-related majors. To unravel the underlying mechanisms that drive the observed effects, we use video feeds collected during our experiment, allowing us to code the time students spent learning versus being distracted, with or without their smartphones. We observed that students spent a similar amount of time learning versus being distracted across all three experimental conditions. Thus, the time students allocate to learning in each condition does not predict performance. Instead, what matters for their performance is the relative time they spent learning versus being distracted when using their smartphones. We show that the increase in performance when smartphones are used to assist instruction comes from students spending a larger percentage of the time learning during the lecture. Our finding contribute to the literature on technology-assisted learning and offer practical and policy implications that teachers and schools can follow to cautiously allow smartphones in the classroom to improve student success.
“The population” scientific term in the LBC tables

In the tables of Russian Library Bibliographic Classification (LBC), the literature on group populations is separated. Based on the LBC Medium Tables, the recommendations are given on how to analyze documents by content with indexing to follow (for teaching systematization). Diverse resources on population may hamper separating publications. Document analysis enables to present its content as a classification number to be used in catalog and/or in systematic collection arrangement. The comprehensive resource coverage expands catalog retrieval capability and saves user’s time. The LBC tables are used in collection arrangement, catalogs and lists. The content makes the key factor of fixing document position in the tables. The potentiality of systematizing literature by examination aspect is revealed as exemplified by the publications on group populations.