Reviewerly/exHarmony huggingface dataset:
Implementation of python source code comparison results with Java using bubble sort method

Abstract In the implementation of Python and Java source code comparison results are more focused on the scale of the ratio of the number of lines of code, file capacity, and access speed. As for the background of this writing because there are so many programming languages that can be used with the same results but overall we do not know which programming language is more optimal and efficient in terms of the number of lines of code better known in the programming language is LOC (Line of Code) capacity of file access speed. In this study, the authors focus only on Java programming language and python course as a first step to know the ratio of the number of lines of code, file capacity, and access density. To determine the comparison there is a method used is bubble short. The results of the implementation of the comparison of these programming languages for Python programming language to produce the number of LOC (line of code) or the number of lines of code as much as 10, the capacity of the file extension.py by 506 bytes and txt extension of 397 bytes and access speed approximately for less more 4 seconds. While Java produces the number of LOC (line of code) or the number of lines of code as much as 11, the capacity of the file extension. Java of 86.2 Kbytes and extension txt of 477 bytes and access speed for 7 seconds. So do not close the possibility to make other applications python programming language will be more optimal and efficient.
An Efficient Dynamic Background Subtraction Algorithm for Vehicle Detection Tracking System

Background subtraction is an important role in video surveillance system in ITS, yet in complex scenes, it is still a challenging problem; hence, it is required to model the background before subtraction. Various illumination changes and dynamic backgrounds form the major key aspects for background modeling. In this paper, an algorithm (TCO-DBS) is proposed to develop an efficient background subtraction framework to solve the above problems. Here, texture and color features are considered for background modeling, thereby separating the foreground and background video frames. The texture features mainly depend on scale values used, i.e., number of neighboring pixels used for describing local texture description. Among this, local binary pattern (LBP) is mostly used in computer vision applications. LBP texture features along with color feature give a promising result when compared to other methods.
Interface Design: Intervention Criteria and Good Practices

The chapter covers the design areas of digital technology-based communication, training and learning systems. In a knowledge-based society, technology, from personal devices to the network, is increasingly the means to transmit, store and produce information. As a result, accessibility to technology becomes a fundamental requirement in the way we live, work and learn. Despite this, on several occasions, it has been observed that while ICT makes it possible to access what has hitherto been inaccessible, offering considerable opportunities, on the other hand they can at the same time generate exclusion problems vulnerable groups. The focus is on the design of user interfaces and design strategies aimed at their versatility and adaptability to different user profiles and different user contexts. Within this chapter special attention is paid to digital technologies for learning and the functional and cognitive aspects that are able to generate the condition of social inclusion.
Behind the Mask: Understanding the Structural Forces That Make Social Graphs Vulnerable to Deanonymization

The tradeoff between anonymity and utility in the context of the anonymization of graph data sets is well acknowledged; for better privacy, some of the graph structural properties must be lost. What is not well understood, however, is what forces shape this tradeoff. Specifically, for the data practitioner who wants to publish an anonymized graph data set, it is unclear what graph structural properties can be preserved and what are the anonymity costs associated with preserving them. This article proposes a framework that examines the interplay between graph properties and the vulnerability to deanonymization attacks. We demonstrate its applicability via extensive experiments on thousands of graphs with controlled properties generated from real data sets. In addition, we show empirically that there are structural properties that affect graph vulnerability to reidentification attacks independent of degree distribution.
Facial Expressions Recognition Using Markov Stationary Feature - Vector Quantization and Support Vector Machine Method

Abstract Facial expression is a form of nonverbal communication that can convey the emotional state of someone to the person who observes it. Research on the recognition of facial expressions is one of the interesting fields in computer science. This research aims to improve the accuracy of recognition performance. The process carried out in this research is to perform feature extraction and image classification. Markov Stationary Feature - Vector Quantization (MSF-VQ) method is used for feature extraction and Support Vector Machine (SVM) for image classification. Data set in used is 1440 data with six classifications of facial expressions. The results of the testing showed 97.41% which stated that this method could be recommended to be applied in the facial expressions recognition.
End-User and Provider Requirements for Pricing Policies of Infrastructure as a Service

Cloud computing has rapidly transformed ICT industry and numerous cloud providers offer multiple flavors of Infrastructure-as-a-Service solutions (IaaS), aiming at meeting customer demand within a competitive landscape. In addition, cloud users seek the optimal cloud service and usually end-up choosing solutions based mainly on price. Pricing strategy aims to satisfy both parties and depends on many factors, reflecting both cloud provider drive-for-profit and user drive for seeking value-for-money solutions. This article proposes a twofold approach in order to capture the viewpoints of cloud providers and users on the pricing strategy of IaaS services, based on structural-equation-model and pairwise comparison, respectively. Five categories of cloud attributes are considered, and IaaS functional and nonfunctional operation is presented. The contribution of each category to the shaping of final price is examined. The conclusions drawn have important bearing on how the pricing strategy of cloud providers is mainly in agreement with the user point-of-view.
Research on Intrusion Prediction Algorithm Based on Association Attack Diagram ASAD

In view of the problem that the attack diagram model can't accurately reflect the impact of attack evidence on the intrusion intent prediction, an intrusion prediction algorithm, INPA based on association attack diagram, ASAD is proposed. This paper defines the parameters of resource node D, attack evidence M, single-step attack T, etc., and constructs the associated attack diagram model to describe the whole process of the intruder reaching the target node from the initial node, and then, Bayesian inference design and Associated quantization algorithm, Correlate are used to calculate the probability of single-step attack. Finally, combined with the And-Or relationship between the directed edges of ASAD, the expression formula of the reach probability of each node is derived, and INPA is designed to realize the function of dynamic prediction the potential intrusion in the network.
Correction of self-destructive behavior of adolescents by means of art therapy

In modern society, self-destructive behavior continues to spread among adolescents. This is manifested in the use of alcohol, tobacco smoking, drug addiction, victimization and extreme behavior. The effective implementation of prevention and correction of self-destruction is an urgent problem for personality psychology, educational psychology, developmental psychology and other industries. The article proposes a new scientific approach to overcoming the self-destructive behavior of adolescents. Self-destructive behavior is one of the compensatory defense mechanisms of adaptation and stabilization of the personality, the ability to adapt to adverse conditions. A teenager with an unformed or critical thinking disorder, acting under the influence of fear, anxiety, hopelessness, shame, guilt, excitement, fatigue or a strong affective state of anger and anger, does not think about the consequences of his actions. The formation and implementation of self-destructive behavior serves as a means for the adolescent to maintain a satisfying “Self-concept” - avoiding feelings, from realizing one’s inadequacy with one’s own idealized ideas, and feeling helpless.
Information and Communication Technology Development for Entrepreneurs

Abstract The importance of time effectivity makes the entrepreneurs implement information technology in their businesses. This study was aimed to describe the growing Micro, Small, and Medium Enterprises (MSMEs) based on information technology and communication to improve technical entrepreneurship in Bandung. Developing information technology in modern times is crucial. The method of this case study uses a qualitative descriptive approach which is to collect data through facts, circumstances, and phenomena that occur in Bandung. It can be concluded that the competitiveness of MSMEs in information technology that is centered in Bandung is still low because of the lack of technical mastery in the scope of business travelers. Entrepreneurs in this region are still minimal and can change their minds completely because of the lack of confidence to compete.
MedGuide

The information on a non-prescription (over-the-counter) medicine label helps patients to make informed decisions when purchasing a medicine. Since non-prescription medicines can be purchased without consulting a healthcare professional, there is a growing concern that people do not put enough emphasis on reading medicine label information, resulting in drug misuse with possible health consequences. In this paper, we investigate patients' use of medicine labels with the goal of developing a smartphone application to guide them toward reading the important information (e.g., warnings, dosage) on the labels. We first conducted two studies examining (i) users' rating on the information that they commonly read and (ii) healthcare professionals' rating on the information that patients should read before purchasing a non-prescription medicine. Our results revealed that patients put less emphasis on reading many information such as dosage, warnings and precautions, that the healthcare professions highly recommend the patients to read. Inspired by the findings, we designed a smartphone application to make users aware of the important information on non-prescription medicines. Along the way, we conduct a study examining different information presentation techniques to show medicine labels on smartphones, where our results show that icons and texts are more accurate and preferred techniques by users. In a further study where users explore medicines with our smartphone application, we observed a significant increase (mean 27%) in the general users' rating on the information categories that were recommended by healthcare professionals. This suggests that the users were guided to read important information by the smartphone application.
Copy-move forgery detection based on local intensity order pattern and maximally stable extremal regions

To abandon the use of overlapping block division and resist the tampering factor of illumination change, a novel copy-move forgery detection method is proposed based on Maximally Stable Extremal Regions (MSERs) and the Local Intensity Order Pattern (LIOP), that integrates block-based and keypoints- based methods. The method involves the following steps: first, affine transformation invariant MSERs are used to maintain the geometrical transformation invariance and reduce computational complexity; second, LIOP features are used to describe the texture and resist illumination change; and finally, RANdom SAmple Consensus is applied to remove false matches. The experiments indicate that the proposed method has great performance for scaling, rotation and illumination changes. Moreover, the method has the high robustness to Gaussian noise, Gaussian blur and JPEG compression.
Weighted Feature Pyramid Network for One-Stage Object Detection

One-stage object detection methods have attracted much attention for their high speed performance compared with two-stage methods. But one-stage methods under performs with small object detection. Feature Pyramid Network (FPN) was widely used to deal with this problem for its multi-scale feature present ability. However there still remains a few problems that are not considered in FPN, which results in limited improvement in detector performance. We note that FPN does not take the weight and scale distribution between different levels of feature maps into account when merging high-level feature maps and low-level feature maps. We present a network named Weighted Feature Pyramid Network (WFPN) to address these problems. Our experimental results on PASCAL VOC and MS COCO show that WFPN can significantly improve the detector performance, especially on small object detection.
Blurred Template Matching Based on Cascaded Network

Template matching is widely used in computer vision applications, but most matching methods simply assume the ideal images without real-word degradations, such as Gaussian blur. Traditional methods for blurred template matching either first resort to image deblurring and then perform template matching with the recovered image, or joint solve image deblurring and matching based on sparse expression prior. However, these methods always perform poor and the matching speed is slow. In this paper, we propose a blurred template matching method based on a cascaded network, which combines a coarse matching network and a fine matching network. The coarse matching network searches for a small image where the target matching position is located in the reference image, and then the fine matching network calculates the similarity between the blurred template image and all image patches in the small image, thus the matching position is the corresponding position of the image patch with the highest similarity. Extensive experiments demonstrate that our method significantly outperforms the state-of-art on the accuracy, speed and robustness.
Surveillance-based Collision-time Analysis of Road-crossing Pedestrians

In recent years, traffic safety is the main focus of transport agencies in New Zealand. Due to expansions of cities, the extent of infrastructure-based recordings such as CCTVS grows rapidly. Computer vision can play a critical role in using the data and converting it into useful information. In this paper, we apply the latest deep learning technology to detect and classify traffic users including pedestrians and vehicles. The objects are tracked using a Kalman filter, considering the class with the highest probability in the defined estimation error. We calculate a transformation matrix to map recorded video into a top-down view and measure the speed. Associations between pedestrians and a closest approaching vehicle are considered to predict possible crash points. The elapsed time until pedestrians and vehicles reach a crash point is regarded as the risk time in this context. Obtained results can be used in various traffic safety analysis and decisions.
Maintenance Helpdesk Information System in Retail Companies

Abstract This research aims to build a maintenance helpdesk information system to provide effective and efficient repair services. The design of this study used descriptive methods, using a structured approach, using flow map tools, context diagrams, Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD), while the development used a prototype model. One form of service to consumers provided by retail companies is the convenience of shopping. Starting from the condition of the building, room temperature, and lighting in each outlet. However, the system that handles the management of the physical condition of this outlet has become a matter of little concern, as a result of some implementation of repairs and maintenance of outlets, not as expected. This research is designed to provide of the Maintenance Helpdesk Information System in Retail Companies, it can be concluded that this application is able to monitor all physical conditions of existing outlets, and is able to improve performance in the maintenance department and shorten the time in the administrative process.
Ergonomic Analysis of An Affordance Tool for Modern Kitchen Knives

The focus of this study was on how different knife characteristics affect the consumer’s ability to slice vegetables. There are many variables to a knife and there are beliefs about what makes a better knife. There are two common multipurpose knives used for slicing vegetables: the chef knife and the santoku knife. For this study, an affordance tool was designed, manufactured and tested. This tool was designed to be placed on the knife to create an affordance for users to hold the knife in a pinch grip. This grip aligns the wrist and forearm and decreases fatigue and increases stability and control while cutting. The tool was tested with 16 participants to ensure it did not require more muscle activation, time, discomfort, or cause lower slice performance. Ultimately, the designed affordance tool provided an affordance without negatively effecting a cutting task compared to using a knife without the affordance tool.
A Neural Network Component for Knowledge-Based Semantic Representations of Text

This paper presents Semantic Neural Networks (SNNs), a knowledge-aware component based on deep learning.SNNs can be trained to encode explicit semantic knowledge from an arbitrary knowledge base, and can subsequently be combined with other deep learning architectures.At prediction time, SNNs provide a semantic encoding extracted from the input data, which can be exploited by other neural network components to build extended representation models that can face alternative problems.The SNN architecture is defined in terms of the concepts and relations present in a knowledge base.Based on this architecture, a training procedure is developed.Finally, an experimental setup is presented to illustrate the behaviour and performance of a SNN for a specific NLP problem, in this case, opinion mining for the classification of movie reviews.
L2R-QA: An Open-Domain Question Answering Framework

Open-domain question answering has always being a challenging task. It involves information retrieval, natural language processing, machine learning, and so on. In this work, we try to explore some comparable methods in improving the precision of open-domain question answering. In detail, we bring in the topic model in the phase of document retrieval, in the hope of exploiting more hidden semantic information of a document. Also, we incorporate the learning to rank model into the LSTM to train more available features for the ranking of candidate paragraphs. Specifically, we combine the results from both LSTM and learning to rank model, which lead to a more precise understanding of questions, as well as the paragraphs. We conduct an extensive set of experiments to evaluate the efficacy of our proposed framework, which proves to be superior.
Application of data mining for Indonesian products export in South Korea using clustering: Indonesia Trade Promotion Center Busan

Abstract This research purpose is to classify data export products of Indonesia against South Korea. The clustering method used in this research was the K-Means method, in K-Means cluster had a good degree of accuracy. This research examines how the use of the K-means method in case studies export products Indonesia against South Korea. From the results of clustering using K-Means method based on the value of the USD and Kg, then the generated 3 (three) clusters with cluster values are high, medium and low. Based on those results suggest that K-Means method can be used for inventory control of product reference or as Indonesia Indonesia Trade Promotion Center (ITPC) in Busan to view any export products should be retained and what are the export products should be improved to be promoted in South Korea so as to increase the number of export products of Indonesia against South Korea.
On the Interaction Between Deep Detectors and Siamese Trackers in Video Surveillance

Visual object tracking is an important function in many real-time video surveillance applications, such as localization and spatio-temporal recognition of persons. In realworld applications, an object detector and tracker must interact on a periodic basis to discover new objects, and thereby to initiate tracks. Periodic interactions with the detector can also allow the tracker to validate and/or update its object template with new bounding boxes. However, bounding boxes provided by a state-of-the-art detector are noisy, due to changes in appearance, background and occlusion, which can cause the tracker to drift. Moreover, CNN-based detectors can provide a high level of accuracy at the expense of computational complexity, so interactions should be minimized for real-time applications. In this paper, a new approach is proposed to manage detector-tracker interactions for trackers from the Siamese-FC family. By integrating a change detection mechanism into a deep Siamese-FC tracker, its template can be adapted in response to changes in a target's appearance that lead to drifts during tracking. An abrupt change detection triggers an update of tracker template using the bounding box produced by the detector, while in the case of a gradual change, the detector is used to update an evolving set of templates for robust matching. Experiments were performed using state-of-the-art Siamese-FC trackers and the YOLOv3 detector on a subset of videos from the OTB-100 dataset that mimic video surveillance scenarios. Results highlight the importance for reliable VOT of using accurate detectors. They also indicate that our adaptive Siamese trackers are robust to noisy object detections, and can significantly improve the performance of Siamese-FC tracking.
Control of Robotic Arm Based on TSK Fuzzy Model and Hierarchical Genetic Algorithms

This article introduces a novel design of Takagi-Sugeno-Kang (TSK) fuzzy control of robotic arm based on hierarchical genetic algorithms (HGAs). First, the selection of a function matrix Q(x) causes the nonlinearity of the model uncertainty and external disturbances to be trivial. Second, the nonlinear dynamic equation of the robotic arm is described by TSK fuzzy model. Based on the Lyapunov method, the linear matrix inequalities (LMI) can promise the TSK control system is stable. The optimal membership functions and control gains can be simultaneously explored the optimal values by HGAs. Last, the proposed HGAs-based LMI is applied to design parallel distributed compensation (PDC) so that the robotic arm can follow the command signal.