Reviewerly/exHarmony huggingface dataset:
Spatial Design Direction of Smart Home in IoT Paradigm

The purpose of this study was to review the spatial planning directionality and themes for future studies of Smart Home and, through literature analysis and to analyzes the effect of the change of Internet of Things (IoT) informatization paradigm on the characteristics and structure of Smart Home. This study also reclassifies Smart Home's spatial planning elements by considering the interaction between intelligent services, intelligent technology, users and space. The contents of spatial planning directionality corresponding to the results of this study are composed of the establishment of the main functions of the Smart Home considering the trend of IoT technology, the integrated approach to various plan elements constituting the Smart Home, and the necessity of user-centered space design.
Applications of Structural Equation Modelling with R

R software typically is a computer language and run-time environment developed by Ross Ihaka and Robert Gentleman based on older programming language S. The first version of R was released in 1990s and several people have joined this development project for its improvement. In 1995, R is launched as “open-sourced” software and anyone can use and modify it for their problem environment. In 2000, the first version of R (1.0) was released for the public.
A Novel Location Privacy Protection Algorithm for Social Discovery Application

With the development of smartphones and mobile wireless network, social discovery applications are becoming more and more popular now. These applications help users to chat with nearby people and make friends. However, the location of a user could be exploited by attackers in some situations. This paper concentrates on the protection of location privacy in social discovery application. Firstly, we formalize the location attack problem using probability theory. Then we propose a new distance obfuscation method to defend the location inference attack. Guided by entropy theory, we obfuscate the distance between two users with a specific probability instead of random obfuscation to make the inference attack more difficult. To keep the balance between the privacy level and the service quality, we resolve the optimal mechanism. Experimental results show that the proposed method performs well in protecting the users’ locations without significantly compromising the quality-of-service of social discovery applications.
A Novel True Colour Image Bit Modification Technique for Image Steganography

This paper presents a novel method to modify true colour image pixels which provides a base for image steganography with the least distortion and high embedding capacity. In this method, all of the R, G and B planes of true colour image are modified to yield one and/or more than one bit of each byte per plane modifiable for steganography. Through this method, 1–7 numbers of bits in a single plane can be achieved for steganography. Any bit position starting from LSB to 7th bit of the byte can appear modifiable. Any arbitrary positions of bits can be embedded per plane per pixel enabling dynamical and more secured steganography. The method overcomes the shortcomings of LSB steganography and also other steganography techniques where different bit positions (up to 4th from LSB) are used. Without any constraints, random pixels can be utilized for modification. This increases the security. This method provides an optimal level of PSNR values with respect to the modifiability or embedding capacity.
Method for Identifying Dangerous Forum Posts on the Onion Network

This article represents the research about onion users classification, potentially dangerous users detection and tracking their illegal activity. Information about either existing methods for identifying and detecting messages with harmful content or creating user’s behavior profile was gathered and examined. Basing on it was designed another method. Mechanisms for automatic text tonality analyze were considered. During this research was designed set of programs based on neural network. It is able to parse content from onion web page and make conclusions about users basing on their messages. Experiment was conducted in order to estimate quality and accuracy of this utility. Gathered results were analyzed. Inference about further research development was given.
Research on Key Exchange Protocol Based on LWE

With the development of quantum computer, some cryptographic algorithms which are based on integers factorization and discrete logarithm become unsafe. Therefore it is necessary to research post-quantum cryptographic algorithms. In this paper, we mainly study post-quantum (authenticated) key exchangeprotocols. Especially, wefocusonlattice-basedprotocols where hard problem assumption can be reduced from worst-case to average-case. After research on NIST 2-round submission, key establishment schemes account for the majority and most schemes are based on lattice hard problem. LWE problem is an usual assumption. We will roughly introduce Kyber, LAC, FrodoKEM, NewHope, Threebears. How to choose a security model plays an important role for KE (or AKE) protocol. We mainly research on security, construction, efficiency, and innovation of these schemes.
Investigation on the Analysis Model of Cloud Classroom Teaching Activities of PHP Network Development Technology Based on xAPI

With the continuous proposal of “Internet + education”, educational informationization has made rapid development in China, and information technology is playing an increasingly important role in the development of China’s education. In order to promote the further improvement of education level, cloud classroom has been developed. In essence, cloud classroom is a kind of public education service system, which realizes the network sharing of education resources with the help of information technology and belongs to a comprehensive virtual network learning community. As for the actual development of cloud classroom, on the one hand, cloud classroom and education are moving towards deep integration step by step; on the other hand, many problems are exposed in the process of cloud classroom and teaching integration. The purpose of this paper is to promote the further development and application of cloud classroom by constructing and studying the analysis model of cloud classroom teaching activities. This paper firstly gives a brief overview of relevant core concepts such as cloud classroom, xAPI and PHP network development, and then combines the existing research data with the actual situation of current development of cloud classroom. Finally, with the help of cloud area algorithm, it carries out testing experiments on the cloud classroom teaching activity model. The experimental results show that the xap-based analysis model of cloud classroom teaching activities of PHP network development technology proposed in this paper is reliable and can record and analyze the whole process of cloud classroom teaching activities in detail.
Combining Digital Signature with Local Binary Pattern-Least Significant Bit Steganography Techniques for Securing Medical Images

The image authentication is generally based on two different types of techniques: watermarking and digital signature. In watermarking methods, embedded watermarking is often imperceptible and it contains either a specific ID of producer or codes related to content that are used for authentication. Normally a separate file is stored, digital signature is a non-repudiation and encrypted version of the information extracted from the data. A digital signature can be attached to the data to prove the originality and integrity. The proposed work presents a new approach to steganography of medical images that uses modified Least Significant Bit (LSB) based on the Local Binary Pattern (LBP) pattern. As a first step, cover image has been divided as blocks of 3×3 non overlapping masks. Then, the pixel embedding position (clock wise or anti-clock wise) has to be identified using LBP operator. The value of the LBP operator determines how and where to embed secret image pixel. Later, using LSB method, pixel values will be embedded in the cover image pixel. In order to provide the integrity of the data, the proposed work also presents Reversible Watermarking (RW), a Digital Signature (DS) technique. The proposed algorithm of steganography experimented on few medical images and achieved better efficiency with respect to MSE and PSNR values and same is reported in this paper.
The Analysis of Determining Credit Worthiness Using Weighted Product and SMART Methods in SPB Cooperatives

The Savings and Loan Cooperative of Simpeunan Pameungkeut Banda is a cooperative that has been established in Tasikmalaya. The system of lending currently used at Simpeunan Pameungkeut Banda Cooperative is still manual, so the management requires quite a long time. Besides that, the cooperative has not had a system that supports in determining the credit worthiness decision. Thus, the credit is not on target, because it does not fit the specified criteria. For this reason, the writers were to make a research on the comparison of weighted product and smart methods in determining credit worthiness in Pameungkeut Banda Cooperative. The purpose of research was to determine who were eligible and who were not eligible to get the credit given the criteria that have been determined using the Weighted Product method and SMART methods. The results of the analysis using the Weighted Product method obtained the highest value of 0.067 and the lowest value of 0.032, while SMART method obtained the highest value of 79.2 and the lowest value of 25. The results show that SMART method is more effective in determining credit worthiness.
Implementation of Interpolation in Credit Card Fraud Detection

In today’s world, a critical threat to the Banking and Finance sector as well as its customers is the occurrence of fraud in the credit card transactions. Detecting this fraud is extremely arduous as it forms only a small percentage of the total number of transactions. In this paper, an algorithm which uses artificial neural network to detect fraudulent transactions amongst numerous genuine ones has been proposed. The number of fraudulent transactions is very few in comparison with the genuine transactions which introduces skewness in the data and makes the task of fraud detection difficult. In order to reduce the skewness of the dataset, under-sampling and over-sampling techniques have been used. The algorithm has been tested on three different datasets. The results of all the datasets have been compared. Confusion matrix and ROC plots have been compared. Further to improve the classification, generative adversarial networks (GANs) have been used to interpolate the fraudulent data without duplication on one of the datasets. These results show that there is an improvement in classification by using GANs.
Quantifying Information Leakage Using Model Counting Constraint Solvers

This paper provides a brief overview of recent results in quantitative information flow analysis, model counting constraints solvers, side-channel analysis and attack synthesis. By combining model counting constraints solvers with symbolic execution it is possible to quantify the amount of information that a program leaks about a secret input. As discussed below, this type of analysis is crucial for detection and analysis of side channel vulnerabilities.
Materializing Block Chain Technology to Maintain Digital Ledger of Land Records

In most of the administrative sections of the government, the enormous amount of digital data is generated. As expected, there will be continuous data insecurity in different forms like tinkering university marks of students, making up of certificates which are bogus, changing or erasing the digital ledger of land records. We need a reliable method that makes this digital data immutable to generate trust between administrative sections of the government and the citizens. To do this, we can materialize the “Block chain, a peer-to-peer software technology” which is an inherent concept from “Bitcoin” crypto currency (Heng in The Application of Block chain Technology in E-government in China, 2017) [1]. To hedge the digital records insecurity, block chain supports a distributed database which clasps encrypted ledger in a decentralized fashion. Upon the occurrence of a transaction, the transaction should be verified and then placed inside distributed encrypted ledger. Afterward, a set of “N” digital transactions are taken into account to calculate hash and kept in a block along with the timestamp. If the hash, timestamp along with transactions placed in a block, it is challenging to change or expunge transactions inside the block, considering that cryptographic block hash is irretrievable. The recently validated block is then connected and appended after the earlier block of block chain using earlier cryptographic block hash value. We have introduced the different schemes for materializing block chain technology in administrative sections of the government, e.g., in the department stamp and registration duties.
Handwritten Signature Verification Using CNN with Data Augmentation

A signature is a mark or sign which is made by an individual on an instrument or document to signify knowledge, approval, acceptance or obligation. To authenticate writing or a notice of its source and to bind the individual signing which is written by the provisions contained in the document. Signature verification is more important for not only in commercial banks but also with every sector like falsification of documents in numerous financial, legal and other commercial aspects. A signature is an important factor in biometric technique in which it is used to detect forged or genuine signature. This paper concerns offline handwritten signature verification using convolutional neural network (CNN). Here we have used data augmentation with CNN model and also, we have made a comparative study with Multilayer Perceptron (MLP) and Single Layer Perceptron (SLP). The model is tested using 4480 images with 20 subjects where we have found the accuracy of CNN is 82.75% and CNN with data augmentation is 98.33%, SLP is 39.91% and MLP is 63.57%. Based on the comparative study CNN with data augmentation proves the best performance.
Strengthening students’ academic digital competencies through development of learning patterns

Today students are met with expectations of being able to navigate in a broad spectre of digital challenges. The STAK project aims at developing specific digital didactic designs in the form of learning patterns that support and develop students' digital competencies, within areas such as digital sharing, open access, online collaboration and other emerging technological opportunities and challenges.&#x0D; The concept of “Learning Patterns” covers a method used to capture experience about best practice from educators and other experts, and disseminate these into concrete learning patterns and activities. These concrete learning patterns are then systematised and described in such a way that it is possible for others to understand them and reuse them. As a product, learning patterns can be described as a “how-to”-formula that you can transfer to your own teaching.&#x0D; The STAK project aims at developing approximately 300 learning patterns within four categories: Digital information competencies, digital participatory competencies, digital production competencies, and digital responsibility- and security competencies. As the STAK project progresses, the concrete learning patterns will be available on an OER (open educational resource) along with models and templates for how to develop learning patterns.&#x0D; In the presentation, we focus on our work processes with developing learning patterns in the project and present a concrete learning pattern. We are also going to provide you with insight into how you can use this method to develop your own learning patterns.
Intelligent Traffic System Path Planning Algorithm

With the rapid development of economy and the continuous progress of science and technology, people’s material living standard has been significantly improved. The number of cars in China has exceeded 250 million. Along with it, the construction structure of road traffic network becomes more and more complicated, road congestion becomes more and more serious, and safety accidents occur frequently. The root cause is that related traffic facilities have been unable to keep up with the pace of vehicle growth year by year. In this context, the purpose of this paper is to propose a traffic path planning algorithm that can optimize urban traffic and reduce congestion and safety accidents, so as to achieve reasonable distribution of traffic flow. In terms of research methods, this paper uses ant colony algorithm, and fit in the latest traffic information and technology, realized in the road traffic network planning out a optimal path from start to finish, make in the process of route planning, combine the search speed and precision, so as to reduce the purpose of residence time on the road. The simulation results show that the ant colony algorithm can effectively obtain the optimal path, and for different departure times, the estimated travel time obtained by planning is also different, and can effectively improve the overall operating efficiency of the system.
A Bayesian Reasoning for Earthquake Prediction Based on IoT System

Abstract Yogyakarta, one of the cities in Indonesia, has the most active volcano in Indonesia, namely Mount Merapi. Volcanic activity of Merapi often results in earthquakes, either weak or strong earthquakes. Those earthquakes sometimes are dangerous to the community; therefore, early warnings for earthquakes are very necessary. This paper presents development of earthquake prediction system based on internet of things (IoT) that equipped with a well known data mining techniques for prediction called Bayesian reasoning. A sequential data about earthquakes occurrence has been used to predict earthquake occurrence in the future. A cloud database is used to store real time earthquake occurrence data and a mobile application has developed to give early warning and visualise the earthquake data to the users. Testing of the system shows the proposed earthquake prediction system has high accuracy with Mean Squared Error (MSE) value is 0.127.
Cryptograhy and Pk-Anonymization Methods for Secure Data Storage in Cloud

Cloud computing empowers the clients to get outsourced information from cloud storage with no equipment and programming administrations. For compelling the usage of secret information from Cloud Service Provider (CSP), the information owner encodes before storing in the cloud. To secure information in cloud, information protection is a testing assignment. In order to deal this issue, a proficient information security strategy utilizing cryptographic procedures and Pk-Anonymization method is introduced. Accordingly, the proposed technique scrambles the sensitive information, as well as distinguishes the exploitative gathering to get to the information utilizing consolidated hash capacities. Anonymization is fundamental concept for bringing about assurance on users secret information. Protection of Data is likely necessary because of the control lessening system and PK-Anonymization method. Pk-Anonymization and cryptography in Cloud computing to improve data security is introduced in the proposed method.
PERANCANGAN CHATBOT UNTUK INFORMASI PENERIMAAN MAHASISWA BARU PADA STMIK KHARISMA MAKASSAR

The purpose of this research is to design and create chatbots that can answer general questions about information on New Student Admission at STMIK KHARISMA Makassar. The benefit of this research is to help users get information on New Student Admission at STMIK KHARISMA Makassar. The research begins by designing the ENTITY RELATIONSHIP Diagrams (ERD) and Unified Modeling Language (UML). Then the chatbot is implemented using the LINE Messaging API with the PHP programming language and Codeigniter framework. Whereas for admin system to process chatbot, it is made web-based and implemented with PHP programming language and CodeIgniter framework and Bootstrap Admin Klorofil template. The program is tested by using Black Box Testing method. Based on the results of the research it can be concluded that the author has succeeded in designing and creating a chatbot that can help users to get information on New Student Admission at STMIK KHARISMA Makassar.
Penggunaan Algoritma Jaringan Syaraf Tiruan Propagasi Balik Pada Klasifikasi Data Penggunaan Daya Listrik

Electrical Energy has a large contribution to the operational costs that must be incurred. The selection of electrical equipment can be one alternative that might be implemented to reduce operational costs incurred. In its use sometimes users do not know any electrical equipment that uses high electrical power and low electrical power. Therefore a system was made to classify data on electric power usage. This data will be classified into four classes, such as: very efficient, efficient, quite efficient and wasteful. Data classification is done using a back propagation neural network algorithm. The training data set used is 190 data and the test data set is 30 data. Based on the training that has been done, the optimal parameters are learning rate 0.5, target error 0.001, max epoch 10000, and 25 hidden neurons. Tests show that the system is able to recognize data with an accuracy level of 96.67% and MSE of 0.03333. Of the 30 data that have been tested obtained 29 data in accordance with the target. Where the 29 data are classified into 4 classes, namely 9 data classes are very efficient, 6 data classes are efficient, 5 data classes are quite efficient and 9 data classes are wasteful. The results of this study can be concluded that the backpropagation neural network algorithm can be implemented to classify electrical power usage data.
Research on Control Strategy of Hydraulic Control Unit Test Device Control System

Abstract Hydraulic control unit test (HCU) is a complicated process with nonlinear, hysteresis and large parameter change. This paper focuses on the research of PID control strategy which comes from the BP neural network and HCU test strategy, and combines Matlab/Simulink, AMESim and other software for simulation and verification, in order to design a reliable and high-quality control scheme.
Automatic Representative Framelets Selection for Human Action Recognition in Surveillance Videos

In computer vision, intelligent video surveillance is one of the most challenging tasks as it requires real-time processing and high reliability. This paper addresses the problem of processing the long-duration videos for complex activity recognition using automatic representative framelets selection approach. This paper proposes an automatic representative framelets selection approach that uses faster region-based convolutional neural network (R-CNN) object detector to find the human as region of interest (ROI). Detected bounding box is extracted and corner detection method is used to find the significant key points. The Lucas–Kanade (LK) optical flow is used to generate the trajectories from frame to frame. The motion velocity magnitude has been calculated for each frame by accumulating the trajectories or tracks. Motion velocity magnitude is visualized. From the visualization, each hill shape represents a meaningful atomic action. Density-based spatial clustering of applications with noise (DBSCAN) is done to group the actionlets according to their similarities. Single frame is selected from each cluster randomly as a representative framelet. Our experiments demonstrate that the proposed method leads to significant improvement in activity recognition system in terms of space and time consumption. This paper presents representative framelets selection results for four benchmark dataset, namely KTH dataset, Weizmann dataset, UCF-11 dataset, IXMAS dataset and one synthetic dataset with challenging and realistic environment.