Reviewerly/exHarmony huggingface dataset:
A color image watermarking in the frequency domain using a teaching-learning optimization algorithm

This paper presents a color image watermark technique that employs teaching learning-based optimization algorithm (TLBO) and lagrangian twin support vector regression (LTSVR) in the frequency domain. By analyzing the statistical property of the selected wavelet band (LL sub-band) after single-level decomposition, LTSVR is used for extraction of watermark and embedding processes. TLBO is used to find the optimal value of watermark strength for different selected blocks of the image in the wavelet domain. Various kinds of images are considered to test the imperceptibility and robustness of the watermark in experimental results. The metric Peak Signal to Noise Ratio (PSNR) has been used for watermark images to evaluate the: (i) imperceptibility, (ii) quality. Bit error rate (BER) and normalized correlation (NC) value is computed to determine the effectiveness and standards of the extracted watermark. JPEG compression attack with different quality factors (QF) ranging from 10 to 90 is evaluated using robustness to determine the proficiency of the proposed work. Experimental results show that the proposed method is robust to JPEG compression as compared to state of art method.
A Novel Wildlife Poaching Detection Solution using Spatio-temporal Data with Dynamic Time Warping

Wildlife poaching of endangered species such as elephants and rhinoceroses in Africa and Asia for illegal trading has become a biodiversity crisis, which has also been recognized by a United Nations Sustainable Development Goal of halting biodiversity loss. The demand for ivory has decimated the elephant population. The main issue associated with attempts to tackle this crisis is the vast area of the wildlife national parks. Recently, unmanned aerial vehicles (UAVs) equipped with heat-sensing infrared cameras have been deployed to help park rangers survey large areas of national parks at night when > 80 % of poaching occurs. In order to maximize surveillance area (with fixed flight time and battery constraints) while avoiding detection, UAVs need to fly at high altitudes (>400ft). This results in very few pixels associated with the objects of interest, i.e., humans and animals, in these thermal infrared videos. Current state-of-art methods utilize shape-based object detection techniques to identify animals/humans in these thermal images, with detection accuracy of only 20 %. In order to battle this inaccuracy, this research presents a novel solution that exploits video data's spatio-temporal nature (differences in movement pattern of animals/humans over time, i.e. turning radius, speed) to derive unique time series. These spatio-temporal series are then classified as human or animal by leveraging dynamic time warping metrics with K-Nearest Neighbor Clustering. When tested on a real-life thermal infrared videos dataset (BIRDSAI), collected in partnership with four African national parks, this method was able to detect human activity with 94 % sensitivity enabling real-time inferencing. Furthermore, this solution has the potential to eliminate the need for high-resolution high-cost $4,800 nighttime-thermal cameras with commodity thermal cameras (<$250), as demonstrated by a design prototype.
Research on Data Service Directory Construction Technology for Power Grid Enterprise Data Middle Platform

In order to support the effective development of data middle platform construction and data service in power grid enterprises, this paper focuses on the data service directory construction technology for power grid enterprise data middle platform, and analyses the basic connotation of data service. Based on data service requirements, this paper analyses the basic requirements and specifications of data service directory construction in power grid data middle platform, puts forward reference classifications of data analysis service directory for power grid enterprises according to analysis service objects, introduces the basic technical scheme of data service directory construction, and develops data service directory construction for provincial power grid enterprises.
An Analysis of Industry 4.0

Chapter 1 sets the scene by providing an overview of the Industry 4.0 concept that is conjoining a number of different technologies, with various levels of maturity, in order to provide an end-to-end capability. This case study is a good exemplar to tease out many pertinent socio-technical topics where the main contexts will be elaborated on throughout the remainder of the book. In short, a case is made that cyber security is first and foremost a human problem, but also highlights the importance of regulation, standards, and bodies to underpin cyber security. Examples of the opposing forces are covered here that together if unmitigated will contrive to undermine the cyber resilience of the 21st century.
Identification of Tampering Image Using SIFT Descriptor

In the recent era, image tampering has become one of the threatening security problems in digital platforms. There are many software&#x2019;s available for tampering with an image that depicts as an original image. Different tampering techniques are used to hide important portions from an image or document, one very common practice is a copy-move forgery that is quite impossible to distinguish with an open eye. Authentications of such images are an ardent research area in image processing and computer vision but still a challenging problem. This paper presents a method to identify image tampering that is based on SIFT (Scale Invariant Feature Transform) algorithm. SIFT descriptor is used to extract keypoint features from the input image and a hierarchical clustering algorithm is used to improve the accuracy of identifying the tampered location. The execution time of our proposed method is proportional to image resolution. If one portion of the image is copied and pasted on multiple locations on the same image, our proposed method can identify such occurrences. Finally, Homography is used to show the tampering points and their matching.
Enhancement in Buffer Overflow (BOF) Detection Capability of Cppcheck Static Analysis Tool

Buffer overflow (BOF) vulnerability is one of the most dangerous security vulnerability which can be exploited by unwanted users. This vulnerability can be detected by both static and dynamic analysis techniques. For dynamic analysis, execution of the program is required in which the behavior of the program according to specifications is checked while in static analysis the source code is analyzed for security vulnerabilities without execution of code. Despite the fact that many open source and commercial security analysis tools employ static and dynamic methods but there is still a margin for improvement in BOF vulnerability detection capability of these tools. We propose an enhancement in Cppcheck tool for statically detecting BOF vulnerability using data flow analysis in C programs. We have used the Juliet Test Suite to test our approach. We selected two best tools cited in the literature for BOF detection (i.e. Frama-C and Splint) to compare the performance and accuracy of our approach. From the experiments, our proposed approach generated Youden Index of 0.45, Frama-C has only 0.1 Youden&#x0027;s score and Splint generated Youden score of -0.47. These results show that our technique performs better as compared to both Frama-C and Splint static analysis tools.
Prospect Prediction Model of Indonesian Telematics Medium and Large Enterprises Using Ensemble Approach

Medium and Large Enterprises (MLE) play an important role in promoting development and increasing Indonesia's competitiveness in the international arena. Currently, there is not much in-depth analysis related to the prospects for medium and large telematics businesses to find out its relevance to economic conditions and what factors affect business prospects. Whereas the telematics industry sector is a supporting sector for Gross Domestic Product (GDP) with a higher growth rate compared to other sectors. The purpose of this study is to predict the prospects MLE telematics businesses using an ensemble approach with the Boost and Bagging algorithm. Data obtained from the 2016 Economic Census. The target class for business prospects consists of five classes, namely better, the same as bad, worse, and cannot be compared. Optimal results are obtained with the average precision value is 76,8, recall is 79,0 and F1-score is 77,13. The model is able to produce important features that can be validated by experts. The advantages of the prospective MLE telematics feature are the level of sales of goods and services, profits, and the level of education of the business owner.
Some enumeration results on trace forms of low radical over finite fields of characteristic two

We introduce some enumeration results for quadratic forms written as trace forms with very low dimension of radical for different types of degrees of extension over F2. This work shows a different approach to extend earlier enumeration results, for instance, when the degree of extension is product of two odd primes. We also determine the possible pairs of invariants for some trace forms and use those results to construct some new maximal Artin-Schreier curves with co-dimension 4 and 6 when the degrees of extension are divisible by 8 but not by 16.
Brief Notes on The Possibility of Copying Qubits in Quantum Systems

Copying the quantum states is contradictory to classical information processing since the fundamental difference between classical and quantum information is that while classical information can be copied perfectly, quantum information cannot. However, this statement does not rule out the risk of building a device that can reproduce a set of quantum states. This paper investigates the naturally arising question of how well or under what conditions one can copy and measure an arbitrary quantum superposition of states. The CNOT and XOR operation-based quantum circuit is presented that exhibits entanglement of states and allows for measuring the doubled qubits.
Differential Metric based Deep Learning Methodology for Non-Profiled Side Channel Analysis

Power Side-Channel analysis recovers sensitive information not only from physical proximity to a device but also from basic knowledge of sample leaked data collection. With minimum mean squared error metric, power analysis using a deep learning test case increase confidence level of proper identification of leaked data. Comparison with state-of-the art technology in this work shows improved performance in the non-profiled SCA category of detection. The deep learning technique aids in calculating the average loss gradient values and the loss values, both being calculated by taking the traces in mathworks implementation as the training data and the MSB values of the intermediate values as the training labels to reveal the expected secret key. Moreover iterative training of some machine learning techniques with different FPGA boards implementing cryptographic designs increased the accuracy of leakage detection at an earlier stage to a better extent.
CoviChain: A Blockchain based Distributed Framework for Healthcare Cyber-Physical Systems

This is a demo abstract based on our previously published article [1]. Some of the significant challenges of central cloud systems and the blockchain include lack of scalability, security risks and higher energy consumption. As a solution, an application with an off-chain Interplanetary File System (IPFS) distributed storage system is presented as CoviChain. The IPFS is used for storing and hashing different data formats of various sizes into immutable links. The links are passed through smart contracts of the Ethereum blockchain to provide a double hash guarantee, along with user access permissions.
Robust Partitioning Scheme for Accelerating SQL Database

SQL Databases have shown tremendous performance in the last four decades. Data consistency, isolation, and durability are the main strengths of SQL databases. However, in relational databases, computing joins are time-consuming at run time. To address this issue, we propose to utilize the Oracle partitioning technique to enhance specific query performance. Physical database tuning approaches of SQL databases are being used to speed up each SQL statement such as partitioning and indexing. With the partitioning technique, the Oracle 11g RDBMS efficiency performance improves by approximately absolute 50&#x0025;. Additionally, we compare its performance with the state-of-the-art NoSQL graph database (Neo4j).
State of Art Approaches for Giving Related Items in Search Query

Search queries are used to dig out needed information and are one of the most important factors in assessment of search engines results. Search queries are consisting of words, phrases and keywords etc. Related items in the form of different appropriate words that can be used in search query to enhance its precision. Enhancement is done in many ways like suggesting correct spelling of misspelled words, autocomplete etc. To be more precise search queries are pr&#x00E9;cised commonly by query replacement and query enhancement using query substitution and expansion approaches. In this paper we have categorized query precising approaches according to type of query. Query types are classified as transactional, navigational and informational. The paper analyses the pros/con of approaches along with challenges that needs to be addressed which will result in accurate query which eventually helps to get precise search results.
Implementation of K-Medoid and Hierachical Clustering for Strengthening Job Satisfaction on The Teacher Profession

Education is a very important element in social life to support the formation of individual character. The implementation of data mining in the education sector shows an increasing trend. The purpose of this study is to implement strengthening job satisfaction for the teaching profession using the K-medoid and Hierarchical Clustering method. Cluster analysis was carried out on five main factors including salary, promotion, supervisor's supervision, relationships with colleagues and the teaching profession. Tests were carried out on 136 respondents and 39 questions devided into 5 factors. The trial of determining the number of clusters was carried out on 3 trials ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}=2, \mathrm{K}=3$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}=4$</tex> ). The optimal cluster value is obtained at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}=3$</tex> with a Davies Bouldin index (DBI) of 2,823 for the K-Medoid method and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}=4$</tex> with a DBI value of 1,415 for the Hierarchical method. The cluster results show that the priority of strengthening teacher professional satisfaction needs to be increased on the salary indicator by 50% for the K-Medoid method and 65% for the Hierarchical method. While the results of the percentage of priorities that need to be maintained are the indicators of relationships with colleagues by 10% in the K-Medoid method and 5% in the Hierarchical method.
Time Series Anomaly Detection Based on CEEMDAN and LSTM

Time series anomaly detection is getting more and more attention in IT operations. Deep learning is one of the most used technologies in recent years, which realizes automatic anomaly detection on raw data. A novel algorithm combining time series decomposition and Long Short Term Memory (LSTM) is proposed in this paper. This algorithm mainly consists of four steps: 1) we use a density-based clustering algorithm to remove the sharp points in the data, and then use interpolation to complete the data; 2) we use the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to decompose the raw data into a series of relatively simple components, called intrinsic mode functions (IMFs); 3) we use LSTM to predict the IMFs, and the sum of the results represents the predicted results of the original data; 4) we use a dynamic threshold rule and the error between the original value and the predicted value to determine whether the point is an anomaly. The experimental results on the Yahoo Webscope_S5 dataset prove the effectiveness of the proposed model.
The Making of PWCPS Building Video of Riau Province Based on Three Dimensional Animation

A video profile is an important thing in a company because a video profile can simplify introduce the functions and duties of that agency to the public. The research aims to produce a video of Public Work and Compartment Planning Service(PWCPS) building-based 3D-Animation as a part of PWCPS profile videos that is tested by USE Questionnaire to obtain the qualification of video based on Likert Scale. This research can be used as a reference for making 3D_Animation Video. Three Dimensional Animation video is created by making a concept to determine anything to be included in the video. Then, creating a video design by making storyboards and collecting data for video purposes. Next, make animation and video editing. All components of Usability Testing namely Usefulness, Satisfaction, Ease-of-Learning, Ease-of-Use are used in testing the result. The research produces a video profile in 20-minutes that shows Outside and Inside of Riau Province PWCPS Building based on 3D-Animation that is followed voice-over in every scene and Malay music is added into the opening scene. Viewpoints of Riau Province PWCPS Building with 0, 200, and 500 Franes in the timeline show that objects of video can be seen as a whole unit from many viewpoints, even without a drone. The qualification of the system is Very Good due to various values from USE’s components starting 90.6%-100%. In general, the average value of Usability Testing for Video of Rian Province PWCPS Building reaches 96%. Based on the Likert scale, it is categorized as a Very Satisfied qualification.
An Efficient Algorithm for Mining Maximal Co-location Pattern Using Instance-trees

Prevalent co-location patterns, which refer to groups of features whose instances frequently appear together in nearby geographic space, are one of the main branches of spatial data mining. As the data volume continues to increase, it is redundant if all patterns are discovered. Maximal co-location patterns (MCPs) are a compressed representation of all these patterns and they provide a new insight into the interaction among different spatial features to discover more valuable knowledge from data sets. Increasing the volume of spatial data sets makes discovering MCPs still very challenging. We dedicate this study to designing an efficient MCP mining algorithm. First, features in size-2 patterns are regarded as a sparse graph, MCP candidates are generated by enumerating maximal cliques from the sparse graph. Second, we design two instance-tree structures, star neighbor- and sibling node-based instance-trees to store neighbor relationships of instances. All maximal co-location instances of the candidates are yielded efficiently from these instance-tree structures. Finally, a MCP candidate is marked as prevalent if its participation index, which is calculated based on the maximal co-location instances, is not smaller than a minimum prevalence threshold given by users. The efficiency of the proposed algorithm is proved by comparison with the previous algorithms on both synthetic and real data sets.
Research Context and Trends of Educational Games

Educational games are video games with enjoyment and instructions, and game-based learning is a specific application form of educational games based on learning contents and learners' characteristics. As a form of effective learning method, it has attracted wide attentions. This paper took CNKI, China National Knowledge Infrastructure, as the data source, retrieved "educational games" and "game-based learning" as key words, and selected literature review articles for systematic literature review. In order to study the research context and trend of educational games and game-based learning comprehensively, this paper focuses on four aspects, including subject classification, research change, existing problems and future trend.
An Unstructured Array Camera System with Scalable Capability

Unstructured array camera systems break through the optical limitations of a single camera and blaze a new trail to high-performance imaging by combining cameras. However, existing solutions use an array of cameras with fixed focal lengths and the same hardware model, lacking flexibility and practicality. The previous works rely on known focal length values, and the image processing methods are too complex to generate ultra-high-resolution videos in real-time. Inspired by image matching and deep learning methods, we proposed an unstructured array camera system with scalable capability that can freely change the focal length of each camera. The proposed cross-scale image matching method based on feature matching can quickly and accurately search the positions of local-view cameras. The image warping algorithm based on deep neural networks can efficiently composite ultra-high-resolution videos in real-time. Furthermore, the color alignment based on the LAB format is used for addressing the problem of different brightness and color between cameras. Experimental results show that our proposed system can use any camera or phone to composite ultra-high-resolution videos in real-time with flexibility, robustness, and efficiency.
A Novel Approach for Excavating Communication Using Taxonomy and Outline Mechanisms

Social Network is a standout amongst the most prominent intuitive medium to share, impart and disperse data. Informal organization is the stage to manufacture social relations among individuals. Clients can stay in contact with companions by trading distinctive kinds of data or messages. Now and again individuals send mail which causes a difficult issue similar to irritating or extorting to clients. The mail substance might exist inconsiderate. The terminology like hostile, detest, disgusting and so on are accessible in the mail. Those mails are recognizing as spam utilizing data sifting. Information filtering can be done by using synopsis and Machine learning substance gathering methodologies. Neural substance classifier is used for representing summary of delivered messages and associated request. In light of content rendering, we have to check the sent messages are spam or not spam.
A Framework for Securing Data by using Elliptic Curve Cryptography and Reed Solomon Coding Schemes

Every firm survives on data, which is an essential resource. In our contemporaries, it is not just the possession of the data that is crucial but the protection that can be offered to ensure its safety and utilization. This study presents the application of Reed Solomon error-correcting coding scheme and two variants of elliptic curve cryptosystems, namely: Elliptic Curve Integrated Encryption Scheme and Fast Elliptic Curve Digital Signature Algorithm to improve the integrity and confidentiality of data. A data protection framework that combines both encryption and encoding schemes to offer integrity and confidentiality to data. The framework draws on the strengths of both encryption and encoding schemes to secure medium and protect data in transit and at storage. The protection scheme obfuscates and signs data in transit by using the Elliptic Curve Cryptographic algorithms to prevent unauthorized access and modification. The Reed Solomon encoding scheme is utilized to correct any corrupted data, in order to maintain the integrity of data.