Reviewerly/exHarmony huggingface dataset:
Study of Various Parallel Implementations of Association Rule Mining Algorithm

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.
Bounding-box Centralization for Improving SiamFC++

Object tracking is one of the important tasks in the field of computer vision. SiamFC++is an excellent anchor-free single object tracking method. It has high accuracy but low success rate. To improve the success rate of the tracker and enhance its robustness, this paper proposes a bounding-box centralization strategy (BCS) to deal with the problem of low success rate. In this paper, two new bounding boxes of elliptical bounding-box (EB) and rectangular bounding-box (RB) are designed to improve SiamFC++, and tested on OTB and VOT datasets. The test results demonstrate that BCS can improve the performance of SiamFC++.
Index Model o f Polder System Service u sing General Reduced Gradient

Polders were one of the construction that built to minimize the impact of flood, because polders have pond to create a room for water as one of the method in flood risk management. Polders usually build in low land area where water could not flow by gravity with components inside of it, which are pond, pumps, water gate and trash racks. Many cities in Indonesia has already build polders to manage the flood in an area, such as DKI Jakarta, Semarang, Bandung, East Kalimantan and others. To maintain those polder’s function, need an effort and government’s consistency to regulate activity and maintenance cost of each polder. Guideline to find out the service index of those polder are not yet available. This guideline will become important to make prioritize of managing polders. This research analyze technical and non-technical indicators would give impacts to polder’s function and formulate the service of polder system for assessment purposes. As points of this research, eight polders in DKI Jakarta which have complete components were chosen for modelling. The result of this research are technical component are mostly effected to polder system operating than non- technical component. Its proportion are 53% technical aspect and 47% non-technical aspect.
A Secure and Privacy-Preserving Approach to Protect User Data across Cloud based Online Social Networks

The tremendous growth of social networking systems enables the active participation of a wide variety of users. This has led to an increased probability of security and privacy concerns. In order to solve the issue, the article defines a secure and privacy-preserving approach to protect user data across Cloud-based online social networks. The proposed approach models social networks as a directed graph, such that a user can share sensitive information with other users only if there exists a directed edge from one user to another. The connectivity between data users data is efficiently shared using an attribute-based encryption (ABE) with different data access levels. The proposed ABE technique makes use of a trapdoor function to re-encrypt the data without the use of proxy re-encryption techniques. Experimental evaluation states that the proposed approach provides comparatively better results than the existing techniques.
VVC Adaptive Loop Filter Optimization for Subpicture-based Viewport-adaptive Streaming

Virtual reality (VR) systems require delivering high-fidelity 360° video content to immerse viewers to the captured scene. The viewport-adaptive streaming (VAS) methods have been developed to deliver 360° VR content efficiently. The Versatile Video Coding (VVC) standard introduces the subpicture picture partitioning tool, which creates isolated regions suitable for VAS. The usage of Adaptive Loop Filter (ALF) as a VVC in-loop filtering operation is limited in subpicture-based VAS. This paper aims at enabling usage of ALF in subpicture-base VAS through proposing a set of encoding constraints that are standard compliant. While ALF is activated, the proposed constrains guarantee that no coding coordination with respect to sharing of ALF parameters among subpictures is required. This further allows subpicture-based parallel encoding of high-resolution VR content. We study the performance of several methods targeting both single- and multi-thread encoding platforms. The experimental results indicate that VR content encoding can be parallelized at a subpicture-group level, while still preserving most of the ALF gain. The proposed method with subpicture-group encoding parallelization achieves on average -2.4%, -4.0%, and -4.3% Bjøntegaard delta rate reduction for Y, U, and V components respectively, compared to the case where ALF operation is deactivated.
Surveillance Data Analytics

In surveillance, object analysisObject analysis refers to treat an object as a whole, while object analytics means taking each object into multiple parts and analyzing the components from various aspects. In this chapter, we mainly discuss those computable features of a visual object and how to use them for visual object analysis which includes object segmentationObject segmentation, detectionObject detection and recognitionObject recognition, classificationObject classification, trackingObject tracking Murray and Basu (IEEE Trans Pattern Anal Mach Intell 16(5):449–459, 1994), annotation, labeling and ontologyObject ontology, search or retrieval, etc. Bimbo (1999); Hampapur et al. (IEEE Signal Process 22(2):38–51, 2005). We also will introduce deep learning-based visual object analysis in this chapter Goodfellow et al. (2016); LeCun and Bengio (255–258, 1995); LeCun et al. (Nature 521:436–444, 2015).
Artificial Intelligence

Artificial intelligence is a set of theories and techniques that develop complex computer programs that are able to simulate certain traits of human intelligence (reasoning, learning, etc.).
Scribe versus authorship attribution and clustering in historic Czech manuscripts: a case study with visual and linguistic features

Abstract For the identification of scribes and authors in handwritten documents, methods from classical linguistic analysis are combined with modern computer vision approaches to enhance the knowledge discovery process. One important finding is that it is possible to train neural networks for automatic transcription of handwritten documents and to use these transcriptions as input for statistical analysis. Furthermore, hypotheses about scribes can be tested by extracting visual handwriting features and clustering them. From a linguistic point of view, the R package stylo is a useful tool to analyse and cluster texts. Unfortunately, it only achieves a high level of accuracy with longer texts. For texts under 5000 words it is more suitable to measure their Euclidean distance based on a set of linguistic features. Both approaches, the analysis with stylo and the Euclidean distance, in combination with neural networks for automatic transcription and clustering allow for more precise statements about the relationship between texts, authors and scribes, even if the documents are under 1,000 words.
Convolutional Neural Network-driven Optimal Prediction for Image Reversible Data Hiding

Reversible data hiding aims to embed data into cover digital media in a reversible way. The key issue of image reversible data hiding is to construct the sharply distributed prediction error histogram using advanced pixel prediction. Inspired by the progress of image super-resolution exploiting convolutional neural networks (CNN), CNN predictors can improve the prediction accuracy compared with conventional predictors generally. However, CNN predictors fail to achieve the best prediction accuracy in some cases due to the dependence on training data. To remedy this drawback, the CNN-driven optimal prediction for image reversible data hiding is proposed in this paper. Instead of only utilizing one specific predictor for prediction error histogram construction, the optimal prediction mechanism is designed by incorporating CNN predictors and conventional predictors. Extensive experiments demonstrate the merits of the proposed method in terms of prediction accuracy and marked image quality.
Introduction

This chapter is a general introduction of the book. First the motivation of aerial robotic manipulation is presented. Next a very short introduction of unmanned aerial vehicles and aerial robotics is included. The next section is devoted to introduce unmanned aerial vehicles physically interacting with the environment with the environment. Finally, aerial robotic manipulation while flying is introduced.
Empirical Abstraction

Given a program analysis problem that consists of a program and a property of interest, we use an empirical approach to automatically construct a sequence of abstractions that approach an ideal abstraction suitable for solving that problem. This process begins with an infinite concrete domain that maps to a finite abstract cluster domain defined by statistical procedures. Given a set of properties expressed as formulas in a restricted and bounded variant of CTL, we can test the success of the abstraction with respect to a predefined performance measure. In addition, we can perform iterative abstraction-refinement of the clustering by tuning hyperparameters that determine the accuracy of the cluster representations (abstract states) and determine the number of clusters.
An IMU-aided Fitness System

In this work, we present an IMU-aided fitness system for users conducting exercises. The system utilizes the wearable 9-axis IMU sensors to detect three kinds of exercises including squat, bridge exercise, and double leg raise. The sensors are also connected to a smartphone via Bluetooth, and an Android APP is designed for users. The system can instantaneously report the correctness of exercises, show the statistics of workouts, and record the data in the cloud for references.
Fault location measurement of sensor nodes based on fuzzy control algorithm

The sensor nodes are devices with extra capabilities of sensing, communicating and processing of the recorded data. The sensor-based wireless and IoT-enabled networks can contribute a precarious part of army command, defense control, defense communications, computerized based intelligence and surveillance, and above all the targeting systems. Examples of defense applications include monitoring of own army forces and enemy forces; monitoring of ammunition, monitoring of equipment, targeting at certain regions; and nuclear as well as biotechnology-based attack detection. With the advent of AI and latest technologies, a technical paradigm shift can be seen in monitoring the faults in sensor nodes that are capable to collect the data and entire decisions are inferred on the basis of this data. By deploying sensors in critical areas, all the movements can be followed in detail. Hence, instant fault location measurement of sensor nodes with the least complex approaches is the need of the hour. Therefore, a novel soft computing-based approach is proposed in this paper for measuring the fault in locations of sensor-based networks using fuzzy logic and neural networks with high accuracy. The proposed technique allows lower consumption of energy while locating the faults. The results demonstrate the superior performance of the proposed fault location scheme with respect to the network node loss, measurement error, and measurement time taken for detecting faults in locations of sensor nodes. The proposed fuzzy and neural network-based method outperforms the existing benchmarked techniques considered for comparative study.
Improved Artificial Potential Field based Parallel RRT Star for Fast Path Planning

The goal of the motion planning algorithm is to compute an obstacle-free trajectory that satisfies the vehicle dynamics constraints and guides the vehicle from the starting position to the target position. RRT* (Rapidly-exploring Random Tree star) is a sampling-based algorithm which is widely used in many path planning applications due to its speed in quickly finding a trajectory. Although the search of RRT* can convergence to optimal solution, the optimal solution has been proven to take an infinite time to achieve convergence with a slow convergence rate. In this paper, a new parallel version of RRT* algorithm combining the artificial potential field (APF-RRT*) is proposed for 2D and 3D path planning. APF-RRT* gives an advantage over other RRT* algorithms by introducing attractive and repulsive forces into the APF algorithm, allowing for improvement in the near-optimal path. Furthermore, the path planning runs as a parallel process, which can improve the convergence speed to a certain extent. The effectiveness of the algorithm has been demonstrated in simulation based on C++. We compared our algorithm with RRT* and verified the improvement of the parallel APF-RRT* algorithm in terms of path optimization and operation time.
Cryptography in Big Data Security

Information security is a prime goal for every individual and organization. The travelling from client to cloud server can be prone to security issues. The big data storages are available through cloud computing system to facilitate mobile client. The information security can be provided to mobile client and cloud technology with the help of integrated parallel and distributed encryption and decryption mechanism. The traditional technologies include the plaintext stored across cloud and can be prone to security issues. The solution provided by applying the encrypted data upload and encrypted search. The clouds can work in collaboration; therefore, the encryption can also be done in collaboration. Some part of encryption handle by client and other part handled by cloud system. This chapter presents the security scenario of different security algorithms and the concept of mobile and cloud computing. This chapter precisely defines the security features of existing cloud and big data system and provides the new framework that helps to improve the data security over cloud computing and big data security system.
Influence of Perspective on Dynamic Tasks in Virtual Reality

Users are increasingly able to move around and perform tasks in virtual environments (VEs). Such movements and tasks are typically represented in a VE using either a first-person perspective (1PP) or a third-person perspective (3PP). In Virtual Reality (VR), 1PP is almost universally used. 3PP can be represented as either egocentric or allocentric. However, there is little empirical evidence about which view may be better suited to dynamic tasks in particular. This paper compares the use of 1PP, egocentric 3PP and allocentric 3PP for dynamic tasks in VR. Our results indicate that 1PP provides the best spatial perception and performance across several dynamic tasks. This advantage is less pronounced as the task becomes more dynamic.
The Importance of Process Improvement in Web-Based Projects

Process improvement in web-based projects is of growing concern for many reasons including successful delivery of projects. Web application development companies are reluctant to adapt process improvement models and methods because of their complex structure and difficult implementation methods. It has been observed that improvement efforts in web application development companies based on process improvement frameworks which are designed for large organizations fail most of the time. This study attempts to evaluate the suitability of different software process improvement models to meet the special characteristics and requirements for the web-based projects.
The Significance of Direct Contacts in the Process of Building Relations with Student Candidates

Summary In the second decade of the 21st century the graduates of high schools — student candidates — already belong to the next generation, called generation “Z”. This generation has been raised in the virtual world. Constant and unlimited access to the Internet leads to a situation in which it constitutes the most important communication channel and a source of information about the world surrounding them. Appreciating the significance of modern technologies and their role in building relations with student candidates, we can’t forget about the significance of direct contacts. For those facing the choice of university, which to a large extent may determine the kind of future professional career, communication in the virtual world, even though necessary, turns out to be insufficient. Meetings with candidates at universities, or high schools, lectures given by academic teachers to high school students, meetings with students, or other forms of direct contacts are still important elements of establishing and building relations with potential student candidates. The goal of this article is highlighting the significance of direct meetings in the process of establishing and building relations between a university and potential student candidates. To carry out the assumed goal a quantitative research on a group of high school students was carried out by means of the technique of auditorium questionnaire. On the basis of obtained results it was concluded that in the context of looking for information about the offer of universities young people from high schools expect direct meetings. What attracts particular attention are visits to universities, during which young people get the opportunity to participate in various activities. What turned out to be an important element of building direct relations were contacts with the students of a particular university. As the results of research have shown, it is worth engaging university students more often in the communication activities of universities.
Everything You Should Know About Intel SGX Performance on Virtualized Systems

Intel SGX has attracted much attention from academia and is already powering commercial applications. Cloud providers have also started implementing SGX in their cloud offerings. Research efforts on Intel SGX so far have mainly focused on its security and programmability aspects. However, no work has studied in detail the performance degradation caused by SGX in virtualized systems. Such settings are particularly important, considering that virtualization is the de facto building block of cloud infrastructure, yet often comes with a performance impact. This paper presents the first detailed performance analysis of Intel SGX in a virtualized system compareed against bare-metal. Based on our findings, we identify several optimization strategies that would result in performance improvements of Intel SGX on such systems.
Human Cognition and Hyper Intelligence

Human cognition is a complex neural process ranging from sensor-motor tasks to decision making. Hyper intelligence strives to accomplish complex tasks through creation or combination of diverse intelligent systems used by humans or machines based on a variety of methodologies, algorithms, and models in statistics, computation, and mathematics. In this paper, we give perspectives on various aspects of human cognition and hyper intelligence, as well as their relations, interactions, and causal effects. In particular, we focus on a diverse set of topics: cognitive process, functions of consciousness, model fusion, supply chain development, and convergence of technologies. Opportunities and challenges of this emerging field are also discussed. In addition, several issues for further investigation are suggested.
Faculty Professional Development for Culturally Responsive Pedagogy in STEM Higher Education

This chapter showcases the purpose, activities, and outcomes of the Teaching to Increase Diversity and Equity in STEM (TIDES) faculty professional development program, which significantly increased both faculty skill level and confidence with implementing culturally responsive pedagogies in the computer/information science disciplines. Starting with the structure of the application process, TIDES intentionally created an effective learning environment where computer and information science faculty could not only learn about culturally relevant teaching practices and the need for them, but also unlearn what they may have absorbed about historical inequities in society and higher education and address their implicit biases about who can and should succeed in STEM. Evaluative data indicate that TIDES exceeded all its target outcomes, so this chapter is shared to support the replication of a proven practice of professional development to foster culturally responsive pedagogy among STEM faculty and, therefore, to support the recruitment and retention of diverse students in STEM.
Preface

The Second International Conference on Computer Science and Engineering Technology was held and organized by Faculty of Engineering Universitas Muria Kudus in Indonesia on 29 October 2019 at Griptha Hotel Kudus. The second ICCSET placed for academicians, researchers, professional, policy makers, and students to exchange the finding and interact on related issues. Currently, topic of Sustainability is a trending topic of research. Holding discussion related to maintain environmental sustainability in the rapid development of engineering and technology is an important point to open our awareness. The theme opened in this conference were: 1) Control Engineering, 2) Manufacturing Engineering, 3) Sustainable Energy, 4) Design Engineering, 5) Computer Engineering, 6) Information Security, 7) Human Factor Engineering and Ergonomics, 8) Engineering Economics, 9) Telecommunication, 10) and other aspects related to the issues of sustainability in engineering. The second ICCSET was attended by over189 participants and total of 80 well-qualified paper were presented and discussed. The paper were authored by researcher form Vietnam, Indonesia and malaysia. All submitted paper were reviewed by qualified reviewer who provide a critical review and comments. Two keynote speaker from Malaysia and Indonesia were invited to deliver the speech on related topic. We sincerely express our gratitude to the international/national advisory Committee, presenters, participants, organizing committee members, session chairs, the dean and all member of faculty on engineering Universitas Muria Kudus. The success of the conference was due to the collective efforts of a large number of individuals. Last but not the least, we are thankful to IOP JPCS for producing the proceeding. The Editors Salman Alfarisi Robbi Rahim Chairman of The 2 nd ICCSET 2019 Dr. Solekhan, S.T., M.T.
Watermarking Identification Codes with Related Topics on Common Randomness

Watermarking identification codes were introduced by Steinberg and Merhav.
Verification of high-level transformations with inductive refinement types

High-level transformation languages like Rascal include expressive features for manipulating large abstract syntax trees: first-class traversals, expressive pattern matching, backtracking and generalized iterators. We present the design and implementation of an abstract interpretation tool, Rabit, for verifying inductive type and shape properties for transformations written in such languages. We describe how to perform abstract interpretation based on operational semantics, specifically focusing on the challenges arising when analyzing the expressive traversals and pattern matching. Finally, we evaluate Rabit on a series of transformations (normalization, desugaring, refactoring, code generators, type inference, etc.) showing that we can effectively verify stated properties.