Reviewerly/exHarmony huggingface dataset:
High-degree noise addition method for the $k$-degree anonymization algorithm

Social network datasets are a valuable source of information for academic researches as well as business and marketing studies. Since social network datasets contain personal and sensitive information of their users, sharing the data with a third party gives rise to many privacy-preserving issues. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -degree anonymization was developed to protect the users of social networks from the re-identification attack by modifying the network structure with a sequence of edge editing operations. In this paper, we introduce a novel approach for noise addition operation in the well-known <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -degree anonymization algorithm <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -DA. We propose the high-degree noise addition method that modifies the degree sequence anonymized by the degree anonymization procedure of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -DA before it is processed by the graph construction procedure of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -DA. Our proposed method significantly reduces the number of necessary repetitions of the graph constructing algorithm and positively affects the efficiency and runtime of the whole <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -DA algorithm. Moreover, we show that the proposed high-degree noise addition algorithm improves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -DA in terms of data utility. We demonstrate its usability by running experiments on 13 different real-world social network datasets.
Towards Real-Time Homogeneity and Heterogeneity in Student's Beliefs

Finding a similarity in the beliefs of students about trending technology is a challenging task. This paper used cluster analysis to groups the homogeneous beliefs of students concerning the technology provided. For this, we applied the Hierarchical Clustering (HC) approach to primary samples. We used the Agglomerative approach to cluster formation with Ward's method and Squared Euclidean Distance (SED). This technique recommended a maximum of three and a minimum of two optimal clusters. Having three clusters 50% and having two clusters, 100% response beliefs are covered. Automatic detection using the HC method discovered the majority of students' beliefs are positive about use, benefits, outlook, and growth. This paper presented an initial cluster approach to detect the Homogeneity and Heterogeneity of students' responses about the technology. It might help to university management to see the grouping of identical beliefs. This technique can deploy online to frame a new web clustering module of the university.
An Overview and Technological Background of Semantic Technologies

The Semantic Web concept is an extension of the web obtained by adding semantics to the current data representation format. It is considered a network of correlating meanings. It is the result of a combination of web-based conceptions and technologies and knowledge representation. Since the internet has gone through many changes and steps in its web versions 1.0, 2.0, and Web 3.0, this last call of smart web, the concept of Web 3.0, is to be associated with the Semantic Web, since technological advances have allowed the internet to be present beyond the devices that were made exactly with the intention of receiving the connection, not limited to computers or smartphones since it has the concept of reading, writing, and execution off-screen, performed by machines. Therefore, this chapter aims to provide an updated review of Semantic Web and its technologies showing its technological origins and approaching its success relationship with a concise bibliographic background, categorizing and synthesizing the potential of technologies.
Towards immersive comprehension of software systems using augmented reality

While traditionally, software comprehension relies on approaches like reading through the code or looking at charts on screens, which are 2D mediums, there have been some recent approaches that advocate exploring 3D approaches like Augmented or Virtual Reality (AR/VR) to have a richer experience towards understanding software and its internal relationships. However, there is a dearth of objective studies that compare such 3D representations with their traditional 2D counterparts in the context of software comprehension. In this paper, we present an evaluation study to quantitatively and qualitatively compare 2D and 3D software representations with respect to typical comprehension tasks. For the 3D medium, we utilize an AR-based approach for 3D visualizations of a software system (XRaSE), while the 2D medium comprises of textual IDEs and 2D graph representations. The study, which has been conducted using 20 professional developers, shows that for most comprehension tasks, the developers perform much better using the 3D representation, especially in terms of velocity and recollection, while also displaying reduced cognitive load and better engagement.
A Parasitic Mechanism-Based Filter Pruning Method for Deep Convolutional Neural Networks

The convolutional neural network (CNN) shows excellent performances in various applications at the cost of huge storage and calculation overheads. To compress and accelerate the CNN, conventional methods usually prune the unimportant filters under the pre-selected standards, which should be artificially and carefully designed according to the specific application scenarios. In this paper, we propose a general CNN accelerating and compressing method, i.e., the parasitic mechanism (PAM)-based filter pruning algorithm. First, the parasitic layer is constructed on the basis of the convolutional structure and the gate module. Then, the parasitic layers are attached to the original CNN, which carry out the pruning process for the filters in each layer. The above attaching and pruning process is named as the parasitic mechanism. Compared with the traditional filter pruning methods which pre-select the pruning standards artificially, the PAM method intelligently learns the pruning standard in the training process, and prunes the unimportant filters under the constraint of maintaining the performance of the original CNN. As verified by simulations, the proposed PAM algorithm has a better pruning performance than the conventional filter pruning methods.
TCB Minimization towards Secured and Lightweight IoT End Device Architecture using Virtualization at Fog Node

An Internet of Things (IoT) architecture comprised of cloud, fog and resource constrained IoT end devices. The exponential development of IoT has increased the processing and footprint overhead in IoT end devices. All the components of IoT end devices that establish Chain of Trust (CoT) to ensure security are termed as Trusted Computing Base (TCB). The increased overhead in the IoT end device has increased the demand to increase the size of TCB surface area hence increases complexity of TCB surface area and also the increased the visibility of TCB surface area to the external world made the IoT end devices architecture over-architectured and unsecured. The TCB surface area minimization that has been remained unfocused reduces the complexity of TCB surface area and visibility of TCB components to the external un-trusted world hence ensures security in terms of confidentiality, integrity, authenticity (CIA) at the IoT end devices. The TCB minimization thus will convert the over-architectured IoT end device into lightweight and secured architecture highly desired for resource constrained IoT end devices. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device. In this paper we review the IoT end device architectures proposed in the recent past and concluded that these architectures of resource constrained IoT end devices are over-architectured due to larger TCB and ignored bugs and vulnerabilities in TCB hence un-secured. We propose the Novel levelled architecture with TCB minimization by replacing oversized hypervisor with lightweight Micro(μ)-hypervisor i.e. μ-visor and transferring μ-hypervisor based virtualization over fog node for light weight and secured IoT End device architecture. The bug free TCB components confirm stable CoT for guaranteed CIA resulting into robust Trusted Execution Environment (TEE) hence secured IoT end device architecture. Thus the proposed resulting architecture is secured with minimized SRAM and flash memory combined footprint 39.05% of the total available memory per device.
The research of clinical temporal knowledge graph based on deep learning

Temporal knowledge base exists on various fields. Take medical medicine field as example, diabetes is a typical chronic disease which evolves slowly. This paper starts from actual EMR data of hospitals by combination of experience and knowledge of clinical doctors. Link prediction on clinical knowledge base such as diabetic complication requires the analysis on temporal characteristic of temporal knowledge base, which is a great challenge for traditional link prediction models. This paper proposes temporal knowledge graph link prediction model based on deep learning. This model selects the TransR transformation model suitable for big data and makes entity projection in relation space containing different semantic meanings, so as to vector the entities and complex semantic relations in graph. Then it adopts LSTM recursive neural network and adds the top-bottom relational information of the graph for sequential learning. Finally it constantly carries out deep learning through incremental calculation and LSTM recursive network to improve the accuracy of prediction. The incremental LSTM model highlights the hidden semantic and clinical temporal information and effectively utilizes sequential learning to mining forward-backward dependent information. It compensates the deficiency of lower prediction accuracy on timely knowledge graph caused by the traditional link prediction models. Finally, it is proved that the new model has better performance over temporal knowledge graph link prediction.
DEVS-based experimental framework for blockchain services

Blockchain works according to the principles of distributed transactions and processing tiny hash-based blocks. Data located in the blockchain preserves its integrity and transparency, which enables participants to verify it. Today, people apply blockchain technology in domains that must maintain the integrity of the data. However, people can be reluctant to introduce blockchain technology due to the complexity of the technology and the resultant inefficiency. This work presents a DEVS-based experimental methodology for performance evaluation of blockchain-based services. The experimental methodology is tested by implementing a content delivery service by utilizing the Ethereum blockchain framework. As the evaluation result, we show the overall turnaround time of the service and investigate the delay of smart contract function consisting the service. In addition, the evaluation result shows the difference in performance between blockchain-based and database-based services. This work presents a methodology for evaluating blockchain service performance and shows experimental results that can be referenced by people who are planning to implement blockchain-based services.
Performance evaluation of Apache Hadoop and Apache Spark for parallelization of compute-intensive tasks

There have been numerous studies that have examined the performance of distribution frameworks. Most of these studies deal with the processing of large amounts of data. This work compares two of these frameworks for their ability to implement CPU-intensive distributed algorithms. As a case study for our experiments we used a simple but computationally intensive puzzle. To find all solutions using brute-force search, 15! permutations had to be calculated and tested against the solution rules. Our experimental application was implemented in the Java programming language using a simple algorithm and having two distributed solutions with the paradigms MapReduce (Apache Hadoop) and RDD (Apache Spark). The implementations were benchmarked in Amazon-EC2/EMR clusters for performance and scalability measurements, where the processing time of both solutions scaled approximately linearly. However, according to our experiments, the number of tasks, hardware utilization and other aspects should also be taken into consideration when assessing scalability. The comparison of the solutions with MapReduce (Apache Hadoop) and RDD (Apache Spark) under Amazon EMR showed that the processing time measured in CPU minutes with Spark was up to 30 % lower, while the performance of Spark especially benefits from an increasing number of tasks. Considering the efficiency of using the EC2 resources, the implementation via Apache Spark was even more powerful than a comparable multithreaded Java solution.
Transfer Learning in Classifying Prescriptions and Keyword-based Medical Notes

Medical text classification is one of the primary steps of health care automation. Diagnosing disease at the right time, and going to the right doctor is important for patients. To do that, two types of medical texts were classified into some medical specialties in this study. The first one is the keywords-based medical notes and the second one is the prescriptions. There are many methods and techniques to classify texts from any domain. But, textual resources of a specific domain can be inadequate to build a sustainable and accurate classifier. This problem can be solved by incorporating transfer learning. The objective of this study is to analyze the prospects of transfer learning in medical text classification. To do that, a transfer learning system has been created for classification tasks by fine-tuning Bidirectional Encoder Representations from Transformers aka the BERT language model, and its performance has been compared with three deep learning models - multi-layer perceptron, long short-term memory, and convolutional neural network. The fine-tuned BERT model has shown the best performance among all the other models in both classification tasks. It has 0.84 and 0.96 weighted f1-score in classifying medical notes and prescriptions respectively. This study has proved that transfer learning can be used in medical text classification, and significant improvement in performance can be achieved through it.
A Hierarchical Bidirectional LSTM Sequence Model for Extractive Text Summarization in Electric Power Systems

With the increasing volume of documents in electric power systems, it is urgent and necessary for electric power systems managers to efficiently analyze the massive documents and make reasonable decisions by capturing the main points of the document as quickly as possible. The text summarization technique provides a feasible way to efficiently analyze and obtain the main contents residing in the document. In this paper, we present a Hierarchical Bidirectional Long Term Short Memory Sequence model for extractive text summarization in electric power systems in order to efficiently and accurately summarize electric power documents and obtain a summary of the document. Our model is divided into four layers including the embedding layer, the word layer, the sentence layer, and the classification layer in a hierarchical manner. The related experiments were made based on the electric power data set that contains more than 2000 electrical papers, in comparison with the existing approaches based on the CRF, CNN, and RNN models. The experimental results show that the performance based on our approach is superior to the three approaches against the three performance indexes ROUGE-1, ROUGE-2, and ROUGE-L.
A Generalization of the CHOMP Algorithm for UAV Collision-Free Trajectory Generation in Unknown Dynamic Environments

Unmanned Aerial Vehicles (UAVs) are being used in an increasing number of application areas. In many applications, UAVs are expected to operate in unknown environments with dynamic obstacles, hence the growing interest in autonomous navigation systems. In this work, we propose a method to generate collision-free trajectories in an unknown environment with dynamic obstacles, by extending the usual notion of collision cost penalty into Dynamic Obstacle Cost, calculated using Dynamic Cost Map. The Dynamic Cost Map allows to identify future potential collisions by modelling the movements of the dynamic obstacles. We compare the performances of our method with conventional CHOMP and A* with polynomial smoothing in a simulation environment. Our results show that the times of replanning are drastically reduced with a limited increase in the computing time associated to each replanning task.
Imputation Using Machine Learning Techniques

In today's environment to find the missing value in the data set has become the biggest challenge for the industry people, scientists, academicians and for the researchers. With the incomplete dataset we are not able to apply algorithm to find the result. If the dataset is not complete then only we can apply algorithms and then result can be analyzed to get the efficient output. In this paper we have analyzed some of the missing value generation techniques and algorithms and also the result is compared with among techniques and algorithms which gives the best solution for imputing the data in a incomplete dataset. We have considered some of the missing value handling techniques such as deletion, imputation, EM algorithm, KNN algorithm and Random Forest. Also applied on the incomplete dataset MRI of the brain available in the Research Center of Alzheimer's disease in Washington University.
Photorealistic avatars to enhance the efficacy of Selfattachment psychotherapy

We have designed, developed, and tested an Immersive virtual reality (VR) platform to practice the protocols of Self-attachment psychotherapy. We made use of customized photorealistic avatars for the implementation of both the high-end version (based on Facebook's Oculus) and the low-end version (based on Google's cardboard) of our platform. Under the Selfattachment therapeutic framework, the causes of mental disorders such as chronic anxiety and depression are traced back to the individual's insecure attachment with their primary caregiver during childhood and their subsequent problems in affect regulation. The conventional approach (without VR) to Selfattachment requires that the individual uses their childhood photographs to recall their childhood memories and then imagine that the child that they were is present with them. They thus establish a compassionate relationship with their childhood self and then, using love songs and dancing, create an affectional bond with them. Their adult self subsequently role plays a good parent and interacts with their imagined childhood self to perform various developmental and re-parenting activities. The goal is to enhance their capacities for self-regulation of emotion, which can lead them into earning secure attachment. It is hypothesized that our immersive virtual reality platform - which enables the users to interact with their customized 3D photorealistic childhood avatar - offers either a better alternative or at least a complementary visual tool to the conventional imaginal approach to Self-attachment. The platform was developed in Unity 3D, a cross-platform game engine, and takes advantage of the itSeez3D Avatar SDK for generating a customized photorealistic 3D avatar head from a 2D childhood image of the user. The platform also offers facial and body animations for some of the basic emotional states such as Happy, Sad, Scared and Joyful and it allows modifications to the avatar body (height/ width) and clothing color. A study to compare the use of the avatar-based approach (VR) to Self-attachment with the conventional photo-based approach showed promising results. Almost 85% of the participants reported that their photorealistic childhood avatar in VR was more relatable than their childhood photos. Both low-end and high-end VR based approaches were unanimously reported to be more effective than the conventional imaginal approach. Participants reported that the high-end version of the VR platform was more realistic and immersive than the low-end mobile VR version.
Leveraging Collaborative Repository “Resource-In-Common” Model to Find Space – and Solace – In Downsizing Legacy Print Collection: A Case Study

This case study details the techniques, strategies, and workflow used by Sam Houston State University’s (SHSU) Newton Gresham Library in preparing materials to submit and claim with the Texas A&M University System Joint Library Facility (JLF). The article provides an overview of JLF history and operations, and details the critical factors leading to SHSU removing large numbers of print materials from its collection. Additionally, the article discusses why SHSU elected to leverage the services offered by JLF, examines challenges encountered, and reviews techniques employed to enhance the pre-check processing required to participate in JLF’s shared collection.
Automatic Release Notes Generation: A Systematic Literature Review

Release Notes (RNs) play a vital role in software development. RNs are one of the essential artifacts about a new release of the software. Despite their importance, RNs are usually generated manually. Generating them manually prone to errors and time consuming as it contains a description of new features, bug fixes, license changes, deprecated libraries, new Application Program Interface (API), and other changes made to the software. There are different tools available to generate RNs automatically from issue tracker and source code repositories. Researchers are working on the automation of RNs from decades to enhance the software development process. In this paper, we present the algorithms published by different researchers, available plugins, and tools in the market for this task. This survey of the past techniques used for the automation of RNs are useful and broad background knowledge of this research field to future researchers.