Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Multi-output Regression via Correlated Gaussian Processes

Multi-output regression seeks to borrow strength and leverage commonalities across different but related outputs in order to enhance learning and prediction accuracy. A fundamental assumption is that the output/group membership labels for all observations are known. This assumption is often violated in real applications. For instance, in healthcare data sets, sensitive attributes such as ethnicity are often missing or unreported. To this end, we introduce a weakly supervised multi-output model based on dependent Gaussian processes. Our approach is able to leverage data without complete group labels or possibly only prior belief on group memberships to enhance accuracy across all outputs. Through intensive simulations and case studies on insulin, testosterone and body fat data sets, we show that our model excels in multi-output settings with missing labels while being competitive in traditional fully labeled settings. We end by highlighting the possible use of our approach in fair inference and sequential decision making. History: Irad Ben-Gal served as the senior editor for this article. Funding: This research was supported in part by the National Science Foundation’s Cyber-Physical Systems (CPS) Program [Award 1931950]. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/2590027/tree/v1 and at https://doi.org/10.1287/ijds.2022.0018 .
Clustering Performance Analysis

Clustering plays a significant role in identifying the intrinsic structure of data. In this paper, various clustering algorithms are compared on real, numerical, categorical datasets around the cluster size. From the analysis, it is inferred that the repetition of KMeans many times does not bring better significant iterations since it starts randomly. It purely depends on the initial choice of the centroid of clusters. The sum of squared error decreases with increasing cluster size. The Expectation–Maximization (EM) is time-consuming than KMeans.
Identifying Variables Interaction for Black-box Continuous Optimization with Mutual Information of Multiple Local Optima

Identifying the interaction of search variables of black-box optimization problem and exploiting the learned interaction structure back to optimization process is a very meaningful research topic. Evaluating the interaction between variables based on information theory is a popular and effective method. However, very little research pay attention to what kind of data can help identify interactions between variables. In this paper, we propose a method to identify the interaction between variables by using the local optima solutions of the objective function. First, a multimodal optimization algorithm is used to search for multiple local optima of the optimization problem. Then, hierarchical clustering is used to cluster and discretize local optima. Finally, the interaction between variables is quantified using the mutual information of local optima. Experimental results show that the proposed method can use the information of local optima to identify the interaction of search variables.
DESENSITIZATION: Privacy-Aware and Attack-Preserving Crash Report

Software vendors collect crash reports from endusers to assist in the debugging and testing of their products.However, crash reports may contain users' private information, like names and passwords, rendering the user hesitant to share the reports with developers.We need a mechanism to protect users' privacy in crash reports on the client side while keeping sufficient information to support server-side debugging and analysis.In this paper, we propose the DESENSITIZATION technique, which generates privacy-aware and attack-preserving crash reports from crashed executions.Our tool adopts lightweight methods to identify bug-related and attack-related data from the memory, and removes other data to protect users' privacy.Since a large portion of the desensitized memory contains null bytes, we store crash reports in spare files to save the network bandwidth and the server-side storage.We prototype DESENSITIZATION and apply it to a large number of crashes of real-world programs, like browsers and the JavaScript engine.The result shows that our DESENSITIZATION technique can eliminate 80.9% of nonzero bytes from coredumps, and 49.0% from minidumps.The desensitized crash report can be 50.5% smaller than the original one, which significantly saves resources for report submission and storage.Our DESENSITIZATION technique is a push-button solution for the privacy-aware crash report.
Query Expansion for Improving Web Search

The aim of this paper is to retrieve the most relevant expansion words for expanding the initial query of the user in order to enhance the outcomes of web search results. Query expansion plays a major role in reformulating a user’s initial query to a one more pertinent to the user’s intended meaning. The reformulated query is then used to obtain more appropriate outcomes from a large amount of information on the web. The proposed semantic query expansion technique uses Wikipedia and WordNet as data sources. Wikipedia is taken as a base for all query expansions because it is one of the most diversified and relevant databases available on the web. To further improve the proposed query expansion technique,WordNet—a lexical database—is used as the as another data source because the synonyms (synsets) of the query term provided by it can be quite useful for query expansion. The proposed expansion technique successfully combines the two data sources to retrieve the most relevant expansion terms from the data sources in response to the user’s original query. The proposed work has been divided into four phases: (1) extraction of relevant words from Wikipedia (2) extraction of relevant words from WordNet (3) merging of the expansion terms obtained from Wikipedia and WordNet, and (4) query formulation by combining the expansion terms using Boolean operators. This reformulated query is then fired on the web to find the desired result. The Experimental result shows a significant improvement in information retrieval using query expansion.
“I do it because they do it”: Social-Neutralisation in Information Security Practices of Saudi Medical Interns

Successful implementation of information security policies (ISP) and IT controls play an important role in safeguarding patient privacy in healthcare organizations. Our study investigates the factors that lead to healthcare practitioners’ neutralisation of ISPs, leading to non-compliance. The study adopted a qualitative approach and conducted a series of semi-structured interviews with medical interns and hospital IT department managers and staff in an academic hospital in Saudi Arabia. The study’s findings revealed that the MIs imitate their peers’ actions and employ similar justifications when violating ISP dictates. Moreover, MI team superiors’ (seniors) ISP non-compliance influences MI’s tendency to invoke neutralisation techniques. We found that trust between medical team members is an essential social facilitator that motivates MI’s to invoke neutralisation techniques to justify violating ISP policies and controls. These findings add new insights that help us to understand the relationship between the social context and neutralisation theory in triggering ISP non-compliance.
Progress on Quantum Key Distribution Using Ultralow Loss Fiber

We use a 2.5 GHz clocked quantum key distribution system to perform long-distance and high-speed quantum key distribution. Taking benefit from superconducting detectors optimized for each operation regime and low-loss fiber, we achieve state-of-the-art performance.
Feature Selection Using Neighborhood Component Analysis with Support Vector Machine for Classification of Breast Mammograms

Recognition of lumps in the breast region is well exploited through mammography. For radiologist, the identification of cancerous tissues is tedious and time-consuming, and many automated computer-aided techniques have been proposed to enhance the clinical diagnosis. This specific research work suggests the application of neighborhood component analysis (NCA) as a feature selection technique for breast mammograms classification. Tamura features (coarseness, contrast and directionality) which provide the characteristics of image surface and objects appearance in images and statistical features (mean, variance, skewness, kurtosis, energy and entropy) were extracted from the breast mammograms, and NCA was applied to identify the best features. Finally, support vector machine classification was performed to classify abnormal condition from normal. Simulation study using the local hospital datasets revealed an overall classification accuracy of 93% by making use of quadratic kernel with SVM classifier.
Robust Blockchain for IoT Security

Due to the vast, increasing number of devices connected to the internet, their high connectivity, diversity, heterogeneity, and the fact that these devices are incapable of carrying a strong security protocols, a number of security and privacy challenges arise. Thus, the IoT- BlockChain brings a lot of benefits, yet still several challenging issues of security, storage and privacy are being faced. Therefore, this present research work proposes a new architecture with an objective to provide robust security with unlimited storage capability that can be adapted to the IoT resources constraints by introducing the new component Security and Storing Manager (SSM) added to the BlockChain. The concept behind the SSM mechanism is based on both encrypting the main data which will be stored in the locked blocks scattered on the internet and storing only the hash of these data in the BlockChain. Through this process, the SSM achieves a role like burial of the data in an unknown place with an unknown name. Also, the access to the BlockChain is restricted by using the smart contract in the new proposed architecture which allows the BlockChain to manage the data access policy. We evaluated the performance of the proposed architecture using various performance measures. The results of our evaluation show that performance is improved by reducing the size of the BlockChain and achieving the security goals. Consequently, SSM paves the way to make our BlockChain smart.
Solving Maze Problem with Reinforcement Learning by a Mobile Robot

Reinforcement learning has been applied to mobile robot control in various domains. In principle, mobile robots can learn through reinforcement learning, but sometimes it can be very time consuming when learning complex tasks. In this paper, three solution algorithms that can be used in the maze problem are introduced. In this paper, we also introduce important mathematical equations in these algorithms.
Implementation of Fall Detection System Based on 3D Skeleton for Deep Learning Technique

In this paper, a fall detection system combined with traditional algorithm with the neural network was proposed. Firstly, we propose a skeleton information extraction algorithm, which transforms depth information into skeleton information and extract the important joints of fall. Secondly, we propose a high robustness deep convolution neural network architecture using pruning method to reduce parameters and calculations in the network. The low parameter and calculation is very adapted to implement on embedded system. The experiment results show the high accuracy and robustness on the popular benchmark dataset NTU RGB+D. We also implement it on NVIDIA Jetson Tx2 platform.
Open Source Cloud Platform for Academic Systems Monitoring Software

The research consists in the study of the problem arises from the improper use of software, which triggers an excessive investment in tools that do not represent an academic benefit for an institution of higher education. This work proposes the development of an adaptive system which captures the data generated from the execution of any computer application in the laboratories. At the same time, the modules send the data to a server hosted in the cloud where the activities are stored in offline files. On the other hand, the monitoring system tabulates these data and presents them through reports following the XP methodology to facilitate their maintenance. The implementation of the monitoring system made it possible to determine the use of the applications executed in a given time, for the proper control of academic systems.
<i>Bi-TCCS</i>: Trustworthy Cloud Collaboration Service Scheme Based on Bilateral Social Feedback

As a complementary technology to traditional network security, trust computing scheme has been playing an increasingly important role in providing cloud service. However, many organizations constantly face trust computing challenges; moreover, establishing a highly trustworthy cloud ecosystem can be costly and time-consuming. In this article, we originally propose the conceptual model and formal definitions for a trustworthy collaboration service ecosystem, and construct a Bi-trustworthy cloud collaboration service ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi-TCCS</i> ), which is a scheme based on an innovative bilateral social feedback (referred to as “bi-feedback”) scheme. First, a trust-aware collaboration service model is proposed based on cloud service brokerages (CSBs), which can provide intermediation and aggregation capabilities to enable organizations to deploy their services across a collaborative cloud environment. Then, we propose a bi-feedback scheme based on the inherent social relationship among three network communities, which are composed of three types of network entities: cloud users, CSBs, and cloud service providers. The proposed scheme is effective and reliable against garnished and bad-mouthing attacks resulting from the traditional social feedback scheme. Moreover, we innovatively adopt an aggregating method for overall trust based on deviation analysis. This method can minimize errors and overcome the limitations of traditional schemes, where trust attributes are weighted manually. Theoretical analysis and experiments verify the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi-TCCS</i> . Compared with existing approaches, the service successful ratio of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi-TCCS</i> increased by 12 percent under highly dishonest cloud environment. These results also indicate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi-TCCS</i> is more adaptable both in the random walk and cheating profiles, which represents a substantial improvement in tracking the dynamic behavior of cloud services.
Threat Modeling and Attack Simulations of Smart Cities: A Literature Review and Explorative Study

Threat Modeling and Attack Simulations of Smart Cities : A literature review and explorative study
Hybrid Sampling and Random Forest Based Machine Learning Approach for Software Defect Prediction

The software has turn into an imperious part of human’s life. In the recent computing era, many large-scale complex network systems and millions of modern technological devices produce a huge amount of data every second. Among these data, the amount of imbalanced data is relatively excessive. The machine learning model is miss leaded by these imbalanced data. Software Defect Prediction (SDP) is a standout amongst the most helping exercises during the testing phase. The estimated cost of finding and fixing defects is approximately billions of pounds per year. To reduce this problem, software defect prediction has come forth but need fine tuning to have expected efficiency. In this chapter, we have proposed a new model based on machine learning approach to predict software defect and identify the key factors that may help the software engineer to identify the most defect-prone part of the system. The proposed model works as follows. First, need to remove highly correlated features and turn all the feature in the same scale using the scaling feature approach. Second, we have used Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic (ADASYN) and Hybrid sampling method to balance highly imbalanced datasets. Third, Random Forest Importance and Chi-square algorithms are chosen to find out the factors which have high effect on software defect. Cross validation is used to remove overriding problem. Scikit-learn library is used for machine learning algorithms. Pandas library is used for data processing. Matplotlib, and PyPlot are used for graph and data visualization respectively. The hybrid sampling method and Random Forest (RF) algorithms achieved the highest prediction accuracy about 93.26% by showing its superiority.
Handling 3vs of Big Data Through Swarm Intelligence

Nowadays, the enormous information has pulled in considerations from an ever-increasing number of specialists. The enormous information is characterized as the data set whose estimate is past the handling capacity of run-of-the-mill database. It may be viewed as mining or handling the enormous information, and precious data could be recovered from expansive data set. The properties of enormous information examination can be packed in three sections: vast volume, wide range of sources, and quick expanding pace, i.e., speed. The computations ought to be successful to settle extensive scale, dynamic the enormous information analytics problems. Swarm intelligence (SI), which depends on a populace of people, is a gathering of nature-propelled seeking procedures. To look through an issue area, a swarm intelligence computations forms a populace of people. Every individual speaks to a potential arrangement of the issue being enhanced. In swarm knowledge, a computations keeps up and progressively increases a populace of feasible arrangements unless certain halting state is encountered. The arrangements are instated arbitrarily in the inquiry area, and are directed from strength to strength regions in the form of communication between arrangements over cycles. The swarm insight computations have indicated noteworthy accomplishments on solving real scale, dynamical, and multitarget issues. With the use of the swarm knowledge, increasingly quick and successful strategies can be intended to take care of enormous information investigation issues.
Isu-Isu Kosakata Matematis dalam Pembelajaran Matematika

This study aims to examine the problem of mathematical vocabulary as the main issues of mathematics education. Through library research with literature review and interviews with several mathematics teachers, data obtained are relevant to the purpose of the study. Descriptively, the data collected from the results of interviews with the teacher regarding his textbook for teaching were analyzed by interactive models. The results showed that the mathematical vocabulary was not yet the basis of school mathematics learning, because the textbooks he used lacked the mathematical vocabulary and were directly used for teaching.
Impact of Information and Communication Technology for Rural Development Current Interventions and Opportunities for action

Technology transfer has been a longstanding issue in rural development. The key concerns relate to efficiency and effectiveness, how to translate and adopt the technology developed in rural societies. The process of technology transfer falters not at the microlevel pilot study or test plot but at the point when the technology is expected to be adopted and used both efficiently and effectively on a larger scale. This paper investigates and identifies the role of Information and Communication Technology (ICT) in rural societies. It assesses the potential for pluralistic approaches to encourage widespread adoption of ICT. The need for flexible and decentralised models for using ICTs is discussed in the context of `know and how'. Besides the paper also overviews the role and function of the Government and its' programmes for rural development in India. Science and technological interventions in the field of rural development have been discussed briefly and efforts being made to document some of the appropriate technologies developed by several research institutes, organizations suitable for application in rural society.
Breast cancer classification from digital breast tomosynthesis using 3D multi-subvolume approach

Digital mammography (DM) was the most common image guided diagnostic tool in breast cancer detection up till recent years. However, digital breast tomosynthesis (DBT) imaging, which presents more accurate results than DM, is going to replace DM in clinical practice. As in many medical image processing applications, Artificial Intelligence (AI) has been shown promising in reducing radiologists reading time with enhanced cancer diagnostic accuracy. In this paper, we implemented a 3D network using deep learning algorithms to detect breast cancer malignancy using DBT craniocaudal (CC) view images. We created a multi-sub-volume approach, in which the most representative slice (MRS) for malignancy scans is manually selected/defined by expert radiologists. We specifically compared the effects on different selections of the MRS by two radiologists and the resulting model performance variations. The results indicate that our scheme is relatively robust for all three experiments.
Tackle Cybersecurity and AWIA Compliance With AWWA's New Cybersecurity Risk Management Tool

Journal AWWAVolume 112, Issue 3 p. 70-73 Security & Preparedness Tackle Cybersecurity and AWIA Compliance With AWWA's New Cybersecurity Risk Management Tool Andrew Ohrt, Search for more papers by this authorKevin M. Morley, Search for more papers by this authorDan Groves, Search for more papers by this authorJoel Cox, Search for more papers by this author Andrew Ohrt, Search for more papers by this authorKevin M. Morley, Search for more papers by this authorDan Groves, Search for more papers by this authorJoel Cox, Search for more papers by this author First published: 03 March 2020 https://doi.org/10.1002/awwa.1466Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Citing Literature Volume112, Issue3March 2020Pages 70-73 RelatedInformation
Development of digital mine information platform based on Internet of things technology

Digital mine is the core content of mine industry informatization. According to the life cycle theory of software engineering, this paper studied the digital mine construction of Internet of things and big data as the core technology. Firstly, this paper starts with the main functions of mine production planning and production scheduling, analyzes the general demands of digital mines in detail, then designs the physical platform architecture and functional architecture of digital mines, and finally verifies the effectiveness of the analysis and design process through examples.
Data visualization: A unique storyteller

Abstract Science and technology have changed all aspects of our lives, including the mode of narration, from traditional stories to data stories. Storytellers have been integrating visualizations into their narratives. From the case studies of some artworks and our students' works to visualization research, we have found distinct genres of narrative visualization and the education method for university students. We describe the differences between these artworks, together with interactivity and information transmission. Some small experiments and some examples of students' works will be shown to explore the visual narrative. We suggest new design strategies including how to make invisible things visible.