Reviewerly/exHarmony huggingface dataset:
Digital Marketing through Physical Context Awareness

Over the past two decades, digital marketing has disrupted the advertising industry, seeing increasing growth in spending each year, and it is forecast to overtake spending on traditional advertising. With the advent of the Internet of Things (IoT), the potential benefits of incorporating fine-grained contextual data is being explored. However, with existing metrics for digital marketing being focused on web based interactions, it is unclear how they can be adapted to interactions in the physical environment. The paper defines new metrics for capturing customer interactions in a physical environment and explores how they relate to eventual purchase decisions. This paper shows how such metrics can be measured for indoor retail environments, through detection of shopping intent. Finally, usage of the proposed metrics are demonstrated using a real world example.
Implementation of OLAP and K-Medoids Clustering for Accreditation Data Analysis of Study Programs

Abstract At present to maintain the quality of the data in the study program is very much needed, given the current accreditation based on PDDIKTI(Pangkalan Data Pendidikan Tinggi) data which must be reported every semester. Higher education data are generally still separate, there is no valid data warehouse and system for data analysis so that it complicates data quality control. The purpose of this study is to apply data warehouse, OLAP (Online Analytical Processing) and k-medoids clustering for data prediction and control according to the study program’s accreditation self-evaluation report matrix. This study uses data the last 3 years for prospective new students, students, students, study periods, achievements and lecturers to be analyzed using OLAP and the k-medoids cluster. The results showed OLAP can see information in an informative, real-time data accreditation matrix and the k-medoids cluster produces accurate cluster estimates with an evaluation value of Davies Bouldin Index of 0.2927 and said to be a good cluster
Towards Privacy-anomaly Detection: Discovering Correlation between Privacy and Security-anomalies

In this paper a notion of privacy-anomaly detection is presented where normative privacy is modelled using k-anonymity. Based on the model, normative privacy-profiles are constructed, and deviation from normative privacy-profile at runtime is labelled as a privacy-anomaly. Furthermore, the paper investigates whether there is a correlation between security-anomalies and privacy-anomalies, that is, whether the privacy-anomalies labelled by privacy-anomaly detection system are detected by conventional security-anomaly detection system used for detecting malicious accesses to databases by insiders.
Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities

Vulnerabilities with publically known exploits typically form 2-7% of all vulnerabilities reported for a given software version.With a smaller number of known exploited vulnerabilities compared with the total number of vulnerabilities, it is more difficult to model and predict when a vulnerability with a known exploit will be reported.In this paper, we introduce an approach for predicting the discovery pattern of publically known exploited vulnerabilities using all publically known vulnerabilities reported for a given software.Eight commonly used vulnerability discovery models (VDMs) and one neural network model (NNM) were utilized to evaluate the prediction capability of our approach.We compared their predictions results with the scenario when only exploited vulnerabilities were used for prediction.Our results show that, in terms of prediction accuracy, out of eight software we analyzed, our approach led to more accurate results in seven cases.Only in one case, the accuracy of our approach was worse by 1.6%.
Optimization of Classification Thresholds for States of Transionospheric Radio Links Described by the Normal Distribution for Ensuring the Accuracy of UAV Positioning

Nowadays the development of satellite navigation systems is largely integrated into modern society. The GPS (USA) and GLONASS (Russia) navigation systems are most commonly used. However, all the satellite navigation systems are negatively affected by the artificial irregularities arising in the ionosphere, which introduce the greatest error in the accuracy of an unmanned aerial vehicle (UAV) positioning. In such cases, radio link perturbations are defined by one of the random distributions: normal distribution, Rayleigh distribution, Rice distribution, Nakagami distribution. Existing methods of counteracting the perturbations cannot adequately suit the emerging practical needs since they are based on the predictive models. One of the promising ways to solve this problem is using of the methods of automated monitoring and control of satellite links state. The control and monitoring of such systems is determined by solving a problem of recognition and classification of an object, but such approach leads to inevitable occurrence of type I (false alarm) and type II (anomaly undetection) errors. In order to minimize the anomaly undetection errors when crossing classes, it is necessary to determine the classification threshold.. In this case, it is possible to solve the problem mathematically using the available values of the system parameters. The solution to the problem is expressed with simultaneous equations which are put in a convenient form for solving by three quasi-Newtonian methods that allow to simplify the algorithm (Powell, Broyden, and Krylov-Newton). #CSOC1120.
Document Clustering Using Hybrid LDA- Kmeans

This paper presents a Hybrid Latent Dirichlet Allocation – Kmeans (HLDA-Kmeans) Algorithm for document clustering. The overload information has became a challenge for users due to the existence of abundance information and heterogeneous nature of the Web. Researchers such as academician as well as people who are involved in text analytics have encountered challenges to analyze documents because of ambiguity in keywords/keyphrases. Hence, the objective is to perform document clustering analysis using HLDA - Kmeans algorithm to discover the clusters among the unlabelled text data, classify the keyphrases based on topics and visualize the clustering results. Online news from Oil and Gas is used as a dataset for training and testing using 70%–30% split. The system performance of the proposed HLDA - Kmeans algorithm was assessed using Precision, Recall and F-Score Formulas. Experimental results show that the proposed HLDA - Kmeans has achieved clustering results satisfactorily.
Transport Riding Wheelchair

This paper describes the assisting navigation system for wheelchairs. Maneuvering of an electric wheelchair is to assist the paralyzed people in the difficult situation. The proposed design can benefit disabled people and improve their survival and quality of life. The wheel chair is the most ubiquitously used aid to enhance the personal accessibility of people with disabilities. For those who are unable to propel a manual wheel chair, power wheel chairs are useful. Disable people face difficulties to travel here and there, this device will help them to climb the transport from the bus stop. This paper describes the overall architectural design of hardware and software, including the system of communication. Arduino, Solid-works and Proteus were used for software and hardware design and a joystick and keyboard were used to operate the chair.
Building Serverless Website on GitHub Pages

Abstract GitHub provides the environment for developing and publishing website in integrated environment. By GitHub Pages, we can host the static web easy, fast and free. It helps the developer for integrating development and deployment process, because GitHub Pages is integrating with GitHub environment that support continuous integration and continuous delivery, and also become a content delivery network (CDN), as a part of JAMstack building block. Unfortunately, the usage GitHub as web hosting and JAMstack as an approach for developing web is not very popular among developer in Indonesia. The purpose of this research is to show how implement the JAMstack approach on designing and building website and host it on the GitHub. The website we designed using JAMstack approach by implementing Hugo as static site generator (SSG) and GitHub as content delivery network (CDN). We also insert API for showing the easiness of integrating lots of components on JAMstack approach. We do several tests to check the quality of the website, such as functional test and performance test. The performance test results show that Google PageSpeed score is 97% (A) and YSlow score is 91% (A). It shows that the website can run in good performance although it connected with others services through API.
Empirical Software Measurements with Machine Learning

This chapter discusses the empirical approach for software measurements using machine learning (ML) techniques. It demonstrates the usage of ML techniques for both software quality and quantity measurements. With a basic introduction to the current trends of the field and moving through problem definition, the author reaches the experimental set-up and then draws inferences from the experiments. The chapter aims to provide the reader practical and applicable knowledge of ML and deep learning for empirical software measurements. Several software engineering tasks like various development and maintenance tasks that come under software engineering can be formulated as learning problems and can be handled as application of learning algorithms. The desired output is over a range of numbers, hence, the problem can be formulated as a regression-based learning problem. In empirical software measurement, effort estimation problem is formulated as a regression-based supervised learning problem.
Muon-Ra: Quantum random number generation from cosmic rays

True Random Number Generators (TRNGs) are the cornerstone of modern cryptographic applications. In this work, we present the first quantum <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> random number generator based on muon detection. The proposed implementation utilizes silicon photomultipliers and plastic scintillators to convert the time interval between crossing muons to random bits. Compared to the state-of-the-art, this design operates using a passive entropy source, scaling down its power consumption significantly. Additionally, the proposed muon-based TRNG can be fully integrated in modern computer hardware, making it suitable for low-power embedded device applications. We evaluate the proposal on its throughput and ability to pass standard randomness tests. Our method is successful in passing the NIST STS SP 800-22 and Dieharder evaluations. Finally, the implementation is compared to other well-established methods of generating random numbers. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We use the term “quantum” to denote the utilization of elementary particles as the output generation source, and not necessarily their properties, similar to related work [1], [2].
Protecting Data By Socket Programming Steganography

Abstract This paper aims to observe the techniques and methods of securing data using socket programming steganography. This work observes the development of the art of steganographic techniques which focus on computer network devices by visiting articles which published in journals and proceedings. The results obtained that the development of steganography is not only on data which stored on local disk, but also utilizes computer network devices to transmitting the data. Steganography techniques have several modifications in the algorithm to secure data on the network using stegnography that uses socket programming. This results show the evolutions of steganography techniques on network devices for better data security.
Ontology-based Dynamic Semantic Annotation for Social Image Retrieval

To mitigate the semantic gap between a visual feature and linguistic representation in social image retrieval, researchers have proposed an automatic image annotation approach, which employs multiple visual features and text matching to label images. This paper used a linked open data approach and ontology to construct a model, an automatic semantic image annotation model for social image retrieval. These models enable users to label images through automatic semantic annotation and to identify the underlying intents of semantics, thereby fulfilling user needs and enhancing the retrieval accuracy.
Fast Spectral Graph Layout on Multicore Platforms

We present ParHDE, a shared-memory parallelization of the High-Dimensional Embedding (HDE) graph algorithm. Originally proposed as a graph drawing algorithm, HDE characterizes the global structure of a graph and is closely related to spectral graph computations such as computing the eigenvectors of the graph Laplacian. We identify compute- and memory-intensive steps in HDE and parallelize these steps for efficient execution on shared-memory multicore platforms. ParHDE can process graphs with billions of edges in minutes, is up to 18 × faster than a prior parallel implementation of HDE, and achieves up to a 24 × relative speedup on a 28-core system. We also implement several extensions of ParHDE and demonstrate its utility in diverse graph computation-related applications.
A Prototype of a Secured File Storing and Sharing System for Cloud Storage Infrastructure

With the increased popularity of third-party cloud storage services, privacy has become a major concern. As cloud storage services can be availed at a minimum price now-a-days, it comes with a greater cost, vulnerability of being accessed by anyone else. To ensure reliability, most of the cloud service providers keep multiple copies of files. This becomes a serious privacy issue when the replicated copies remain in the server even after the owner has deleted the original file. To solve this problem, several models such as FADE, MADS, SFADE+ etc. were proposed that ensures assured file deletion but has limitations of their own. We have designed a system using the existing cryptographic approaches which ensures assured deletion of files resolving the limitations that remained un-patched by the previous models. The primary focus of our proposed model is to ensure user privacy and security through a user-friendly system which is computationally less intensive and does not rely upon any trusted entity. For factual exhibition, we have implemented a prototype named CryptedCloud which assures file deletion without relying upon any trusted body with no compromise to the performance and user-friendliness.
Context-Aware Transfer of Task-Based IoT Service Settings

The number of available Internet of Things (IoT) devices is growing rapidly, and users can utilize them via associated services to accomplish their tasks more efficiently. However, setting up IoT services based on the user, and environmental context, and the task requirements is usually a time-consuming job. Moreover, these IoT services operate in distributed computing environments in which spatially-cohesive IoT devices communicate via an ad-hoc network, and their availability is not predictable due to their mobility characteristic. To the best of our knowledge, there have been no researches done on saving and recovering users’ task-based IoT service settings with considering the context and task requirements. In this paper, we propose a framework for describing task-based IoT services and their settings in a semantical manner, and providing semantic task-based IoT services in an effective manner. The framework uses a machine learning technique to store and recover users’ task-based IoT service settings. We evaluated the effectiveness of the framework by conducting a user study.
Support Vector Regression for GPA Prediction

Abstract This study aims to predict student GPA. This research began by collecting data. The features used in predicting GPA are semester 1 and semester 1 IP grades. The process of GPA prediction uses SVM regression, Linear Regression, and Simple Linear Regression. Based on testing with normalized data, the smallest error is obtained by the SVM regression method with Kernel RBF which is equal to 0.1505. Whereas by using standardized data, the smallest error is obtained by using the SVM regression improve method with the Kernel RBF, which is 0.1487. Based on this research, in order to obtain prediction results that are closer to the actual values, it is better to standardize the data first and to predict the process using the SV Regression Improve method using the Kernel RBF.
Recovering CRT-RSA Secret Keys from Noisy Square-and-Multiply Sequences in the Sliding Window Method

We discuss side-channel attacks on CRT-RSA encryption or signature scheme (the RSA scheme with the Chinese remainder theorem) implemented via the sliding window method. The sliding window method calculates exponentiations through repeated squaring and multiplication. These square-and-multiply sequences can be obtained by side-channel attacks, and there is the risk of recovering CRT-RSA secret keys from these sequences. Especially, in CHES 2017, it is proved that we can recover secret keys from the correct square-and-multiply sequences in polynomial time when the window size w is less than 4. However, there are errors in the obtained sequences. Oonishi and Kunihiro proposed a method for recovering secret keys from noisy sequences when $$w=1$$ . Although this work only addresses the case with $$w=1$$ , it should be possible to recover secret keys for larger values of w. In this paper, we propose a new method for recovering secret keys from noisy sequences in the sliding window method. Moreover, we clarify the amount of errors for which our method works.
SpeCS — SPARQL Query Containment Solver

With increasing popularity and importance of Semantic Web and its application, SPARQL, as a standard language for querying RDF data, gains more importance and receives additional attention from both practitioners and researchers coming from various domains. In database world, the query containment problem is a fundamental problem, crucially important in verification and optimization of queries. In this paper, we present our work on developing SPECS, an efficient solver for this problem in SPARQL query language. Our approach reduces query containment problem to the satisfiability problem in theories of the first order logic, and exploits SMT solver Z3 for checking the constructed formula. We present an evaluation that shows that our solver is much faster and covers more language features than the other available state-of-the-art solvers.
Neuraltran: Optimal Data Transformation for Privacy-Preserving Machine Learning by Leveraging Neural Networks

In this work, we develop a new data transformation technique to mediate privacy-preserving access to data while achieving machine learning (ML) tasks. Specifically, we first leverage mutual information in information theory to quantify the utility-providing information (corresponding to any ML task) and the privacy information (could be arbitrary information specified by the users). We further convert the optimization of utility-privacy tradeoff into training a novel neural network (named as NeuralTran) which consists of three modules: transformation module, utility module and privacy module. NeuralTran can be leveraged to automatically transform the input data to ensure that only utility-providing information is kept while the private information is removed. Through extensive experiments on real world datasets, we show the effectiveness of NeuralTran in balancing utility and privacy as well as its advantages over previous approaches.
Impact Of Age And Ageing On Face Recognition Performance

This paper is concerned with the effect of ageing on biometric systems and particularly its impact on face recognition systems. Being biological tissue in nature, facial biometric trait undergoes significant changes as a person ages. Consequently, developing biometric applications for long-term use becomes a particularly challenging task. The idea behind the investigation presented here is that biometric systems have uneven difficulty in recognising people from different ages. Some algorithms may perform better for certain age groups. Therefore, a carefully optimised multi-algorithmic system can reduce the error rates. A subset of 100 subjects from the MORPH-II database has been selected to test the performance of a face verification system. The population is split into 5 age bands (≤19, 20-29, 30-39, 40-49, ≥50 years) based on their age during enrolment. The facial image database used in the experiments here contains images acquired over a period of five years. In the proposed multi-classifier scheme, features extracted from face images are transformed by different projection algorithms prior to matching. It has been observed that all the age groups showed improved performances when compared to the single classifier error rates. Of all the groups, the EER were highest for the younger population (≤ 19 year olds).
Analysis and Classification of Urinary Stones Using Deep Learning Algorithm: A Clinical Application of Radiology-Common Data Model (R-CDM) Data Set

Recent advances in artificial intelligence have been used for a variety of clinical interests, such as lesion detection, segmentation, classification, and differentiation in medical images, and a great deal of research is underway. A good algorithm is also needed to obtain meaningful artificial intelligence research results, but a correctly created data set is also important. To provide these data sets, we proposed the radiology-common data model (R-CDM), as an expansion of OMOP-CDM. With the use of R-CDM, this study created a data set to distinguish urinary diseases for 873 patients and showed significance using an artificial intelligence algorithm that modified GoogleNet, a deep-learning algorithm based on CNN. In this experiment, 99% accuracy and AUROC 0.9 were obtained. External validation was performed using urinary stones data sets for 200 patients at other hospitals to increase confidence in the results of deep learning. In external validation, the results of 90% sensitivity, 97% specificity, and AUROC 0.984 confirm that the data sets and algorithms have very high efficiency. The study will help researchers who perform artificial intelligence through medical imaging.
Metallogenesis and fluid evolution of the Devonian porphyry Cu deposits in the Central Asian Orogenic Belt: the Xiaotuergen example

Xinxia Genga, Fuquan Yanga*, Chengdong Yanga , Jihua Qinb & Chaoquan Wenb a Key Laboratory of Metallogeny and Mineral Assessment, Ministry of Natural Resources, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, PR Chinab No. 4 Geological Team of the Xinjiang Bureau of Geology and Mineral Exploration and Development, Altay, Xinjiang, PR China
US Traffic Sign Recognition Using CNNs

Traffic Sign recognition is the technology that gives a vehicle the ability to recognize everyday traffic signs that are put on the road. Detection methods are usually classified as color based, shape based, and learning based methods. Recently, Convolutional Neural Networks (CNN) have shown to become the popular solution to image recognition problems. Thanks to the quick execution and high recognition performances the CNNs have greatly enhanced many computer vision tasks. In this paper, we propose a traffic sign recognition system by applying CNN architectures on the LISA traffic sign dataset.