Reviewerly/exHarmony huggingface dataset:
Why Your Heart Was Beating: Poster

The use of wireless electrocardiograms, wearable as well as implants, enable long-term and unobtrusive monitoring of patients in their everyday living and working environments. If enriched by environmental contexts, these devices can be vital for early detection of cardiovascular diseases. Often cardiologists encourage patients to keep medical journals in order to contextualise the measurements of electrocardiograms. Experiences show, however, journal entries can be inconsistent or incomplete. In this paper we associate the measurements of a wireless electrocardiogram with the measurements of inertial sensors in order to reason about the activities of a person. We put together the raw measurements and their wavelet transform in a three-way tensor and apply tensor decomposition to uncover hidden features which can be vital for detecting the underlying activities. We model and reason about six everyday activities, namely, cycling, climbing up and down a staircase, jumping, push-ups, running, and skipping.
Design and Implementation of Academic Search Aggregation Engine

Abstract With the rapid development of the global Internet, academic search engines have become indispensable tools for academic research like literature, papers, books, preprints, and abstracts. The total amount of network information is growing rapidly today, and the global paper output doubles every five years. For academic researchers, it is very difficult to find papers that meet the needs in a large number of articles. Additionally, as the quality of journal papers varied, finding high-quality papers and accurately locating them is getting harder and harder. Many researchers find it tiring and inefficient that when looking for research fields, they need to read through a large number of articles in search results, and manually classify them. Through the clustering algorithm and data visualization and other related technologies, the systematic integration and classification of the search papers will help the researchers to accurately locate the required articles, and can greatly reduce the time for searching the documents. Regarding the previously stated problems, this thesis designs and implements an academic search aggregation engine (referred to as “aggregation engine”) based on cloud computing and some clustering algorithms. On the one hand, the aggregation engine distributes the user’s search request to multiple academic search engines, and then collects and summarizes the paper results, thereby uniformly displaying the search results; on the other hand, from the large number of papers obtained from academic search engines, the aggregation engine analyzes their titles, abstracts, and keywords, clusters papers with similar research points, and visualize clustering results to users, so that the result can be macroscopic and intuitive, grasping all the papers and the research points they involve. This paper first introduces the research background of the subject, through the investigation of the existing search engine and the existing aggregate search engine, it analyzes the needs of the researcher and other customers for the paper search; according to the demand analysis, we proposes a system design that distribute the search request to multiple academic search engines and then aggregated results. Finally, we realised the implementation of the aggregate search engine, and verified the effectiveness of the system through tests
Decoding Algorithm for Modified Linear Block Code with Syndrome Polynomial

This article gives the encoding and decoding methodology for a Modified Linear Block Code (MLBC) from the solution space of a completely controllable discrete-time system for any k length message bits. Such an established code is (n, k) linear modified block code and it is a perfect code. The decoding algorithm is developed from the syndrome polynomial. The system avoids the limitation of multiple solutions and Maximum Likelihood decoding associated with Linear Block Codes. The code is designed for minimum Hamming distance, d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> of 3. The system can be generalized for d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> = 4 and above. The performance of the said MLBC is evaluated with the help of simulation and check on all the bit positional errors has been carried out.
Multilevel Thresholding Image Segmentation based on Autonomous Groups Particle Swarm Optimization

Abstract Multilevel thresholding problems based on Otsu criteria are discussed in this paper. One weakness of the Otsu method is that computational time increases exponentially according to the number of thresholding dimensions. In this paper, a modified Particle Swarm Optimization (PSO) algorithm called Autonomous Groups Particles Swarm Optimization (AGPSO) is proposed to reduce two problems trapped in local minima and a slow convergence rate in solving high-dimensional problems. AGPSO is used for multilevel thresholding image segmentation. The performance of AGPSO is compared with standard PSO on three natural images. The parameters used to compare the performance of AGPSO and PSO are SSIM, PSNR, Computation Time, optimal threshold obtained from each algorithm. From the experimental results show that AGPSO is better when compared to PSO in image segmentation, from the resulting fitness value and higher SSIM and PNSR values.
English language mastery as an ease factor to access health information in industrial revolution 4.0: A local challenge for global opportunities

Access to health information in Industrial Revolution 4.0 is eased by English language mastery. This study aims to describe university students’ English proficiency based on their TOEFL prediction scores and to explain how it can influence their access to health information. This research is a descriptive study that describes the results of students’ English language proficiency in one of the universities in Manado, Indonesia. The method used was quantitative method. The instrument in this study was the TOEFL Prediction test with three main components of Listening Comprehension, Structure and Written Expression, and Reading Comprehension. The data analysis was conducted statistically to see the students’ English proficiency levels and in particular the differences between female and male students. English mastery is needed for the access to health information in Industrial Revolution 4.0. The average proficiency level of the university students is still a bit low with the average score of 415.78, although there are a few students who have managed to achieve above-average scores. This indicates that average students may still have difficulty to access health information. In terms of gender differences, the percentage of male students mastering the language is a bit higher than that of female students, but the highest score belongs to a female student. English language mastery for the access to health information in Industrial Revolution 4.0 is still a challenge for university students, particularly in Manado. Efforts are needed to increase their language proficiency level for global opportunities.
Improved Processing of LP-residual Information for Detection of Replay Signals

Replay attack is a method of unauthorized access to automatic speaker verification system by using pre-recorded speech samples of any target. Recently, linear prediction (LP) residual signal is extensively explored for detection of replay signals. The latest development is the mel-cepstral processing of the analytic representation of the LP residual signal. In that work, the conventional cepstral processing method is followed and the effect of zeroth coefficient (c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) is ignored. In this work, the effect of c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is investigated. In particular, we have used residual Hilbert envelope mel frequency cepstral coefficient (RHMFCC) and residual phase-mel frequency cepstral coefficient (RPMFCC) feature to design the replay detection system. The experiments are carried out in ASVspoof 2017 version 1.0 database. With the incorporation of c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , the performance of RHM-FCC based system is degraded slightly (around 1%), but the performance of RPMFCC based system is improved significantly. This improvement is significant enough to provide a relative improvement of 11.78% in the case of combined RHMFCC+RPMFCC based system with c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> compared to the system without c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . The system performance with constant-Q cepstral coefficient with log energy (CQCC-L) feature is improved slightly from 12.72% to 12.47% with c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . The final system of CQCC-L+RHMFCC+RPMFCC feature with c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> shows a relative improvement of 20.51% compared to the system without c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . These results show the importance of c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> in excitation signal based replay detection system.
Automatic Prosodic Structure Labeling using DNN-BGRU-CRF Hybrid Neural Network

The speech corpus with labeled prosodic structure information is crucial for text-to-speech (TTS) synthesis to train a reliable model that can generate high quality natural synthetic speech. Traditional manual prosodic structure labeling is laborious and time-consuming and may encounter an inconsistency problem caused by different annotators. Automatic prosodic labeling is thus desirable, which can not only speed up the labeling process, but also protect the labeling results from the inconsistency problem. This paper presents a DNN-BGRU-CRF hybrid neural network, which aggregates the advantages of deep neural network, bidirectional gated recurrent units and conditional random fields, to label three-level prosodic structure boundaries. It exploits both text and acoustic cues in a neural network framework. Experimental results demonstrate the effectiveness of the proposed model.
Ontology based Semantic Description and Registration of Mathematical Web Services

A web service is a piece of software exposed over the internet, which can use XML messaging system to communicate with other systems to carry out some kind of operation. The fascination of the mathematical community when it comes to utilizing the Web to facilitate the use of mathematics has lead to the development of mathematical Web services. Since mathematics is a structured domain, it needs to be defined and registered by its provider incorporating semantics, and afterwards discovered by service requesters. In this research work, discussed about the development of an interface that handles registration for the service providers named web service registration interface. It provides the capability for semantic description and registration while making use of an ontology tree to catalogue the characteristics of the mathematical web services, functional as well as non-functional. An XML file and a sorted file are used to store the registration details. The usability of user interface is analyzed and compared.
Necessity of Fourth Factor Authentication with Multiple Variations as Enhanced User Authentication Technique

Authentication plays a vital role in granting access to a user by any computing system. There are several existing authentication techniques used for different scenarios which include one-factor, two-factor, and three-factor authentication, where the user submits required credentials and gains access to the system. When a legitimate user is unable to provide his credentials at any point of time, the user becomes unauthenticated. At this juncture, we have proposed a fourth factor for authentication where a legitimate user can seek assistance from any of his close associates like a spouse, friend, and colleague and can gain access to the system for a single time. In this paper, we consider multiple variations of fourth-factor authentication and compare them within the framework and explore the mechanism by applying various metrics such as key size, the ease of use, time and space complexity of the algorithms, and security considerations.
Classification of Farmer’s Eligibility as Recipients of Subsidized Fertilizer Assistance with C4.5 Algorithm

Abstract Farmers are the main actors in agricultural development, have a major contribution in realizing Indonesia’s agricultural vision. To support the realization of agricultural production and productivity targets, the government’s supports is by issuing policies to provide subsidized fertilizer assistance. The criteria for farmers who are eligible to receive subsidized fertilizer assistance are having land less than 1.9 hectares, have been members of farmer groups, and having work as a farmer or a private employee. Data collection was carried out using the method of observation and interviews with farmer groups in Gunung Kidul. Data classification is done by applying the C4.5 algorithm to 300 data of prospective farmers receiving subsidized fertilizer assistance. Application development includes interface design, making data flow diagrams, implementation and testing. From the research that has been done by applying the C4.5 Algorithm for farmer data, there are 300 data with 113 training data. Produce a data classification of farmers who are eligible to receive assistance at 95,58% accurate and targeted to the needs of farmers. This means that the C4.5 algorithm can be used as a classification for determining the eligibility of farmers to receive subsidized fertilizer.
Trend Analysis of Small Sample Operation and Maintenance Data Based on Function Fitting

Abstract After years of informatization construction, the State Grid Corporation’s data centers have reached a very large scale in the headquarters and provincial company’s network companies, providing good support for the deployment of various business systems. Compared with the rapid development of the data center infrastructure construction, the data center-related operation and maintenance management work concept is relatively lagging behind, the data center generates a large amount of operation and maintenance data every day, and relevant analysis and processing work of the data was ignored. In response to the overall requirements of the State Grid Corporation to build a ubiquitous power Internet of Things, the operation and maintenance of the computer room has been upgraded to a new level, and the operation and maintenance work will be transformed from the traditional lag problem solving problem to the forward-looking work of predicting the operation and maintenance risk in advance. Through the analysis and processing of operation and maintenance data, this paper establishes the functional relationship between operation and maintenance indicator data and time, so as to predict the events that may cause system downtime in the future.
Data Randomization for Multi-Variant Execution Environment

The majority of embedded software is written in C/C++ language, which suffers from an abundance of memory vulnerabilities which open gate to attackers to infiltrate into the computer system. Multi-variant execution environment (MVEE) has been proposed to utilize multi-core embedded processors to provide efficient protection against attackers utilizing memory vulnerabilities. The security provided by MVEE depends on the degree of randomization between the variants. Existing MVEEs typically randomize the data layout between the variants, which can fail to detect some attacks based on relative memory errors. In this paper, we propose to apply data space randomization (DSR) to the variants of MVEE to strengthen the security provided by the MVEE. Experiments show that it adds reasonable performance overheads.
Analysis of different camera calibration methods on a camera-projector measuring system

The accuracy of metrology equipment formed by a camera-projector pair depends directly on the calibration procedure used. This kind of equipment allows to perform the calibration by two different approaches: as a whole system or separately. The most common approach is the second one: conventional calibration. Studies show that the uncertainty of the camera parameters from its calibration propagates to the projector parameters. The objective of this study is to have a clear comprehension of the relationships between the camera and projector parameters and of how uncertainty is propagated. This will be done by using a camera previously calibrated by Tsai, Zhang or Direct Linear Calibration (DLC) methods, followed by the calibration of the projector using DLC.
Algorithms of Transformation in Problems of Fuzzy Comparison of Graphic Images

This article discusses how to compare photos using neural networks, namely explores how to identify similar files. The analysis of already designed and trained models is carried out, the uniqueness of each of them is determined. A detailed overview of the network structure is carried out. The article describes how and where mathematical models can be applied in practice and what is their superiority over outdated comparison algorithms, for example by generating a hash value from the contents of a given file.
Augmented Primitive Data Types of Programming Language

Learning programming language for the first timer is critical and difficult to understand the concepts properly. It will be easier for the beginners to understand the concepts if any visual representation is available for them. Augmented reality (AR) is one of the most preferable medium in recent time. In this paper, we present an experiment of augmentation of primitive data types of C programming language in mobile platform. The visualization includes the structure of the data types, such as, memory block and space taken by each data type.
Video-Text Compliance: Activity Verification Based on Natural Language Instructions

We define a new multi-modal compliance problem, which is to determine if the human activity in a given video is in compliance with an associated text instruction. Solutions to the compliance problem could enable automatic compliance checking and efficient feedback in many real-world settings. To this end, we introduce the Video-Text Compliance (VTC) dataset, which contains videos of atomic activities, along with text instructions and compliance labels. The VTC dataset is constructed by an auto-augmentation technique, preserves privacy, and contains over 1.2 million frames. Finally, we present ComplianceNet, a novel end-to-end trainable compliance network that improves the baseline accuracy by 27.5% on average when trained on the VTC dataset. We plan to release the VTC dataset to the community for future research.
Schematron-based Semantic Constraints Specification Framework and Validation Rules Engine for JSON

Business data is a critical asset for organizations. Data quality issues can creep in at multiple stages during the creation and processing of the business data. Syntax and semantic validation of business data at inception and during processing are of utmost importance to mitigate financial and other types of risks. Currently, XML is a predominant format for business data. XML has a mature syntax and semantic constraint specification standards and validation toolset. JSON is an emerging format for business data. It has a relatively mature syntax specification standard and a variety of validation tools but it lacks a common semantic/co-constraints specification standard and reusable validation toolset. This research developed a system to overcome these serious limitations. The system consists of a) an ISO/IEC 19757- 3 Schematron compliant framework to specify the semantic constraints in JSON itself and b) a JavaScript-based reusable and extensible validation component. The semantic constraints specification framework can be leveraged to specify arbitrarily complex constraints whereas the validation tool can be used as a standalone component or can be embedded in other data processing enterprise systems as a module. Together this system can be used as a test bed for further research in the area of semantic validation for JSON data. This paper will describe in detail the first component while briefly touching upon the second component.
Multiple Hypothesis Tracking for Processing Tracklets

Multiple hypothesis tracking (MHT) addresses difficult data association problems by maintaining multiple association hypotheses over multiple frames of data. In many multiple target tracking (MTT) problems, sensor measurements that originate from the same targets can be grouped into tracklets for further processing by another tracker. This processing approach improves the efficiency of MHT because association is performed on tracklets instead of measurements. This paper introduces two types of tracklets: pure tracklets representing single targets and ambiguous tracklets representing multiple targets. It discusses how MHT can be used for stitching tracklets from a single sensor and associating tracklets from multiple sensors.
Simple Photo Blemish Retouching Using Iterative Singular Value Decomposition

Retouching like removing the unwanted blemish from photos is mostly performed in software such as Photoshop, Gimp, and CorelDraw. It is interesting to find that simple blemish such as face moles and pimples can be retouched using iterative singular value decomposition. The algorithm has been experimented on a few portraits and the effects are sightly to see.
A Low Performance-Overhead ORAM Design for Processor System with Un-trusted Off-chip Memory

Information leak is a fundamental concern in most computing systems. One security weak point in the processor-based system is the bus between the processor chip and un-trusted off-chip memory, where data can be snooped by an attacker. For the data confidentiality, encryption is commonly used. However, encryption alone is not secure since the information of the system can still be revealed through the plain memory access trace of the processor. A possible solution to such a problem is obscuring the access trace with an oblivious random-access memory (ORAM) scheme where true memory accesses are covered by random dummy accesses to the memory. But existing ORAM designs involve large number of dummy accesses for each true access, which adds significant performance overhead to the execution. In this paper, we propose a low performance-overhead design, RF-ORAM, in which a true memory access is hidden in the dummy accesses to a small flock of random memory locations. The design has two features: one, the accessed memory data are shuffled not only within the current flock but randomly across multiple flocks, and flocks are not correlated with each other so that the randomness of the access trace can be easily achieved with small flocks, hence the performance overhead can be reduced; two, the operations in the ORAM are allowed to be overlapped, therefore, further performance improvement can be achieved. Our experiment on the Xilinx XC7VX330T FPGA platform shows that for a true memory request, RF-ORAM can reduce the performance overhead by more than 5 times when compared to the state-of-the-art design.