Reviewerly/exHarmony huggingface dataset:
Heterogeneous sensor network for micro-satellite anomaly detection and event recording

Avio-launched micro satellites with re-entry capability are useful to acquire data which can be used in future space operations to safely accommodate payloads into the cargo compartment. A deployable aero-brake and a thermal-shield with a detachable payload capsule are expected to protect the satellite and allow the recovery phase. This paper presents the contribution to the preliminary project of payload sensor network to perform anomaly detection and direct measure of relevant flight quantities. In particular, during its operation the satellite needs to estimate temperature, pressure, velocity variations, atmospheric drag, position and energy consumptions. The possibility of a direct measurement by means of an heterogeneous sensor network would improve these estimations. A survey on hardware technologies available is among the objectives of this work, and it will constitute the basis for further investigations on appropriate data fusion techniques.
Safeguarding Data Consistency at the Edge

We propose to design and implement a secure edge storage system. Edge computing is a paradigm that extends cloud computing with storage and processing capacity close to the edge of the network, supporting new applications that require low latency. It assumes the availability of fog nodes that are located close to the edge. However, fog nodes are likely to be vulnerable to tampering. A malicious fog node can manipulate, create or delete data from edge applications, leading this application into a fail state, impacting the quality of service. Therefore, it is important to secure the functions fog nodes provide. To achieve our goal we plan to leverage the use of secure hardware (e.g., Intel SGX) as a means to harden the implementation. However, as we discuss here, SGX alone is not enough to achieve the qualities we consider necessary to support edge applications, such as low latency, scalability, and multiple models of data consistency. In this work, we present the main challenges in the design of a secure edge storage system and point to the research directions that we plan to follow to address these challenges.
Modeling of human smooth pursuit by sparse Volterra models with functionally dependent parameters

This paper deals with the identification of Volterra models that capture the dynamics of smooth pursuit eye movements recorded by an eye tracker. The framework is motivated by neurological applications but can also be useful in biometrics. In healthy subjects, ocular dynamics are predominantly linear, while neurological conditions inflict nonlinearity on smooth pursuit eye movements. Besides overparameterization, Volterra models may also exhibit functional dependence among the model coefficients. A combination of sparse estimation and Principal Component Analysis is shown to be instrumental in estimating parsimonious Volterra models from eye-tracking data. The efficacy of the approach is demonstrated on experimental data collected from Parkinsonian patients as well as healthy controls.
Mine Pressure Prediction Study Based on Fuzzy Cognitive Maps

The study on the prediction of mine pressure, while exploiting in coal mine, is a critical and technical guarantee for coal mine safety and production. In this paper, primarily due to the actual demand for the prediction of mine pressure, a practical prediction model Mine Pressure Prediction (MPP) was proposed based on fuzzy cognitive maps (FCMs). The Real Coded Genetic Algorithm (RCGA) was proposed to solve the problem by introducing the weight regularization and dropout regularization. A numerical example involving in-situ monitoring data is studied. Mean Square Error (MSE) and fitness function were used to evaluate the applicability of MPP model which is trained by RCGA, Regularization Genetic Algorithm (RGA) and Weight and Dropout RGA optimization algorithms. The numerical results demonstrate that the proposed Weight and Dropout RGA is better than the other two algorithms, and realizing the requirement for prediction of mine pressure in the coal mine production.
Anomaly Analyses to Guide Software Testing Activity

Software testing is a quality activity with the purpose of revealing errors not yet discovered. Also, this activity verifies if the software realizes its functionalities conformance with its specification. As software testing activity evolves during software development, a large set of test cases can be generated, compromising the effort during the regression testing, for instance. Regression testing is a type of software testing to confirm that a new program or code change has not adversely affected existing features of the software. In this activity, the tester may not have enough time to run all test cases, needing to decide which test cases are best in terms of effectiveness in reveal faults. In another way, during code refactoring activities, code smells can be identified, which represents unwanted situations that can negatively impact software quality. This project proposes an approach that employs information about code smells to guide software testing activity. Our hypothesis is that the code smells can guide, for instance, the prioritization of test cases, focusing on effort reduction of software testing. I would like to acknowledged my advisor, Simone R. S. Souza for guiding and supporting in this work.
SWMapper: Scalable Read Mapper on SunWay TaihuLight

With the rapid development of next-generation sequencing (NGS) technologies, high throughput sequencing platforms continuously produce large amounts of short read DNA data at low cost. Read mapping is a performance-critical task, being one of the first stages required for many different types of NGS analysis pipelines. We present SWMapper — a scalable and efficient read mapper for the Sunway TaihuLight supercomputer. A number of optimization techniques are proposed to achieve high performance on its heterogeneous architecture which are centered around a memory-efficient succinct hash index data structure including seed filtration, duplicate removal, dynamic scheduling, asynchronous data transfer, and overlapping I/O and computation. Furthermore, a vectorized version of the banded Myers algorithm for pairwise alignment with 256-bit vector registers is presented to fully exploit the computational power of the SW26010 processor. Our performance evaluation shows that SWMapper using all 4 compute groups of a single Sunway TaihuLight node outperforms S-Aligner on the same hardware by a factor of 6.2. In addition, compared the state-of-the-art CPU-based mappers RazerS3, BitMapper2, and Hobbes3 running on a 4-core Xeon W-2123v3 CPU, SWMapper achieves speedups of 26.5, 7.8, and 2.6, respectively. Our optimizations achieve an aggregated speedup of 11 compared to the naïve implementation on one compute group of an SW26010 processor as well as a strong scaling efficiency of 74% on 128 compute groups.
Tripod: Performance, Media, Cybernetics

American AnthropologistVolume 122, Issue 3 p. 684-690 MULTIMODAL ANTHROPOLOGIES Tripod: Performance, Media, Cybernetics Jennifer Cool, Corresponding Author Jennifer Cool cool@usc.edu University of Southern California Email: cool@usc.eduSearch for more papers by this author Jennifer Cool, Corresponding Author Jennifer Cool cool@usc.edu University of Southern California Email: cool@usc.eduSearch for more papers by this author First published: 06 August 2020 https://doi.org/10.1111/aman.13436Read 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 onFacebookTwitterLinkedInRedditWechat Volume122, Issue3September 2020Pages 684-690 RelatedInformation
Application of virtual technologies for the representation of physical phenomena in maintenance tasks

Abstract Maintenance during industrial development has evolved, always seeking the in-depth study of the physical phenomena that occur in the elements and systems that have suffered functional failures. Maintenance has been adapted to each challenge presented. The progress and development of computer science have opted for the massive use of information and communication technologies that support industrial processes, generating new possibilities in maintenance management and operation. These tools could contribute to improving the efficiency of maintenance processes, taking into account that these costs represent a third of the total production cost in an organization. This work seeks to identify the evolution of these technological tools in the applications that they have had in representing the physical phenomena of maintenance management. This research also seeks to establish the minimum requirements that these tools must consider in their applications and uses. Considering the appropriation of these technologies in Colombia, in order to determine where the country is in terms of development and technological use, being important information for the recognition of the scenario and decision-making that prevail in a competitive Colombian industry world level.
Neural network under external stimulus: Improving storage capacity and reactions

Motivated by the way animals react in nature, we introduce a fully-connected neural-network model, which incorporates the external pattern presented to the system as a fundamental tool of the recognition process. In this new scenario, in the absence of this external field, memories are not attractors inside basins of attraction, as in usual attractor neural networks, although basins may be created according to the external pattern, thus allowing storing a much larger number of memories. The key point consists in calibrating the influence of the external pattern such as to cancel the noise generated by those memories not correlated with the external pattern. We illustrate how this proposal works by including this new contribution in the standard Hopfield model, showing a significant increase in its recognition capacity (typically by a factor 1 0 2 ). As an additional feature of this model, we show its ability to react promptly to changes in the external environment, a crucial attribute of living beings. This procedure can be applied to a wide variety of neural-network models to increase considerably their recognition and reaction capabilities. • A new pattern-recognition approach is proposed, based on the external stimulus. • The intensity of the external stimulus is crucial for recognizing patterns. • The procedure is illustrated for the standard Hopfield model. • It is shown that a significant increase in the storage capacity is achieved. • The ability to react promptly under changes in the environment is verified.
Information Technology in Mathematics Training

There are three reasons for the need to apply information technology in mathematics training and for an innovative digital education environment: the digital economy and the new human resources requirements it creates; digital generation (new generation of students with special socio-psychological characteristics); new digital technologies shaping and evolving the digital environment. The main idea of the work is to identify the peculiarities of mathematics training in the conditions of the economy and education digitalization and to build from these positions the technique of mathematics training. The analysis of literature has led to the conclusion that in order to achieve success in professional activity SoftSkills and DigitalSkills are required first of all. The main socio-psychological characteristics of the modern student are the increase in visual perception; clip thinking; the desire to work together in the form of “Wiki”; information congestion. The corresponding technique of mathematics training with continuous application of information technologies is described. For the construction of the electronic course the LMS Moodle system is chosen. #CSOC1120.
Using Optimized Butterworth-Based $\Sigma\Delta$ Bitstreams for the Testing of High-Resolution Data Converters

Testing high-resolution data converters is often performed using Automatic Test Equipment (ATE) that has lower resolution Integrated Chips (ICs). Hence, indirect voltage measurements are required. To perform these measurements programmable DC voltage sources are essential. An area efficient technique in generating DC voltage sources is the one that saves a ΣΔ bitstream generated in software in the memory of an ATE and apply it periodically to a low-pass filter (LPF). The Noise Transfer Function (NTF) of the ΣΔM has been selected from the Butterworth, Butterworth with zeros, Bessel and Gaussian families for ΣΔM orders from 1 to 4. An investigation was conducted to figure out the best ΣΔM type and order that should be used. It will be shown that using a high-order Butterworth-based ΣΔ bitstream yields the lowest settling time. This means the DC voltage levels will be available for usage quicker than the other ΣΔM types. Hence, reducing the time taken to test an IC, which in-turn minimizes the time to market and maximizes profit margins.
Priority Union and Feature Logic in Phonology

Phonological rules built from a single operation, priority union, can model both feature-filling and feature-changing processes. The distinction is handled by specifying which argument of priority union is defeasible. Priority union should be considered as a candidate for inclusion in phonological Universal Grammar.
EARS: Enabling Private Feedback Updates in Anonymous Reputation Systems

Reputation systems, designed to remedy the lack of information quality and assess credibility of information sources, have become an indispensable component of many online systems. A typical reputation system works by tracking all information originating from a source, and the feedback to the information with its attribution to the source. The tracking of information and the feedback, though essential, could violate the privacy of users who provide the information and/or the feedback, which could both cause harm to the users' online well-being, and discourage them from participation. Anonymous reputation systems have been designed to protect user privacy by ensuring anonymity of the users. Yet, current anonymous reputation systems suffer from several limitations, including but not limited to a)lack of support for core functionalities such as feedback update, b) lack of protocol efficiency for practical deployment, and c) reliance on a fully trusted authority. This paper proposes EARS, an anonymous reputation system that ensures user anonymity while supporting all core functionalities (including feedback update) of a reputation system both efficiently and practically, and without the need of a fully trusted central authority. We present security analysis of EARS against multiple types of attacks that could potentially violate user anonymity, such as feedback duplication, bad mouthing, and ballot stuffing. We also present evaluation of the efficiency and scalability of our system based on implementations.
Design and Application of a Portable Fingerprint System for Student Attendance Web-Based and Telegram Using Raspberry Pi

Abstract This paper presents the results of the portable fingerprint system design that is specifically applied for student attendance. The purpose of designing this system is to make it easier for teachers or school administrators to monitor and to process student data so that it can be shared via websites and telegram applications. In addition, there are also features for parents to monitor their child’s online presence at school. The methods used were the design and implementation method. The design of this system was built using hardware from Raspberry Pi, where students fingerprint is recorded and can be sent to the website, and also through the telegram application. The system built has been able to function and be implemented properly. The system test results showed a percentage of success of 100% from a number of student entry samples. The fingerprint application that has been made is expected to be used by schools to expedite the process of recording student attendance, and also parents of students can monitor the presence of their children in real time in the teaching and learning process especially in Indonesia.
Cons-free Programs and Complexity Classes between LOGSPACE and PTIME

Programming language concepts are used to give some new perspectives on a long-standing open problem: is logspace = ptime ?
Brain MRI image segmentation using nature-inspired Black Hole metaheuristic clustering approach

The primary goal of brain image segmentation is to partition the brain image into its constituent homogeneous regions. It is very challenging to classify the pixels into homogeneous regions due to noise and inhomogeneous intensity distribution. This work proposes a framework for MRI brain tissue segmentation based on nature-inspired Black Hole (BH) clustering. The framework consists mainly of two stages. The first stage is to remove nonbrain tissues using a hybrid watershed algorithm (HWA) skull-stripping method. Thereafter, a nature-inspired Black Hole clustering technique is used to segment brain tissues from the skull-stripped image. The proposed framework has been applied on several simulated T1-weighted brain images and the performance can be compared with well-known existing clustering segmentation techniques, such as fuzzy c-means (FCM), k-means (KM), and the gravitational search algorithm (GSA). Experimental results show that the proposed framework performs better than FCM, KM, and GSA on the basis of visual inspection as well as on the basis of JSC, DSC, similarity structure index, and segmentation accuracy evaluation metrics.
Efficient Data Access Control for Cloud Computing With Large Universe and Traceable Attribute-Based Encryption

Ciphertext-policy attribute-based encryption (CP-ABE) schemes provide fine-grained access control for the data stored in cloud computers. However, commercial CP-ABE applications need a new encryption scheme for providing two properties such as: supporting large universe attribute and traceability. First, a large universe attribute allows the attribute authority to use any number of attributes in the system. i.e., the attribute universe is dynamic, and it is not fixed at the setup phase. Second, traceable CP-ABE systems trace the dishonest users who intentionally leak the private key for their profit. In this article, a large universe CP-ABE system with white box traceability has been proposed. The attribute universe of the proposed technique is exponentially larger, and it is polynomially unbound. Further, this technique will trace the identity of users who involve in malicious activities. In addition, the proposed scheme can express any kind of monotonic tree access policies into linear secret sharing structure (LSSS). Compared with the existing schemes that are presented to achieve the same property, proposed scheme has achieved better experimental results and so it is applicable for commercial applications.
Document Image Extraction System Design

Abstract The design of the document image extraction system aims to provide an overview of the process in a system that converts the image of a document into text so that it is easier to use (save, manage or search for information) or representing in interesting visualization. The system design based on specific cases such as decision letters, certificates, and assignments. The document identified data that might be needed. After that, other processes are carried out based on each document. The design results show the flow of the system starts from data input, scanning, pre-processing to the document image, character classification, normalization process, and extraction process. This is done because the uniqueness of each document requires a unique process so that it cannot be treated in general. The design of the document image extraction system focuses more on the process of character recognition. The uniqueness of a document has an impact on the extraction process. Then the system design needs to be added to selected the type of document to be extracted.
Innovation Development of Web-Based Dormitory Information System at Boarding School SMA Terpadu Krida Nusantara

Abstract The main problem in this research is the innovation of the development of web-based dormitory management information system at SMA Terpadu Krida Nusantara. Current conditions, the boarding management system at the school is still manual and raises many problems, such as system for monitoring and reporting student data recap, resulting in the accumulation of documents in the room, and the difficulty of searching student data, as well as reporting complicated and untidy data. With the web-based dormitory management information system, it is expected to be able to simplify the process of student data recapitulation, the process of finding data, the process of documenting data, and the reporting process. The design of this study was in the form of designing and manufacturing dormitory management information systems, and the methodology used in this study was RUP (Rational Unified Process). There are four stages in the use of the RUP methodology, namely the stages of inception, elaboration, construction, and transition. By looking at the design process that has been carried out, this management information system will be very helpful in managing dormitories at SMA Terpadu Krida Nusantara.
Maximally Stable Extremal Regions and Naïve Bayes to Detect Scene Text

Abstract This study examines the performance of Maximally Stable Extremal Regions (MSER) and Naïve Bayes in detecting scene text. The variance of types and sizes of fonts, uneven lighting conditions, the text orientation, a complex background, occlusion, and the presence of objects that resemble text, make the scene text detection is quite challenging. The initial stage of the detection process is to use MSER to get the candidate characters in the image. The validation process of the candidate character uses the Naïve Bayes classifier, which we trained using char74k and CIFAR10 data sets. The classification process used HOG as the extracted features. The system validates the candidates by comparing the Naïve Bayes probability value with the specified threshold value. By using 100 images from ICDAR 2015, the research obtains a 50% reduction of the candidate with an accuracy increase of 8% for Naïve Bayes using threshold values. The result shows that Naïve Bayes with the thresholding value is better than the usual Naïve Bayes classification in selecting candidates.
GradeSense: Gradation Aware Storage for Robust Activity Recognition in a Multimodal Smarthome

A wide range of multimodal sensors such as sensors, video cameras, wearables worn by users in an IoT powered smarthome provide important albeit huge amount of data through which applications derive meaningful Activities of Daily Living (ADLs). Storing this massive amount of data is a significant challenge for efficient execution of the corresponding applications to meet real-time demands; there is scope for improving it as it is found that a substantial amount of the data produced may be unimportant. In this paper, we propose an end-to-end system GradeSense, which implements a grading mechanism based on multimodal data fusion by categorizing ‘important’ data. GradeSense is made complete by an applicationindependent storage module that leverages our grading scheme (as opposed to traditional usage-based models) for efficient storing. Activity prediction algorithms perform well (up to 17% improvement) with this now-fused and important data which is a mere fraction of the entire data, achieving 87% data reduction on average in faster storage tier. The throughput of GradeSense, measured through runtime, gives improvement up to 46% due to our enhanced data-categorization and storing mechanism.