Reviewerly/exHarmony huggingface dataset:
Automation of Non-Classroom Courses using Machine Learning Techniques

Non-classroom courses in higher education significantly encourage creative thinking. Awarding credits for accomplishments through non-classroom courses encourage students for more participation and improve their problem-solving skills. Machine learning techniques help to automate the maintenance of such non-classroom courses. This paper proposes a process to introduce and automate such a non-classroom. Firstly, students will submit a softcopy of the certificates/proofs of their accomplishments such as winning/participating in a competition/event through a web-based user interface. Secondly, the authenticity of these proofs/certificates is tested using text mining, image processing, and web scraping techniques. Text is extracted from the submitted certificates using Tesseract Optical Character Recognition (OCR) Engine and stored as a MySQL database which is matched with the information provided by the student in the web interface and other sources such as flyer of the event. Further, the extracted text is used as search criteria for web scraping and matched with the scraped text. Further, the authentication test can be performed with these keywords and tags by search through social media. Facial Encodings and Recognition is done on the images submitted to authorize the user. Finally, the evaluation is performed and the final result indicating the credits/grade is revealed to the user.
Video based human activities recognition using deep learning

Human activities recognition from motion capture data is a challenging problem in the computer vision due to the fact that, in various human activities, different body components have distinctive characteristics in terms of the moving pattern. In this paper, a learning method of detecting an activity from different angles based on various sources of information is proposed. with high accuracy. The bottom up approach is used in OpenPose which is the tool used in this paper's experiments. The proposed method achieves promising results on Berkeley Multimodal Human Action Database (MHAD) datasets at 98% accuracy.
Continual leakage resilient SM2 signature scheme

The following topics are dealt with: mobile robots; autonomous aerial vehicles; learning (artificial intelligence); collision avoidance; control system synthesis; multi-robot systems; optimisation; aircraft control; path planning; position control.
Dual Graph regularized NMF with Sinkhorn Distance

Many researchers have paid more attention to the application of non-negative matrix factorization (NMF) in data representation. Recently, some regularization methods can improve the performances by utilizing the data and feature manifold, simultaneously. In this work, a new method, named dual graph regularized NMF with Sinkhorn distance (DSDNMF) is presented. It not only synchronously takes the data structure and feature structure into consideration, but also measures the reconstruction error by adopting the Earth Mover's Distance (EMD) to make full use of the feature correlation. Therefore, DSDNMF can effectively explore the semantic structure information of data in contrast to traditional methods. Besides, we introduce an efficient strategy to optimize our proposed model. Comprehensive experiments on the COIL20 and PIE datasets manifest the superiority of DSDNMF.
Knowledge Base System (KBS) Uses Ternary Grid in Islamic Philosophers

Knowledge is the most important component in the architecture of artificial intelligence that is applied to a variety of applications of artificial intelligence, one of which is a knowledge-based system. One part of KBS is the inference engine (inference engine) whose task is to determine the decision on the facts of input provided by the user. Therefore, the performance of the inference engine has a responsibility for the quality of decisions that are produced. To overcome this problem, this research will develop a Ternary Grid-based knowledge base that can be used to overcome errors in rules and facts and optimize existing material, content, or information. The ternary grid will be applied in the case of learning Islamic philosophy. The results of this study are the design of knowledge base system applications that can be used to study philosophy.
Searching the Hyper-heuristic for the Traveling Salesman Problem with Time Windows by Genetic Programming

This paper focuses on the solving constrained traveling salesman problem with time windows indirectly by finding useful hyper-heuristic via genetic programming. Resulting hyper-heuristic represents calculation of city priority along the traveling salesman path. The influences of different city properties (position in the Cartesian coordinate space, sum of distances to the n nearest cities, polar angle from the beginning of the coordinate system, etc.), math functions (addition, subtraction, division, sine, cosine, tangent) and penalty functions are tested. Trigonometric functions have no positive influence, best results are achieved with Cartesian coordinates and sum of distances to the n nearest cities. Polar angle gives more diverse solutions. Resulting hyper-heuristics are good on training sets. When they are tested on another datasets, they find relatively short paths, but there are problems with respecting constraints in the form of time windows.
WITHDRAWN: An evolutionary approach to load balancing using forest optimization algorithm

Cloud computing can be described as a distributed computing framework that provides computational facilities, where information and assets are recovered from cloud service providers via the web, by means of a well-framed online application. But resource sharing often leads to unavailability of resources, resulting in a deadlock situation. One approach to avoid this is by disseminating the workload uniformly between the encumbered and idle machines. This is called Load Balancing. The aim of doing this is to reduce the average response time and maximize resource utilization. Forest Optimization Algorithm (FOA) is based on governance of trees in the forest and survival of distinct trees. These trees have proper geological developing conditions. The proposed Algorithm is an attempt to find these distinguished solutions from the pool to avoid starvation of the tasks, at the same time improving the average response time by utilizing the process of seed dispersal in forests.
Centroiding and Connected Component Labeling for Radar Images Using Image Processing Algorithms

The main objective of this paper is to identify centroids for the given image using connected component labeling technique. Imaging radars are used to acquire the images of the required target and it produces 2D images. The imaging radar used here is wall penetrating radar which explores the targets behind the wall. The images obtained from this imaging radar are subjected to some processes like thresholding and filtering in order to improve the image characteristics. The region of interest is then defined using a technique called connected component labeling where the image is scanned and the pixels grouped into components based on pixel connectivity. All the pixels in the connected component of an image exhibit same pixel intensity values which are connected together. When all the groups in an image have been identified then each pixel will be labeled with a gray level or color labeling based on the component it was specified. Finally, these individually identified objects are located with centroid, these centroids show the presence of the targets. The main goal is to remove noise/clutters in an image in an efficient manner and to locate individual object in an image by a single point called centroid.
Action Recognition Using Frame Average Feature Map with 2D Convolutional Neural Network for Real-Time Video Analysis

A typical video action recognition system has a high computational cost and is not suitable for real-time applications. To solve this problem, we propose an action recognition method using a two-dimensional convolutional neural network (2D CNN), which has a significantly lower computational cost than a 3D CNN. In addition, the proposed method uses a small number of frames from the video for an accurate result. The proposed method consists of: i) pretrained VGG16,which is a2D CNN, to train the action of the video andii) a test with an average of ten frames per dataset. The proposed method improved the recognition performance with a reduce computational cost by using the average of several frames instead of directly analyzing all the frames for real-time video analysis environments.
Data Collection Strategies for Sharing Lived Experiences: Low-Income Mothers’ Perceptions of Text (SMS) and Multimedia (MMS) Data Collection

The purpose of this study was to explore low-income lone mothers’ perceptions of their engagement with a text (SMS) and multimedia message (MMS) qualitative study. Study participants were asked to submit text and pictures via SMS and MMS that represented their reflections, observations, and experiences accessing community support services over a 6-week period. After engaging in the study, participants were asked to complete an evaluation questionnaire. The resoundingly positive feedback received in the evaluation—and the researchers’ satisfaction with the data collected—suggests that researchers should consider employing SMS and MMS data collection strategies to gain open-ended insights into the daily experiences of marginalized groups. This method may be particularly well suited to feminist research designs and research with populations underrepresented in the literature due to barriers presented by traditional data collection strategies.
Comparative study of degradation between the carbon black back coat layer and magnetic layer in magnetic audio tapes using attenuated total reflectance Fourier transform infrared spectroscopy and machine learning techniques

Abstract Most magnetic audio tapes in the industry were initially made with only two layers, the base film with polyethylene terephthalate (PET) and the binder layer with polyester polyurethane (PEU). The binder layer seems to undergo degradation from hydrolysis, causing the condition “sticky shed syndrome (SSS).” Hydrolysis is a reversible reaction where atmospheric moisture reacts with weak ester bonds to form degradation products, alcohols, and carboxylic acids, which produce sticky materials, hindering the playability of tapes. Later, another layer called “back coat” was introduced to magnetic audio tapes. This layer consists of carbon black embedded in PEU or polyether polyurethane. Carbon black particles in this layer absorb water, facilitate high‐speed tape winding, and remove static electricity charges. However, due to the ability to absorb water, hydrolysis can occur in the back coat layer as well. For this reason, there is a study claiming that the back coat is responsible for the degradation of magnetic audio tapes, and by removing it, SSS can be overcome. To test the validity of the above claim, this study uses attenuated total reflectance Fourier transform infrared spectroscopy (ATR FT‐IR) with multivariate statistics to determine the degradation of the back coat layer and compare the results with the magnetic layer of the same tape identities. Results obtained show poor predictability in the back coat test set when compared with the results collected from the magnetic side, which indicates that to determine the playability status of this specific magnetic audio tape collection, the preferred side to obtain spectra is the magnetic layer.
Motion matters: Comparing naturalness of interaction with two locomotion interfaces using decision-making tasks in virtual reality

Virtual environments can replicate the visual appearance of terrain conditions, but movements involved in using the interfaces confer their own bodily sensations, which can be incongruent with the visual presentation. Assuming that more natural interfaces produce more natural locomotor behaviors, we propose a framework for assessing the quality of a locomotion interface. Using this framework, we studied the interaction of different locomotion interfaces with visual information on wayfinding decisions in a virtual environment. We compared decisions made using a dual joystick gamepad with a walking-in-place metaphor. Paths presented on a given trial differed visually in one of the following aspects: (a) slope, (b) friction, (c) texture, and (d) width. In this experiment, choices made with the walking-in-place interface more closely matched visual conditions which would minimize energy expenditure or physical risk in the natural world. We provide some observations that would further validate this approach and improve this method in future implementations. This approach provides a way of both studying factors in perceptual decision making and demonstrates the effect of interface on natural behavior.
Image Segmentation Algorithm for Semantic Segmentation with Sharp Boundaries using Image Processing and Deep Neural Network

Deep neural network (DNN)-based image segmentation has the endemic problems caused by iterative sampling, i.e., inaccurate and uncertain boundaries. On the other hand, conventional image segmentation based on image processing tends to extract well-aligned object boundaries, but has been difficult in semantic segmentation. In this paper, we propose an image segmentation method that exploits both image processing techniques and a DNN to extract semantic objects with well-aligned boundaries. In the experiments, it is confirmed that the proposed algorithm improves DNN-based segmentation effectively.
Path Planning and Design of Loitering Munition Based on DSP

With the increasing complexity of battlefield environment and combat missions, the onboard computer of the loitering munition demands higher performance to calculate and transfer the data. On one hand, considering the existing online planning system, which execute the multiplied missions divided sequentially in modules, this paper proposed the design for the hierarchy, modules and ports of the path planning system supported by the master and slave structure of TMS3206678 multi-core DSP based on the idea of encapsulation and stratification. The cores are allocated according to the parallel and serial running sequence of data interaction. On the other hand, considering the difference between the traditional programming simulation environment and the real time operation of the electronic device, such as the Digital Signal Processor, an improved 3d sparse A* algorithm is migrated in the environment of Code Composer Studio 5.5.3 to determine the simulation time under the clock rate of 40MHz, so as to verify the reliability and real-time performance of the algorithm.
Forensic Source Identification of OSN Compressed Images

Source camera identification is used in legal applications involving cybercrime, terrorism, pornography, etc. It is a digital forensic way to map the image to its authentic source. In today's digital era online social networks have become a great source of image transmission as well as mapping of the OSNs images to its source device has become difficult due to its implicit compression technique and altering of metadata. In this paper, we propose a deep learning model for SCI, on images downloaded from Facebook, and Whatsapp. We adapt the ResNet50 network and add our own layer head to fine-tune the model for the classification of source cameras of OSNs compressed images. It can be seen by observing the experimental results that the proposed technique addresses the results efficiently for images downloaded from OSNs.
Reference Model to Identify the Maturity Level of Cyber Threat Intelligence on the Dark Web

In this article, we propose a reference model to identify the maturity level of the cyber intelligence threat process. This proposal considers the dark web as an important source of cyber threats causing a latent risk that organizations do not consider in their cybersecurity strategies. The proposed model aims to increase the maturity level of the process through a set of proposed controls according to the information found on the dark web. The model consists of three phases: (1) Identification of information assets using cyber threat intelligence tools. (2) Diagnosis of the exposure of information assets. (3) Proposal of controls according to the proposed categories and criteria. The validation of the proposal was carried out in an insurance institution in Lima, Peru, with data obtained by the institution. The measurement was made with artifacts that allowed to obtain an initial value of the current panorama of the company. Preliminary results showed 196 emails and passwords exposed on the dark web of which one corresponded to the technology manager of the company under evaluation. With this identification, it was diagnosed that the institution was at a “Normal” maturity level, and from the implementation of the proposed controls, the “Advanced” level was reached.
Open proceedings of scientific events as an object of information and library services

The task of creating an open access repository for social sciences in the framework of the development of the information system of the Institute of Scientific Information for Social Sciences of the Russian Academy of Sciences (INION RAS) is formulated. The repository is to acquire and store he programs of workshops, webinars, conference and symposia as well as the presentations and paper abstracts. The findings of the study of open materials science events loaded to the websites of RAS scientific institutions of social and humanitarian profile as potential sources of information for the Repository are cited. For 2019–2020, over 1,2 thousand events held by more than 100 academic institutions were selected. Distribution by the number of events, their categories and status, topics, and type of available materials is given. The data supports the Pareto principle: 20% institutions hold up to 70% events. Thirteen scientific event types are specified, with 5 most popular of them accounting for 90% events with the leading disciplines of history, economics and literary studies. The available materials are unified and their 16 types are specified. Distribution by the number of events, their categories and status, topics, and type of available materials is given. The possibility for using these materials in various information and library services, building e-libraries and union bibliographic databases, and current and advanced selective information awareness services, is discussed.
DIARI: A Coordination Framework for Systems Design and Integration Management

Systems design and integration are two phases of the system lifecycle that inherit a high degree of dependency from the interaction required from the organizations that need to come together to perform them. Current scaled agile frameworks assume the organizational structure to be a unified entity that delivers a system in form of a single output. In reality, a system is not a single output up to the moment it becomes operational. Until then it is an evolving aggregate and so are the organizations that jointly deliver it. In this paper, we demonstrate that this aggregate of organizations behaves as a System-of-Systems and that the current scaled agile frameworks are therefore not addressing the management of systems design and integration in an appropriate way. We propose a new coordination framework for managing systems design and integration - the DIARI Framework - that adapts to and scales with the complexity of the existing organizational structure.
Anonymous Author Identifier Using Machine Learning

Cyber security companies predict potential attacks on their clients by monitoring deep and dark web forums where actors conspire and discuss potential bugs and vulnerabilities of clients that could be exploited to attack them. There are existing solutions that analyze texts and state if it's a threat purely based on content. These solutions tend to trigger a lot of false alarms because the discussion content is indiscernible from actual threat. The key indicator of authenticity of a discussion being a threat is its author. There are red hot malicious actors who are notorious for attacks in the past and if we see a threat from them, there's a high chance that it would lead to a potential attack. The catch is, the malicious actors cover up their digital signatures effectively—making it impossible to attribute an anonymous threat to a known actor. However, the most intrinsic feature of such anonymous content is, the author's writing style. In this paper we have tried to find out the answer of the question, Given a collection of document, how to cluster the document that are possibly written by the same author?.
A KG-based Enhancement Framework for Fact Checking Using Category Information

The massive spread of false information has brought about severe security-related problems to individuals and society. To debunk misinformation automatically, fact checking has become an important task that aims at retrieving evidence from external sources to verify the truthfulness of a given claim. As knowledge graph (KG) is a classic external source for retrieving relevant evidence. Previous methods typically check a claim by making inferences over it. Entity category information can be utilized to strengthen both the learning and verification process. However, this information was largely ignored in previous research. To make better use of the category information, in this paper, we propose a category-based framework for improving the performance of fact checking with KGs. We first learn prototypes for each category as their representatives, and then propose a prototype-based learning technique for effectively modeling the entity dependency in KG. We further develop a prototype matching technique to explore the category-level relations between head and tail entities for more robust verification. Experimental results on two benchmark datasets and a real-world dataset show that our framework can significantly improve the reasoning abilities of KG reasoning methods on Fact Checking task.
Concentric Morphology Model in Detection of Masses from Mammogram: A Study

Breast cancer is a victor among the most comprehensively saw malignancies among ladies around the world. Clump territory from mammogram helps in right on time affirmation for breast tumor. A PC-aided detection structure which will see also perceive the subverting lumps in person’s breast in an unmistakable and fiscally able way is required. It is the fantasy and reason for experts to have a CAD framework with most important affectability and low fake positives per picture (FPI). Affirmation of clump from person’s breast is badly arranged considering its plenteous geomorphological attributes and questionable edges. Clump territory execution can be overhauled by utilizing productive preprocessing instrument and geomorphological features of the clump area. As a clump makes, it enrages the breast parenchyma and widens by stirring up different coaxial layers. Geomorphological assessment of these coaxial layers is a foundation in clump conspicuous verification figuring. Particular CAD structures utilizing coaxial morphology show exist as a bit of the creation. In this article, an endeavor has made to plot a piece of the current CAD frameworks which utilize coaxial morphology appear for precisely on time and exact zone of clumps.
The Imperceptible Trembling of Things

Previous articleNext article No AccessThe Imperceptible Trembling of ThingsMark LewisMark Lewis Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Afterall Volume 50Autumn/Winter 2020 Published by Central Saint Martins, University of the Arts London Article DOIhttps://doi.org/10.1086/712843 Views: 238Total views on this site © 2020 by Central Saint Martins College of Art and Design, University of the Arts London. All rights reserved.PDF download Crossref reports no articles citing this article.