Reviewerly/exHarmony huggingface dataset:
Lossless Visual Knowledge Discovery in High Dimensional Data with Elliptic Paired Coordinates

Data with more than two or three dimensions are difficult for humans to conceptualize and facilitate knowledge discovery. Novel Elliptic Paired Coordinates (EPCs) allow for multidimensional data to be represented in 2-D without loss of multidimensional information. In addition, EPC halves the required visual elements in the graph in comparison with parallel and radial coordinates. This research explores the effectiveness of constructing predictive machine learning models interactively using EPC visualizations. For this research EllipseVis, an interactive software system, was developed to process high-dimensional datasets, create corresponding EPC visualizations, and build predictive classification models based on dominance rules. The EllipseVis system allows both interactive and automatic discovery of areas that are located with a high percentage of single-class dominance. The experiments using it on benchmark datasets suggest EPC approach is a promising method for discovering predictive models with high coverage and precision that could be useful in many fields allowing for visually appealing dominance rules to be easily interpreted in the application domains.
Contradictions in the traditional methods of electrodynamics teaching as a determinant of its update

Abstract The article deals with the theoretical analysis of the traditional approaches to electrodynamics teaching. The authors pay attention to the contradictions arising in the process of combined application of Biot Savart and Coulomb’s laws. The thesis of some limitation of the traditional presentation of the course of electrodynamics is substantiated. In this connection the necessity of updating methodology of electrodynamics teaching is determined. As an innovative approach to solving this problem, it is proposed to analyze and address the existing contradictions of electrodynamics teaching using a specially developed (copyright) methodology - the methodology of the development of students’ critical thinking (MDCT). Using specific examples, the content of each of the stages of the proposed methodology, which can be used in the study of electrodynamics in secondary and higher school, is characterized.
Gender and Age detection using Deep Learning

Mankind has advanced technology to the extent that the 21st century is the crack of the dawn for unimaginable achievements. The aforementioned technology can be used for our benefit in identifying one's age and even their gender just based on their glimpse from a camera, image and even a video. This research paper will methodically chalk out the whole procedure, multiple methodologies and algorithms that can be used, which one is the most accurate and how it all comes together. It will also stress on its importance and how it can be implemented to benefit our day to day life. The paramount objective of the paper is to build a gender and age detector that can approximately guess the gender and age of the face of an individual in a picture using Deep Learning on the audience dataset. Moreover, to get the most effective predictions and result by overcoming the problem of accuracy and time. Moreover, the map for the ways this technology can be used to our benefit and look at the huge spectrum where it can be implemented: ranging from security services, CCTV surveillance and policing to dating applications, matrimonial sites.
Scaling Event Aggregation at Twitter to Handle Billions of Events per minute

Log files consisting of events from different services are a rich source of information for large scale analytics. Events can be as simple as log line or as complex as nested structured objects like thrift or protobuffers. At Twitter every service logs events for a particular category and publishes them to the Event Log Aggregation framework. This framework aggregates events of the same category into log files, usually stored on a distributed file system like the Hadoop Distributed File System (HDFS). Large Scale multi-petabyte analytics use these files across hundreds of projects. In this paper we provide an overview of the Event Aggregation framework used at Twitter, highlight its advantages, and compare it with similar frameworks. We also introduce the concept of category group and aggregator group in our architecture. Services at Twitter generate trillions of events with aggregate size exceeding multiple petabytes of data every day. At present this framework handles over three billion events per minute. The main focus of our efforts has been efficient use of hardware resources, scalability and reliability of the framework.
Discord Forensics: The Logs Keep Growing

Voice over Internet Protocol (VoIP) applications are some of the most widely used applications for keeping in contact with people. These popular applications, such as WhatsApp, Skype, and Viber, although prevalent on mobile devices, also offer desktop applications. There has been significant Digital Forensics research into those popular applications, both on mobile and desktop devices. However, research has been lacking regarding more recent VoIP applications such as Discord, which is fast becoming one of the most used communication methods, having millions of registered users. A forensic investigation of Discord can contain valuable information on a suspect's mental state, whereabouts, and even direct evidence of criminal activity. This paper investigates the desktop application for Discord on Windows 10 in terms of forensic merit and cybersecurity. Our results show that plaintext copies of typed message drafts can be recovered along with cached pictures that were sent/received. We discuss the security aspects of any information saved locally and provide guidelines for forensic investigators and users concerning their data privacy.
Leveraging Differential Privacy in Geospatial Analyses of Standardized Healthcare Data

We present a collection of geodatabase functions which expedite utilizing differential privacy for privacy-aware geospatial analysis of healthcare data. The healthcare domain has a long history of standardization and research communities have developed open-source common data models to support the larger goals of interoperability, reproducibility, and data sharing; these models also standardize geospatial patient data. However, patient privacy laws and institutional regulations complicate geospatial analyses and dissemination of research findings due to protective restrictions in how data and results are shared. This results in infrastructures with great abilities to organize and store healthcare data, yet which lack the innate ability to produce shareable results that preserve privacy and conform to regulatory requirements. Differential privacy is a model for performing privacy-preserving analytics. We detail our process and findings in inserting an open-source library for differential privacy into a workflow for leveraging a geodatabase for geocoding and analyzing geospatial data stored as part of the Observational Medical Outcomes Partnership (OMOP) common data model. We pilot this process using an open big data repository of addresses.
HDNet: Hybrid Distance Network for semantic segmentation

Semantic segmentation is currently solved as a pixel-wise labeling task, which predicts the label of each pixel based on its features. However, current methods isolate the relations of points in a feature map and cause the discontinuous segmentation results. In order to solve this problem, we propose a Hybrid Distance Network to measure the distance from two aspects. First, the Hybrid Distance Relation is proposed to model the relations between a point and its context regions to capture contexts in a feature map by an elegant combination of positional distance and high-dimension feature distance. Then, a Location Aware Attention module is proposed to efficiently sample the contexts by the positional distance and produces sparse Hybrid Distance Relations. It synthesizes the different contexts of each point and generates position-wise attention value to compact object-level representation. During the training step, High-dimension Feature Distance loss is also presented as an auxiliary loss to compact category-level representation in feature space. Experiments show that the proposed HDNet achieves state-of-the-art performance with interpretability and efficiency on three challenging semantic segmentation benchmarks: Pascal Context, ADE20K, and COCO Stuff 10 K.
Towards effective learning for face super-resolution with shape and pose perturbations

Recent development of convolutional neural networks (CNNs) has activated a lot of studies and boosted the performance greatly on image super-resolution. This paper addresses the issue of face super-resolution, which attracts a lot of interests in the photographic industry. We propose to make use of the face-specific priors to enhance the performance of face super-resolution with the convolutional neural networks. Classical facial prior models represent the 2D facial shape in a compact low-dimensional space expressed by principal components. Here, we impose perturbations on the low-dimensional space and generate face samples with novel appearance. First, we conduct 2D facial image perturbations through 2D facial landmarks. Then, we carry on the study with perturbations on 3D facial landmarks. Facial pose and shape are perturbated to generate novel appearances of a single 2D facial image. These novel facial samples are then fed into the training process of the convolutional neural networks for face super-resolution. The experimental results demonstrate that the proposed method is adaptable to various networks and achieves superior performance for the face super-resolution task.
Story CreatAR: a Toolkit for Spatially-Adaptive Augmented Reality Storytelling

Headworn Augmented Reality (AR) and Virtual Reality (VR) displays are an exciting new medium for locative storytelling. Authors face challenges planning and testing the placement of story elements when the story is experienced in multiple locations or the environment is large or complex. We present Story CreatAR, the first locative AR/VR authoring tool that integrates spatial analysis techniques. Story CreatAR is designed to help authors think about, experiment with, and reflect upon spatial relationships between story elements, and between their story and the environment. We motivate and validate our design through developing different locative AR/VR stories with several authors.
Don’t Touch My MIDI Cables: Gender, Technology and Sound in Live Coding

Live coding is an embodied, sensorial and live technological–human relationship that is recursively iterated through sonic and visual outputs based on what we argue are kinship relations between and through bodies and technology. At the same time, and in a familiar moment of déjà vu for feminist scholars, live coding is most often discussed not in relation to the lived and sensory human–technology kinship, but in terms of fetishised code or software, output and agency. As feminist scholars have long argued, emphasising and fetishising code or software, and celebrating output and agency are normatively masculine, white and Western conceptions of technology that feed into the growing valorisation of accelerationist logic whilst also negating embodied, not to mention other (non-white, Western, masculine) bodies, expertise or histories per se. In this article, we want to redress this by drawing on our empirical material on live coding to focus on human–technology kinship and, in so doing, think about failure, slowness and embodiment and about human–technology relations that are more akin to what Alison Kafer (drawing on the work of Donna Haraway) has termed ‘becoming with’ or ‘making kin’. This, we argue, has the potential to shift the focus from the potentialities of technologies on or through the body, towards the generative capacities of mediation (including failure), which are caught up in lived experiences. The question is not only about how the relations of bodies and technologies are played out in certain circumstances but about what might be played out if we reconceptualise these relations in these terms.
Analysis of phishing emails

<abstract> This research aims to describe and analyze phishing emails. The problem of phishing, types of message content of phishing emails, and the basic techniques of phishing email attacks are explained by way of introduction. The study also includes a review of the relevant literature on Web of Science and analyzes articles that deal with the threat of phishing attacks and defense against them. Data collected within a time interval of two months from two email accounts of one of the authors of the study was used for the analysis of 200 email messages. Data has been resented in tabular form, to allow further statistical processing using functions such as sum, average and frequency analysis. The core part of the study involved the classification and segmentation of emails according to the main goals of the sent message. The text analytical software Tovek, was used for the analysis, Contribution of the manuscript is in the understanding of phishing emails and extending the knowledge base in education and training in phishing email defense. The discussion compares the results of this research with those of the studies mentioned in the "Introduction" and "Literature review" sections. Furthermore, the emerging problems and limitations of the use of text analytical software are described, and finally the issue is devoted to problems with obtaining personal data from recipients' emails. The "Conclusion" section summarizes the contributions of this research. </abstract>
Digital Image Processing using Singular Value Decomposition

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Searchable Turkish OCRed historical newspaper collection 1928–1942

The newspaper emerged as a distinct cultural form in early 17th-century Europe. It is bound up with the early modern period of history. Historical newspapers are of utmost importance to nations and its people, and researchers from different disciplines rely on these papers to improve our understanding of the past. In pursuit of satisfying this need, Istanbul University Head Office of Library and Documentation provides access to a big database of scanned historical newspapers. To take it another step further and make the documents more accessible, we need to run optical character recognition (OCR) and named entity recognition (NER) tasks on the whole database and index the results to allow for full-text search mechanism. We design and implement a system encompassing the whole pipeline starting from scrapping the dataset from the original website to providing a graphical user interface to run search queries, and it manages to do that successfully. Proposed system provides to search people, culture and security-related keywords and to visualise them.
Integrated Image Sensor and Light Convolutional Neural Network for Image Classification

Deep learning has accomplished huge success in computer vision applications such as self-driving vehicles, facial recognition, and controlling robots. A growing need for deploying systems on resource-limited or resource-constrained environments such as smart cameras, autonomous vehicles, robots, smartphones, and smart wearable devices drives one of the current mainstream developments of convolutional neural networks: reducing model complexity but maintaining fine accuracy. In this study, the proposed efficient light convolutional neural network (ELNet) comprises three convolutional modules which perform ELNet using fewer computations, which is able to be implemented in resource-constrained hardware equipment. The classification task using CIFAR-10 and CIFAR-100 datasets was used to verify the model performance. According to the experimental results, ELNet reached 92.3% and 69%, respectively, in CIFAR-10 and CIFAR-100 datasets; moreover, ELNet effectively lowered the computational complexity and parameters required in comparison with other CNN architectures.
Spartan: A Sparsity-Adaptive Framework to Accelerate Deep Neural Network Training on GPUs

Deep Neural Networks (DNNs) have emerged as an important class of machine learning algorithms, providing accurate solutions to a broad range of applications. Sparsity in activation maps in DNN training presents an opportunity to reduce computations. However, exploiting activation sparsity presents two major challenges: i) profiling activation sparsity during training comes with significant overhead due to computing the degree of sparsity and the data movement; ii) the dynamic nature of activation maps requires dynamic dense-to-sparse conversion during training, leading to significant overhead. In this article, we present Spartan, a lightweight hardware/software framework to accelerate DNN training on a GPU. Spartan provides a cost-effective and programmer-transparent microarchitectural solution to exploit activation sparsity detected during training. Spartan provides an efficient sparsity monitor, a tile-based sparse GEMM algorithm, and a novel compaction engine designed for GPU workloads. Spartan can reduce sparsity profiling overhead by 52.5× on average. For the most compute-intensive layers, i.e., convolutional layers, we can speedup AlexNet by 3.4×, VGGNet-16 by 2.14×, and ResNet-18 by 2.02×, when training on the ImageNet dataset.
Mineral Exploration: Discovering and Defining Ore Deposits

Editor’s note: The Geology and Mining series, edited by Dan Wood and Jeffrey Hedenquist, is designed to introduce early-career professionals and students to a variety of topics in mineral exploration, development, and mining, in order to provide insight into the many ways in which geoscientists contribute to the mineral industry. Abstract For economic geologists, mineral exploration has a specific objective: the discovery of mineral concentrations that can be recovered economically to provide resources essential for society. This was achieved consistently until the first decade of the current century, but exploration since then has been wealth destructive. This outcome is a major issue for the mining industry unless reversed. We believe the technologies presently used to discover ore deposits will be as useful in making future discoveries as they were previously. However, we argue that a new approach is required in how exploration is conducted and in how these and emerging technologies are applied. The required changes in approach include improved business models for conducting exploration and acceptance that fewer deposits are likely to be discovered near the surface. We argue that discovery of deeper deposits will be facilitated if exploration teams (1) seek to identify subtle evidence of mineralized rock recognizable within 500 m of the surface, (2) conduct follow-up investigations with a clear understanding of the volumetric dimensions of the discovery target, and (3) drill boldly as a critical exploration tool. We propose that improving the way geoscientists think when exploring—being more predictive—is the immediate key to increasing the number of discoveries.
The Implications of the No-Free-Lunch Theorems for Meta-induction

The important recent book by Schurz (2019) appreciates that the no-free-lunch theorems (NFL) have major implications for the problem of (meta) induction. Here I review the NFL theorems, emphasizing that they do not only concern the case where there is a uniform prior—they prove that there are "as many priors" (loosely speaking) for which any induction algorithm A out-generalizes some induction algorithm B as vice-versa. Importantly though, in addition to the NFL theorems, there are many free lunch theorems. In particular, the NFL theorems can only be used to compare the expected performance of an induction algorithm A, considered in isolation, with the expected performance of an induction algorithm B, considered in isolation. There is a rich set of free lunches which instead concern the statistical correlations among the generalization errors of induction algorithms. As I describe, the meta-induction algorithms that Schurz advocates as a "solution to Hume's problem" are simply examples of such a free lunch based on correlations among the generalization errors of induction algorithms. I end by pointing out that the prior that Schurz advocates, which is uniform over bit frequencies rather than bit patterns, is contradicted by thousands of experiments in statistical physics and by the great success of the maximum entropy procedure in inductive inference.
VaBank: Visual Analytics for Banking Transactions

To analyse and detect fraudulent patterns in banking transactions, most fraud analysts use spreadsheets which makes the overall process time-consuming and complex. In this article, we propose a visualization tool that aims to ease the analysis of banking transactions over time and the detection of the transactions' topology and of suspicious behaviours. Our main contributions are: (i) a user-centred visual tool, developed with the aid of fraud experts; (ii) a method that characterises the transactions topology through a self-organising algorithm; (iii) the visual characterisation of transactions through complex glyphs; and (iv) a user study to assess the tool effectiveness.
Multiobjective Sensor Ontology Matching Technique with User Preference Metrics

Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state-of-the-art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the users with different preferences is a challenging problem. To face this challenge, in our work, a Multiobjective Evolutionary Algorithm (MOEA) is used in determining different nondominated solutions. In particular, the evaluating metric on sensor ontology alignment’s quality is proposed, which takes into consideration user’s preferences and do not need to use the Reference Alignment (RA) beforehand; an optimization model is constructed to define the sensor ontology matching problem formally, and a selection operator is presented, which can make MOEA uniformly improve the solution’s objectives. In the experiment, the benchmark from the Ontology Alignment Evaluation Initiative (OAEI) and the real ontologies of the sensor domain is used to test the performance of our approach, and the experimental results show the validity of our approach.