Reviewerly/exHarmony huggingface dataset:
Hidden Concepts of Library and Information Science in Information Seeking Behavior Models

Modeling information seeking behavior was started in the early 1980s pioneered by Wilson. More than 70 models in the last four decades on information seeking behavior have emerged. The models delineate the concepts from cognate subjects such as psychology and sociology. The authors made an analytical study of these models and identified many concepts of library and information science (LIS); from information processing and retrieval, reference service case studies and the evaluation experiments of information retrieval systems intrinsically are hidden in these models that have not been explicitly expounded as part of information seeking traits. The Chain Indexing devised by Ranganathan has an inclusive concept into information seeking with the phrase "Sought Link" to select the relevant subject heading(s) derived from the document(s) for indexing and to assign as the sought term(s) in the subject catalogue. Lancaster in the evaluation of MEDLARS identified phrases; serendipity and novelty ratio in response to users queries to the system. The paper after an in depth study of these ideas and with an analytical review of the models implies many of the classical concepts from LIS are implicit-in information seeking patterns of users. The paper conjures up to investigate them in the models of information seeking behavior as pragmatic rather only on user perceptions.
Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes

Digitization of large parts with tight geometric tolerances is a time-consuming process that requires a detailed scan of the outer surface and the acquisition and processing of massive data. In this work, we propose a methodology for fast digitization using a partial scan in which large regions remain unmeasured. Our approach capitalizes on a database of fully scanned parts from which we extract a low-dimensional description of the shape variability using Statistical Shape Analysis. This low-dimensional description allows an accurate representation of any sample in the database with few independent parameters. Therefore, we propose a reconstruction algorithm that takes as input an incomplete measurement (faster than a complete digitization), identifies the statistical shape parameters and outputs a full scan reconstruction. We showcase an application to the digitization of large aeronautical fuselage panels. A statistical shape model is constructed from a database of 793 shapes that were completely digitized, with a point cloud of about 16 million points for each shape. Tests carried out at the manufacturing facility showed an overall reduction in the digitization time by 80% (using a partial digitization of 3 million points per shape) while keeping a high accuracy (reconstruction precision of 0.1 mm) on the reconstructed surface.
Flex Sensor Dataset: Towards Enhancing the Performance of Sign Language detection System

Whether it&#x0027;s God&#x0027;s creation or science, the earth has evolved and life came into existence. Not all have the same character instead exhibit similar. As of reports from the National Institute on Deafness and Communication related disorders 18.5 million(approx.) have speech related disorders. Many are born with speech inability and many get speech-related disorders and are unable to speak. As communication is key for an effective society and environment, a person with speech inability looks different from a normal person. A communication gap exists between a normal person and a person with speech inability. As technological revolutions have taken the world to a new level, still a better and efficient process to make a speech disorder person a normal person is not available. There are many processes and products available but the efficient and reliable ones are not available. The use of Machine Learning and Artificial Intelligence in many fields has high success rates. In this project, a wearable device is to be made with the help of flex sensors and an accelerometer sensor which is to be used by a speech disorder person to communicate with others. There are many sign language systems available, considering the American Sign Language (ASL) system for each alphabet and each numerical dataset is to be created. A comparative study is to be made by applying different machine learning algorithms for the created dataset. The ML algorithm with better performance is identified and to be deployed in microcontroller in the future.
Edge detection using multiplication imaging conditions

This paper presents a new algorithm to generate edge detection images using a migrated image. The key idea is that discontinuity response are comprised of all dip angle ranges and thus after F-K filtering, they can exist in both images with negative-only and positive-only dips. However, reflection responses can only exist in either image with negative-only dips or image with positive-only dips. Applying multiplication imaging condition (MIC) to two images with opposite dips can generate an edge detection image. This method can be easily extended to 3D cases. More specifically, two strategies are proposed. The first one involves applying a 3D F-K filter to calculate four components with different dip and azimuth angles, then multiplying the decomposed four components to generate the final edge detection image. The other one consists of splitting the 3D data volume into 2D sections and then applying the 2D methodology to generate the edge detection image. Both synthetic and real data tests will be employed in 2D and 3D to demonstrate the validity of edge detection using MIC.
A Survey on Image Memorability Prediction: From Traditional to Deep Learning Models

Image memorability prediction is an interesting and challenging task which aims to create a representative model of memorable images, in order to recognize memorable ones from others. Actually, memorability can be valuable in different areas and several applications. In recent years, image memorability has received considerable interest in computer vision and machine learning communities. Different approaches have been proposed which have made a significant impact on memorability analysis performances. Thus, the purpose of this paper is to expose different methods and techniques used to predict and analyze image memorability. Therefore, we expose and compare datasets and evaluation metrics used for performance memorability estimation. Then, we discuss many challenges and opportunities for image memorability prediction improvement, which can provide some future guidelines.
Multi region integrated energy system planning method considering the dynamic characteristics of heat supply network

Abstract In view of the poor effect of current heat supply network energy system planning, a multi regional comprehensive energy system planning method based on the dynamic characteristics of heat supply network is proposed, the equipment structure of energy hub is optimized, and the evaluation index and algorithm of multi regional comprehensive energy system planning are constructed to ensure the effect of multi regional comprehensive energy system planning with the dynamic characteristics of heat supply network. Finally, it is confirmed by experiments. In practical applications, the method in this paper is highly adaptable and can meet the research requirements.
Improvement of Human Tracking Based on an Accurate Estimation of Feet or Head Position

In this paper a method is presented to estimate the position of feet/head of objects in various camera views. In this method, first, all objects in the scene are detected using the background subtraction. Then, human and non-human objects are separated via the support vector machine (SVM) that is trained based on local binary patterns (LBP) features. The basic idea of the next step of this work is that the feet/head of an object are the group of pixels that are projected to small region on ground/top plane by corresponding homography matrix. This idea is expressed via an optimization problem which avoids partitioning out small group of pixels. Experimental results show that the proposed methods can improve the accuracy of the object tracking.
A luminance control method for OLED burn-in prevention using user information

In this paper, we propose a novel luminance control method utilizing user information (UI) to prevent burn-in in organic light emitting diode (OLED) displays. To control the luminance on a burn-in potential region (BPR), we first calculate the visual angle of each pixel on display utilizing UI and design a visual acuity model based on the cortical magnification theory. Next, we generate a UI-based saliency map by combining the visual acuity model and an image saliency map. Finally, the luminance of the BPR is adjusted based on the UI-based saliency map and the local contrast of the BPR. Experimental results show that the proposed method not only controls the luminance of the BPR to a degree of which users barely notice the change but also greatly extends the lifetime of the OLED.
Supporting Reusability in the Scrum Process

The combination of agile software development and reusability makes a lot of benefits to the client, work team and quality of the project. Several types of artifacts can be used in the future (exp code, design, document, model, etc.). To be more effective, they must be organized and structured in an artifacts’ repository. However, few approaches have been proposed to address this problem. In this work, we have incorporated reuse into most of the scrum process activities through a repository of reusable artifacts. This repository allows the work team on the one hand to store and classify new artifacts detected during software development and on the other hand to search and retrieve the available artifacts. This retrieval is facilitated by an evaluation for each artifact. To validate our proposed approach, we have experimented our idea in a project to develop an intelligent system for assisting a COVID19 crisis unit.
Tree-based dynamic classifier chains

Abstract Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in practice, this order has a substantial impact on the quality of the final prediction. Dynamic classifier chains denote the idea that for each instance to classify, the order in which the labels are predicted is dynamically chosen. The complexity of a naïve implementation of such an approach is prohibitive, because it would require to train a sequence of classifiers for every possible permutation of the labels. To tackle this problem efficiently, we propose a new approach based on random decision trees which can dynamically select the label ordering for each prediction. We show empirically that a dynamic selection of the next label improves over the use of a static ordering under an otherwise unchanged random decision tree model. In addition, we also demonstrate an alternative approach based on extreme gradient boosted trees, which allows for a more target-oriented training of dynamic classifier chains. Our results show that this variant outperforms random decision trees and other tree-based multi-label classification methods. More importantly, the dynamic selection strategy allows to considerably speed up training and prediction.
Melodic Transformation in George Garzone’s Triadic Chromatic Approach; or, Jazz, Math, and Basket Weaving

Abstract The Triadic Chromatic Approach (TCA) is a melodic improvisation technique, created by the saxophonist and jazz educator George Garzone. With the TCA, successive triads are connected by a semitone and must have different inversions. The resulting lines mysteriously resolve to any underlying harmony. This article develops transformational and Markov-chain models of the TCA, showing how this melodic process approaches a probability distribution where every pitch class is equally likely to appear. Nonetheless, theorizing the TCA is different from performing it, and insights from Garzone’s students—and the anthropology of weaving—suggest that goals are less fundamental here than characteristic gestures.
Influence of structures and lithologies in the morphology and localization of Au–Ag mineralization at Martinetas deposit, Don Nicolas mine, Deseado Massif, Argentina

This work describes the structural and lithological controls that affect the distribution, orientation, and precipitation mechanism of mineralization in the Martinetas district, from the structural context and the analysis of the role played by the geological units that host the mineralization. Martinetas is a low sulfidation epithermal gold and silver mining district located in the northeastern sector of the Deseado Massif, Argentina. The district is characterized by numerous quartz veins, sheeted veinlets and breccias, developed in pyroclastic and volcanic rocks of the Bahia Laura Volcanic Complex. The analysis of fractures, faults and host rocks of the vein systems show that the main factors controlling the formation and distribution of the mineralization are the lithology, deformation style and fault kinematics. Their interplay led to positive feedback between the evolution of plane aperture through time, strain localization and the resulting mineralization. Vein orientations are indicative of a NE-SW extensional stress field at the time of hydrothermal fluid ascend, which favored fluid input and pervasive flow within the active fractures and the matrix of the geological units. Subsequently, cyclic channelized fluid flow during repeated fluid input caused extensive veining and numerous filling events. A link between hydrothermal organic matter and the presence of gold in the vein system has been recorded. Knowledge of the structural and lithological controls that set the structure and the distribution of higher grades ore shoots of the mineralization in the Martinetas district constitutes information of high value as an exploratory tool for the region.
Mapping Speech Intonations to the VAD Model of Emotions

Human speech carries information not only in its semantics but also in intonation, that delivers a vast amount of knowledge that is usually ignored in modern text-to-speech (TTS) technologies. Development of methods to define and further to compute dependencies between speech and emotions is considered a promising field of research. After looking into the role of intonation in the Russian language an experiment has been suggested to create a mapping between intonational configurations of spoken Russian language and sentiments. The impact of emotions on individuals is tangible on many levels, that is the reason why they are a key part of modelling affective states. As understanding sentiments is as important as expressing them, ways to define them vary from discrete sets to multidimensional spaces. Therefore the suggested mapping is expected to be used further in the task of synthesising more natural and emotion-saturated speech.
Applying PSO to improve the pedestrian fall detection rate using wearable sensor data

Pedestrian fall detection attracts a lot of research due to its importance in building a warning system to avoid negative consequences. There are many techniques for fall detection that have used different devices such as cameras, environmental sensors, wearable sensors, etc. However, the popularity of wearable devices with many embedded sensors has motivated research on detecting abnormal activity such as falling based on sensor data. High accuracy of fall detection in daily life activities is always a practical challenge. This paper investigates different classifiers using a combined feature set that computes from Time, Frequency, and Hjorth domains using the wearable device’s accelerometer data only. This study also employs Particle Swarm optimisation (PSO) to find the parameters of the Random Forest classification, which aims to improve the accuracy of the fall detection system. The experimental results on the UP-Fall dataset have shown a better performance than the study of L. Martínez-Villaseñor et al. Combining the RF classifier with PSO also improves the stability and performance of the model.
Multidimensional Scaling Analysis and an Application

The aim of this study is to determine the factors affecting student’s failure. For this, a survey study was carried out. In the questionnaire applied to people of all ages and occupations living in Turkey between 2013 and 2015, inferences were made on the answers of 4183 people, 2928 of whom were men and 1255 were women, regarding the relationship between 26 variables that are closely related to the success factor. In this study, non-metric multidimensional scaling technique was used considering the Euclidean distance. This is because the data is not quantitative and smaller size solutions can be obtained. As a result of the analysis, Kruskal's stress value was observed as 0.00 and no significant difference was observed between the variables. Therefore, the observation of stress values in the range of (0-0.025) clearly reveals that there is a "complete" agreement between the variables. Another one words, the measure of the actual figure compatibility chart obtained S=0.00 and shows full compliance. In this case, we can say that the results obtained adequately reflect the data set we have.
Visual Pattern Recognition: Identity Authentication Technology Based on Eye Movement Patterns

Abstract Existing identity authentication techniques are primarily based on passwords or physical characteristics and are therefore prone to theft and forgery. This can lead to potential security risks for identity recognition. In this paper, we propose a new identity authentication technique based on reading eye movements by introducing eye tracking technology. In this approach, a deep neural network based on multi-input architecture is used to construct a computational model. The model uses the text sequence, the fixation point sequence, and the linguistic feature as inputs, and output the recognition by a neural network. The simulation results showed a recognition accuracy of 89.9%. The eye tracking technique developed in this study may be used as a new and efficient identity authentication method. The data set used in the experiment and the associated codes have been released on GitHub.
‘<i>Suiran danshi</i>’

Abstract ‘ Suiran danshi (‘although but’)’ used on Chinese social media is a novel variant of ‘ suiran (‘although’)… danshi (‘but’)’, a typical pairing of adversative connectives in Mandarin Chinese. This article hypothesizes that ‘ suiran danshi ’ is a memetic expression resulting from pragmatic ellipsis and repeated use for special functions in the context of online communication. To verify this hypothesis, the authors collected data from Weibo and WeChat, two most widely used social media platforms in China, and analyzed the use of ‘ suiran danshi ’ in context. It is argued that as an elliptical form, ‘ suiran danshi ’ complies with the requirements of the Economy Principle and the Politeness Principle. As a meme, the emergence and popularity of the variant are ascribable to its special functions of teasing and facilitating social bonding, as well as to the vagueness of language use and the richness and complexity of cyber-context afforded by the Internet.
DomainPrio: Prioritizing Domain Name Investigations to Improve SOC Efficiency

Security Operations Centers (SOCs) are in need of automation for triaging alerts.Current approaches focus on analyzing and enriching individual alerts.We take a different approach and analyze the population of alerts.In an observational study over 24 weeks, we find a surprising pattern: some domains get analyzed again and again by different analysts, without coming to a final evaluation.Overall, 19% of the domains trigger 74% of all investigations.The most time-consuming domains are classified as false positives and ''potentially unwanted programs''-the lowest threat level.To increase SOC efficiency, we design DomainPrio, a tool that prioritizes domains that are likely to be the subject of repeated, incomplete investigations.This enables us to indicate to the first analyst encountering this domain that the investigation should be, if possible, completed on this first attempt, so future investigations on the same domain can be prevented.DomainPrio is able to predict these domains with 89% accuracy and does so with an interpretable and auditable logistic regression model.When evaluating our tool on 100 days of data from a production setting, we find that it can potentially reduce the number of alert investigations by up to 35%, presenting the SOC with very substantial efficiency gains.
Visual Typer for the Handicapped

One of the methods for handicapped people who can’t talk nor use their hands to communicate with the external world is using their eyes-gaze. Most of the commercial eye trackers used for typing are expensive and not user-friendly. The aim of this paper is to present a low-cost eye-tracking system that can be used easily by consumers. The basic idea of this project is as follows: An eye image is acquired from the web camera, and then the pupil will be detected using image processing techniques. Based on the estimated gaze direction, specific commands will be used to control the Graphical User Interface (GUI) which displays a screen that contains letters and special function buttons.
Stereo Image Analysis by Octonion Fractional-Order Orthogonal Color Moments

Polar harmonic Fourier moments(PHFMs) are popular for image analysis due to their excellent performance of the image representation. However, the traditional PHFMs are unavailable for color stereo image analysis on the one hand, and on the other hand the polar harmonic polynomials with integer-order are not able to extract fine features. In this paper, a new category of moments named octonion fractional-order PHFMs(OFrPHFMs) are proposed using the fractional-order basis functions of PHFMs and octonion theory. The proposed moments can be viewed as a generalization of quaternion orthogonal moments. More importantly, some numerical instability and calculation issues are discussed and a fast computational framework using matrix operation and block Gaussian numerical integration is developed to improve the accuracy and efficiency of the proposed OFrPHFMs. Finally, some correlated image experiments are conducted and the results show that the proposed OFrPHFMs have favorable performance in the field of color stereo image analysis.
Role of Machine Learning in Authorship Attribution with Select Stylometric Features

The author of a manuscript leaves behind footprints which can be traced back to identify the person. This paper attempts to solve such a text categorization issue by distinguishing the actual author of a document from within a pool of claimants using various Machine Learning tools. The techniques have been shown to achieve success in resolving controversies as and when they arise in the domain of Authorship Attribution. The corpus comprises assorted documents of three separate short-story writers. The process involves utilizing unsupervised K-Means Clustering technique initially to extract some select stylometric features or attributes from the text corpus. Thereafter several Supervised Learning techniques have been implemented to classify the author’s identity correctly. Amongst these the ANN classifier emerges as the best technique with an accuracy of 93.33%.