Reviewerly/exHarmony huggingface dataset:
Improving Accessibility in Interlibrary Loan Using OCR

AbstractOptical character recognition (OCR) is used to identify typed, printed and handwritten text, and encode it in a digital format. OCR has been used in libraries since the 1990s, but only recently have libraries started using it in interlibrary loan (ILL). This study focused on seeing if OCR could be applied to scans made in an ILL unit and sent to patrons while incurring minimal costs and requiring few new technical skills. Copies of 20 articles requested through ILL were used to test the process and accuracy of Adobe Acrobat Pro DC in creating searchable PDFs. The process involved calculating the accuracy of automatic OCR results, accuracy after manual corrections were made, and noting any problems that arose. The results showed that searchable PDFs could be created using Adobe Acrobat Pro DC that were 95% accurate or more in 12 of the 20 test files. Adobe Acrobat Pro DC had difficulties processing some information and text, but this provides a good starting point for an ILL unit desiring to provide their patrons with accessible materials.Keywords: Accessibilityinterlibrary loan departmentsOCRresource sharing AcknowledgmentsI would like to thank Dr. Lenese Colson for her assistance in the editing and proofreading of this paper. I would like to thank Jonathan Lee and Robert Lee for their expertise and assistance in designing the study methodology. I would also like to thank Sheryl Williams, Tricia Clayton, and Braegan Abernethy for supporting me in pursuing this project.Disclosure statementThe author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Sparse Group Regression Classification for Face Recognition

Abstract Face recognition has been widely used in biometric verification which is a significant issue in system control in computer based communication. Sparse regression classification (SRC) has been an important method for face recognition. SRC assumes its regression coefficients are globally sparse. However, these regression coefficients correspond to a class-specific gallery may be locally dense. In order to get some regression coefficients that are globally sparse and locally dense, sparse group regression (SGR) is introduced in this paper for face recognition. By use of SGR, we expect to get some regression coefficients which are not only sparse as a whole, but also locally dense in some part. Variable splitting and alternating direction method of multipliers (ADMM) are used for solving the problem of SGR. The proposed algorithm, named Sparse Group Regression Classification (SGRC), is extensively evaluated on four standard databases. A comparative study with some state-of-the-art algorithms reflects the efficacy of the proposed approach.
Türk Bireylerin Kekemeliği Olan Bireylere Yönelik Dinleyici Tepkileri: Kültürlerarası Bir Karşılaştırma

Social attitudes and reactions towards people who stutter (PWS) are generally examined through scales.The purpose of this study is to compare the attitudes and behaviors of Turkish and non-Turkish individuals in their interactions with PWS in an experimental condition.The participants were two PWS and 16 adults as interaction partners, including eight Turkish and eight non-Turkish individuals.In the experiment, each interaction partner engaged in a conversation with PWS on spontaneous themes.Four raters scored the attitudes of the interactors by watching the recorded videos via a questionnaire consisting of 49 antonym adjectives.Two raters calculated the interactive behaviors of the interaction partners.Based on the qualities with an agreement rate over .7 according to the reliability findings, the groups were compared with independent samples t-test.In the behavioral sense, the Turkish group exhibited significantly more 'sentence completion' and 'asking consecutive questions' behaviors compared to the non-Turkish group.In terms of interaction attitudes, the Turkish group received significantly higher scores in responding in the first syllable, completing the statement, being serious and anxious.In comparison to the Turkish group, the non-Turkish group received significantly higher scores in using gestures/facial expressions and being empathetic, warm, positive, sincere, sympathetic, and open.
Extracting Camera Pose Using Single Image Super Resolution Networks

This work proposes a mechanism which can be used as a basis for allowing camera POSE information to be maintained reliably during loss or interference with inertial motion unit or positioning system integration. This basis is formed by employing image synthesis networks with atypical data for the network type: inputs are normal down scaled source imagery while outputs are native resolution images composed of the contents of the same scene viewed from a fixed offset position. The goal of this application is to simulate the presence of a binary camera from monocular hardware, which makes feasible certain POSE estimation workflows which would normally require binary cameras on monocular platforms. Being able to rapidly synthesize images of additional camera positions without having to physically navigate to those positions allows for two methods to build off each other. First, knowing that the model should consistently maintain a specific POSE from the source camera allows synthetic images to be used to artificially inflate available data during structure from motion processing with confidence in the accuracy of synthetic points. It also enables the comparison of an image at an actual physical location with the synthetic one later as a measure of POSE accuracy which can be incorporated into a solution for computing POSE of the image source.
Multi-Robot Coordinated Planning in Confined Environments under Kinematic Constraints

We investigate the problem of multi-robot coordinated planning in environments where the robots may have to operate in close proximity to each other. We seek computationally efficient planners that ensure safe paths and adherence to kinematic constraints. We extend the central planner dRRT* with our variant, fast-dRRT (fdRRT), with the intention being to use in tight environments that lead to a high degree of coupling between robots. Our algorithm is empirically shown to achieve the trade-off between computational time and solution quality, especially in tight environments. We also demonstrate the ability of our algorithm to be adapted to the online planning problem while maintaining computational efficiency. The software implementation is available online at https://github.com/CMangette/Fast-dRRT.
Classifier Design Based on Class-Wise Fast Topological CIM-Based Adaptive Resonance Theory

Clustering algorithms can flexibly extract useful knowledge from input data. Therefore, clustering algorithms are often applied to data preprocessing such as dimensionality reduction and feature extraction. Clustering algorithms can also be applied to classifiers thanks to a good knowledge extraction ability. As a conventional study of applying clustering algorithms to classifier design, the algorithm has been proposed that explicitly learns decision boundaries by applying a clustering algorithm to each class of data. However, there are some problems such as instability of learning and slow processing. In this study, we propose a classifier by utilizing the Fast Topological CIM-based Adaptive Resonance Theory (FTCA) that achieves both excellent self-organization performance and high-speed learning. Our experimental results in this paper show that the proposed algorithm has better classification performance compared to other clustering-based classifiers.
Modeling Information Pooling Bias in Incident Response Teams: An Agent Based Modeling Approach

Security analysts regularly correlate disparate incidents to detect cyber-attacks. However, past research shows that team-based incident correlation analysis may be affected by information pooling bias. This article presents findings from an agent-based model used to explore the cognitive processes hypothesized to be causing this bias during information exchange within a team. The model simulated information exchange between three analysts conducting incident correlation analysis by searching for information available with them about the different incidents. Three models of memory search process were compared: Random, Local, and Memory-aided search. Results from the simulation show that agents in a local search model, compared to memory-aided search model, shared more often the information known to majority in the team. Comparing model results with data from lab experiments suggest that teams, by default, may be employing a heuristic search process during information exchange leading to sub-optimal team processes and performance.
An Efficient Holistic Schema Matching Approach

Schema matching allows a certain way of communication between heterogeneous, autonomous and distributed data sources. We choose the matching approach depending on the number of sources we wish to integrate: pairwise matching approaches for a small to a medium total number of data sources, and holistic matching approaches for a big to a huge number of data sources. Nevertheless, current matching approaches were proven to achieve a very moderate matching accuracy. Moreover, holistic matching approaches operate in a series of two-way matching steps. In this paper, we present hMatcher, an efficient holistic schema matching approach. To execute holistic schema matching, hMatcher captures frequent schema elements in the given domain prior to any matching operation. To achieve high matching accuracy, hMatcher uses a context-based semantic similarity measure. Experimental results on real-world domain show that hMatcher performs holistic schema matching properly, and outperforms current matching approaches in terms of matching accuracy.
Reinforcement Learning For Walking Robot

Abstract Reinforcement learning namely Deep Q Learning neural network is used to make a robot walk in a controlled environment. The main objective of this paper is to make a robot learn to walk by building a model in hardware with six forms of movement. The basic alignment of robot at any moment depends on the direction of movement of stepper motor of a leg joint by test and trial method without any preordained model. The robot is made to freely walk which is stopped without falling down and is able to move freely over a transportable platform. Hardware robot communicates to the learning model which is run on the local machine through a USB port connection.
Fixational Feature-Based Gaze Pattern Recognition using Long Short-Term Memory

The pattern of eye gaze is increasingly powerful for human-computer interaction tasks. Understanding of gaze pattern can provide valuable information regarding to users’ attention. Certainly, the patterns of eye gaze known as eye accessing cues are related to the cognitive processes of the human brain. In this paper we propose a method for gaze patterns recognition, where a gaze data was collected from eye tracker. Consequently, a gaze fixation feature and Long Short- Term Memory technique is employed in this work for the recognition. To evaluate the performance of the proposed method, we have an experiment with 7 examiners, in which they have to looked at 3 tasks of point, rotate and slide on screen. The experimental results show the proposed method offering favorable performance on a standard eye tracker, respectively.
CNN을 활용한 카오스 신호 분류 검증

The aim of the study was to classify the chaotic time-series data with the nonlinear problem using the convolutional neural network (CNN), and to determine and verify the chaotic characteristics from a deterministic system. The classical nonlinear differential equation established by the Rossler model was used, and the chaotic characteristics were determined by the Lyapunov exponent. The chaotic properties was visualized using an unthresholded recurrence plot through the proposed procedure. A simple CNN model was developed to learn the extracted image using the proposed feature-visualization technique. As a result, the chaotic characteristics were classified with an accuracy of 99% or more.
Mining social collaboration patterns in developer social networks

Software development is extremely complex, requiring collaboration between teams and developers who collaborate on various tasks; these activities lead to the generation of an implicit developer social network (DSN). The authors’ aim to understand the development process in terms of collaboration between developers. In this work, they conducted an empirical study on mining social collaboration patterns of DSNs for open source software projects based on an integrated approach involving the identification of global and local collaboration patterns among developers based on social network analysis. The bug tracking system-based DSN (BTS-DSN) is chosen as an example over the other DSNs since it incorporates larger collaboration activities and actors. The empirical results show that the DSNs, specifically BTS-DSN, exhibits three different coordination pattern levels (Plan, Aware, and Reflexive) based on their collaboration activities. The mean time to repair metric proves that the Reflexive level occupies the fastest bug fixing time, then the Plan level comes secondly, and lastly the Aware level. In addition, each level group shows different collaboration behaviours among developers; thus, this information can be useful as a resource for better understanding of developer collaboration and collaboration awareness.
A Contrast Optimal Visual Cryptography Scheme for Half-Tone Images

In this paper, we propose a new contrast optimal visual Cryptography Scheme for Half-tone images. This scheme can share Secrete Half-tone Image among n members and all n number of members can reveal the secrete by superimposing the n share together and n − 1 members can not reveal the secrete. The proposed method generate the noise like shares but share size and the image recovered after superimposition is of same size which is equal to original secrete image. It reduces the pixel expansion based on the designed codebook and matrix transposition. It also reduces the transmission speed, complexity computation, security issue and restore the secrete image without any distortion. The scheme proposed can share the black& white and gray level image and produces the more accurate results and is easy to implement.
Research on Intelligent Sampling Inspection Platform for Distribution Network Materials

This paper introduces the current status of distribution network material sampling inspection in power grid. Aiming at solving the existing problems, an intelligent sampling inspection platform for distribution network materials is designed. This platform is based on the Internet of Things technology, and a machine learning algorithm, Local Outlier Factor, is employed to quantitative evaluate the inspection reports. This sampling inspection platform designed can greatly improve the efficiency of material inspection and achieve refined sampling inspection.
Multi Sensorial Stimulation Lab: A New Approach for Severe Dementia

The MS-Lab project wants to implement a non-pharmacological method of multisensory stimulation aimed at reducing the sense of discomfort and improving the psycho-behavioral disorders typical of subjects with severe dementia, with the aim of improving the quality of life of patients, verifying effectiveness and safety of this method. This technological implementation is made possible by a wide and complex collaboration between organizations and companies with different skills. As described by practice and theory, appropriate sensory stimulation can lead to several improvements, such as: greater initiative of patients, greater vigilance, improvement in mood and interpersonal relations, greater attention and confusional state reduction. Moreover, positive effects could derive from the possible reduction of drugs and/or psychotropic drugs, with consequent reduction of pharmacological side effects and reduction of health costs, as well as decrease in the need for physical restraint. MS-Lab is experimenting a non-pharmacological therapy through a multisensory stimulation and intend to overcome some limits detected in this type of treatment, improving its effectiveness by supporting a framework designed as a complex system of input and output devices driven by an intelligent system able to modulate sensory stimuli according to the continuously measured biological parameters. The project also proposes to define a detailed protocol with an algorithm at the base of a dedicated software, in order to make this experience repeatable, producing data that can be freely used by experts, re-proposing it in a clinical contest or in any other environments to be defined ad hoc. Moreover, a multiplatform software layer will be designed and implemented to support Clinical Decisions (CDSS), supporting caregivers through suitable graphic interfaces on mobile devices, suggesting the most appropriate domains to be stimulated on the basis of current and past clinical parameters acquired from sensors’ ecosystem. MS-Lab will be installed at “Casa Amata”—a nursing home located in Taviano (Le), Italy—and at “Casa Sollievo della Sofferenza” —an hospital and Research Institution located in S. Giovanni Rotondo (Fg), Italy—with the aim of conducting simultaneous and co-planned experiments through the proposed treatment able to represent the best answer to the end-user needs.
High-Parallelism Hash-Merge Architecture for Accelerating Join Operation on FPGA

Join is a data-intensive and compute-intensive operation in database systems. As most existing solutions to accelerate the hash join operation on field programmable gate array (FPGA) are focused on N-to-1 join relationships, their performances rapidly decline on N-to-M joins. To resolve this shortcoming, this brief proposes a novel architecture combining hash and sort-merge algorithms for join acceleration. In the build phase, the architecture utilizes a single hash function to build hash tables for two data tables, and the hash collisions are addressed by building ordered linked lists according to their join attributes. In the merge phase, mapped buckets in two hash tables are merged one-to-one to find matching tuples. This architecture lends itself to high parallelism to improve its performance. Experimental results show that the design on a FPGA achieved a high join throughput of 194.0 million tuples per second, which is better than the reported FPGA implementations. Moreover, the architecture is perfectly compatible with both N-to-1 and N-to-M join relationships.
Angle-Based Non-local Recurrent Network for Single Image Deraining

Single image deraining is always an important problem in the field of image processing. With the development of deep learning over the years, image rain removal has also made substantial progress. However, the existing deep learning networks tend to regard rain streaks as a neat pattern, without considering the direction and physical characteristics of rain streaks, and they can not completely remove long rain streaks. To solve these problems, We proposed an Angle-based Nonlocal Recurrent Network(ANRN), which consists of Angle information extraction block and non-local recurrent convolution block. Angle information extraction block can effectively model rain streaks, and generates abstract feature maps and weights based on the amount of rain streaks, making full use of the direction and physical characteristics of rain streaks. In order to get rid of long rain streaks, we have used a non-local recurrent convolution structure, which can better connect long distance contextual information and reduce the size of the network. We did a large number of experiments in both synthetic and real data sets, and the results had shown that our network can remove rain streaks in various directions and densities, perform better on long rain streaks, and is superior to current state-of-the-art methods.
Linearized Polynomials and Their Adjoints, and Some Connections to Linear Sets and Semifields

Throughout this paper we let p be a prime number, let $$q=p^r$$ and let $$\mathbb {F}_{q^n}$$ denote a finite field with $$q^n$$ elements, where n is a positive integer.
Salerno Telling: An Approach for Cultural Heritage Enhancing

The cultural tourism market is evolving towards a dimension of complete fulfilment of the tourist’s needs. On the one hand, it is enhancing the centrality of the cultural aspect in a 360° travel experience and on the other is paying attention to the choice of one’s journey, completed by, according to each context, all needed services. The information overload, the substantial changes of the traveller’s decisions and the enlargement of the tools of Knowledge available to each connected user are today’s main levers of change and require the use of Context-Aware Systems. In this scenario, a context-aware framework for the fruition of e-Tourism contents and services will be discussed. The proposed system is able of supporting not only tourists but also Public Institutions and professionals, through the dynamic design of core and services for tourism promotion. In order to test the proposed methodology, a prototype has been developed, obtaining promising results.
WITHDRAWN: Asana posture correction using centre of gravity calculated using skeletal information

Abstract Life without medical assistance is a gift from god. To lead a good healthy life physical fitness is very much essential. To avoid issues in bones and joints one should maintain proper posture in all the activities this is inevitable in exercising and practices like yoga. Mal-postures will lead to issues simple from muscle pain to complicated issues like fracture. In order maintain balance while practicing yoga having the Centre of Gravity (CoG) in proper location is important. For those who practice by themselves (i.e without trainer’s help) by using assistive systems, giving the location of CoG for every step in the asana will be a vital assistance. The trainer module can check the captured postures and calculate the CoG for that posture will be compared with the CoG of that posture stored in the database. Proper weight distribution of the body parts will lead to maintain the CoG in correct location. In this paper postures in every step of Warrior I are captured and using the formula CoG for the postures are calculated. Mean value for every posture is considered as correct CoG value for that posture.
Near and Dear: Designing Relatable VR Agents for Training Games

Agents are becoming increasingly ubiquitous in people’s lives. There is potential for agents to aid scalability of digital learning and training. Research has noted the reduction in personal interactions as such applications reach a broader population. There is a gap in literature with regards to virtual agents as relatable learning companions. This paper presents the findings from three ideation workshops with different expert groups centered around designing playful interactions with a digital agent in Virtual Reality (VR), in the context of cybersecurity training. The workshop discussions were subjected to thematic analysis to extract design insights. The design insights fall into three broad categories: thematic (e.g., different metaphors to translate cybersecurity concepts), stylistic (e.g., less realistic art), and mechanical (e.g., VR gestures). This work contributes to HCI by producing a set of design insights to inform design of agent based VR learning environments.