Reviewerly/exHarmony huggingface dataset:
Exploiting the Relationship between Pruning Ratio and Compression Effect for Neural Network Model Based on TensorFlow

Pruning is a method of compressing the size of a neural network model, which affects the accuracy and computing time when the model makes a prediction. In this paper, the hypothesis that the pruning proportion is positively correlated with the compression scale of the model but not with the prediction accuracy and calculation time is put forward. For testing the hypothesis, a group of experiments are designed, and MNIST is used as the data set to train a neural network model based on TensorFlow. Based on this model, pruning experiments are carried out to investigate the relationship between pruning proportion and compression effect. For comparison, six different pruning proportions are set, and the experimental results confirm the above hypothesis.
Image Captioning with Unseen Objects

Image caption generation is a long standing and challenging problem at the intersection of computer vision and natural language processing. A number of recently proposed approaches utilize a fully supervised object recognition model within the captioning approach. Such models, however, tend to generate sentences which only consist of objects predicted by the recognition models, excluding instances of the classes without labelled training examples. In this paper, we propose a new challenging scenario that targets the image captioning problem in a fully zero-shot learning setting, where the goal is to be able to generate captions of test images containing objects that are not seen during training. The proposed approach jointly uses a novel zero-shot object detection model and a template-based sentence generator. Our experiments show promising results on the COCO dataset.
Efficient Hardware Acceleration of Convolutional Neural Networks

Convolutional neural networks (CNNs) have emerged as the most efficient technique for solving a host of machine learning tasks. Compute and memory intensive nature of CNN has stimulated lot of work in hardware acceleration of these network models. FPGAs have emerged as a promising approach for accelerating CNNs, due to its high performance, flexibility and energy efficiency. We propose a unified architecture named UniWiG, where both Winograd based convolution and general matrix multiplication (GEMM) can be accelerated using the same set of processing elements. Proposed architecture has been used to accelerate AlexNet and VGG-16 models on FPGA with a performance of 433.63 GOPS and 407.23 GOPS respectively. We have also analyzed the performance with varying Winograd tile sizes and found out the most appropriate tile sizes for maximizing the performance while reducing on-chip memory resource.
Numerical personal threpsology: the problems and solutions

О р д е н а Л е н и н а ИНСТИТУТ ПРИКЛАДНОЙ МАТЕМАТИКИ имени М.В.Келдыша Р о с с и й с к о й а к а д е м и и н а у к
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering.

Visual question answering (Visual QA) has attracted significant attention these years. While a variety of algorithms have been proposed, most of them are built upon different combinations of image and language features as well as multi-modal attention and fusion. In this paper, we investigate an alternative approach inspired by conventional QA systems that operate on knowledge graphs. Specifically, we investigate the use of scene graphs derived from images for Visual QA: an image is abstractly represented by a graph with nodes corresponding to object entities and edges to object relationships. We adapt the recently proposed graph network (GN) to encode the scene graph and perform structured reasoning according to the input question. Our empirical studies demonstrate that scene graphs can already capture essential information of images and graph networks have the potential to outperform state-of-the-art Visual QA algorithms but with a much cleaner architecture. By analyzing the features generated by GNs we can further interpret the reasoning process, suggesting a promising direction towards explainable Visual QA.
DIGITAL IMAGING BASED ON FRACTAL THEORY AND ITS SPATIAL DIMENSIONALITY

Due to the complexity of digital imaging targets and imaging conditions, fractal theory techniques in existing digital imaging systems still have various shortcomings. In this paper, a digital imaging processing method based on fractal theory is proposed for the first time. For X-ray images, the rapid calculation method of H-parameters is derived based on the fractional Brownian random field model. The H-parameters of X-ray images are calculated point by point. After that, all the singular points are connected, which is the edge of the defect in the image. We apply this method to analyze and process the X-ray images with defects such as missing joints, skins and hollows. Secondly, by means of fractal geometry, the contour slice measurement of the digital imaging space of this fractal is studied. The approximate index value is the digital imaging section profile dimension (D1 dimension) and the section shadow dimension (D2 dimension), so that the dimension determines the complexity of the form and detail of digital imaging. Finally, it can be seen from the experimental results that this method is effective and explores a new way for the development of digital imaging technology. At the same time, it is of great significance to the automatic pattern recognition of the application.
An experimental estimate of the impact produced on PNU by new generation video codecs

The resolution of video cameras has increased considerably in recent years leading to a new generation of codecs with higher compression rates, such as H.265, VP9 and AV1. However, compression can heavily affect the noise present in each frame. Other encoder specific features, like the intra-frame prediction for H.265 and AV1, can flatten the pixel non-uniformity (PNU) noise. We implemented a test-bed to establish whether source camera identification can still be achieved using the PNU when videos are encoded in those formats. The experiments proved that the results are less accurate than those obtained from videos encoded with traditional codecs. Nevertheless, the most original result achieved was the methodology we designed. In order to avoid hidden artifacts the source videos were initially acquired as a raw data stream from a dedicated embedded system and then recorded with an open-source YUV encoder.
The Uniform Distribution Is Complete with Respect to Testing Identity to a Fixed Distribution.

Inspired by Diakonikolas and Kane (2016), we reduce the class of problems consisting of testing whether an unknown distribution over [n] equals a fixed distribution to the special case in which the fixed distribution is uniform over [n]. Our reduction preserves the parameters of the problem, which are n and the proximity parameter \(\epsilon >0\), up to a constant factor.
PSO Optimized Fuzzy Controller for Mobile Robot Path Tracking

An essential aspect of Industry 4.0 is autonomous production methods, powered by Industry of Things (IoT) based on a connected mesh of objects, devices and computer machines, which can communicate with each other. Many things can be transported across the factory via autonomous mobile robots, avoiding obstacles and coordinating with other robots. Autonomous robots are fully integrated with the manufacturing system in Industry 4.0 to a greater extent than ever before. This paper addresses the design and implementation of PSO optimized fuzzy control for mobile robot path tracking. Firstly, fuzzy control is applied to track the desired trajectory. Secondly, this path tracking has been improved with the PSO optimized fuzzy controller. The proposed PSO fuzzy controller was investigated based on several conducted MATLAB simulation scenarios for a mobile robot. The simulation results show better performances with the proposed PSO optimized fuzzy controller when it is compared with the fuzzy controller without optimization.
Combining metadata and co-citations for recommending related papers

Identification of relevant documents is performed to keep track of the state-of-the-art methods and relies on research paper recommender systems. The proposed approaches for these systems can be classified into categories like content-based, collaborative filtering-based, and bibliographic information-based approaches. The content-based approaches exploit the full text of articles and provide more promising results than other approaches. However, most content is not freely available because of subscription requirements. Therefore, the scope of content-based approaches is limited. In such scenarios, the best possible alternative could be the exploitation of other openly available resources. Therefore, this research explores the possible use of metadata and bibliographic information to find related articles. The approach incorporates metadata with co-citations to find and rank related articles against a query paper. The similarity score of metadata fields is calculated and combined with co-citations. The proposed approach is evaluated on a newly constructed dataset of 5116 articles. The benchmark ranking against each co-cited document set is established by applying Jensen--Shannon divergence (JSD) and results are evaluated with the state-of-the-art content-based approach in terms of normalized discounted cumulative gain (NDCG). The state-of-the-art content-based approach achieved an NDCG score of 0.86 while the traditional co-citation-based approach scored 0.72. The presented method achieved NDCG scores of 0.73, 0.77, and 0.78 by incorporating the title, co-citation and title, and abstract, respectively, whereas the highest NDCG score of 0.77 was achieved by combining co-citations with metadata. However, better results are achieved by incorporating the title and abstract with NDCG score of 0.81. Therefore, it can be concluded that the proposed approach could be a better alternative in cases where content is unavailable.
Touch the Wall: Comparison of Virtual and Augmented Reality with Conventional 2D Screen Eye-Hand Coordination Training Systems

Previous research on eye-hand coordination training systems has investigated user performance on a wall, 2D touchscreens, and in Virtual Reality (VR). In this paper, we designed an eye-hand coordination reaction test to investigate and compare user performance in three different virtual environments (VEs) – VR, Augmented Reality (AR), and a 2D touchscreen. VR and AR conditions also included two feedback conditions – mid-air and passive haptics. Results showed that compared to AR, participants were significantly faster and made fewer errors both in 2D and VR. However, compared to VR and AR, throughput performance of the participants was significantly higher in the 2D touchscreen condition. No significant differences were found between the two feedback conditions. The results show the importance of assessing precision and accuracy in eye-hand coordination training and suggest that it is currently not advisable to use AR headsets in such systems.
Composing through Interaction: a framework for collaborative music composition based on human interaction on public spaces

Urban public art is a kind of art that is produced and demonstrated in public places, based on the function and connotation of the city itself exerts. As an essential artistic content in the contact of human life, the introduction of technology is a significant trend in public art, and with it, the interaction has become an increasingly relevant aspect of public art in the digital context. In this way, this work presents an environment for creating random collaborative music from interaction in public spaces using mobile technology. The result is a composition that goes towards to John Cage’s methods. However, in our case, all participants are composers and their interactions with space work as the component that brings randomness to composition. A case study was conducted with volunteer students divided into groups. Participants made use of two versions of Compomus - an app developed for immersive interaction with sound. One version encourages movement through the environment, while the other explores the spatiality of sound in a simulated public environment within the university. The interaction of the participants generated ten compositions, five from the first version and five compositions from the second version of the developed application. The sounds resulting from the interaction were made available to the public through a website.
Histogram-based approach for mass segmentation in mammograms

Segmentation of abnormalities in mammographic images has been a challenge due to poor contrast between the abnormality and surrounding area. In general Computer Aided Diagnostic (CAD) systems are composed of three major step: i.e segmentation followed by feature extraction and classification. The ultimate results of CAD systems are strongly influenced by the results of segmentation, as poor segmentation leads to incorrect features being extracted and ultimately incorrect classification. In this paper we propose a novel method for the segmentation of masses in mammographic images by using a simple method based on the information provided in histograms. After applying some preprocessing on the mammographic images in order to smooth the image histograms, optimal threshold values were obtained for each mammogram which can deal with uni-modal and bi-modal histograms. Segmentation results show an improved area for the mammogram masses, representing the true shapes of the abnormalities. We tested our algorithm on 233 benign and 233 malignant abnormalities. Results are in line with state of the art methods.
Perceção sobre os Conhecimentos, Habilidades/Competências e Aptidões dos Profissionais da Informação: uma comparação de realidades

Information professionals' Knowledge, Abilities/Skills, Competencies and Aptitudes are concepts widely discussed in the literature, both in academic and professional references, and the term 'information professional' is not rarely used in the literature, in different ways.Despite the published works, there are differences between the concepts and their application, especially when analyzing works by authors from different countries and different approaches.The purpose of the ongoing research work is to find out if there are differences regarding the main Knowledge, Abilities/Skills, Competences and Aptitudes of information professionals, perceived either by Portuguese or Brazilian professionals, from the perspective of Information Science.This article presents a discussion of the main concepts related to information professionals, as well as a brief description of the research work that is intended to be carried out.
An Intelligent Multi-Resolutional and Rotational Invariant Texture Descriptor for Image Retrieval Systems

To find out the identical or comparable images from the large rotated databases with higher retrieval accuracy and lesser time is the challenging task in Content based Image Retrieval systems (CBIR). Considering this problem, an intelligent and efficient technique is proposed for texture based images. In this method, firstly a new joint feature vector is created which inherits the properties of Local binary pattern (LBP) which has steadiness regarding changes in illumination and rotation and discrete wavelet transform (DWT) which is multi-resolutional and multi-oriented along with higher directionality. Secondly, after the creation of hybrid feature vector, to increase the accuracy of the system, classifiers are employed on the combination of LBP and DWT. The performance of two machine learning classifiers is proposed here which are Support Vector Machine (SVM) and Extreme learning machine (ELM). Both proposed methods P1 (LBP+DWT+SVM) and P2 (LBP+DWT+ELM) are tested on rotated Brodatz dataset consisting of 1456 texture images and MIT VisTex dataset of 640 images. In both experiments the results of both the proposed methods are much better than simple combination of DWT +LBP and much other state of art methods in terms of precision and accuracy when different number of images is retrieved. But the results obtained by ELM algorithm shows some more improvement than SVM. Such as when top 25 images are retrieved then in case of Brodatz database the precision is up to 94% and for MIT VisTex database its value is up to 96% with ELM classifier which is very much superior to other existing texture retrieval methods.
Hybrid Wizard of Oz: Concept Testing a Recommender System

In this paper, we report the results from a Hybrid Wizard of Oz experiment consisting of a critical incident interview and an in-situ simulation. The study aimed at validating the need for a contextualised and personalised Point-Of-Interest (POI) recommender and understanding the detailed user needs for it. Our key findings include: feeling bored as a key trigger to search for POIs, trust issues with the existing recommendation sources, intent to find free activities, information needs on areas-of-interest beyond points-of-interest, support for socialising, and language barriers. With this study, we also exemplify a cost- and time-effective approach for design of intelligent systems.
MultiSeg: Parallel Data and Subword Information for Learning Bilingual Embeddings in Low Resource Scenarios

Distributed word embeddings have become ubiquitous in natural language processing as they have been shown to improve performance in many semantic and syntactic tasks. Popular models for learning cross-lingual word embeddings do not consider the morphology of words. We propose an approach to learn bilingual embeddings using parallel data and subword information that is expressed in various forms, i.e. character n-grams, morphemes obtained by unsupervised morphological segmentation and byte pair encoding. We report results for three low resource morphologically rich languages (Swahili, Tagalog, and Somali) and a high resource language (German) in a simulated a low-resource scenario. Our results show that our method that leverages subword information outperforms the model without subword information, both in intrinsic and extrinsic evaluations of the learned embeddings. Specifically, analogy reasoning results show that using subwords helps capture syntactic characteristics. Semantically, word similarity results and intrinsically, word translation scores demonstrate superior performance over existing methods. Finally, qualitative analysis also shows better-quality cross-lingual embeddings particularly for morphological variants in both languages.
Designing Automatic Speech Recognition Technologies to Improve Accessibility for Deaf and Hard-of-Hearing People in Small Group Meetings

Deaf and hard of hearing (DHH) individuals face several barriers to communication in the workplace, particularly in small-group meetings with their hearing peers. The impromptu nature of these meetings makes scheduling sign-language interpreting or professional captioning services difficult. Recent advances in Automatic Speech Recognition (ASR) technology could help remove some of these barriers that prevent DHH people from becoming involved in group meetings. However, ASR is still imperfect, and it contains errors in its output text in many real-world conversation settings. My research proposes to investigate whether there are benefits in using ASR technology to aid understanding and communication among DHH and hearing individuals. My dissertation research will evaluate the effectiveness of using ASR in small group meetings (through empirical studies with DHH and hearing participants), as well as develop guidelines for system design to encourage hearing participants to communicate and speak more clearly.
Role-Based Perceptions of Computer Participants in Human-Computer Co-Creativity

The purpose of this ongoing research is to better un- derstand the potential contributions that computers can play in sit- uations where people interact with computers towards creative pur- suits and goals. Past research has provided sets of definitions of dif- ferent roles that a computer plays in human-computer creative col- laboration. Thus far, we look into the advantages and limitations of having such roles. In particular, this paper contributes an analysis and categorisation of the coverage of existing role classifications for computational participants in co-creativity. This analysis is comple- mented by a comparative review of the use of roles to understand and structure creative collaboration between people only (i.e. without any computational participants involved). Our wider project investigates whether these defined sets of roles are a. adequate and b. helpful for understanding the perception of computational contributions in co-creativity, with a study planned to investigate the roles of current systems in practice. This project considers both co-creative computer systems that currently exist, and systems that could potentially exist in the future. Our goal is to reach a point where the perception of what is possible in human-computer co-creative collaboration is en- abled and boosted (but not constrained) by a definitive set of roles.
I_ConvCF

Item-based collaborative filtering is widely used in industry to build recommendation systems because of its explanatory and efficiency in personalized recommendation. However, item-based collaborative filtering is mostly a shallow linear model, which cannot well mine the complex relationship between items. Therefore, in this work we propose a item-based convolution collaborative filtering model (I_ConvCF). Using a convolution neural network to extract the nonlinear relationship characteristics of Historical interaction/non-interactive items as a low dimensional latent factor. The target item is regarded as another low dimensional latent factor, and their product is regarded as the feature of the target item. We demonstrate their superiority in personalized ranking tasks on two real data sets.
ManiGaze: a Dataset for Evaluating Remote Gaze Estimator in Object Manipulation Situations

Gaze estimation allows robots to better understand users and thus to more precisely meet their needs. In this paper, we are interested in gaze sensing for analyzing collaborative tasks and manipulation behaviors in human-robot interactions (HRI), which differs from screen gazing and other communicative HRI settings. Our goal is to study the accuracy that remote vision gaze estimators can provide, as they are a promising alternative to current accurate but intrusive wearable sensors. In this view, our contributions are: 1) we collected and make public a labeled dataset involving manipulation tasks and gazing behaviors in an HRI context; 2) we evaluate the performance of a state-of-the-art gaze estimation system on this dataset. Our results show a low default accuracy, which is improved by calibration, but that more research is needed if one wishes to distinguish gazing at one object amongst a dozen on a table.
Adaptive Square-Root Unscented Kalman Filter: Implementation of Exponential Forgetting Factor

This paper proposes a new form of adaptive square root unscented Kalman filter that implements an exponential forgetting factor to update the filter. It aims at estimating the states of nonlinear systems without a priori knowledge about the statistics of noises. The filter updates the estimation of covariances of noises with time, and the updated covariances are used to update the states of the system. The proposed approach is implemented to a servo-hydraulic system which states and measurements are affected by time varying noises with time-varying statistics. The obtained results along with the mean square errors of the estimation of states confirmed the performance and precision of the proposed filter.