Reviewerly/exHarmony huggingface dataset:
Estimation of disparity maps through an evolutionary algorithm and global image features as descriptors

In several approaches that include analysis processes – the most well-known being object tracking, video understanding, automatic surveillance systems, and image reconstruction – there are basic tasks to be performed. One of these tasks is related to how to select an image feature window in a frame and then compute its displacement in another frame. In the literature, the last two tasks represent an open research topic because (1) estimation of the similitude for a region window involves a set of invariants that are scene-dependent; (2) a general method for detecting the best-fitting region criterion to compute the displacement is dependent on the similarity criterion and numerical approaches for estimating the displacement; and (3) the type of conditions must be warranted so that an image feature has a high probability of estimating the displacement and numerically reaching a convergence state. In this paper, we propose a framework to estimate the displacement of an image feature from a reference image to another image. The proposal uses a generalization of the optimization concept, that is, a random search process in the dissimilarity metric space. This approach is carried out in a discrete space by mapping the variable domain to be estimated to a symbolic space with a set of operators, where a random search method is described through a uniform sampling process and genetic operators. The approach searches for the best suboptimal solution of the locality under a predefined metric criterion, avoiding divergence for the worst suboptimal solutions. The proposal is based on the formalization of the Lucas and Kanade approach. It considers as a metric-space solution the proposal of the Shi and Tomasi approach, but instead of a Taylor series expansion and step-descendant approach to solve the system, an evolutionary algorithm is used. The reference approach is well accepted as one of the most important approaches in motion displacement. To test our approach, we take the task of building a disparity map for 3D geometry extraction given the number of times the displacement computation is performed (once for each pixel). Finally, the results demonstrate that the evolutionary approach increases the repeatability and robustness of the distance estimation.
Fall Detection in Clinical Notes using Language Models and Token Classifier

Electronic health records (EHR) are a key source of information to identify adverse events in patients. The largest category of adverse events in hospitals is fall incidents. The identification of such incidents guide to a better comprehension of the event and enhance the quality of patient health care. In this initial work, we compare the performance of SentenceClassifier (StC) against the Token-Classifier (TkC) with state-ofthe-art recurrent neural networks (RNN) to detect fall incidents in progress notes. Our experiments show that the use of deeplearning algorithms as token-classifier outperforms text-classifier. It improves fall identification using StC from 65% to 92% with TkC (F-Measure). Additionally, the token classifier is able to explain which words are most important in positive detection.
A Comparative Analysis of Quality Page Object and Screenplay Design Pattern on Web-based Automation Testing

In software engineering, a design pattern is a standard solution for solving problems that often occur in the design, including developing automation testing. Today, page object and screenplay design patterns are often used by quality assurance engineers to develop the web-based automation testing design. The application of the design pattern for automation testing cannot be arbitrary because selecting the right design pattern should be able to support the optimization of automation testing. Therefore, in this study, a comparative analysis of the two design patterns was proposed using the QMOOD (Quality Model of Object-Oriented Design) model. The QMOOD model has the characteristics of four levels and three mappings. The main assessment of the QMOOD model shows in the first level, i.e., Functionality, Reusability, Flexibility, Understandability, Effectiveness, and Extendibility. While the three mappings contained in QMOOD serve as a link between levels so that inter-levels on QMOOD can be integrated. By applying the QMOOD model as a parameter to measure the quality of the page object design and screenplay pattern, we know that the two design patterns have their own superiority. This research can become a reference or reference source for quality assurance engineers in optimizing automation testing by knowing the quality values of the page object design and screenplay design.
Black-Box Saliency Map Generation Using Bayesian Optimisation

Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches to saliency map generation are available, but most require access to model parameters. This work proposes an approach for saliency map generation for black-box models, where no access to model parameters is available, using a Bayesian optimisation sampling method. The approach aims to find the global salient image region responsible for a particular (black-box) model's prediction. This is achieved by a sampling-based approach to model perturbations that seeks to localise salient regions of an image to the black-box model. Results show that the proposed approach to saliency map generation outperforms grid-based perturbation approaches, and performs similarly to gradient-based approaches which require access to model parameters.
Introduction and Correction of Bengali-Hindi Noise in Large Word Vocabulary using RNN

Word correction or Spell Checking is essential for fundamental applications such as text editors and social media based chat platforms. A lot of works have been reported in English, Chinese, and other European languages with available standard datasets. However, there is a lack of sizeable standard datasets with Indic languages, which is one of the causes not to observe enough contribution in this field. As the possibility of error in a word for an Indic script is different than that of English due to the presence of diacritics(matra), the synthetic noise introduction should be very specific. In the work, we focus on creating two large standard noisy datasets using the probabilistic rule on alphabets for Hindi and Bengali languages. Each dataset contains a vocabulary size of 150000 words. We tested the datasets using two recurrent neural networks (RNN) based models which provide 80% accuracy on both the datasets.
Driving Value Creation Through Proper Design of Goal Realization Frameworks

Organizations have become increasingly aware that customer value creation is critical to remain relevant and to stay in business. Hence, most organizations have incorporated these elements into their overall mission statement and their organization-wide strategy. Yet many of them struggle to create an organization where employees focus effectively on executing the formulated strategy and on delivering and continuously improving customer value. This article describes how clever and tailored design of goal realization in organizations can foster value creation and embed it in the daily work routines of employees. The key to success is to adopt goal realization in such a way that it creates an environment where employees feel safe, empowered and confident to go after aspirational, value-based goals while creating alignment and connecting employees to the overall goals and objectives of the organization. This is illustrated in a practical manner by presenting the framework as adopted by Google.
Pose Estimation on 3-D Models Using ConvNets

Pose estimation of various human activities has seen tremendous progress over the past few decades due to the advancement of computer vision technology. However, the current status of pose estimation is quite stagnated when seen in context to human behaviour analysis by using the pose. In this paper, pose estimation has made significant advancement in terms of dexterity and ingenuity, which is sufficient for human pose estimation. This work includes a large dataset of various human activities. The collected images cover a wide range of human activities that include various activities in various postures in different angles of viewpoints. We have annotated all images for various joint locations such as head, elbow, hands, and eyes via CSV data file. The pose analysis of this work will provide various ongoing research insights about the use of computer vision and Convolution neural networks (ConvNets) for practical purposes and would further instigate this technique as an alternative for VFX in the film industry.
Performance Analysis of Classification Techniques for Car Data Set Analysis

Data mining which is also known as data or knowledge discovery is an important technique to analyze data and discover important information from different data bases. Data mining consists of various classification and prediction algorithms. Classification is an important technique with a broad area of application. To analyze different algorithms of classification is a complex task. In this paper Waikato Environment for Knowledge Analysis (WEKA) tool is used for classification of an automobile data set. Here, 66 different classifiers are applied for this data set. The aim of this work is to compare and analyze different classification algorithms on WEKA tool and find out which classification algorithm is most suitable to work with automobile data set.
A Multi-Information Fusion Correlation Filters Tracker

In recent years, trackers based on correlation filters have attracted more and more attention due to the impressive tracking accuracy and real-time performance. However, in real scenarios, the tracking results are often been interfered with by the occlusion, illumination variation, appearance variation and background clutter. In order to find a tracker with better tracking performances, this paper proposed a multi-information fusion correlation filter tracker, which uses channel and spatial reliabilities and time regularization information on samples for filter training, and which not only extends the target search areas but also has a stronger ability to track the targets with significant appearance variations. Thus, results from extensive experiments conducted on OTB100, VOT2016, TC128, and UAV123 data sets show that our tracker with only directional gradient histogram (HOG) and color name (CN) features, performs favorably against the state-of-the-art trackers in terms of tracking precision, tracking success rate, tracking accuracy, and A-R rank.
A Synthetic Video Dataset Generation Toolbox for Surveillance Video Synopsis Applications

Video synopsis technique aims to condense long duration videos into its compact representation for efficient browsing and retrieval of surveillance videos. The synopsis videos depend on the results of object detection and tracking in input video. However, there is a lack of publicly available commonly used datasets that are properly annotated for training trackers and analyzing the performance of various approaches in video synopsis. This paper introduces an interactive toolbox that allows users to generate synthetic videos with user-defined parameters and related tube information, eliminating the steps of detection and tracking. The proposed toolbox enables users to simulate general and specific scenarios of interest, which are expected to be observed in surveillance videos. We present experiments that show the usability and effectiveness of using this toolbox in evaluating different video synopsis methods.
A revised open source usability defect classification taxonomy

: Reporting usability defects is a critical part of improving software. Accurately classifying these reported usability defects is critical for reporting, understanding, triaging, prioritizing and ultimately fixing such defects. However, existing usability defect classification taxonomies have several limitations when used for open source software (OSS) development. This includes incomplete coverage of usability defect problems, unclear criticality of defects, lack of formal usability training of most OSS defect reporters and developers, and inconsistent terminology and descriptions. : To address this gap, as part of our wider usability defect reporting research, we have developed a new usability defect taxonomy specifically designed for use on OSS projects. : We used Usability Problem Taxonomy (UPT) to classify 377 usability defect reports from Mozilla Thunderbird, Firefox for Android, and the Eclipse Platform. At the same time, we also used the card-sorting technique to group defects that could not be classified using UPT. We looked for commonalities and similarities to further group the defects within each category as well as across categories. : We constructed a new taxonomy for classifying OSS usability defects, called Open Source Usability Defect Classification (OSUDC). OSUDC was developed by incorporating software engineering and usability engineering needs to make it feasible to be used in open source software development. The use of the taxonomy has been validated on five real cases of usability defects. However, evaluation results using the OSUDC were only moderately successful. : The OSUDC serves as a common vocabulary to describe and classify usability defects with respect to graphical user interface issues. It may help software developers to better understand usability defects and prioritize them accordingly. For researchers, the OSUDC will be helpful when investigating both trends of usability defect types and understanding the root cause of usability defect problems.
Secure <i>k</i>-ish Nearest Neighbors Classifier

Abstract The k -nearest neighbors ( k NN) classifier predicts a class of a query, q , by taking the majority class of its k neighbors in an existing (already classified) database, S . In secure k NN, q and S are owned by two different parties and q is classified without sharing data. In this work we present a classifier based on k NN, that is more efficient to implement with homomorphic encryption (HE). The efficiency of our classifier comes from a relaxation we make to consider κ nearest neighbors for κ ≈k with probability that increases as the statistical distance between Gaussian and the distribution of the distances from q to S decreases. We call our classifier k -ish Nearest Neighbors ( k -ish NN). For the implementation we introduce double-blinded coin-toss where the bias and output of the toss are encrypted. We use it to approximate the average and variance of the distances from q to S in a scalable circuit whose depth is independent of | S |. We believe these to be of independent interest. We implemented our classifier in an open source library based on HElib and tested it on a breast tumor database. Our classifier has accuracy and running time comparable to current state of the art (non-HE) MPC solution that have better running time but worse communication complexity. It also has communication complexity similar to naive HE implementation that have worse running time.
Innovation in Communication and Media Studies: Reflections from South African Academics

This article discusses the possibility of a discipline of communication and media studies that is innovative, pluralistic and open in ways that conduce to development. Based on a set of in-depth interviews with a select group of South African communication and media studies scholars, the article discusses critically how, and if, communication and media studies as a field is innovative. Innovation here talks to a discipline that is imaginatively open to a myriad of different, diverse and divergent contributions relevant to the human endeavour of understanding the world in ways that better humanity. In doing this, the authors critically explore how the discipline is perceived variously by the scholars interviewed as enabling, encompassing and embodying innovation in research, teaching, curricula, theory, methodology, resourcing, and community outreach. As such the article addresses a span of issues that either support or inhibit innovation.
High Temperature Deformation Field Measurement Using 3D Digital Image Correlation Method

A full-field, three-dimensional and non-contact deformation field measurement method under high temperature environment based on 3D digital image correlation (3D-DIC) is introduced. In order to reduce the impact of high temperature radiation on the image quality, a band-pass filter is placed in front of the camera lens. The two cameras simultaneously take pictures of the object before and after deformation, and use 3D-DIC to measure the three-dimensional deformation field of the object surface. The high temperature deformation field measurement test shows that 3D-DIC can accurately and conveniently measure the deformation field of an object under high temperature environment.
Privacy-Preserving Gesture Recognition with Explainable Type-2 Fuzzy Logic Based Systems

Smart homes are a growing market in need of privacy preserving sensors paired with explainable, interpretable and reliable control systems. The recent boom in Artificial Intelligence (AI) has seen an ever-growing persistence to incorporate it in all spheres of human life including the household. This growth in AI has been met with reciprocal concern for the privacy impacts and reluctance to introduce sensors, such as cameras, into homes. This concern has led to research of sensors not traditionally found in households, mainly short range radar. There has been also increasing awareness of AI transparency and explainability. Traditional AI black box models are not trusted, despite boasting high accuracy scores, due to the inability to understand what the decisions were based on. Interval Type-2 Fuzzy Logic offers a powerful alternative, achieving close to black box levels of performance while remaining completely interpretable. This paper presents a privacy preserving short range radar sensor coupled with an Explainable AI system employing a Big Bang Big Crunch (BB-BC) Interval Type-2 Fuzzy Logic System (FLS) to classify gestures performed in an indoor environment.
DrunaliaCap: Image Captioning for Drug-Related Paraphernalia With Deep Learning

Image captioning is a process of generating textual descriptions of images.In recent years, research on publicly available large-scale datasets and deep learning-based algorithms has promoted the development of this field.However, little research has been conducted on captioning images of drug-related paraphernalia that, despite being an important topic for both drug prevention and police enforcement, is not covered by existing image captioning studies.In this paper, we propose DrunaliaCap-a deep learning-based system for autogenerating both ''factual'' (what is in the image) and ''functional'' (the usage of each paraphernalia during drug-taking) descriptions of images of drug-related paraphernalia.We constructed a new dataset containing 20 categories of drug-related items and trained deep learning-based models for the proposed system.We further proposed a method to evaluate and optimize the generation of captions to prevent them from missing important knowledge.Experiments were conducted to validate the performance of the newly proposed dataset and method.We analyzed the experimental results and discussed the significance, limitations, and potential applications of our work.
Modeling the Paraphrase Detection Task over a Heterogeneous Graph Network with Data Augmentation

Paraphrase detection is a Natural-Language Processing (NLP) task that aims at automatically identifying whether two sentences convey the same meaning (even with different words). For the Portuguese language, most of the works model this task as a machine-learning solution, extracting features and training a classifier. In this paper, following a different line, we explore a graph structure representation and model the paraphrase identification task over a heterogeneous network. We also adopt a back-translation strategy for data augmentation to balance the dataset we use. Our approach, although simple, outperforms the best results reported for the paraphrase detection task in Portuguese, showing that graph structures may capture better the semantic relatedness among sentences.
Secure and Intelligent Energy Data Management Scheme for Smart IoT Devices

The renewable energy plays an increasingly important role in many fields such as lighting, automobile, and electric power. In order to make full use of the renewable energy, various smart Internet of Thing (IoT) devices are deployed. However, in the field of energy management, the two-way mismatch between the demand and the supply of the renewable energy will greatly affect the efficiency of the renewable energy. In addition, the security threat of the energy data and the privacy leakage of the user may hinder the further development of smart IoT devices. Therefore, how to achieve consistency and balance between the demand and the renewable energy supply and how to guarantee the security and privacy of smart IoT devices become the key problems of the energy-efficient smart environment. In this paper, a secure and intelligent energy data management scheme for smart IoT devices is proposed. It is worth noting that, with the help of artificial intelligence (AI) technologies and secure cryptography primitives, the proposed scheme realizes high-efficient and secure energy utilization in a smart environment. Specifically, the proposed scheme aims at improving the efficiency of the energy utilization in the multidimensions of a smart environment. In order to realize the fine-grain energy management of smart IoT devices, strategies of three different dimensions are considered and realized in the proposed scheme. Moreover, technologies in AI are applied and integrated into the energy management scheme. The analysis shows that the proposed scheme can make full use of the renewable energy in smart IoT devices.
A new colour image copyright protection approach using evolution-based dual watermarking

Multiple watermarking techniques for images is receiving more attention in recent years for its wide variety of applications in different fields such as piracy of digital data, and copyrights protection. Current approaches rely on adding many watermarks in different bands or channels utilising scaling factor, and embedding locations that are mainly defined by experts. This brought many challenges in achieving equilibrium between security, robustness, and quality. Aiming to fill the gap of determining stationary scaling factor and embedding locations, the work proposed in this paper adapts genetic algorithm to find the optimality of these issues; that will enhance both of watermarking capacity and imperceptibility in colour images. To increase security, the suggested model encodes the watermarks using 2-D Walsh transform (WHT). Furthermore, Singular Value Decomposition (SVD) as a dimension reduction tool is utilised to factorise Wavelet coefficients to determine the most salient embedding locations. Experimental results show that the proposed model is more robust against common image manipulation attacks in terms of PSNR and NCC.