Reviewerly/exHarmony huggingface dataset:
A hybrid feature descriptor with Jaya optimised least squares SVM for facial expression recognition

Facial expression recognition has been a long-standing problem in the field of computer vision. This paper proposes a new simple scheme for effective recognition of facial expressions based on a hybrid feature descriptor and an improved classifier. Inspired by the success of stationary wavelet transform in many computer vision tasks, stationary wavelet transform is first employed on the pre-processed face image. The pyramid of histograms of orientation gradient features is then computed from the low-frequency stationary wavelet transform coefficients to capture more prominent details from facial images. The key idea of this hybrid feature descriptor is to exploit both spatial and frequency domain features which at the same time are robust against illumination and noise. The relevant features are subsequently determined using linear discriminant analysis. A new least squares support vector machine parameter tuning strategy is proposed using a contemporary optimisation technique called Jaya optimisation for classification of facial expressions. Experimental evaluations are performed on Japanese female facial expression and the Extended Cohn–Kanade (CK+) datasets, and the results based on 5-fold stratified cross-validation test confirm the superiority of the proposed method over state-of-the-art approaches.
Cricket Video Events Recognition using HOG, LBP and Multi-class SVM

Abstract The world has witnessed a growth in multimedia data, especially video data over the past few years due to increased internet bandwidth and higher processing power of computers. Having a large number of video data also require techniques to store, summarize, index and information retrieval. More attention has been given in recent years to develop techniques which summarize, index and retrieve sports videos due to its commercial aspects. This paper proposes a framework which classifies a cricket video into one of the four events namely Bowled Out, Caught Behind, Catch Out and LBW Out. The framework uses training videos from each category of event and summarizes the videos into key frames. HOG and LBP features are computed for key frames and fused to form a single feature vector which will be labeled accordingly which represents a single event in video. Feature vector is given to a Multi-Class SVM which classifies the video into one of the four events. The experimental results show that the Precision of our technique is 77.23%, Recall is 77.86%, F-Measure is 77.55% and the Accuracy is 65.62%. The evaluation metrics of our technique are promising, because in the literature there is no other technique present so far for event detection &amp; classification in cricket videos.
Information Technology to the Rescue? Explaining the Acceptance of Emergency Response Information Systems by Firefighters

Improving the efficacy of emergency responses with digital means is receiving increasing attention. Currently, several innovative information technologies and systems are being developed to raise the situation awareness of first responders like firefighters. Among them, emergency response information systems (ERIS) appear to provide a particularly promising platform, which helps to gather, analyze, and share relevant information during emergencies. However, the conditions under which firefighters accept or reject such systems remain unclear. Existing theories explain the acceptance of information technologies only on a general level that does not consider the specific usage constraints existing in the firefighter domain. To fill this literature gap, we propose a detailed, domain-specific acceptance model with factors that explain the acceptance of ERIS by firefighters. It combines findings of the user satisfaction and the technology acceptance literature and was developed based on the input of 82 domain experts. An evaluation of the acceptance model in a survey with 212 firefighters from Germany indicates that it is effective in predicting a firefighter's intention to use an ERIS. The identified acceptance factors provide guidance for the design and evaluation of ERIS, enabling the so far mostly theoretical benefits of ERIS to be transferred into practical applications more effectively.
eIDAS Implementation Challenges: The Case of Estonia and the Netherlands

Solid eID (electronic identification) infrastructures form the backbone of today’s digital transformation. In June 2014, the European Commission adopted the eIDAS regulation (electronic identification and trust services for electronic transactions in the internal market) as a major initiative towards EU-wide eID interoperability; which receives massive attention in all EU member states in recent years. As a joint effort of Estonia and the Netherlands, this study provides a comparative case study on eIDAS implementation practices of the two countries. The aim was to analyze eIDAS implementation challenges of the two countries and to propose a variety of possible solutions to overcome them. During an action learning workshop in November 2019, key experts from Estonia and the Netherlands identified eIDAS implementation challenges and proposed possible solutions to the problems from the policy maker, the service provider and the user perspective. As a result, we identified five themes of common challenges: compliance issues, interpretation problems, different practices in member states, cooperation and collaboration barriers, and representation of legal persons. Proposed solutions do not only involve changes in the eIDAS regulation, but different actions to develop an eIDAS framework and to improve cross-border service provision - which has recently become an important topic among member states. Eventually, the study provides practical input to the ongoing eIDAS review process and can help member states to overcome eIDAS implementation challenges.
Stable Iterative Neural Network Training Algorithms Based on the Extreme Method

The article deals with the construction of neural network training algorithms using the extreme method. For the error propagation method to function, it is necessary to perform initial initialization and train of the neural network neural network when in the operational mode. As a method of training a neural network at the stage of network initialization, an extreme training method is implemented. Pseudo-conversions of matrices from the used recurrent algorithms based on the bordering method are also considered. These relations allow us to perform recurrent pseudo-circulation of matrices in the neural network training algorithm and thus implement the vectors of the input and output weights of the hidden layer neuron using the extreme training method.
Hybrid analytic hierarchy process-based quantitative satisfaction propagation in goal-oriented requirements engineering through sensitivity analysis

In the early phase of Requirements Engineering (RE), Goal-Oriented Requirements Engineering (GORE) has been found to be a valuable tool. GORE plays a vital role in requirements analysis such as alternative selection decision-making process. This is carried out to determine the practicability and effectiveness of alternative approaches to arriving at quality goals. Most GORE models handle alternative selection based on an extremely coarse-grained qualitative approach, making it impossible to distinguish two alternatives. Many proposals are based on quantitative alternative choices, yet they do not offer a clear decision-making judgement. We propose a fuzzy-based quantitative approach to perform goal analysis using inter-actor dependencies in the i* framework, thereby addressing the ambiguity problems that arise in qualitative analysis. The goal analysis in the i* framework was performed by propagating the impact and weight values throughout the entire hierarchy of an actor. In this article, the Analytic Hierarchy Process (AHP) is adapted with GORE to discuss the evaluation of alternative strategies of the i* goal model of interdependent actors. By using a quantitative requirement prioritisation method such as the AHP, weights of importance are assigned to softgoals to obtain a multi-objective optimised function. The proposed hybrid method measures the degree of contribution of alternatives to the fulfillment of top softgoals. The integration of AHP with goal anlaysis helps to measure alternative options against each other based on the requirements problem. This approach also includes the sensitivity analysis, which helps to check the system behaviour for change in input parameter. Hence, it facilitates decision-making for the benefit of the requirements’ analyst. To explain the proposed solution, this paper considers a telemedicine system case study from the existing literature.
Comparison of SARIMA and SVM model for rainfall forecasting in Bogor city, Indonesia

Abstract Heavy rainfall and flash floods are still significant for several cities in Indonesia, especially Bogor City. Rainfall prediction is essential to develop early warning systems for early detection of massive and flash floods. The accurate forecasting method is needed to produce accurate predictions of rainfall. Several methods have been developed to get a precise rainfall forecast, such as the classical approach, such as seasonal autoregressive integrated moving average (SARIMA), and machine learning approach, for example, gradient boosting and support vector machine (SVM). In this study, we introduce for forecasting rainfall at Bogor city, Indonesia. The SVM provides an accurate result with a small MAPE.
A Hybrid Approach to Data Pre-processing Methods

This is an era of big data, as data is growing exponentially and resources are running out of infrastructure, so it is required to accommodate all the data that gets generated. We collect data in enormous amounts to derive meaningful conclusions, perform effective data analytics and improve decision making. As we don't have enough infrastructures to support data storage for huge volumes, it is needed to clean the data in compulsion. It is a mandatory to carry out a step before doing anything with the data. We call it pre-processing of data and this is carried out in various steps. Pre-processing includes data cleaning, data integration, data filtering, and data transformation and so on. As such preprocessing is not limited to the number of steps or a number of methods or definitive methods. We must innovatively preprocess the data before it is being consumed for data analytics. It has become a responsibility for every data analyst or big data researcher to handpick data for his or her analytics. Considering all these techniques in mind we are proposing a hybrid technique to leverage various algorithms available to pre-process our data along with minor modifications such as at the run time, choosing an algorithm or technique wisely based on the data that we have.
A Framework for Banana Plantation Growth Using Blockchain Technology

Bananas are stacked with a rich source of nutrients and minerals that drive it to be one of the major staple crop consumption in India. The integrity and quality of bananas during its growth are a major concern for society. All the stages of the growth of the banana fruit are vital. Protection of the fruit starting from the planting stage until the marketing stage is crucial, since, the quality of banana fruit depends on how well it is supervised, during all the stages of its growth. As a solution, we propose a Banana Agro System, and the system monitors the growth of the banana fruit and its supply chain. Blockchain technology is used to store the attributes of all the growth stages of banana. Blockchain provides a secure method of storing and overseeing the data, which encourages the development and utilization of data-driven transformation for the smart farming systems.
Ontology-driven Event Type Classification in Images

Event classification can add valuable information for semantic search and the increasingly important topic of fact validation in news. So far, only few approaches address image classification for newsworthy event types such as natural disasters, sports events, or elections. Previous work distinguishes only between a limited number of event types and relies on rather small datasets for training. In this paper, we present a novel ontology-driven approach for the classification of event types in images. We leverage a large number of real-world news events to pursue two objectives: First, we create an ontology based on Wikidata comprising the majority of event types. Second, we introduce a novel large-scale dataset that was acquired through Web crawling. Several baselines are proposed including an ontology-driven learning approach that aims to exploit structured information of a knowledge graph to learn relevant event relations using deep neural networks. Experimental results on existing as well as novel benchmark datasets demonstrate the superiority of the proposed ontology-driven approach.
RefiNet: 3D Human Pose Refinement with Depth Maps

Human Pose Estimation is a fundamental task for many applications in the Computer Vision community and it has been widely investigated in the 2D domain, i.e. intensity images.Therefore, most of the available methods for this task are mainly based on 2D Convolutional Neural Networks and huge manuallyannotated RGB datasets, achieving stunning results.In this paper, we propose RefiNet, a multi-stage framework that regresses an extremely-precise 3D human pose estimation from a given 2D pose and a depth map.The framework consists of three different modules, each one specialized in a particular refinement and data representation, i.e. depth patches, 3D skeleton and point clouds.Moreover, we present a new dataset, called Baracca, acquired with RGB, depth and thermal cameras and specifically created for the automotive context.Experimental results confirm the quality of the refinement procedure that largely improves the human pose estimations of off-the-shelf 2D methods.
Representing EHRs with Temporal Tree and Sequential Pattern Mining for Similarity Computing

The ability to rapidly identify at scale patients that are similar based on their electronic health records (EHRs) is fundamental for a number of clinical informatics applications, such as clinical decision support, cohort selection, treatment recommendation, among others. The effective representation of EHR data is paramount to effective computational similarity methods. Such representation would take into account the complex properties of EHR data including temporality and multivariaty. Of critical importance for this is the modelling of: (i) compound information – multiple medical events for a patient occur in order and may be at the same time, (ii) clinical patterns – frequent common sequential patterns that are associated with specific sequences of clinical events. To model these, in this paper we exploit the recently proposed Temporal Tree technique to capture compound information and we further apply sequential pattern mining (SPM) with gap constraint to discover more complex clinical patterns. The effectiveness of the proposed EHR representation method is evaluated using a real EHR dataset, MIMIC III, based on two task types within an Intensive Care Unit setting: (i) similar patients retrieval (ii) sepsis prediction and mortality prediction. The empirical results show that representation of EHRs with Temporal Tree and SPM, used in conjunction with traditional similarity measures or more complex embedding methods, delivers significant improvements in effectiveness in the considered tasks.
Comparative Study of Layout Analysis of Tabulated Historical Documents

Nowadays, the field of multimedia retrieval system has earned a lot of attention as it helps retrieve information more efficiently and accelerates daily tasks. Within this context, image processing techniques such as layout analysis and word recognition play an important role in transcribing content in printed or handwritten documents into digital data that can be further processed. This transcription procedure is called document digitization. This work stems from an industrial need, namely, a Swedish company (ArkivDigital AB) has scanned more than 80 million pages of Swedish historical documents from all over the country and there is a high demand to transcribe the contents into digital data. Such process starts by figuring out text location which, seen from another angle, is merely table layout analysis. In this study, the aim is to reveal the most effective solution to extract document layout w.r.t Swedish handwritten historical documents that are featured by their tabular forms. In short, outcome of public tools (i.e., Breuel's OCRopus method), traditional image processing techniques (e.g., Hessian/Gabor filters, Hough transform, Histograms of oriented gradients -HOG- features), machine learning techniques (e.g., support vector machines, transfer learning) are studied and compared. Results show that the existing OCR tool cannot carry layout analysis task on our Swedish historical handwritten documents. Traditional image processing techniques are mildly capable of extracting the general table layout in these documents, but the accuracy is enhanced by introducing machine learning techniques. The best performing approach will be used in our future document mining research to allow for the development of scalable resource-efficient systems for big data analytics.
Quality assessment of machine translation output

Despite fast development of machine translation, the output quality is less than acceptable in certain language pairs. The aim of this paper is to determine the types of errors in machine translation output that cause comprehension problems to potential readers. The study is based on a reading task experiment using eye tracking and a retrospective survey as a complementary method to add more value to the research as eye tracking as a method is considered to be problematic and challenging (O’BRIEN, 2009; ALVES et al., 2009). The cognitive evaluation approach is used in an eye tracking experiment to determine the complexity of the errors in the English–Lithuanian language pair from easiest to hardest as seen by the readers of a machine-translated text. The tested parameters – gaze time and fixation count – demonstrate that a different amount of cognitive effort is required to process different types of errors in machine-translated texts. The current work aims at contributing to other research in the Translation Studies field by providing the analysis of error assessment of machine translation output.
Viewpoint Transform Matching model for person re-identification

Person re-identification is a challenging problem that aims at matching persons across multiple non-overlapping cameras. Previous works on person re-identification mainly focus on solving the problems caused by pose variations, backgrounds, and occlusion while ignoring the viewpoint variations. To address the viewpoint problem, we propose a Viewpoint Transform Matching (VTM) model, which reduces the inference of viewpoints by feature-level viewpoint transformation. Overall, we design a framework by using viewpoint specific branches to separately extract the representation of different viewpoints. Specifically, we first select the branch for an input image, according to its viewpoint information. In the branch, we transform the feature to other viewpoints by establishing a mutually transformable connection between the features of different viewpoints. To suppress the interference among viewpoints, we propose a viewpoint transform classifier module to independently train a classifier for each viewpoint. To improve the effectiveness of the transform, we propose a viewpoint transform loss to guarantee the consistency of the original features and the transformed features. Experiments conducted on Market-1501, DukeMTMC-reID and CUHK03 show the effectiveness of the proposed method.
Improving accuracy of gaze‐control tools: Design recommendations for optimum position, sizes, and spacing of interactive objects

Abstract To investigate a suitable design of interactive objects in the fixation‐triggered eye‐control interface, this study conducted two ergonomic experiments based on the location of an object, area of the object, and distance between adjacent objects. Experiment 1 investigated the priority distribution of different regions and recommended values of object sizes in different regions. The results showed that middle‐middle (MM) area is the optimal area, while middle‐left (ML) area, middle‐right (MR) area, and upper‐left (UL) area are suboptimal areas, and the remaining are general areas. The recommended minimum sizes for the optimal area, suboptimal area, and general area are 2.636° (200 px on the screen of a 1920 px × 1080 px 14 inch monitor), 2.978° (225 px on the screen of a 1920 px × 1080 px 14 inch monitor) and 3.322° (250 px on the screen of a 1920 px × 1080 px 14‐inch monitor), respectively. Experiment 2 conducted ergonomic experiments of different distances between adjacent objects in horizontal and vertical distributions. The results showed that the appropriate setting of the distribution and size of the objects showed no significant difference between different levels of spacing on the trigger accuracy rate and task completion time. Further, 1.032° (75 px on the screen of a 1920 px × 1080 px 14‐inch monitor) could be recommended as the most suitable spacing. According to the conclusions of Experiments 1 and 2, an optimized eye‐control interface and the NASA‐TLX scale were used to verify the validity and scientificity of the experimental results. The conclusions can be employed to improve the interaction efficiency of the eye‐control system and user experience and expand the application field of eye control.
RETRACTED: Simulation of English translation text filtering based on machine learning and embedded system

The translation is generally assumed to distinguish it from the original language text in the same area. Therefore, say these constitute a unique multilingual translation, commonly referred to as translation; translation is also affected by the source language and exhibits different characteristics according to the source language. Therefore, claim that these variants constitute the same target language translated into additional "dialog." Using Machine Learning English and embedded technologies investigated the differences between the general characteristics of different translation sources and language translation. There is little research corpus between the complicated relationship between translation and the original text and translation itself for very different language types. May does not translate enough knowledge of the subject areas covered. There are many guidelines to help authors clearly express their ideas to promote translation; scientists and engineers find it difficult to apply the procedures required for a high degree of speech recognition. For financial reasons, non-essential text may be edited publication. In this case, as described in the article, the author can take some precautions to express terms of intelligibility. Machine Learning and embedded systems determine the importance of various features for data collection and the importance of its characteristics compared with previously reported studies in English. Also, the method allows us to add the grammatical function of translation studies rarely used. The results show that, even if only based on five features, the full translation can be reliably separated from the non-translated region.