Reviewerly/exHarmony huggingface dataset:
Multifold Classification for Human Action Recognition

Identification of human actions from the video samples is suffered from various challenges like cluttered background, brightness and motion variation. This paper uses the multifold classification for this purpose. The UT interaction video data dataset is used to validate the work. The UT interaction dataset consists of 20 sequence videos with the six different interaction categories of the human-human interaction. The input video is segmented into the frames using a python script. The global features are evaluated with the Histogram of Gradient (HOG) and Histogram of Optical Flow (HOF). The dimension of the HOF and HOG feature vector is reduced by the Principle Component Analysis (PCA) method. The extracted features are fed to the SVM classifier which provids two different classes of six human-human interaction action. Class 1 contains the fighting interaction like Kicking, Punching and Pushing and Class 2 contains the normal interaction like Pointing, Hugging and Hand Shaking. At the second level, motion features Speed up Robust Features (SURF) are extracted. The detected features are represented as the visual words by using the Bag of Visual Word (BoVW) approach. The output of SURF-BoVW features is fed to the Neural Network classifier, which classifies the human-human interaction. The proposed method provided efficient results than the single-level classier like SVM and KNN. The proposed method provides the accuracy upto 99 % for the UT interaction dataset.
Towards an Adaptive Blockchain for Internet of Vehicles

Cybersecurity is an essential requirement to enable the deployment of Cooperative-Intelligent Transportation Systems (C-ITS). In vehicular networks, Blockchain is a solution often considered to secure exchanges between nodes. In fact, this technology could enable each vehicle to check by itself the content of each message it receives, without the need to trust its neighbors. Nevertheless, Blockchain was initially designed for wired networks. Therefore, its deployment in vehicular networks would imply considering the features of this environment: high mobility, variable C-ITS applications’ requirements, limited vehicles capabilities, etc. That is why, in this paper, after comparing existing solutions, we introduce a new adaptive Blockchain framework, designed to address the vehicular environment challenges. We also describe a Blockchain architecture that could support this framework and present future directions that will contribute to its implementation and evaluation.
(Deep) induction rules for GADTs

Deep data types are those that are constructed from other data types, including, possibly, themselves. In this case, they are said to be truly nested. Deep induction is an extension of structural induction that traverses all of the structure in a deep data type, propagating predicates on its primitive data throughout the entire structure. Deep induction can be used to prove properties of nested types, including truly nested types, that cannot be proved via structural induction. In this paper we show how to extend deep induction to GADTs that are not truly nested GADTs. This opens the way to incorporating automatic generation of (deep) induction rules for them into proof assistants. We also show that the techniques developed in this paper do not suffice for extending deep induction to truly nested GADTs, so more sophisticated techniques are needed to derive deep induction rules for them.
GAMMA SPECTROMETRY AND NEUTRON ACTIVATION ANALYSIS OF TOOT OILFIELD WELLS OF DATTA FORMATION, UPPER INDUS BASIN, DISTRICT ATTOCK, PUNJAB, PAKISTAN

Present study was aimed to investigate trace elemental composition and possible elemental correlation with depth of some Pakistani wells. Exploratory oil wells of Toot oilfield from Potwar region of upper Indus basin were selected for this purpose. Samples of well cuttings and soil sections were collected with the assistance of Oil and Gas Development Company Limited. Elemental analyses were performed using instrumental neutron activation analysis. This study is the first of its kind to report detailed elemental profile of Toot oilfield, Attock, Punjab, Pakistan, via instrumental neutron activation analysis (INAA). Source formation of Toot oilfield is characterized by Jurassic Datta formation. Around 19-26 elements were identified by INAA. Major, minor and trace elemental concentrations varied with depths along the sampling lines. Data analysis was performed by the application of principal component analysis (PCA). PCA was useful in differentiating between surface and depth samples. Elemental volumes of different wells were established through bi-plots. In extension, naturally occurring radioactive materials and technologically enhanced naturally occurring radioactive materials were also determined for the wells of Toot oilfield. Annual dose rates and activity levels were calculated in oil well formations. Measurements of annual dose rates fall within safe limits, indicating anodyne atmosphere. Contamination indices, such as enrichment factor with respect to Al, geo accumulation index (Igeo), pollution index and integrated pollution index, were also determined. Cr and Zn were found to cross the safe limits, which may be due to the local industrial and anthropogenic activities.
Graph Data Mining in Recommender Systems

With the rapid development of e-commerce, massive data is generated from various e-commerce platforms. Most of the generated data can be represented in the forms of graph, which is capable to demonstrate the complicated relations among various entities, for example, graphs describe the interactions history between users and items. It is critical for the platforms to mine graph data to formulate recommendation strategy to gain more profits. For instance, in a user-item interaction graph, we can utilize graph data mining techniques to capture users’ behavioral patterns to make personalized recommendation strategies. Graph data mining in recommendation is currently a research topic attracts more and more attentions from industry and academic fields. In this half-day tutorial, we will present some key graph data mining methods and its applications in recommendation. We hope to find out the directions for the future work and that more theoretical models can be applied under real-world scenarios.
Discrete versus Ordinal Time-Continuous Believability Assessment

What is believability? And how do we assess it? These questions remain a challenge in human-computer interaction and games research. When assessing the believability of agents, researchers opt for an overall view of believability reminiscent of the Turing test. Current evaluation approaches have proven to be diverse and, thus, have yet to establish a framework. In this paper, we propose treating believability as a time-continuous phenomenon. We have conducted a study in which participants play a one-versus-one shooter game and annotate the character&#x0027;s believability. They face two different opponents which present different behaviours. In this novel process, these annotations are done moment-to-moment using two different annotation schemes: BTrace and RankTrace. This is followed by the user&#x0027;s believability preference between the two playthroughs, effectively allowing us to compare the two annotation tools and time-continuous assessment with discrete assessment. Results suggest that a binary annotation tool could be more intuitive to use than its continuous counterpart and provides more information on context. We conclude that this method may offer a necessary addition to current assessment techniques.
Learning Platforms in the Context of the Digitization of Education: A Strong Methodological Innovation. The Experience of Latvia

Abstract The modernization of the education system, the digitalization of the educational environment and learning management systems (LMS), where one of the solutions is learning platforms, are the most urgent directions today’s pedagogical work is taking to reap the benefits of the digital environment. Education quality can be improved in different ways: by changing the content of learning, forms of learning, learning methods and teaching aids; promoting the use of learning platforms in schools; introducing programming and robotics; using learning management systems and other systems. Technologies and digital solutions are transforming the educational landscape in technology-enhanced learning environments. On one hand, there are many possible solutions that provide technology-enhanced learning; while on the other, there is a need to transform educational processes, to transform competence in teaching, to analyze learning outcomes so that technology-enhanced environments can support knowledge construction. The authors of this paper analyze the results of research on learning platforms, in which several research methods were used: systematic literature analyses; development of learning platform evaluation tools; analyses of learning platforms; and surveys on teachers’ attitudes to learning platforms. Altogether 705 teachers expressed their opinion on using learning platforms as a tool for enhancing knowledge construction, providing feedback and analyzing students’ learning results. In this paper, the authors will discuss the results of analyses on nine learning platforms developed in Latvia, conducted using an evaluation tool with 22 criteria and 43 sub-criteria.
Automatic Detection Metastasis in Breast Histopathological Images Based on Ensemble Learning and Color Adjustment

Breast cancer is more common neoplasm among women. The cure of the disease depends on early identification and treatment of the tumor to avoid advanced stages such as metastasis of the initial lesion. Recently, studies showed that computational methods could help specialists in this process and even provide advantages over the traditional analysis method. Thus, this work proposes a tumor cell detection method based on an ensemble of convolutional neural networks (CNN) trained with normalized images using different color adjustment techniques. Analyzes of eight different color spaces and their channels, and two color normalization methods were performed to reduce the effects caused by color variation of histopathological images on the generalization of predictive models. The proposed approach evaluated six CNN architectures with color adjustment methods to define which ones improve and preserve important characteristics for the classification task. The ensemble that constitutes the proposed method comprises three models of the VGG19 architecture trained in images generated through from the color space HSV, from the color channel RED of the RGB images, and the Reinhard normalization method, respectively. This approach was evaluated using a public image.
Real-time Multi Pose Trajectory Tracking based on OpenPose Keypoints

Currently, there exist several open-source computer vision libraries designed for human pose estimation from photos and videos. They are mainly focused on the possibility of detecting individuals in the image and returning their skeleton determinants. An often overlooked or underdeveloped functionality is tracking the trajectory of the detected people, which presents a serious problem in the process of design automated video surveillance systems. In this work, the author attempts to develop an algorithm for tracking multiple human poses in real-time, based on simple decision filters. The developed solution is designed to work with keypoints obtained from a selected open-source human poses recognition library. The author reveals details related to the method of processing and analysing obtained keypoints, describes the concept of decision filters and presents the results of the software implementation.
Quantum Algorithm for Method of Moment in Electromagnetic Computation

We propose a quantum algorithm for method of moments (MOM) that is able to efficaciously calculate the relative surface current density and electromagnetic radar cross section (RCS) problem for electrically large size targets. This quantum algorithm can be implemented by the dense Hamiltonian simulation technology and the method of solving the inverse of the dense matrix. We put forward a feasible algorithm for the dense Hamiltonian simulation where a novel tree access structure is applied, and perform the Fourier approach to approximate to the inverse of the dense matrix with the result of gate complexity.
An Innovative Method for Automatic American Sign Language Interpretation using Machine Learning and Leap Motion Controller

Millions of people globally use some form of sign language in their everyday lives. There is a need for a method of gesture recognition that is as easy to use and ubiquitous as voice recognition is today. In this paper we explore a way to translate from sign language to speech using an innovative method, utilizing the Leap Motion Controller and machine learning algorithms to capture and analyze hand movements in real time, then converting the interpreted signs into spoken word. We seek to build a system that is easy to use, intuitive to understand, adaptable to the individual, and usable in everyday life. This system will be able to work in an adaptive way to learn new signs to expand the dictionary of the system and allow higher accuracy on an individual level. It will have a wide range of applications for healthcare, education, gamification, communication, and more. An optical hand tracking piece of hardware, the Leap Motion Controller will be used to capture hand movements and information to create supervised machine learning models that can be trained to accurately guess American Sign Language (ASL) symbols being signed in real time. Experimental results show that the proposed method is promising and provides a high level of accuracy in recognizing ASL.
Explorative Study of Explainable Artificial Intelligence Techniques for Sentiment Analysis Applied for English Language

Sentiment analysis or opinion mining for English language is a crucial machine learning task which enables various AI applications. Different machine learning models have been optimized to achieve breakthrough performances on the sentiment analysis task. Recently, most machine learning models are going through scrutiny in the AI industry to ensure their faithfulness in their decision making process. Artificial neural network (ANN)-based models are particularly labelled as black-box models due to their lack of transparency. Various explainable artificial intelligence (XAI) techniques have been developed to explain the decision-making process of the artificial neural network-based models. Usage of artificial neural network-based sentiment analysis systems is widespread among AI practitioners, and we believe exploring explainability in these systems is a valuable task. In this work, we explore one pedagogical XAI technique (LIME: Local Interpretable Model-Agnostic Explanations) and one decompositional XAI technique (LRP: Layer-wise Relevance Propagation) on artificial neural network (black-box)-based sentiment analysis model and compare their performance quantitatively based on simulatability test. The results indicate that both techniques are capable of increasing the human understanding of the black-box sentiment analysis model, thus highlighting their important role in bringing faithfulness to the model.
How real is real enough? Participant feedback on a behavioral simulation used for information-seeking behavior research

A challenge of studying information-seeking behavior in open web systems is the unpredictability of those systems. One solution to counteract this issue is employing a simulation to ensure experimental control. However, concerns arise over the realism of such an environment. This paper assesses the realism of a behavioral simulation used to study the evaluation behavior of 175 students from fourth grade through graduate school. We assess realism through the examination of targeted participant feedback about what would have made the simulated environment and tasks more realistic to these participants. Based on this feedback, we reflect on decisions made in designing the simulation and offer recommendations for future studies interested in incorporating behavioral simulation in their research design. We find that a thoughtfully designed simulation can elicit naturalistic behavior when the controlled environment is designed to be realistic in meaningful ways. Because the simulation does not have to perfectly match reality to elicit these behaviors, designing a simulation that is real enough is an effective method to study information-seeking behavior.
Quantifying the Intensity of Toxicity for Discussions and Speakers

In this work, from YouTube News-show multimodal dataset with dyadic speakers having heated discussions, we analyze the toxicity through audio-visual signals. Firstly, as different speakers may contribute differently towards the toxicity, we propose a speaker-wise toxicity score revealing individual proportionate contribution. As discussions with disagreements may reflect some signals of toxicity, in order to identify discussions needing more attention we categorize discussions into binary high-low toxicity levels. By analyzing visual features, we show that the levels correlate with facial expressions as Upper Lid Raiser (associated with ‘surprise’), Dimpler (associated with ‘contempť), and Lip Corner Depressor (associated with ‘disgust’) remain statistically significant in separating high-low intensities of disrespect. Secondly, we investigate the impact of audio-based features such as pitch and intensity that can significantly elicit disrespect, and utilize the signals in classifying disrespect and non-disrespect samples by applying logistic regression model achieving 79.86% accuracy. Our findings shed light on the potential of utilizing audio-visual signals in adding important context towards understanding toxic discussions.
Using Convolution Neural Networks to Build a LightWeight Anomalies Detection Model

With the development of the State Grid, the number of transmission equipment and the cost of later maintenance increases. Currently, the maintenance of power transmission equipment usually requires the dispatch of drones to take patrol photos and then detect the photos. The detection process usually requires high cost in manpower and time. Applying machine learning to the photo detection can overcome these limitations in some way. And it’s necessary to propose a lightweight model and computational method due to the limited computational resources of end devices. Therefore, this paper proposes a lightweight model, which named Lightweight convolution neural network (Light_CNN), based on deep neural network to detect anomalies in equipment pictures. The experiment on our self-constructed datasets shows that the model outperforms state-of-the-art baselines. In addition, the number of parameters and flops of Light_CNN is much smaller than other models, which can be applied to many terminal devices that are limited by computational resources.
To download, or not to download, that is the question: Investigating Belgian residents’ motivation to download or not download the COVID-19 contact-tracing app Coronalert

This research explores COVID-19 contact-tracing apps (CTAs) from a citizen perspective by looking at the meaning they attribute to CTAs and their motivation to not use such kinds of apps. As such, it looks at the Belgian CTA Coronalert and semi structured interviews were used to investigate Belgian residents’ opinions.What emerged from the interviews is that the meaning participants attribute to the CTA Coronalert is different from the meaning the app itself has and the meaning the government gave to the app. The app was created as a safe and privacy-preserving solution, however, participants expressed concern over privacy violations and lack of data transparency.
Almost Difference Sets From Singer Type Golomb Rulers

Let <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> be an additive group of order <inline-formula> <tex-math notation="LaTeX">$v$ </tex-math></inline-formula>. A <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-element subset <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> is called a <inline-formula> <tex-math notation="LaTeX">$(v, k, \lambda, t)$ </tex-math></inline-formula>-almost difference set if the expressions <inline-formula> <tex-math notation="LaTeX">$g-h$ </tex-math></inline-formula>, for <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> in <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>, represent <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> of the non-identity elements in <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> exactly <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> times and every other non-identity element <inline-formula> <tex-math notation="LaTeX">$\lambda + 1$ </tex-math></inline-formula> times. Almost difference sets are highly sought after as they can be used to produce functions with optimal nonlinearity, cyclic codes, and sequences with three-level autocorrelation. A set of positive integers <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> is called a Golomb ruler if the difference between two distinct elements of <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> are different. In this paper, we use Singer type Golomb rulers to construct new families of almost difference sets. Additionally, we constructed 2-adesigns from these almost difference sets.
Vastr-GAN: Versatile Apparel Synthesised from Text using a Robust Generative Adversarial Network

The development of the fashion industry has increased the demand for customised and meticulously designed clothes. This poses a challenge to fashion designers who need to create novel clothing designs based on the requirements specified by the customers. This work presents a generative adversarial network (GAN) based text-to-image synthesis model for fabricating intricate Indian apparel designs. We introduce an architecture that strategically combines multiple trained GAN models for a streamlined text-to-image generation. Existing fashion datasets with elaborate image descriptions cater to western fashion only. We have extracted traditional Indian images like kurtis, kurtas, etc., and then combined with an existing dataset to create an Indian Fashion dataset of around 16000 images with their corresponding text descriptions. On carrying out elaborate testing on our dataset we have achieved good visual results that can capture the details given in the text descriptions.
STAT Simple Text Annotation Tool (STAT): Web-based Tool for Creating Training Data for spaCy Models

In machine learning, text annotation is used to train models. Annotation is a precursor to named entity recognition (NER) in natural language processing problems. It is the labeling or highlighting of certain keywords, phrases or sentences through means of tags to help learning models better understand the complexities of human languages. During the annotation process, the features of the dataset are marked up using metadata tags. Annotation tools can make this process easier for users, reducing manual labor involved. A simple web tool has been developed which allows only authenticated users to manually upload and annotate text files and download the output in JSON format for internal use by the employees of optimum data analytics to prepare training data for spaCy models. Its UI is simple and intuitive, making it easy to use by the average user for NLP tasks. This paper discusses in detail about the need for STAT and its implementation aspects.