Reviewerly/exHarmony huggingface dataset:
Exploring Body Gestures for Small Object Selection in Dense Environment in HMD VR for Data Visualization Applications

Recent years have seen incredible growth of Virtual Reality (VR) interfaces. In the area of data visualization and analytics, VR applications offers immense opportunities to interact, modify and explore 3D data in an interactive manner, which help in establishing new trends and patterns. In this regard, object selection is of primary importance. It is the fundamental and initial task in any immersive VR. However, the current literature is limited in investigating the effectiveness of object selection techniques in different VEs including dense and occluded dense VE, varied object sizes, proximity, and distances in the area of Immersive Analytics. In this paper, I present my ongoing PhD research to explore controller-less gestures for nail-size object selection on HMD-VR interface for dense and occluded dense VE. I describe the experiments, the findings and my future studies. I believe the outcome of these experiments in the form of guidelines or framework will enable researchers to design controller-less body gestures for object selection task.
A Cost-Effective Framework for Developing and Testing Autonomous RC Cars

In this paper, we propose a cost-effective framework for developing and testing autonomous toy RC cars. In addition, we present our on-going work on developing an autonomous toy RC car that is being built using the proposed framework.
Drone-based Risk Management of Autonomous Systems Using Contracts and Blockchain

The drones provide an active measure to identify, monitor, analyze and resolve risks of autonomous systems during operational phase. To date, however, the published studies have not considered them for managing risks in a dynamic manner. The capability to deal with unknowns and uncertainties during operational phase is regarded as essential to exploit the autonomous systems at their full potential. This paper targets the drone-based assurance of autonomous systems. The hazard and threat analyses are performed during design and development phase by using the Hazard and Operability (HAZOP) and Threat and Operability (THROP) techniques, respectively. Based on the analyses results, the safety and security requirements are derived. The assume-guarantee contracts are also derived for uncertainty sources; they are integrated in the blockchain-based smart contracts. The simulators are leveraged for performing the verification and validation as well as improving systems. For assuring safety and security during operational phase, the contracts derived for uncertainty sources are checked. In case of divergence, the drones provide assistance; otherwise, depending on the severity risk factor, system control is taken to avoid the mishap risk. The applicability of the proposed methodology is exemplified in the context of a quarry site production scenario.
Query by documents on top of a search interface

Document repositories often provide a keyword-based query interfaces to allow users to search for documents. These interfaces typically have rate limits or monetary cost per access operation. Constrained search interfaces include legal or medical data sources, social networks and the Web. We study the problem where a user has a set of input documents, and wants to discover other similar documents using a constrained search interface. Specifically, given a set of input documents and an access budget, we present principled techniques to generate a list of queries to submit. Our technique’s key intuition is to compute the best set of queries to return the input documents, which, as we show experimentally, also return other relevant documents. We show that our techniques are superior to the state-of-the-art work, according to several intuitive document relevance metrics, on several real benchmark datasets. We show results for two problem variants: finding queries to return in the highest positions the input documents (Docs2Queries-Self problem) and other relevant documents (Docs2Queries-Sim problem).
Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining

In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. The multitask cooperative voting decision tree was designed to correct the membership relationship of classification results of each layer. Ten years of signal switch machine fault data of high-speed railway are used for experimental analysis; the experiment shows that the multilevel classification model can effectively improve the classification of signal equipment fault multilevel classification task evaluation index and can ensure the correctness of the subordinate relations’ classification results.
Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection

State-of-the-art pedestrian detectors have achieved significant progress on non-occluded pedestrians, yet they are still struggling under heavy occlusions. The recent occlusion handling strategy of popular two-stage approaches is to build a two-branch architecture with the help of additional visible body annotations. Nonetheless, these methods still have some weaknesses. Either the two branches are trained independently with only score-level fusion, which cannot guarantee the detectors to learn robust enough pedestrian features. Or the attention mechanisms are exploited to only emphasize on the visible body features. However, the visible body features of heavily occluded pedestrians are concentrated on a relatively small area, which will easily cause missing detections. To address the above issues, we propose in this paper a novel Mutual-Supervised Feature Modulation (MSFM) network, to better handle occluded pedestrian detection. The key MSFM module in our network calculates the similarity loss of full body boxes and visible body boxes corresponding to the same pedestrian so that the full-body detector could learn more complete and robust pedestrian features with the assist of contextual features from the occluding parts. To facilitate the MSFM module, we also propose a novel two-branch architecture, consisting of a standard full body detection branch and an extra visible body classification branch. These two branches are trained in a mutual-supervised way with full body annotations and visible body annotations, respectively. To verify the effectiveness of our proposed method, extensive experiments are conducted on two challenging pedestrian datasets: Caltech and CityPersons, and our approach achieves superior performance compared to other state-of-the-art methods on both datasets, especially in heavy occlusion cases.
High success perfect transmission of 1-qubit information using purposefully delayed sharing of non-maximally entangled 2-qubit resource and repeated generalized Bell-state measurements

We propose a new scheme in which perfect transmission of 1-qubit information is achieved with high success using purposefully delayed sharing of non-maximally entangled 2-qubit resource and repeated generalized Bell-state measurements (GBSM). Alice possesses initially all qubits and she makes repeated GBSM on the pair of qubits, consisting of (1) the qubit of information state and (2) one of the two entangled resource qubits (taken alternately) until transmission with perfect fidelity is indicated. Alice then sends to Bob, the qubit not used in the last GBSM and also the result of this GBSM and Bob applies a suitable unitary transformation to replicate exactly the information state. Continued probabilistic transmission with unit fidelity is achieved by changing continuously the generalized Bell basis and also the pair of measured qubits of the collapsed states. We calculate the success probability up to the third repeated attempt of GBSM and plot it with concurrence of the entangled resource state. We also discuss the maximal average fidelity.
CCOnto: The Character Computing Ontology

Especially in light of the COVID-19 pandemic which is influencing human behavior, it is clear that there is a rising need for joining psychology and computer science to provide technology interventions for people suffering from negative feelings and behavior change. Behavior is driven by an individual’s character and the situation they are in, according to Character Computing and the Character-Behavior-Situation (CBS) triad. Accordingly, we developed the first full ontology modeling the CBS triad with the aim of providing domain experts with an intelligent interface for modeling, testing, and unifying hypotheses on the interactions between character, behavior, and situation. The ontology consists of a core module modeling character-based interactions and use-case and domain-specific modules. It was developed by computer scientists and psychology domain experts following an iterative process. The main contributions of this paper include updating the earlier prototypical version of the ontology-based on feedback from psychology experts and existing literature, adding more tools to it for enabling domain expert interaction, and providing the final ontology. Steps taken towards evaluating and validating the ontology are outlined.
Deciding Koopman's qualitative probability

In their recent paper in this journal, Delgrande, Renne and Sack study qualitative probability and discuss its role in Artificial Intelligence. Building on related work by de Finetti, Scott, Segerberg and others, the authors provide general axioms for qualitative probability. In this paper we investigate Koopman's conditional qualitative probability from the computational viewpoint. An important part of his work, published in the Annals of Mathematics in 1940-1941, deals with finite conjunctions K of statements of the form "the probability of a given h does not exceed the probability of b given k", with a,b,h,k elements of a boolean algebra. Upon coding these elements by boolean formulas, we provide a decision procedure to check the consistency of any such K. As an immediate consequence, also inferences in Koopman's probability theory are shown to be computable. These problems of qualitative probability theory significantly generalize Boole's (typically quantitative) problem of estimating the possible probabilities of a new event given the probabilities of other events. Boole's classical problem today is known as the optimization version of the probabilistic satisfiability problem PSAT. In 1986 Nilsson published an influential paper on this subject in this journal. The scope of our results is much larger than that of PSAT, because Koopman's conjunctions K also formalize the key notion of independence. Some familiarity with boolean logic and finite boolean algebras is the only prerequisite for this paper.
Sketchnote Components, Design Space Dimensions, and Strategies for Effective Visual Note Taking

Sketchnoting is a form of visual note taking where people listen to, synthesize, and visualize ideas from a talk or other event using a combination of pictures, diagrams, and text. Little is known about the design space of this kind of visual note taking. With an eye towards informing the implementation of digital equivalents of sketchnoting, inking, and note taking, we introduce a classification of sketchnote styles and techniques, with a qualitative analysis of 103 sketchnotes, and situated in context with six semi-structured follow up interviews. Our findings distill core sketchnote components (content, layout, structuring elements, and visual styling) and dimensions of the sketchnote design space, classifying levels of conciseness, illustration, structure, personification, cohesion, and craftsmanship. We unpack strategies to address particular note taking challenges, for example dealing with constraints of live drawings, and discuss relevance for future digital inking tools, such as recomposition, styling, and design suggestions.
Expert Finding In Scholarly Data: An Overview

In the era of digital transformation, when scholarly literature is rapidly growing, there are hundreds of papers published online daily in different fields, especially in the academic field. The huge volume of research papers published makes it difficult to find an expert/scholar to collaborate within a specific research area. This considers one of the most challenging factors in academia. Many researchers have proposed several methods to rank the authors based on their expertise in a specific area, focusing on co-citation, using keywords and their principal areas of research. The significant relationship or collaborations between scholars are credible. This paper explores the existing methods and tools in related studies to obtain author expertise among the scholarly network in determining the expertise in a specific area. Also, the semantic relations in the heterogeneous networks are presented with an overview of building a visual scholarly network that is beneficial in many pieces of research in data mining and related areas.
Freeform surface adaptive interferometry assisted with simulated annealing-hill climbing algorithm

The freeform surface adaptive interferometer (FSAI) recently has been employed to realize the unknown freeform surface metrology. A near null interferogram should be acquired from the initial interferogram with undistinguished fringes even dark areas. The direct optimization object in FSAI is just the interferogram rather than the focusing intensity characterization in traditional wavefront-sensorless (WFS) adaptive systems. The simulated annealing-hill climbing (SA-HC) mixed algorithm is employed in the FSAI, which has much better convergence than the stochastic parallel gradient descent (SPGD) algorithm, almost as much as the genetic algorithm (GA). At the same time, it is much faster than GA and thus applicable to the test of volume-produced in the optical shop. Simulations and experiments validating the algorithm feasibility are presented.
Deep 3D caricature face generation with identity and structure consistency

This paper proposed a novel approach to generate face caricatures automatically from a single portrait image. We decompose the process of 3D face caricatures generation into two independent subtasks: appearance transfer of texture and the geometry transfer of mesh. For the appearance transfer, we design a GAN-based network named CariFaceGAN to learn the style mapping from portrait to caricature, in which facial features are leveraged to preserve identity consistency. For geometry transfer, we first learn the transformation of the landmarks between portraits and caricatures in an embedded space obtained with Locally Linear Embedding method, and then Kriging interpolation is used to manipulate the portrait mesh constructed from a single image. The experimental results show that our proposed CariFaceGAN outperforms the state-of-the-art methods in terms of maintaining identity consistency and providing satisfactory visual effects.
An Algebraic View on p-Admissible Concrete Domains for Lightweight Description Logics

Concrete domains have been introduced in Description Logics (DLs) to enable reference to concrete objects (such as numbers) and predefined predicates on these objects (such as numerical comparisons) when defining concepts. To retain decidability when integrating a concrete domain into a decidable DL, the domain must satisfy quite strong restrictions. In previous work, we have analyzed the most prominent such condition, called \(\omega \)-admissibility, from an algebraic point of view. This provided us with useful algebraic tools for proving \(\omega \)-admissibility, which allowed us to find new examples for concrete domains whose integration leaves the prototypical expressive DL \(\mathcal {ALC}\) decidable.When integrating concrete domains into lightweight DLs of the \(\mathcal {EL}\) family, achieving decidability is not enough. One wants reasoning in the resulting DL to be tractable. This can be achieved by using so-called p-admissible concrete domains and restricting the interaction between the DL and the concrete domain. In the present paper, we investigate p-admissibility from an algebraic point of view. Again, this yields strong algebraic tools for demonstrating p-admissibility. In particular, we obtain an expressive numerical p-admissible concrete domain based on the rational numbers. Although \(\omega \)-admissibility and p-admissibility are orthogonal conditions that are almost exclusive, our algebraic characterizations of these two properties allow us to locate an infinite class of p-admissible concrete domains whose integration into \(\mathcal {ALC}\) yields decidable DLs.
Handwritten Tamil Character Recognition using Convolutional Neural Network

A handwritten Tamil character recognition system using deep learning algorithm is proposed. Convolutional Neural Network (CNN) is used for image classification and text classification. In this project the character image is converted into text and also represented as an audio output with the help of Google cloud Translation API. Using Deep learning algorithm, the undigitized Tamil letters are converted into readable format. We have trained for 12 vowels (soul letters), 18 consonants (body letters), 90 soul-body letters and one special character, in total we trained 121 characters out of 247 for which the dataset is collected from students as well as from Kaggle website. Thus, we obtained around 40,000 image samples. Out of 121 characters we get an accurate result (99%-100%) for 114 characters. The other seven characters will be accurate if the characters are captured in a proper lighting environment. The model accuracy is 95%. The system can also recognise the words with the combination of the above-mentioned letters.
Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge

Noisy data, crawled from the web or supplied by volunteers such as Mechanical Turkers or citizen scientists, is considered an alternative to professionally labeled data. There has been research focused on mitigating the effects of label noise. It is typically modeled as inaccuracy, where the correct label is replaced by an incorrect label from the same set. We consider an additional dimension of label noise: imprecision. For example, a non-breeding snow bunting is labeled as a bird. This label is correct, but not as precise as the task requires. Standard softmax classifiers cannot learn from such a weak label because they consider all classes mutually exclusive, which non-breeding snow bunting and bird are not. We propose CHILLAX (Class Hierarchies for Imprecise Label Learning and Annotation eXtrapolation), a method based on hierarchical classification, to fully utilize labels of any precision. Experiments on noisy variants of NABirds and ILSVRC2012 show that our method outperforms strong baselines by as much as 16.4 percentage points, and the current state of the art by up to 3.9 percentage points.
An AI-Based Solution to Reduce Undesired Face-Touching as a Precautionary Measure for COVID-19

The ongoing health crisis continues to impact all walks of life, demanding radical lifestyle changes, and forcing us to comply with a whole new host of precautionary regulations. Poor hygiene practices are in part responsible for the soaring COVID-19 cases, self-inoculation being one of them. Recent studies in this area have highlighted how face-touching provides us with immediate relief from temporary discomforts such as muscle tension, and is a way of regulating emotions and stimulating memory. This paper is aimed at exhibiting the role of Artificial Intelligence and Machine Learning in developing a fully software-based system to help restrict this instinct. A web application has been designed, which takes in real-time input of a person through a built-in PC webcam, and detects the intersection between the hands and facial regions, resulting in a warning alert as an output to the user. Making use of BodyPix 2.0 in TensorFlow, this novel method has been found to have an accuracy of 91.3%, with proper synchronization and minimum delay in detection. The feasibility of such an approach has thus been discussed herein, giving an insight into how smart hygiene control techniques could help in the management of any such future catastrophes stemming from similar deadly biological agents, lest history repeats itself.
Parallel algorithm for modeling temperature fields using the splines method

The paper is devoted to the construction of an algorithm for parallelization of temperature field modeling processes based on bicubic spline and the problems that arise in it. It takes a lot of time and memory on a computer to model existing large-scale signals of temperature fields in three-dimensional graphical form. Solving these problems on the basis of the proposed parallel algorithm, the development of parallel algorithms and software tools to qualitatively determine the coordinates of high-temperature hotspots of temperature fields and allow them to be monitored continuously, and to calculate the digital processing of existing large-scale signals of temperature fields. Based on this, the article presents a parallel algorithm for digital processing of signals of temperature fields on the basis of bicubic spline and ways to parallel its computational processes using Open MP technology.
Combined Approach of Supervised and Unsupervised learning for Dog Face Recognition

One would be surprised to hear the lost dog rates around the world. Even though it is something that one doesn't ponder a lot about, lost dogs are a problem that most dog owners fear. Dogs provide humans with companionship, protection, and unconditional love, and to the dogs; their whole world revolves around their owner and their family members. Therefore, when a pet dog goes missing, not only the dog owner but also the pet dog is affected. Unfortunately, in Sri Lanka, a lost dog being found is a very rare occurrence. A reason for this can be pointed out as the lack of an easily-accessible, public platform for lost dogs. In this research project, a solution to this problem has been implemented using image processing. This research study is about image classification and recognition using the Convolutional Neural Network (CNN) or also known as Shift Invariant or Space Invariant Artificial Neural Network (SIANN) by using TensorFlow framework as well as Keras library. The VGG16 model was customized for being used feature extraction. The implementation was a combination of both Machine Learning and Deep Learning. The platform to upload the found dog is also a continuous and inter-related subcomponent that provides a happy and healthy life for stray dogs too. That idea is providing them a higher chance to find a safe place to survive and also a home where they will be loved. The results are discussed in terms of the accuracy of the image recognition and classification in percentage. Each group of dogs get around 90% accuracy or above.