Reviewerly/exHarmony huggingface dataset:
Semantic Constraints Specification and Schematron-Based Validation for Internet of Medical Things’ Data

Fitness and activity trackers are hugely popular wearable devices that monitor various health-related metrics, such as step count, heartbeat rate, or even oxygen saturation. Utilizing personal health information obtained by users&#x2019; personal trackers provides promising results in the fields of telemedicine and personal well-being. However, we face challenges such as data quality, privacy and compliance with standards and regulations. This paper addresses such challenges, with the focus on the last one. Semantic constraints for healthcare datatypes are defined to ensure compliance with standards, making the information medically valid and relevant. A process of semantic verification and Schematron-based validation is proposed. The validation process suggested in this paper will enable the data to be transferred and incorporated into a formal Electronic Health Record. The process is then verified using datasets containing various health-related data types. The aim is to integrate personal health data into Electronic Health Record, which forms a part of Central Health Information System. This would provide personalized medical services to patients and help physicians to make more informed decisions.
A Multi-objective Approach for Inter-cluster and Intra-cluster Distance Analysis for Numeric Data

A measure of how closely a set of data items are connected is determined based on the similarity or dissimilarity between the data items. Inter-cluster and intra-cluster distances are two metrics that exhibit how the data items can group accordingly to form clusters. Usually, the data items with high similarities are in the same cluster, and the distance between them is significantly less. A good clustering algorithm always maintains high similarity within the cluster, thereby maximizing the distance between the data items having higher dissimilarities in distinct clusters. These measures play a crucial role in identifying the patterns among the data objects. Euclidean distance acts as a distance measure for determining these two metrics. This paper proposes a multi-objective approach for clustering to establish the relationship between inter-cluster and intra-cluster distances. It aims to perform a comparative analysis of the sum of inter-cluster and intra-cluster distances. It reveals that when the sum of intra-cluster distance is minimizing, then the sum of inter-cluster distance is maximizing for a given dataset.
Robust Video Watermarking Using Normalized Zernike Moments

Digital video watermarking has become a hot research topic in recent years due to the increasing demand of protecting the intellectual property of video data. Even though many conventional video watermarking methods have been reported in past years, few of them are resistant to high-intensity geometric attacks, which motivates the authors in this paper to propose a video watermarking technique that is robust against high-intensity geometric distortion. To this purpose, the proposed method embeds the watermark information into the normalized Zernike moments of the target frames of the cover video sequence. The advantage is that the normalized Zernike moment preserves a strong invariance to geometric distortions such as rotation and scaling attacks. During data embedding, secret bits are embedded into adaptively selected moments with slight modifications to provide good robustness while maintaining the imperceptibility. The chrominance channel of the video data rather than the luminance one is used in our algorithm, as distortion in the former channel is less sensitive to the human visual system. Experimental results show that, compared with the existing scheme, the PSNR values of the proposed method gain about 7 dB averagely, meaning that the proposed method achieves high imperceptibility. Moreover, it is demonstrated that the proposed method is more robust against geometric distortions such as rotation and upscaling, which has verified the applicability and superiority of the proposed work.
Emergency Vehicle Detection Using Deep Convolutional Neural Network

In densely populated cities, emergency vehicles getting caught in traffic is a regular occurrence. As a result, emergency vehicles arrive late, resulting in asset and human life losses. It is critical to treat emergency vehicles differently to avoid losses. The purpose underlying this research is to preserve human lives and reduce losses. For this, an automated method for detecting emergency vehicles is implemented. Ambulance and fire trucks are considered an emergency, and other vehicles are considered non-emergency vehicles in the proposed method. Initially, it identifies several vehicles from an image. The YOLOv4 object detector accomplished this part of the method. The identified vehicles are the region of interest for the rest of the research. Finally, the method classifies the vehicles into emergencies or non-emergencies. This study contributes by developing a model based on rigorous testing and analysis and includes a viral algorithm in deep learning: convolutional neural network (CNN). Furthermore, the transfer learning technique with VGG16’s fine-tuned model is employed for emergency vehicle detection. On the Emergency Vehicle Identification v1 dataset, this model had an average accuracy of 82.03%.
Polarization-based research on a priori defogging of dark channel

The image enhancement method based on dark channel priori defogging performs well in target detection, but it takes the light intensity information as the carrier and the single optical dimension leads the target characterization efficiency to decline. Based on the sensitivity of polarization to physical properties, in this paper a proposal is made that polarization information is introduced into the traditional dark channel priori defogging method to enhance the recognition degree between different objects. The theory of backscattering and polarization detection in dark channel priori defogging method is studied, and the mechanical polarization filtering imaging equipment is built to verify the target characterization function of the proposed method in foggy environment. The research shows that the dark channel priori defogging method based on polarization can obtain the light intensity and polarization information of the object at the same time. Compared with the traditional dark channel priori defogging method, using the polarization difference between the target and the background can significantly improve their contrast. This research result can be applied to the existing polarization imaging instrument system to realize real-time backscattering and polarization information extraction, and further improve the efficiency of target detection and characterization in fog.
Entity-based Neural Local Coherence Modeling

In this paper, we propose an entity-based neural local coherence model which is linguistically more sound than previously proposed neural coherence models.Recent neural coherence models encode the input document using large-scale pretrained language models.Hence their basis for computing local coherence are words and even sub-words.An analysis of their output shows that these models frequently compute coherence on the basis of connections between (sub-)words which, from a linguistic perspective, should not play a role.Still, these models achieve state-of-the-art performance in several end applications.In contrast to these models, we compute coherence on the basis of entities by constraining the input to noun phrases and proper names.This provides us with an explicit representation of the most important items in sentences leading to the notion of focus.This brings our model linguistically in line with pre-neural models of computing coherence.It also gives us better insight into the behaviour of the model thus leading to better explainability.Our approach is also in accord with a recent study (O'Connor and Andreas, 2021), which shows that most usable information is captured by nouns and verbs in transformer-based language models.We evaluate our model on three downstream tasks showing that it is not only linguistically more sound than previous models but also that it outperforms them in end applications 1 .
Research on custom-tailored swimming goggles applied to the internet

Custom-tailored designs have attracted increasing attention from both consumers and manufacturers due to increasingly intense market competition. We propose and verify a method for custom designing swimming goggles that is suitable for use on the Internet. Twenty-five points representing head features were first identified, and the relationship between these points and the size of the goggles were confirmed. The correct position for photography was then experimentally determined, and a camera-position corrector was designed and manufactured. A three-dimensional (3D) scanning model was divided into 18 planes based on the feature points, and the contour curve of the surface on each plane was extracted. Secondly a Hermite interpolation curve was then used to describe the contour curve for the head, and a parametric 3D head model was established. The method of using orthographic photographs with patches to obtain 3D data was summarized to determine the size of the user’s head, and a 3D model of the user’s head and the 3D model of the goggles were established. Lastly, we developed an algorithm for eliminating errors in the photographs. We also produced an operational flowchart for an application (APP) following the research approaches and then determined the page structure of the APP based on the flowchart to verify the validity of our proposed method and ultimately to establish an APP for interactively designing swimming goggles. The entire APP operation process was completed using a volunteer as an experimental subject when a model for custom-tailored goggles was obtained. The model was then processed and applied using 3D printing. The volunteer confirmed the model by declaring that the goggles were comfortable to wear and perfectly positioned on his face, thereby verifying the validity of the method.
A Distributed Fault Tolerant Algorithm for Load Balancing in Cloud Computing Environments

Cloud computing is a promising paradigm that provides users with higher computing benefits in terms of cost, availability and flexibility. Nevertheless, with potentially thousands of connected machines, faults become more frequent and may have an adverse effect on the application. Consequently, fault-tolerant load balancing becomes necessary in order to optimize resource utilization while ensuring the reliability of the system. Different approaches have been proposed in the literature for fault tolerance in cloud computing. However, they suffer from several shortcomings: some fault tolerance techniques use task replication which reduce the cloud’s efficiency in terms of resource utilization. While other models rely on checkpoint recovery to tolerate failures, resulting in an increase in the mean response time. To address these shortcomings, an efficient and adaptive fault tolerant algorithm for load balancing is proposed. Based on the CloudSim simulator, some series of test-bed scenarios are considered to assess the behavior of the proposed algorithm.
Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning

Prompt-based paradigm has shown its competitive performance in many NLP tasks.However, its success heavily depends on prompt design, and the effectiveness varies upon the model and training data.In this paper, we propose a novel dual context-guided continuous prompt (DCCP) tuning method.To explore the rich contextual information in language structure and close the gap between discrete prompt tuning and continuous prompt tuning, DCCP introduces two auxiliary training objectives and constructs input in a pair-wise fashion.Experimental results demonstrate that our method is applicable to many NLP tasks, and can often outperform existing prompt tuning methods by a large margin in the few-shot setting.
Basics of Cryptographic Primitives for Blockchain Development

Cryptographic algorithms are the basic building blocks of a secure system and protocols. A security protocol is a set of measures to accomplish required security objectives by employing suitable security mechanisms. Security mechanisms are generally referred to as cryptographic functions, which have the fundamental property of representing the data in another form. Diverse kinds of security protocols are in practice, such as authentication, non-repudiation, and key management protocols. As one of the crypto-intensive technologies, Blockchain has become a scorching topic. Many security and privacy issues have been addressed for Blockchain supported by cryptographic primitives. Basic cryptographic primitives include hash primitives, digital signature, and encryption primitives which are incorporated in Blockchain. This chapter has discussed hash primitives such as SHA-256, SHA-512, and Ethash. Digital signatures such as Elliptic Curve Digital Signature Algorithm are the current signature scheme in Bitcoin. Schnorr signatures and BLS signatures rectify issues faced by ECDSA. All the algorithms work on symmetric and asymmetric key encryption, which are discussed in detail. This chapter also provided implementations for some of these topics, which will help in understanding the concepts better. The study of cryptographic primitives will be helpful for cryptographers to research and evaluate cryptographic solutions in Blockchain.
Design and Implementation of Teaching Assistant System for Mechanical Course based on Mobile AR Technology

Augmented reality technology has become a hot spot in many fields because of its unique real-time interaction and the ability to add virtual objects in 3D video space.To enable students better understand and master traditional mechanical courses and solve the limitations of the courseware teaching method, a teaching assistant system based on Vuforia platform is designed and developed by combining augmented reality technology with existing teaching methods.On the smartphone application side, the 3D model of parts can be displayed by scanning mechanical engineering drawings, which can rotate, zoom, and section view the model.It realizes 3D visualization and interactive operation of 2D drawings in textbooks and achieves the purpose of image, diversity and efficient teaching.The results show that this method can not only promote the cognition of spatial relations based on visualization, but also create a situational learning environment based on experience, which can effectively improve the learning effect of courses and enhance students' interest and enthusiasm in learning.
Particle Swarm Optimization with Multi-Chaotic Scheme for Global Optimization

Particle swarm optimization (PSO) is widely implemented as an optimizer due its characteristics of simple implementation and fast convergence speed. However, it has tendency to cause premature convergence and population diversity loss if the population is not well-initialized due to its randomness. In this research, a PSO with Multi-Chaotic Scheme (PSOMCS) is introduced to generate promising initial population to improve the population diversity and fitness of the candidate solution. Multiple chaotic system is dependent on the initial condition that can cover a broader region of the search space. By using multiple chaotic maps, the introduced method is able to solve different types of problems effectively as each type of chaotic maps has better performance in solving a specific problem. The performance comparisons of PSOMCS and the existing PSO variants are conducted by using the test functions of CEC 2014. The simulation analyses reported that PSOMCS outperforms its competitors with respect to total mean fitness.
Zero-Pronoun Annotation Support Tool for the Evaluation of Machine Translation on Conversational Texts

This study proposes a support tool for building zero-pronoun evaluation sets called the zero-pronoun annotation support tool (0Past; pronounced zero-past). The proposed tool provides a chat-like user interface to facilitate the navigation of human annotators. Each conversation is displayed separately, and while the user views a certain conversation, the messages within the conversation are displayed individually with a distinct color for the newest message. Using 0Past, two zero-pronoun evaluation sets are constructed. These evaluation sets are then used to evaluate neural machine translation (NMT) models’ performance translating Japanese conversations to English with the correct pronoun. Additionally, this study builds a zero-pronoun classification model by incorporating newly constructed evaluation sets and enables the tool to provide automated pre-annotation features, which can then be improved manually by human annotators. Finally, this study reports the evaluation results of training a Japanese-English neural machine translation model and compares its performance with two publicly available pretrained models in translating parallel conversational sentences from Japanese to English, which contains many omitted pronouns. The results confirm that phenomenon-specific evaluation sets are essential for better measuring NMT models when handling conversational sentences in Japanese, which is heavy on the anaphoric zero-pronoun phenomenon.
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text

Toxic span detection is the task of recognizing offensive spans in a text snippet.Although there has been prior work on classifying text snippets as offensive or not, the task of recognizing spans responsible for the toxicity of a text is not explored yet.In this work, we introduce a novel multi-task framework for toxic span detection in which the model seeks to simultaneously predict offensive words and opinion phrases to leverage their inter-dependencies and improve the performance.Moreover, we introduce a novel regularization mechanism to encourage the consistency of the model predictions across similar inputs for toxic span detection.Our extensive experiments demonstrate the effectiveness of the proposed model compared to strong baselines.
Deepfake Video Detection using Neural Networks

In today’s era, software tools based on deep learning have made the people work easier to make credible faces exchanges in video with little signs of manipulation, nicknamed “DeepFake” videos. Manipulation in digital media has been performed for decades through the appropriate use of visual effects; nevertheless, current breakthroughs occurred in deep learning have resulted in a significant rise to gain reality of fake material or contents using the simple ways. This are Artifical Intelligence-generated media (known as DF). Using tools of artificial intelligence to create the DF is an easy task. However, detecting these DF poses a significant barrier. Because it is difficult to teach the algorithm to detect the DF. Using Convolutional Neural Networks and Recurrent Neural Networks, we have made progress in detecting the DF. The system employs a Convolutional Neural network (CNN) on frame level to extract features. These observations are noted and this can train a Recurrent Neural Network (RNN), which has the ability to learn and classify whether or not a video has been tampered with and identified the temporal irregularities in the frame introduced by DF tools. We demonstrate how utilizing a simple architecture, our system may get competitive outcomes in this job.
BpHigh@TamilNLP-ACL2022: Effects of Data Augmentation on Indic-Transformer based classifier for Abusive Comments Detection in Tamil

Social Media platforms have grown their reach worldwide.As an effect of this growth, many vernacular social media platforms have also emerged, focusing more on the diverse languages in the specific regions.Tamil has also emerged as a popular language for use on social media platforms due to the increasing penetration of vernacular media like ShareChat and Moj, which focus more on local Indian languages than English and encourage their users to converse in Indic languages.Abusive language remains a significant challenge in the social media framework and more so when we consider languages like Tamil, which are low-resource languages and have poor performance on multilingual models and lack language-specific models.Based on this shared task, "Abusive Comment detection in Tamil@DravidianLangTech-ACL 2022", we present an exploration of different techniques used to tackle and increase the accuracy of our models using data augmentation in NLP.We also show the results of these techniques.
The influence of media literacy on the culture of critical thinking

Digital technologies affect the way of life of both individuals and the modern society as a whole. Active participation on social networks directs us to frequent connections with social media. However, the field of electronic media is often a platform for sharing content that does not aim to place true information, but to influence public perception. In order for media users to be able to recognize the goal of media information, it is necessary for them to master knowledge and competencies. Media literacy plays a key role in this. The goal we set in this paper is to determine to a what ectent media literacy is recognized and what is the perception of respondents about how knowledge / ignorance of media literacy affects the culture of critical thinking and willingness of media users to accept cultural diversity. The paper presents a part of the research conducted by the author during 2021, the part related to the impact of media literacy on the goal set in this paper, and presents the results which confirm that a higher degree of mastery of media literacy competencies has a positive effect on a culture of critical thinking, and that a more developed critical capacity to assess information and the media provides a greater opportunity for media users to embrace cultural diversity.
Improved reinforcement learning algorithm for mobile robot path planning

In order to solve the problem that traditional Q-learning algorithm has a large number of invalid iterations in the early convergence stage of robot path planning, an improved reinforcement learning algorithm is proposed. Firstly, the gravitational potential field in the improved artificial potential field algorithm is introduced when the Q table is initialized to accelerate the convergence. Secondly, the Tent Chaotic Mapping algorithm is added to the initial state determination process of the algorithm, which allows the algorithm to explore the environment more fully. In addition, an ε-greed strategy with the number of iterations changing the ε value becomes the action selection strategy of the algorithm, which improves the performance of the algorithm. Finally, the grid map simulation results based on MATLAB show that the improved Q-learning algorithm has greatly reduced the path planning time and the number of non-convergence iterations compared with the traditional algorithm.
Iterative Solution for the Narrow Passage Problem in Motion Planning

Finding a path in a narrow passage is a bottleneck for randomised sampling-based motion planning methods. This paper introduces a technique that solves this problem. The main inspiration was the method of exit areas for cavities in protein models, but the proposed solution can also be used in another context. For data with narrow passages, the proposed method finds passageways for which sampling-based methods are not sufficient, or provides information that a collision-free path does not exist. With such information, it is possible to quit the motion planning computation if no solution exists and its further search would be a loss of time. Otherwise, the method continues to sample the space with sampling-based method (a RRT algorithm) until a solution is found or the maximum number of iterations is reached. The method was tested on real biomolecular data - dcp protein - and on artificial data (to show the superiority of the proposed solution on better-imagined data) with positive results.