Reviewerly/exHarmony huggingface dataset:
Tracking Multiple Unmanned Aerial Vehicles on SE(3) Using a Monocular Camera

This article presents MTT- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{SE}\mathopen {}(3)\mathclose {}$</tex-math></inline-formula> , a vision-based multiple-target tracking algorithm for targets that evolve on the special Euclidean group <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{SE}\mathopen {}(3)\mathclose {}$</tex-math></inline-formula> in dense clutter. The target state consists of the pose in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{SE}\mathopen {}(3)\mathclose {}$</tex-math></inline-formula> and the twist of the vehicle. Contributions of the article include the development of a robust track initialization scheme designed on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{SE}\mathopen {}(3)\mathclose {}\times \mathbb {R}^{6}$</tex-math></inline-formula> , and a track-to-track association and fusion algorithm to merge similar tracks evolving on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{SE}\mathopen {}(3)\mathclose {}\times \mathbb {R}^{6}$</tex-math></inline-formula> . The performance of MTT- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{SE}\mathopen {}(3)\mathclose {}$</tex-math></inline-formula> is verified in hardware by simultaneously tracking three multirotors using a stationary monocular camera. The results are compared to a multiple-target tracking algorithm that uses linear, time-invariant nearly constant velocity, acceleration, and jerk models, where it is shown that MTT- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{SE}\mathopen {}(3)\mathclose {}$</tex-math></inline-formula> improves several traditional tracking metrics.
DGR: Decomposition Graph Reconstruction for Question Understanding

To tackle the understanding of complex questions, Question Decomposition Meaning Representation (QDMR) decomposes a complex question into a sequence of atomic simple questions. However, state-of-the-art QDMR parsers neglect the type information of simple questions and the dependency information between simple questions, leading to limited overall performance. In this paper, we propose a Decomposition Graph Reconstruction (DGR) model to induce the information by introducing two additional tasks: simple question type classification and dependency relationship detection. DGR can generate sub-questions with the expected type and organize sub-questions with correct logical connections. Experimental results on the BREAK QDMR dataset demonstrate the effectiveness of our method compared with previous methods.
Exploration Versus Exploitation in Model-Based Reinforcement Learning: An Empirical Study

Model-based Reinforcement Learning (MBRL) agents use data collected by exploration of the environment to produce a model of the dynamics, which is then used to select a policy that maximizes the objective function. Stochastic Value Gradient (SVG) methods perform the latter step by optimizing some estimate of the value function gradient. Despite showing promising empirical results, many implementations of SVG methods lack rigorous theoretical or empirical justification; this casts doubts as to whether good performance are in large part due to the benchmark-overfitting. To better understand the advantages and shortcomings of existing SVG methods, in this work we carry out a fine-grained empirical analysis of three core components of SVG-based agents: (i) the gradient estimator formula, (ii) the model learning and (iii) the value function approximation. To this end, we extend previous work that proposes using Linear Quadratic Gaussian (LQG) regulator problems to benchmark SVG methods. LQG problems are heavily studied in optimal control literature and deliver challenging learning settings while still allowing comparison with ground-truth values. We use such problems to investigate the contribution of each core component of SVG methods to the overall performance. We focus our analysis on the model learning component, which was neglected from previous work, and we show that overfitting to on-policy data can lead to accurate state predictions but inaccurate gradients, highlighting the importance of exploration also in model-based methods.
Impact Analysis of RGB Channels to the Quality of Imperceptibility in Image Steganography

In computers, color images generally have three color channels, namely red (R), green (G), and blue (B). Embedding a message in the image can change the pixel value. This change certainly adjusts the image pixel value, luminance, contrast, structure, and even image characteristics. So, it is necessary to know how much effect the pixel changes have on each channel. By learning how the effects occur, future research can find solutions to minimize the impact of changes. Based on testing of 40 standard image datasets with four different steganographic methods. The red channel has the highest sensitivity to embedding based on MSE, PSNR, and SSIM measurements. The green has better SSIM, and the blue channel has better PSNR and MSE. However, overall, the difference of MSE, PSNR, dan SSIM from the three channels is less significant
Contrastive Co-training for Diversified Recommendation

Beyond accuracy, diversity has become a crucial factor to evaluate a recommendation system as higher diversity helps mitigate echo chamber issue and improve user satisfaction. Recently, great success has been made to improve diversity, but the approaches often sacrifice much lower accuracy. Herein this work, we propose contrastive co-training for diversified recommendation that improves diversity greatly and achieves comparable or even better accuracy. Specifically, we keep two user-item graph views for recommendation and contrastive learning, respectively. Pseudo edges are predicted from current graph view to augment the other graph view by mining the novel items that users might be highly interested in. However, merely leveraging co-training hurts the accuracy since the pseudo labels are sometimes noisy. Therefore, we propose diversified contrastive learning that not only is robust to the noisy pseudo edges but also improves the diversity further by alleviating the popularity and category biases by re-balancing item-level popularity and category-level advantage. The extensive experiments on three public datasets show the superiority of our proposed model in terms of accuracy and diversity compared with strong baselines.
Twitter Data Analysis Using Apache Streaming

Real-time data from social network sites like Twitter or Facebook has been a popular source for analytics and researchers in the recent years due to various factors like large amount of data, structured-ness, and popularity. Analyzing data is a very common requirement today, but such requirements become difficult when there is a bulk of data which needs to processed and analyzed in real time. Analyzing large number of tweets from Twitter to get different patterns and extract useful information is a massive challenge. Apache Spark is a platform that can be used to handle big data efficiently, and it offers faster solutions compared to Hadoop. This chapter addresses the issue of real-time analyzing and filtering the tweets as per the user's requirements from among the millions of other streaming tweets and classifies them into various categories. It creates an interactive automatic system that splits data based on important keywords and displays a graphical representation of connected tweets using Apache Spark.
Convergent Usability Evaluation of the HFES Website

The purpose of this usability analysis was to utilize convergent methods to evaluate the Human Factors and Ergonomics Society (HFES) website and provide recommendations to improve user experience. Three methods were utilized including the Mouloua and Alberti Usability Questionnaire (MAUQ), the System Usability Scale (SUS), and Net Promoter Score (NPS). An in-depth heuristic analysis using Nielsen's 10 Usability Heuristics was conducted in concordance with the previous scales to provide specific implementation recommendations for improving user experience. Our findings suggest that implementing convergent methods leads to robust knowledge about the user experience of a digital product. Both theoretical and practical implications are discussed, alongside recommendations for design improvements, and directions for future research are presented.
Training Data Generation for U-Net Based MRI Image Segmentation using Level-Set Methods

Image segmentation has been a well-addressed problem in pattern recognition for the last few decades. As a sub-problem of image segmentation, the background separation in biomedical images generated by magnetic resonance imaging (MRI) has also been of interest in the applied mathematics literature. Level set evolution of active contours idea can successfully be applied to MRI images to extract the region of interest (ROI) as a crucial preprocessing step for medical image analysis. In this study, we use the classical level set solution to create binary masks of various brain MRI images in which black color implies background and white color implies the ROI. We further used the MRI image and mask image pairs to train a deep neural network (DNN) architecture called U-Net, which has been proven to be a successful model for biomedical image segmentation. Our experiments have shown that a properly trained U-Net can achieve a matching performance of the level set method. Hence we were able to train a U-Net by using automatically generated input and label data successfully. The trained network can detect ROI in MRI images faster than the level-set method and can be used as a preprocessing tool for more enhanced medical image analysis studies.
Extractive summarization in Hindi using BERT-based ensemble model

The past few years have seen a massive growth in the number of daily internet users whose primary language of communication is Hindi. Hindi is now one of the most spoken languages in the world and the official language of the Indian Government. Given this considerable rise in the amount of data in Hindi, managing, analyzing, and summarizing documents becomes a significant task with many applications. But language models and Natural Language Processing tasks catering to this demographic have been very limited in scope. Even state-of-the-art multilingual models cannot handle the nuances of the language. To bridge this gap, the MuRIL [37] language model was implemented and trained on large-scale Indian text corpora. The present work focuses on the summarization task for Hindi documents. We leverage the power of the MuRIL model and develop a novel extractive summarization-based solution using the language model’s embeddings. Newspaper articles spanning several categories are extracted as our training data, and comprehensive testing shows that our model exceeds the performance of the previous baselines on the accuracy metric.
A Conceptual Study on Artificial Intelligence in Human Resources and its Effects on Future

Artificial intelligence is used in many areas, including recruiting, selection, hiring, performance analysis, employee data collection, real-time information, and correct information.The employment of artificial intelligence in the human resources department, on the other hand, necessitates the creation of a skill set for employees.Adapting and understanding artificial intelligence technologies, as well as becoming adept in the area of digital technology, may be difficult for employees.Tianyi, (2021) investigates artificial intelligence's definition and categorization.Based on theories on business dynamic capabilities, excellent marketing, and consumers' perceptions of products and values as well as artificial intelligence technology characteristics, the process of artificial intelligence technology integration and commercialization is developed An organization's features, technological innovation and commercialization integration, integration results, consumer perception of product and service brand value, and the technical environment are all part of the mechanism's components.According to this research, the relevance of market expertise and customers' need for integration are highlighted.As part of our investigation, we look at how artificial intelligence is employed in management, as well as the potential privacy and ethical issues that arise.QiongJia, (2018) works shows that the six HRM pillars and the present status of AI technology are combined to provide a conceptual AI framework for HR management.Face-recognition and natural
Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization

One key challenge in multi-document summarization is to capture the relations among input documents that distinguish between single document summarization (SDS) and multi-document summarization (MDS). Few existing MDS works address this issue. One effective way is to encode document positional information to assist models in capturing cross-document relations. However, existing MDS models, such as Transformer-based models, only consider token-level positional information. Moreover, these models fail to capture sentences' linguistic structure, which inevitably causes confusions in the generated summaries. Therefore, in this paper, we propose document-aware positional encoding and linguistic-guided encoding that can be fused with Transformer architecture for MDS. For document-aware positional encoding, we introduce a general protocol to guide the selection of document encoding functions. For linguistic-guided encoding, we propose to embed syntactic dependency relations into the dependency relation mask with a simple but effective non-linear encoding learner for feature learning. Extensive experiments show the proposed model can generate summaries with high quality.
Unlocking New Opportunities Towards Smart Contracts for Transactive Energy Systems: Technical and Legislative Perspectives

Integration of distributed energy resources (DES), especially renewable energy and storage resources, into the electrical power grid has introduced new challenges such as bidirectional power flow, distributed energy markets, trust between participants, etc. As a result, it is needed to share and trade energy among participants in a trusted environment for distributed markets, either as individuals or aggregators, by negotiating based on demand, price, time of day or others. Transactive Energy Systems (TESs) provide a unique environment to engage end participants and non-traditional resources to address the grid challenges, while Blockchains provide a unique technology to address the trust problem through the use of a distributed ledger, cryptocurrencies, and the execution of smart contracts. This study explores the automation features - commonly referred to as smart contracts - of blockchain technology. This paper also aims to reflect the international industrial, academic and entrepreneurial perspectives.
Improved (t,n)-Threshold Proxy Signature Scheme

With the increasing maturity of (t, n) threshold signatures and proxy signatures, (t, n) threshold proxy signatures have been increasingly studied. The (t, n) threshold proxy signature scheme allows t or more proxy signers in a proxy group to sign messages on behalf of the original signer, but less than t signers cannot. In this paper, by studying a (t, n) threshold proxy signature scheme, we find that their scheme cannot resist forgery attack and does not meet the requirements of anonymity and traceability. Therefore, we propose an improved (t, n) threshold proxy signature scheme which remedies the security problems mentioned above. It is proved by analysis that our scheme not only can resist forgery attack, collusion attack and public-key substitute attack, but also has anonymity, distinguishability, and traceability. Moreover, the improved signature scheme is ultimately somewhat lower in terms of computational complexity.
Validation of Speech Data for Training Automatic Speech Recognition Systems

Recent automatic speech recognition systems are largely based on deep neural networks that need large amounts of labelled speech data to train. This can be a problem, especially for languages for which large speech databases are not available. To facilitate the construction of a speech database suitable for training automatic speech recognizers, we propose a tool that enables the validation of audio recordings from collected speech recordings. The developed tool allows the user to check the com-pliance with the predefined requirements regarding the correct audio format, the appropriate speech volume, the compatibility of the spoken text with the reference text and the suitability of the length of the non-spoken segments. The applicability of the developed tool is demonstrated by the creation of the Slovene speech corpus from audio recordings collected within the project Development of Slovene in a Digital Environment, although the tool is also suitable for all other languages supported by the used automatic speech recognizer.
Development of multipurpose bed cum wheel chair for paralytic attacked and elderly persons

The objective of this system is to provide mobility and maximum comfort to the patient without the help of a care giver. The back rest and leg rest of the wheelchair is controlled by linear actuator. Design concept is carried out through Solid works. Navigation is enabled through joystick which in turn controls the motors of the wheelchair. The wheelchair offers more comfort features than previously available wheelchair versions. The main features that are to be implemented in this model are pressure sore prevention and monitoring the vital body signs of the patient continuously. Pressure sore prevention enables the patient to use the chair for a long time with ease. The main components used in the system are DC Motor, battery, linear actuator, leadscrew. In a nutshell, the outcome of this wheelchair is that it will be able to provide mobility and comfort to the patient by operating solely by the patient himself.
Bidirectional Network for Image Generation and Classification

Since a large number of convolutional neural network-based image generation and classification networks are now implemented in two separate models, this is a great waste of researcher’s effort and hardware resources, and also limits the scope of application of the models. In order to solve this problem, we propose a bidirectional network–-CGNet for image generation and classification tasks. Firstly, we design a low depth convolutional network model to ensure that the output of the terminal convolutional layer has enough information of the original image features. Secondly, we add a reverse data stream inside the network model to achieve the purpose of bidirectional network. Thirdly, using only one fully-connected layer after the terminal convolution layer, which reduces both the number of parameters in the model and the complexity of training. Finally, the training method of Generative Adversarial Nets (GAN) is used for the training of the inverse network model. The experimental results show that the classification accuracy of the proposed bidirectional network reaches over 82% on the CIFAR-10 dataset, and the generated images have clear semantics and high image quality.
Towards Characterization of General Conditions for Correlated Differential Privacy

Differential privacy is a widely-used metric, which provides rigorous privacy definitions and strong privacy guarantees. Much of the existing studies on differential privacy are based on datasets where the tuples are independent, and thus are not suitable for correlated data protection. In this paper, we focus on correlated differential privacy, by taking the data correlations and the prior knowledge of the initial data into account. The data correlations are modeled by Bayesian conditional probabilities, and the prior knowledge refers to the exact values of the data. We propose general correlated differential privacy conditions for the discrete and continuous random noise-adding mechanisms, respectively. In case that the conditions are inaccurate due to the insufficient prior knowledge, we introduce the tuple dependence based on rough set theory to improve the correlated differential privacy conditions. The obtained theoretical results reveal the relationship between the correlations and the privacy parameters. Moreover, the improved privacy condition helps strengthen the mechanism utility. Finally, evaluations are conducted over a micro-grid system to verify the privacy protection levels and utility guaranteed by correlated differential private mechanisms.
An Illegal Data Supervision Scheme for the Consortium Blockchain

With the rapid development of blockchain, many enterprise-level applications have used consortium blockchain to ensure the system’s immutability and decentralization. However, due to the immutability of blockchain, the cost of removing illegal data on the blockchain is relatively high. Although the permission blockchain has restricted users’ actions, there will still be illegal data on the chain, which brings great regulatory difficulties to the government. Therefore, how to efficiently supervise the blockchain has become an important issue. In this article, in order to supervise illegal data with different impacts, we propose a hierarchical blocking scheme for illegal data in consortium blockchain. First, for illegal data with little impact, we use regular expressions on the application side to prevent users from accessing illegal data. Second, the smart contract can be used by supervisors to mark illegal data with greater impact. The experimental results show the effectiveness and practicability of our scheme.
Autonomous Anti - interference Identification of $\text{IoT}$ Device Traffic based on Convolutional Neural Network

Network traffic classification plays a vital role in many fields such as intrusion detection, network management, and network security. As the proportion of IoT device traffic increases, many approaches to identifying IoT device types through traffic have emerged. Specifically, Deep Learning (DL) has been proven to be a more efficient approach for encrypted traffic identification than other traditional methods. However, most existing classification models are created in static datasets from the closed world, so they can only classify within a limited domain. In this case, interfering traffic in the open world is easily misidentified by classifiers as IoT device traffic. An autonomous framework is proposed to tackle this issue, effectively identifying the device type according to the grayscale graph generated by packet payload and automatically updating to adapt to the unknown environment in the open world. The core of the proposed framework consists of a packet graph-vector transformer, a CNN-based classifier, and an autonomous optimizer. The optimizer can filter interfering data and optimize the model by updating the training dataset. We comprehensively evaluated the proposed framework on two datasets, one taken from the UNSW IoT traces and the other collected by our experiments, containing traffic generated from two devices and three open-world scenarios. The results demonstrate that the proposed framework can update the training dataset by unsupervised filtering interference packets, enabling the model to automatically suit complex environments for accurate and robust IoT device type identification in the open world.