Reviewerly/exHarmony huggingface dataset:
Design of Human Motion Training Motion Capture Model Based on Improved PSO Algorithm

Sports training is an effective way to improve athletes’ performance and psychological quality. Various computer hardware and software systems have been greatly improved, which provides technical support for sports training. In order to effectively reduce the loss caused by athletes’ wrong actions and improve their hit rate, this paper explores the human motion training motion capture model based on improved PSO algorithm. In this paper, the auxiliary training system of improved PSO algorithm and human motion capture model is studied by means of experimental comparison and algorithm comparison. Experimental data show that the number of iterations of the improved PSO algorithm is mostly less than 200. In this paper, the improved PSO algorithm can reasonably meet the needs of motion capture auxiliary model.
Redefining the Performance Management using Emotional Artificial Intelligence

Over the last two decades, the relevant literature has focused a lot on the human intelligence of (EI) and the machine intelligence of AI. The present research integrates these two schools of thinking and examines how employee retention and productivity are impacted by mental agility and artificial intelligence. How successfully individuals execute tasks during their internal and external services contacts with clients and colleagues, respectively. These interactions may take place either internally or outside and are categorized as either internal or external. The research demonstrates that emotive artificial intelligence has a major effect on both the performance of employees and their retention rates.
Forecasting of E-Commerce System for Sale Prediction Using Deep Learning Modified Neural Networks

E-commerce is the practice of purchasing and selling goods over the Internet. Customers will appreciate the simplicity of not having to make physical purchases using e-primary commerce. They will order the item online and deliver it to their home as soon as feasible. This research aimed to create deep learning algorithms capable of forecasting e-commerce sales. This project aims to develop and test a model for predicting online product sales over a wide range of online product types. It bases its decisions on the requirements for online product sales, the factors influencing online product sales in various industries, and the benefits of the deep learning algorithm. A continuous Stochastic Fractal Search (SFS) method for optimizing the parameters of a deep learning-modified neural network (DLMNN) is introduced in this paper. In e-commerce demand forecasting studies, a time series dataset is also analyzed. The DLMNN model’s performance improvements across multiple product categories are illustrated using a non-deep learning model as a baseline comparison. The experiment also shows that the unsupervised pretrained DLMNN model outperforms the competition in terms of sales predictions. For root mean square error, the proposed technique obtained RMSE, Mean, and Standard Deviation.
Peer-to-Peer Energy Trading Using Blockchain in Microgrid

Over these years, there has been an observable effect on the environment due to Global Climatic Change. Scientists all over the world have predicted many of these disasters, and the main reason behind this is the emission of greenhouse gas. The use of fossil fuels to generate electricity contributes significantly to greenhouse gas emissions. If this conventional fossil fuel-based energy is replaced by renewable energy resources like solar energy, wind energy, etc., more than half of the emissions can be curbed. Increasing the usage of renewable sources of energy, mostly photovoltaic devices, and enabling distributed energy services will thereby encourage an energy trading environment among the users. This paper focuses on providing an approach to implement a prototype that integrates a microgrid and blockchain to trade electricity without an intermediary. It comprises of smart contracts and an interface which together forms a microgrid management system creating a peer-to-peer platform for users to trade electricity and thereby contributing towards sustainable and affordable energy.
Virtual Medical Assistant in English and Kannada Languages

Health care and medicine are two crucial facets of human existence. This guarantees patients the right to accurate and timely clarification of any questions they may have about their health state. A virtual assistant built with the help of Natural Language Processing [NLP] makes it simpler for consumers to consult the medical personnel in the pandemic situation from the comfort of their homes without any direct contact. This work discusses various methods for building virtual medical assistants that may interact with people to answer simple questions about health characteristics and recommend necessary actions to the user. A unique inclusion in this paper is the support to a regional language which is kannada in this case. In order to identify the ailment, the system extracts essential keywords from the chat and applies predictive analysis. The user is advised to see a doctor, take medication, or visit the hospital, depending on the severity of the symptoms. The Kannada voice-bot has a higher processing time when compared to the English voice-bot due to change in dialects. The real goal of this job is to address the user’s symptoms and offer appropriate medical advice in order to shorten the process’s time and expense requirements.
Solar Irradiation Forecast Enhancement Using Hybrid Architecture

Power balancing at the grid is much more involved process due to the fact that solar power generation is primarily weather dependent, as it is relied on solar irradiation, which is very volatile and unpredictable. Accurate solar irradiation forecasting can significantly increase the performance of solar power plants. This research is motivated by the current advancements in deep learning (DL) models and its practical use in the green energy field. The proposed model combines two DL architectures: convolutional neural network (CNN) and long short-term memory (LSTM). The effectiveness of the same is analysed by comparing with recurrent neural network (RNN) family architectures. The RNN family models are Long Short Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU). The simulations are conducted on a publicly available data set from Desert Knowledge Australia Solar Centre (DKASC), Australia. A meteorological station across the Northern Territory (NT Solar resource) collects high resolution solar and climate data from Darwin location, which is used for the experiment. From the results, it is evident that each of the bidirectional model outperform its unidirectional equivalent architectures. However, the hybrid network (CNN-LSTM) outperforms all the individual models as per the error metric analysis.
Privacy-Preserving and Verifiable Decentralized Federated Learning

Federated learning is a machine learning technique that allows multiple devices to collaboratively train a machine learning model without sharing their data with a central server. The data is kept on the local device, and the model is trained on the device itself. It also reduces the risk of data breaches and enhances privacy by keeping the data local to the device. It can improve the speed and efficiency of training large-scale models by distributing the workload across multiple devices. This paper proposes a solution to the security challenges faced by the federated learning framework, which allows global model construction without sharing raw data. Privacy-preserving and verifiable decentralized federated learning (PPVD-FL) is a framework designed for secure deep learning model training. It's a decentralized federated learning framework that pre-serves privacy and ensures verification. The framework uses an efficient and verifiable cipher-based matrix multiplication algorithm along with a suite of decentralized algorithms. It maintains the confidentiality of the global model and local updates and verifies every training step. PPVD-FL can protect privacy against various inference attacks and ensure training integrity, as shown by security analysis. Real-world experiments on datasets demonstrate its accuracy and practical performance.
Administrator Controlled Timetable Based Automatic Facial Recognition Attendance System

A facial recognition attendance system is a technology-based solution for automating the process of taking attendance in schools, offices, or other organizations. The present work mainly focuses on image capturing, training and recognition in real-time using free source Python modules. The system uses computer vision algorithm such as the Open Source Computer Vision (OpenCV) Library to recognize and identify individuals based on their facial features. Additionally, the Numerical Python (NumPy) library is used to store and manipulate arrays of facial recognition data, such as feature vectors or embeddings. The Tkinter GUI is employed as an interface for managing attendance and allows the administrator to access the system and view attendance records for individual students. The ESP32-CAM module and thin-film-transistor liquid-crystal (TFT) display has been used together to provide a live video stream, while the server processes the video and records attendance. This system would improve accuracy of attendance records because it will remove all the hassles of roll calling (in case of classroom attendance of students) and will save valuable time of the teachers as well as students.
Gone but Not Forgotten: A Novel Approach for Collecting Deleted Tweets and TTL Prediction

Social media networks, including Twitter, allow users to delete their posts. Analysing deleted tweets is crucial for identifying spam or malicious accounts and gaining insights into communication patterns, biases, political discourse, and data accuracy. Curating a dataset of deleted tweets is therefore important for a comprehensive analysis of problems in the social media landscape. However, traditional methods of collecting deleted tweets are costly and require a paid subscription. In this paper, we propose a novel method for curating a dataset of deleted tweets using publicly available information. Using the proposed methodology, we introduce a large-scale dataset of deleted tweets, ‘Tweeleted’, which includes over a million deleted tweets with associated metadata such as hashtags, account mentions, and location information. We perform an initial analysis of the dataset and propose a machine learning-based approach to predict the time to live (TTL) for a tweet, which can be useful for researchers, businesses, and legal teams in prioritising the tweets to analyse or collect as evidence.
PCTS: Partition Based Clustering for Text Summarization

The exponential growth of digital data has resulted in an unprecedented amount of information being generated on a daily basis. It has become increasingly difficult to keep up with the sheer volume of information, and manual text summarization has become a tedious and time-consuming task. As a result, text summarization has grown in significance as a field of study in natural language processing. This study offers a text summarizing method that identifies a text's key sentences using partition-based clustering and similarity metrics. The sentence similarity score is computed using Euclidian Distance (Euc), Cosine Similarity (Cos), and Jaccard Similarity (Jac). The proposed model uses possible combinations of clustering and similarity algorithms and is validated over the Document Understanding Conferences (DUC) dataset. The proposed model combination of K-Mean clustering with cosine similarity shows significantly better results than the other summarizers. Overall, this paper provides an efficient and effective way to generate text summaries that capture the essential information in a given text.
How Different are the Cloud Workloads? Characterizing Large-Scale Private and Public Cloud Workloads

With the rapid development of cloud systems, an increasing number of service workloads are deployed in the private cloud and/or public cloud. Although large cloud providers such as Azure and Google have published workload traces in the past, prior work has not focused on analyzing and characterizing the differences between private and public cloud workloads in detail. Based on our experience working with Azure, one of the most widely used cloud platforms in the world, we find that the workload characteristics are different between the private and public cloud workloads. Specifically, compared with the public cloud workloads, the private cloud workloads tend to be more homogeneous in both deployment sizes and utilization patterns, more static with occasional bursts in deployment characteristics, and more region-agnostic regarding the sensitivity to deployed regions. Our findings gain several insights and implications on cloud management and motivate us to build a centralized workload knowledge base.
SecDDR: Enabling Low-Cost Secure Memories by Protecting the DDR Interface

The security goals of cloud providers and users include memory confidentiality and integrity, which requires implementing replay attack protection (RAP). RAP can be achieved using integrity trees or mutually authenticated channels. Integrity trees incur significant performance overheads and are impractical for protecting large memories. Mutually authenticated channels have been proposed only for packetized memory interfaces that address only a very small niche domain, require fundamental changes to memory system architecture, and assume fully-trusted modules. We propose SecDDR, a low-cost RAP that targets direct-attached memories, like DDRx. SecDDR avoids memory-side data authentication, and thus, only adds a small amount of logic to memory components and does not change the underlying DDR protocol, making it practical for widespread adoption. In contrast to prior mutual authentication proposals, which require trusting the entire memory module, SecDDR targets untrusted modules by placing its limited security logic on the DRAM die (or package) of the ECC chip. Our evaluation shows that SecDDR performs within 1% of an encryption-only memory without RAP and that SecDDR provides 18.8% and 7.8% average performance improvements (up to 190.4% and 24.8%) relative to a 64-ary integrity tree and an authenticated channel, respectively.
Efficient and Privacy-Preserving Cloud-Based Medical Diagnosis Using an Ensemble Classifier With Inherent Access Control and Micro-Payment

Decision tree (DT) models are widely used in medical applications where the size of the data sets is usually small or medium. Moreover, DT ensemble models are preferred over single DT models because of their higher accuracy in spite of the need for more overhead due to using multiple trees. Several schemes have been proposed for privacy-preserving cloud-based medical diagnosis using ensemble models. However, these schemes suffer from several limitations. First, they suffer from high computation/communication overheads due to using inefficient public-key cryptosystems. Second, none of them can simultaneously protect the intellectual property of the model and preserve the privacy of the patients’ data and diagnosis results. Finally, they do not provide inherent access control for the outsourced model and micropayment, in which only the registered patients can use the model and pay for the service. In this article, we develop a lightweight and privacy-preserving cloud-based medical diagnosis scheme using ensemble models with high accuracy and acceptable overhead. Using our scheme, the model owner can control the patients who can use the model. Also, for each classification operation, patients must make a micro-payment to pay for the diagnosis service. Our analysis indicates that our scheme can protect the model’s intellectual property and diagnose diseases without leaking any sensitive information about the patients’ medical data and the diagnosis results. Our experimental results demonstrate that our scheme requires less communication/computation overhead compared to the existing schemes.
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.
Directed Acyclic Transformer Pre-training for High-quality Non-autoregressive Text Generation

Abstract Non-AutoRegressive (NAR) text generation models have drawn much attention because of their significantly faster decoding speed and good generation quality in machine translation. However, in a wider range of text generation tasks, existing NAR models lack proper pre-training, making them still far behind the pre-trained autoregressive models. In this paper, we propose Pre-trained Directed Acyclic Transformer (PreDAT) and a novel pre-training task to promote prediction consistency in NAR generation. Experiments on five text generation tasks show that our PreDAT remarkably outperforms existing pre-trained NAR models (+4.2 score on average) and even achieves better results than pre-trained autoregressive baselines in n-gram-based metrics, along with 17 times speedup in throughput. Further analysis shows that PreDAT benefits from the unbiased prediction order that alleviates the error accumulation problem in autoregressive generation, which provides new insights into the advantages of NAR generation.1
TrackDLO: Tracking Deformable Linear Objects Under Occlusion With Motion Coherence

The TrackDLO algorithm estimates the shape of a Deformable Linear Object (DLO) under occlusion from a sequence of RGB-D images. TrackDLO is vision-only and runs in real-time. It requires no external state information from physics modeling, simulation, visual markers, or contact as input. The algorithm improves on previous approaches by addressing three common scenarios which cause tracking failure: tip occlusion, mid-section occlusion, and self-occlusion. This is achieved through the application of Motion Coherence Theory to impute the spatial velocity of occluded nodes, the use of the topological geodesic distance to track self-occluding DLOs, and the introduction of a non-Gaussian kernel that only penalizes lower-order spatial displacement derivatives to reflect DLO physics. Improved real-time DLO tracking under mid-section occlusion, tip occlusion,and self-occlusion is demonstrated experimentally. The source code and demonstration data are publicly released.
Reasoning over Public and Private Data in Retrieval-Based Systems

Abstract Users an organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private context is important to personalize open-domain tasks such as question-answering, fact-checking, and personal assistants. State-of-the-art systems for these tasks explicitly retrieve information that is relevant to an input question from a background corpus before producing an answer. While today’s retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, users are often unable or unwilling to expose their private data to entities hosting public data. We define the Split Iterative Retrieval (SPIRAL) problem involving iterative retrieval over multiple privacy scopes. We introduce a foundational benchmark with which to study SPIRAL, as no existing benchmark includes data from a private distribution. Our dataset, ConcurrentQA, includes data from distinct public and private distributions and is the first textual QA benchmark requiring concurrent retrieval over multiple distributions. Finally, we show that existing retrieval approaches face significant performance degradations when applied to our proposed retrieval setting and investigate approaches with which these tradeoffs can be mitigated. We release the new benchmark and code to reproduce the results.1
Adversarial Multi-Task Learning for Mandarin Prosodic Boundary Prediction With Multi-Modal Embeddings

Prosodic boundaries are still crucial to the naturalness of end-to-end speech synthesis systems. This article proposes to use adversarial multi-task learning to predict prosodic boundaries. Adversarial multi-task learning is utilized to transfer knowledge from an auxiliary POS tagging task to a prosodic boundary prediction task. Furthermore, multi-modal embeddings are composed of contextual word and speech embedding features obtained from the pre-trained bidirectional encoder representations from transformers (BERT) model and Speech2Vec. We can utilize linguistic and acoustic information from large amounts of external text and speech data without prosodic boundary labels. At the inference stage, the prosodic boundary predicting model can use the syntactic features learnt from the POS tagging task without any extra computation cost due to only employing the prosodic boundary predicting task to decode. We conducted experiments on Mandarin datasets. The results show that the models using multi-modal embeddings from the pre-trained BERT and Speech2Vec outperform the models trained with single modal embedding. Furthermore, the models trained with adversarial training obtain further performance gains by up to 2.95% in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F_{1}$</tex-math></inline-formula> score.