Reviewerly/exHarmony huggingface dataset:
An overview of Markov Chains for monitoring indoor movements and ambient motion detection

Background:In recent years and due to the availability of non-wearable sensing technologies, there has been a widespread interest in developing methods for monitoring the movements of older adults and further estimating any anomalies.Objective: Low-cost motion detection has been gaining particular attention due to its widespread availability, ease of deployment, and inherent property in protecting privacy.In addition, and due to the cost-benefits, it has become an attractive alternative for the low-income sector of society.This paper presents an overview of a monitoring framework and a review of the literature using motion detection sensors.Method: Markov Chains (MC) have been explored by many researchers as a suitable framework for monitoring and estimating sequences of events associated with movements and activities which can further be used as a part of an anomaly detection system in a sensor network.This paper presents an overview of this method and related literature.Results: A brief overview of MC and the related literature with some insights and challenges associated with the potential limitations and future extensions.One of the challenges of utilizing MC is the definition of what can be considered the state of movements and activities and what can be used as a measure of such state.In this context, various extensions of MC have been utilized where the state of the system can not be measured directly and are defined in a form of Hidden Markov Models (HMM).Conclusion: Proper deployment of motion detection sensors and associated MC for monitoring requires an in-depth understanding and co-designing of the system with the family members, care providers, and engineers in order to fully take advantage of such technology.Depending on the number and properties of the selected sensors (such as their effective range and the inherent time delay), particular attention needs to be paid to what can constitute a measurable (observable) state and what can be further defined as a hidden state.
DISTIN螩T: Data poISoning atTacks dectectIon usiNg opt蟤ized jaCcard燿isTance

Machine Learning (ML) systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system's performance.Data poisoning attacks are challenging to detect, and even more difficult to respond to, particularly in the Internet of Things (IoT) environment.To address this problem, we proposed DISTINÏCT, the first proactive data poisoning attack detection framework using distance measures.We found that Jaccard Distance (JD) can be used in the DISTINÏCT (among other distance measures) and we finally improved the JD to attain an Optimized JD (OJD) with lower time and space complexity.Our security analysis shows that the DISTINÏCT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINÏCT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.
Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB.The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance.At the same time, optimal hyperparameter tuning process plays a vital role to enhance overall results.This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification (TLGODL-CBC) model in Social Media.The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context.Initially, the input data is cleaned and pre-processed to make it compatible for further processing.Followed by, independent recurrent autoencoder (IRAE) model is utilized for the recognition and classification of CBs.Finally, the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work.To assuring the improved outcomes of the TLGODL-CBC approach, a wide range of simulations are executed and the outcomes are investigated under several aspects.The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.
Arabic Articulation Disorder Detection System Using Long Short-Term Memory

The Arabic language has always been an immense source of attraction to various people from different ethnicities by virtue of the significant linguistic legacy that it possesses. Consequently, a multitude number of people from all over the world are yearning to learn it. However, people from different mother tongues and cultural backgrounds might experience some hardships regarding articulation due to the absence of some particular letters only available in the Arabic language that could hinder the learning process. As a result, a speaker-independent and text-dependent efficient system that aims to detect articulation disorders has been implemented. In this design, we highlight the importance of speech signal processing in diagnosing Arabic articulation disorder using the Mel-Frequency Cepstral Coefficients (MFCCs) as the optimum extracted features. Furthermore, Long Short-Term Memory (LSTM) has also been utilized for the classification process. Our results show that using the LSTM network significantly enhances mispronunciation detection. Thus, the LSTM models have achieved an average accuracy of 98.6% in the proposed system, reflecting a high performance compared to that of previous miss pronunciation detection systems. Additionally, the system has attained the highest accuracies of 99.789% and 99.827% when uttering the letter ج/ʒ/ alone or in the middle of a word, respectively.
Detecting and Classifying Vehicles Under Uncontrolled Environmental Conditions: A Transfer Learning Based Approach

Vehicle detection and classification is an active area of research in vehicle surveillance system and has numerous applications in intelligent transportation system. In vehicle surveillance system, identifying the make and model of a vehicle is crucial for traffic monitoring. Due to intraclass diversity, view-point variation, and variable illumination conditions, identifying vehicle make and model is a difficult process. Past researchers have used different datasets for the detection and classification of vehicle as per their make and model. However, majority of these studies have used datasets of high-quality images with clear images. The present study uses the dataset with vehicle images taken under uncontrolled environmental conditions such as images with different illumination, shadowing, reflection from vehicle surface etc. and uses transfer learning approach to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify vehicles. The study further compares and analyses the accuracy and precision of two transfer Learning techniques. The study deploys machine learning classifiers for classifying the vehicles as per their models.
Joint Strategic Emerging Issues Team-Submission of Comments re Profit Shifting

The attached letter includes concrete suggestions for the IRS Joint Strategic Emerging Issues Team to consider. The approaches suggested attack profit-shifting structures and have the potential to collect additional taxes avoided through aggressive and often sham uses of these structures.In particular, the letter and its appendices focus on (i) the application of the periodic adjustment mechanism (which implements the section 482 "commensurate with income" Congressional mandate in transfer pricing for intangible transfers) and (ii) effectively connected income taxation. The letter also refers to four articles authored or co-authored by Stephen Curtis that use publicly available information to calculate amounts of tax due under the periodic adjustment mechanism for four multinationals (Facebook, Apple, Google, and eBay). These articles also make the case for application of ECI taxation to these four multinationals. The letter refers to these as "low-hanging fruit" for the IRS to pursue.
Teda:Tensor Embedding Domain Adaptation for Automatic Modulation Classification

Automatic modulation classification (AMC) facilitates important IoT device identification. Recently, transfer learning models have been used for modulation recognition in order to solve the problem of small samples or zero samples of signals, which outperforms direct training on small samples or human empirical analysis. However, the application of transfer learning to AMC remains challenging for the following reasons. Model-based TL is convenient and thus popular in AMC, but it assumes rich sample labels in the source domain, which is increasingly difficult in IOT scenarios, while data/feature-based DA methods are mostly in the visual domain, so it is difficult to control the effect applied to AMC. To address these challenges, we propose a Tensor Embedded Domain Adaptation (TEDA) approach to address these challenges. TEDA directly learns the latent subspace of the tensors formed by the time-frequency maps of the signals, avoiding the loss of information converted into vectors, and at the same time using the multi-dimensional domain information of the signals to jointly learn the shared subspace of the source domain and the target domain. And perform dynamic distribution alignment in the shared subspace while learning a structural risk minimization classifier. The experimental results show that under the modulated signal data, compared with the state-of-the-art DA algorithm, TEDA has less requirements on the number of samples and categories in the source and target domains, and has strong anti-noise ability.
Reconciliation of Mental Concepts with Graph Neural Networks

In the digital age, knowledge processes can be formalized and simplified using task management systems. As they evolve, so must the underlying schemata to retain harmony and concurrency with the real world. In this work we present a graph neural network model that can help in reconciling these data. It can do so by leveraging a novel propagation rule that does not presume reciprocal dependency but is able to represent it still. Thereby it can predict structures in the form of usage links with high accuracy and assist in the reconstruction of missing information. We evaluate this model on a new knowledge management dataset and show that it is superior to traditional embedding methods. Further, we show that it outperforms related work in an established general link prediction task.
A Divergent Index Advisor Using Deep Reinforcement Learning

A divergent design index is a tuning method that employs replication to specialize the index configuration of each replica in a replicated database for a subset of a workload to minimize the total processing cost of the workload. Studies show that this tuning method improves the workload performance in comparison with the case that all replicas have the same index configuration. Current divergent design algorithms do not have any mechanism to learn about the effectiveness of the recommended index sets. Moreover, they solely rely on the query optimizer's cost estimation, which can be inaccurate. To tackle these problems, we introduce a new divergent index advisor, DINA, that learns the goodness of the workload partitioning among replicas and the efficiency of their index configurations by employing a Deep Reinforcement Learning (DRL) algorithm. The DRL agent explores various possible workload partitions and learns the benefit of their index configurations via performance observation. We conduct experiments using the TPC-H and TPC-DS database benchmarks to evaluate the performance of DINA. The experiments show that DINA yields better query execution time than the existing algorithms.
Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

When utilizing the deep learning models in some real applications, the distribution of the labels in the environment can be used to increase the accuracy.Generally, to compute this distribution, there should be the validation set that is labeled by the ground truths.On the other side, the dependency of ground truths limits the utilization of the distribution in various environments.In this paper, we carried out a novel system for the deep learning-based classification to solve this problem.Firstly, our system only uses one validation set with ground truths to compute some hyper parameters, which is named as one-shot guidance.Secondly, in an environment, our system builds the validation set and labels this by the prediction results, which does not need any guidance by the ground truths.Thirdly, the computed distribution of labels by the validation set selectively cooperates with the probability of labels by the output of models, which is to increase the accuracy of predict results on testing samples.We selected six popular deep learning models on three real datasets for the evaluation.The experimental results show that our system can achieve higher accuracy than state-of-art methods while reducing the dependency of labeled validation set.
Enhancing Blockchain Security Using Ripple Consensus Algorithm

In the development of technology in various fields like big data analysis, data mining, big data, cloud computing, and blockchain technology, security become more constrained.Blockchain is used in providing security by encrypting the sharing of information.Blockchain is applied in the peerto-peer (P2P) network and it has a decentralized ledger.Providing security against unauthorized breaches in the distributed network is required.To detect unauthorized breaches, there are numerous techniques were developed and those techniques are inefficient and have poor data integrity.Hence, a novel technique needs to be implemented to tackle the new breaches in the distributed network.This paper, proposed a hybrid technique of two fish with a ripple consensus algorithm (TF-RC).To improve the detection time and security, this paper uses efficient transmission of data in the distributed network.The experimental analysis of TF-RC by using the metric measures of performance in terms of latency, throughput, energy efficiency and it produced better performance.
A Deep Dive into Deep Cluster

Deep Learning has demonstrated a significant improvement against traditional machine learning approaches in different domains such as image and speech recognition. Their success on benchmark datasets is transferred to the real-world through pretrained models by practitioners. Pretraining visual models using supervised learning requires a significant amount of expensive data annotation. To tackle this limitation, DeepCluster - a simple and scalable unsupervised pretraining of visual representations - has been proposed. However, the underlying work of the model is not yet well understood. In this paper, we analyze DeepCluster internals and exhaustively evaluate the impact of various hyperparameters over a wide range of values on three different datasets. Accordingly, we propose an explanation of why the algorithm works in practice. We also show that DeepCluster convergence and performance highly depend on the interplay between the quality of the randomly initialized filters of the convolutional layer and the selected number of clusters. Furthermore, we demonstrate that continuous clustering is not critical for DeepCluster convergence. Therefore, early stopping of the clustering phase will reduce the training time and allow the algorithm to scale to large datasets. Finally, we derive plausible hyperparameter selection criteria in a semi-supervised setting.
Single Input-Based CNN-LSTM and CNN-GRU Based HAR Using Wearable Sensors

Human Activity Recognition (HAR), in the past few years, has been an audience-attracting domain. Human activity recognition (HAR) can prove to have great benefits in various application areas, including elderly care, health sector, entertainment, rehabilitation, and monitoring. Existing techniques, like deep learning, have been deployed for specific activity recognition, but very less for the transitional activities’ recognition. The actual motive behind this study is to realize human behavior and inherently predict human intentions. The activity data of human beings obtained from various wearable sensors like accelerometers and gyroscopes are obtained in the time-series data format because there is a date and time associated with every reading. It is crucial for HAR to bring out the purposeful temporal features from the unprocessed data obtained from sensors. There is a good quantity of feature engineering and data preprocessing involved behind most of the HAR approaches, which requires domain expertise as well. There is one issue associated with such approaches that they consume a lot of time and are application-specific in nature. As far as this study is concerned, a model based on a Deep Neural Network, that further makes use of the CNN, and the GRU is put forward as an end-to-end model which is capable of performing classification of the activities and self-sustainable (automatic) feature extraction. The unprocessed data acquired from the wearable sensors were used for experimental purposes with nominal preprocessing. About 96.20% accuracy was obtained on the UCI-HAR datasets. The results of the experiments established that there was superior classification performance achieved by the proposed model compared to that of other similar architectures of this data. After this, we compile this model, and then this model is being trained using CNN and GRU, and LSTM. Then this trained model is used to make predictions accordingly.
A Deep Learning Approach for Crowd Counting in Highly Congested Scene

With the rapid progress of deep convolutional neural networks, several applications of crowd counting have been proposed and explored in the literature.In congested scene monitoring, a variety of crowd density estimating approaches has been developed.The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task, as a large number of individuals stand nearby and, it is hard for detection techniques to recognize them, as the crowd can vary from low density to high density.To deal with such highly congested scenes, we have proposed the Congested Scene Crowd Counting Network (CSCC-Net) using VGG-16 as a core network with its first ten layers due to its strong and robust transfer learning rate.A hole dilated convolutional neural network is used at the back end to widen the relevant field to extract a large range of information from the image without losing its original resolution.The dilated convolution neural network is mainly chosen to expand the kernel size without changing other parameters.Moreover, several loss functions have been applied to strengthen the evaluation accuracy of the model.Finally, the entire experiments have been evaluated using prominent data sets namely, ShanghaiTech parts A, B, UCF_CC_50, and UCF_QNRF.Our model has achieved remarkable results i.e., 68.0 and 9.0 MAE on ShanghaiTech parts A, B, 199.1 MAE on UCF_CC_50, and 99.8 on UCF_QNRF data sets respectively.
Word Alignment Based Transformer Model for XML Structured Documentation Translation

In the context of globalization, the development of localized translation technology for enterprise online documents is crucial for business promotion. The enterprise online documents are represented by semi-structured text documents with markup tags, while the mainstream neural machine translation methods focus on only the plain text translation. In this research, a Word Alignment based Transformer Model was proposed for markup language translation. Experiments conducted on the Salesforce XML English-Chinese datasets, and the result demonstrated that adding a word alignment model to the translation model can improve the translation model's performance in translating text with makup tags.
Two-Fold and Symmetric Repeatability Rates for Comparing Keypoint燚etectors

The repeatability rate is an important measure for evaluating and comparing the performance of keypoint detectors.Several repeatability rate measurements were used in the literature to assess the effectiveness of keypoint detectors.While these repeatability rates are calculated for pairs of images, the general assumption is that the reference image is often known and unchanging compared to other images in the same dataset.So, these rates are asymmetrical as they require calculations in only one direction.In addition, the image domain in which these computations take place substantially affects their values.The presented scatter diagram plots illustrate how these directional repeatability rates vary in relation to the size of the neighboring region in each pair of images.Therefore, both directional repeatability rates for the same image pair must be included when comparing different keypoint detectors.This paper, firstly, examines several commonly utilized repeatability rate measures for keypoint detector evaluations.The researcher then suggests computing a two-fold repeatability rate to assess keypoint detector performance on similar scene images.Next, the symmetric mean repeatability rate metric is computed using the given two-fold repeatability rates.Finally, these measurements are validated using well-known keypoint detectors on different image groups with various geometric and photometric attributes.
Mining Fluctuation Propagation Graph Among Time Series with Active Learning

Faults are inevitable in a complex online service system. Compared with the textual incident records, the knowledge graph provides an abstract and formal representation for the empirical knowledge of how fluctuations, especially faults, propagate. Recent works utilize causality discovery tools to construct the graph for automatic troubleshooting but neglect its correctness. In this work, we focus on structure discovery of the fluctuation propagation graph among time series. We conduct an empirical study and find that the existing methods either miss a large proportion of relations or discover almost a complete graph. Thus, we propose a relation recommendation framework named FPG-Miner based on active learning. The experiment shows that operators' feedback can make a mining method to recommend the correct relations earlier, accelerating the trustworthy application of intelligent algorithms like automatic troubleshooting. Moreover, we propose a novel classification-based approach named CAR to speed up relation discovery. For example, when discovering 20% correct relations, our approach shortens 2.3–42.2% of the verification quota compared with the baseline approaches.
Skeleton-Based Mutual Action Recognition Using Interactive Skeleton Graph and Joint Attention

Skeleton-based action recognition relies on skeleton sequences to detect certain predetermined types of human actions. The existing related works are inadequate in mutual action recognition. We thus propose an innovative interactive skeleton graph to represent the skeleton data. In addition, because the GCN pays attention to the information about the edges in the skeleton graph which represent the interaction between joints, we propose a joint attention module that assists the model in paying attention to the pattern of vertices which represent the joints in the skeleton graph. We validate our model on the NTU RGB-D datasets, and the experimental results demonstrate the superiority of our model against other baseline methods in terms of recognition effectiveness in understanding mutual actions.