Reviewerly/exHarmony huggingface dataset:
Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm

Capturing time and frequency relationships of time series signals offers an inherent barrier for automatic human activity recognition (HAR) from wearable sensor data. Extracting spatiotemporal context from the feature space of the sensor reading sequence is challenging for the current recurrent, convolutional, or hybrid activity recognition models. The overall classification accuracy also gets affected by large size feature maps that these models generate. To this end, in this work, we have put forth a hybrid architecture for wearable sensor data-based HAR. We initially use Continuous Wavelet Transform to encode the time series of sensor data as multi-channel images. Then, we utilize a Spatial Attention-aided Convolutional Neural Network (CNN) to extract higher-dimensional features. To find the most essential features for recognizing human activities, we develop a novel feature selection (FS) method. In order to identify the fitness of the features for the FS, we first employ three filter-based methods: Mutual Information (MI), Relief-F, and minimum redundancy maximum relevance (mRMR). The best set of features is then chosen by removing the lower-ranked features using a modified version of the Genetic Algorithm (GA). The K-Nearest Neighbors (KNN) classifier is then used to categorize human activities. We conduct comprehensive experiments on five well-known, publicly accessible HAR datasets, namely UCI-HAR, WISDM, MHEALTH, PAMAP2, and HHAR. Our model significantly outperforms the state-of-the-art models in terms of classification performance. We also observe an improvement in overall recognition accuracy with the use of GA-based FS technique with a lower number of features. The source code of the paper is publicly available here https://github.com/apusarkar2195/HAR_WaveletTransform_SpatialAttention_FeatureSelection.
A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh-Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT's reduced features obtained from the three DL models. Additionally, the three DL models' PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.
Strategies for the determination of the running coupling of ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math> )-dimensional QED with quantum computing

We propose to utilize noisy-intermediate-scale-quantum-era quantum devices to compute short distance quantities in ($2+1$)-dimensional QED and to combine them with large volume Monte Carlo simulations and perturbation theory. On the quantum computing side, we perform a calculation of the mass gap in the small and intermediate regime, demonstrating, in the latter case, that it can be resolved reliably. The so obtained mass gap can be used to match corresponding results from Monte Carlo simulations, which can be used eventually to set the physical scale. In this paper we provide the setup for the quantum computation and show results for the mass gap and the plaquette expectation value. In addition, we discuss some ideas that can be applied to the computation of the running coupling. Since the theory is asymptotically free, it would serve as a training ground for future studies of QCD in $3+1$ dimensions on quantum computers.
Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning

Our target is to learn visual correspondence from unlabeled videos. We develop Liir, a locality-aware inter-and intra-video reconstruction method that fills in three missing pieces, i.e., instance discrimination, location awareness, and spatial compactness, of self-supervised correspondence learning puzzle. First, instead of most existing efforts focusing on intra-video self-supervision only, we exploit cross-video affinities as extra negative samples within a unified, inter-and intra-video reconstruction scheme. This enables instance discriminative representation learning by contrasting desired intra-video pixel association against negative inter-video correspondence. Second, we merge position information into correspondence matching, and design a position shifting strategy to remove the side-effect of position encoding during inter-video affinity computation, making our Liir location-sensitive. Third, to make full use of the spatial continuity nature of video data, we impose a compactness-based constraint on correspondence matching, yielding more sparse and reliable solutions. The learned representation surpasses self-supervised state-of-the-arts on label propagation tasks including objects, semantic parts, and keypoints.
Highly-efficient Incomplete Largescale Multiview Clustering with Consensus Bipartite Graph

Multiview clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations. Most previous methods hold the assumption that each instance appears in all views. However, it is not uncommon to see that some views may contain some missing instances, which gives rise to incomplete multi-view clustering (IMVC) in literature. Although many IMVC methods have been recently proposed, they always encounter high complexity and expensive time expenditure from being applied into large-scale tasks. In this paper, we present a flexible highly-efficient incomplete large-scale multi-view clustering approach based on bipartite graph framework to solve these issues. Specifically, we formalize multi-view anchor learning and incomplete bipartite graph into a unified framework, which coordinates with each other to boost cluster performance. By introducing the flexible bipartite graph framework to handle IMVC for the first practice, our proposed method enjoys linear complexity respecting to instance numbers, which is more applicable for large-scale IMVC tasks. Comprehensive experimental results on various benchmark datasets demonstrate the effectiveness and efficiency of our proposed algorithm against other IMVC competitors. The code is available at <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/wangsiwei2010/CVPR22-IMVC-CBG.
Exploring the Benefits and Challenges of AI-Language Learning Tools

AI language learning tools are computer programs or software applications that use artificial intelligence (AI) algorithms to help users learn and improve their skills in a foreign language. These technologies provide a variety of advantages, including the capacity to reduce time and increase learning speed, to give learners personalized learning experiences, and to introduce them to other cultures. In this study, scientific literature has been reviewed in order to analyze opportunities, challenges and limitations to using AI language learning tools, including the need for more human interaction, and contextual nuances of language, and dependence on large amounts of data for training. &#x0D; These AI tools have several potential future advances, including integrating VR and AR technologies, improving natural language processing algorithms, and developing more advanced adaptive learning algorithms. By addressing these issues and constraints, AI learning systems have the potential to become even more powerful and impactful tools for language learning, and their integration plays a crucial role in designing more effective and efficient learning user experience solutions.&#x0D;
Malaria Detection Using Advanced Deep Learning Architecture

Malaria is a life-threatening disease caused by parasites that are transmitted to humans through the bites of infected mosquitoes. The early diagnosis and treatment of malaria are crucial for reducing morbidity and mortality rates, particularly in developing countries where the disease is prevalent. In this article, we present a novel convolutional neural network (CNN) architecture for detecting malaria from blood samples with a 99.68% accuracy. Our method outperforms the existing approaches in terms of both accuracy and speed, making it a promising tool for malaria diagnosis in resource-limited settings. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Additionally, we present an analysis of model performance on different subtypes of malaria and discuss the implications of our findings for the use of deep learning in infectious disease diagnosis.
How artificial intelligence will shape the future of metaverse. A qualitative perspective

The announcement of Facebook's rebranding to Meta and heavy investments in the metaverse by companies like Microsoft have brought attention to the metaverse as the next major technological advancement. The concept of Metaverse Seoul was recently unveiled, providing a virtual communication ecosystem for municipal administrative fields and specific services for persons with impairments utilizing XR technology. This study uses a qualitative perspective to explore how artificial intelligence (AI) will shape the future of the metaverse. Three methodological approaches were employed to address this research: literature review, historical-logical and phenomenological approach, and synthesis analysis. We have carefully considered how artificial intelligence has affected the development of the metaverse and how it can increase user immersion in virtual reality. The introduction to this book introduced the basic concepts of the metaverse, the techniques of artificial intelligence, and the role of artificial intelligence in the metaverse. After that, there are several important technology areas - including NLP, machine vision, blockchain, networks, deep learning, and neural interface - and a wide range of application areas - including healthcare, manufacturing, smart cities, gaming and DeFi. researched. The analyzed AI-based solutions have shown that AI has enormous potential to reinforce system design, improve built-in services in virtual worlds, and improve the 3D immersive experience. Finally, we examined well-known metaverse projects that included AI to improve service quality and consider the metaverse ecology. We will now list many metaverse-based AI research trajectories.
Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review

Abstract The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.
A fuzzy C-means algorithm for optimizing data clustering

Big data has increasingly become predominant in many research fields affecting human knowledge, including medicine and engineering. Cluster analysis, or clustering, is widely recognized as one of the most effective processes to deal with various types of data, especially big data. There has been considerable interest in Fuzzy C-Means (FCM) as a method for clustering data using a short-distance approach in data mining. However, despite its simplicity, this method is not suitable for clustering large data sets due to their complex structure. In particular, FCM is sensitive to cluster center initialization, and an improper initialization can result in slow or non-optimal convergence. In order to solve the FCM convergence problem and find more appropriate cluster centers, optimization methods are generally used. In this study, a whale optimization algorithm is applied to solve the problem. As a solution to the problem of big data clustering, random sampling, clustering on samples, and extending the clustering results to all data are proposed. The proposed algorithm is implemented on several large data sets, both artificial and real, with many features after normalization and standardization. To verify the validity and correctness of the performance of the proposed algorithm, the same data sets have been clustered with other known algorithms, and the results compared using several valid fuzzy indices. Based on the comparison results, it can be concluded that the proposed algorithm is more powerful and efficient than other algorithms and, hence, can be used to effectively cluster large data sets. Our study can benefit organizations and managers who have a large amount of data and are unable to classify or make use of them properly. Using big data takes a lot of time. The features of the proposed algorithm would be of great help to managers allowing them to make better decisions and improve the quality of their work.
Mobility-Aware Multiobjective Task Offloading for Vehicular Edge Computing in Digital Twin Environment

In vehicular edge computing (VEC), vehicle users (VUs) can offload their computation-intensive tasks to edge server (ES) that provides additional computation resources. Due to the edge server being closer to VUs, the propagation delay between the ESs and the VUs is lower compared to cloud computing. Applying digital twin to VEC allows for low-cost trial in task offloading. In real-word, the mobility of VUs cannot be ignored and the downlink delay in receiving process results from ES is related to the mobility of VUs. Therefore, a five-objective optimization model including downlink delay, computation delay, energy consumption, load balancing, and user satisfaction of the VUs is constructed. To solve the above model, an improved CMA-ES algorithm based on the guiding point (GP-CMA-ES) is proposed. When the number of VUs increases, the dimension of variables also increases. Therefore, a convergence-related variable grouping strategy based on the relationship detection between variables and objectives is proposed. The performance of algorithm GP-CMA-ES is compared with five algorithms in the digital twin environment.
Sorting networks: To the end and back again

New properties of the front and back ends of sorting networks are studied, illustrating their utility when searching for bounds on optimal networks. Search focuses first on the "out-sides" of the network and then on the inner part. Previous works focused on properties of the front end to break symmetries in the search. The new, out-side-in, properties shed understanding on how sorting networks sort, and facilitate the computation of new bounds on optimality. We present new, faster, parallel sorting networks for 17–20 inputs. For 17 inputs, we show that no sorting network using less layers exists.
Correlation measure detecting almost all non-Markovian evolutions

We investigate the ability of correlation measures to witness non-Markovian open quantum system dynamics. It is shown that the mutual information and any entanglement measure between the system and an ancilla do not witness all non-Markovian dynamics. A correlation measure is introduced, and it is proven that, in an enlarged setting with two ancillary systems, this measure detects almost all non-Markovian dynamics, except possibly a zero-measure set of dynamics that is non-bijective in finite time-intervals. Our proof is constructive and provides different initial states detecting the non-Markovian evolutions. These states are all separable and some are arbitrarily close to a product state.
Online Bidirectional Trajectory Planning for Mobile Robots in State-Time Space

In this paper, we propose a computationally efficient online local motion planning algorithm for mobile robots in unknown cluttered dynamic environments. The algorithm plans a trajectory incrementally up to the finite horizon in state-time space. Incremental planning method is capable of fast computation but has poor obstacle avoidance performance. To compensate for the drawbacks of incremental planning, a partial trajectory modification scheme is used that sets an interim goal and then plans a trajectory to pass through the interim goal. The interim goal is a temporary desired goal to prevent the robot from falling into the inevitable collision state. By using incremental planning and partial trajectory modification, it is possible to plan collision-free trajectory with small computation even in cluttered dynamic environment. To generate smooth trajectories around given waypoints and goal, we systematize bidirectional trajectory planning with three kinds of trajectories: 1) a forward trajectory from the current robot state; 2) a backward trajectory from the state of current target waypoint; 3) and a connecting trajectory between the forward and backward trajectories. A smooth trajectory is generated around the way point and goal by setting the state of the current target waypoint (or goal), while taking into account the positional relationship among the planned forward trajectory, the target waypoint and the next waypoint. Performances of the proposed algorithm are validated through extensive simulations and experiment with two types of mobile robots: 1) a holonomic mobile robot and 2) a differential drive mobile robot.
Statistical analysis of quantum-entangled-network generation

We develop techniques to analyse the statistics of completion times of non-deterministic elements in quantum entanglement generation, and how they affect the overall performance as measured by the secret key rate. By considering such processes as Markov chains, we show how to obtain exact expressions for the probability distributions over the number of errors that a network acquires, as well as the distribution of entanglement establishment times. We show how results from complex analysis can be used to analyse Markov matrices to extract information with a lower computational complexity than previous methods. We apply these techniques to the Innsbruck quantum repeater protocol, and find that consideration of the effect of statistical fluctuations tightens bounds on the secret key rate by 3 orders of magnitude. We also use the theory of order statistics to derive tighter bounds on the minimum quantum memory lifetimes that are required in order to communicate securely.
Secure Quaternion Feistel Cipher for DICOM Images

An improved and extended version of a quaternion-based lossless encryption technique for digital image and communication on medicine (DICOM) images is proposed. We highlight and address several security flaws present in the previous version of the algorithm originally proposed by Dzwonkowski <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2015). The newly proposed secure quaternion Feistel cipher (S-QFC) algorithm retains the concept of a modified Feistel network with modular arithmetic and the use of special properties of quaternions to perform rotations of data sequences in 3D space for each of the cipher rounds. A new and more secure key generation scheme based on quaternion Julia sets is utilized. We also introduce both-sided modular matrix multiplications for the encryption and decryption processes. The proposed S-QFC algorithm eliminates the major security flaws of its predecessor while maintaining high computation speed in comparison to other algorithms originally embedded in DICOM (i.e., advanced encryption standard and triple data encryption standard). A computer-based analysis has been carried out. Simulation results and cryptanalysis are shown at the end of this paper.
Efficiently Predicting Hot Spots in PPIs by Combining Random Forest and Synthetic Minority Over-Sampling Technique

Hot spot residues bring into play the vital function in bioinformatics to find new medications such as drug design. However, current datasets are predominately composed of non-hot spots with merely a tiny percentage of hot spots. Conventional hot spots prediction methods may face great challenges towards the problem of imbalance training samples. This paper presents a classification method combining with random forest classification and oversampling strategy to improve the training performance. A strategy with an oversampling ability is used to generate hot spots data to balance the given training set. Random forest classification is then invoked to generate a set of forest trees for this oversampled training set. The final prediction performance can be computed recursively after the oversampling and training process. This proposed method is capable of randomly selecting features and constructing a robust random forest to avoid overfitting the training set. Experimental results from three data sets indicate that the performance of hot spots prediction has been significantly improved compared with existing classification methods.