Reviewerly/exHarmony huggingface dataset:
Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control

Modern technology has been improving, as is medical technology. Over the years, rehabilitation medicine is developing and growing. The use of rehabilitation robots to achieve the upper limb motor function of patients with hemiplegia has also become a popular research in academia. Under this background, this paper proposes an upper limb robot rehabilitation system based on Internet of Things remote control. The upper limb robotic rehabilitation system based on the Internet of Things in this paper is composed of upper computer and lower computer. Information is collected by pressure sensor. The transmission process is realized by STM32 controller, which is first transmitted to the upper computer, and then the information needs to be processed After processing, it sends control commands to the lower computer controller to control the motor drive of the rehabilitation robot, so as to realize the rehabilitation training of the patient. In order to verify the reliability of the system in this paper, this paper conducted a motion test and system dynamic performance test. The research results of this paper show that the passive motion accuracy of the system in this paper has reached more than 97%, and the active motion accuracy has reached more than 98%. In addition, the maximum speed response time of the upper limb rehabilitation robot system based on the remote control of the Internet of Things in this paper is 5.7ms. The amount of adjustment is 5.32%, and the dynamic performance is good. The research results of this paper show that the upper limb rehabilitation robot system based on the Internet of Things remote control in this paper has excellent performance, which can provide a certain reference value for the research of rehabilitation robot.
Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.
Object Tracking in Unmanned Aerial Vehicle Videos via Multifeature Discrimination and Instance-Aware Attention Network

Visual object tracking in unmanned aerial vehicle (UAV) videos plays an important role in a variety of fields, such as traffic data collection, traffic monitoring, as well as film and television shooting. However, it is still challenging to track the target robustly in UAV vision task due to several factors such as appearance variation, background clutter, and severe occlusion. In this paper, we propose a novel two-stage UAV tracking framework, which includes a target detection stage based on multifeature discrimination and a bounding-box estimation stage based on the instance-aware attention network. In the target detection stage, we explore a feature representation scheme for a small target that integrates handcrafted features, low-level deep features, and high-level deep features. Then, the correlation filter is used to roughly predict target location. In the bounding-box estimation stage, an instance-aware intersection over union (IoU)-Net is integrated together with an instance-aware attention network to estimate the target size based on the bounding-box proposals generated in the target detection stage. Extensive experimental results on the UAV123 and UAVDT datasets show that our tracker, running at over 25 frames per second (FPS), has superior performance as compared with state-of-the-art UAV visual tracking approaches.
Full-Field Measurement of Residual Stresses in Composite Materials Using the Incremental Slitting and Digital Image Correlation Techniques

The slitting method is a widely used destructive technique for the determination of residual stresses. Because of the rich data content of the full-field methods, optical techniques such as digital image correlation (DIC) are replacing strain gages for surface measurements. The objective of the current paper is to overcome the difficulties that arise in using the DIC technique combined with the slitting method. The present noise, low signal-to-noise ratio, and systematic errors are the main impediments to the use of DIC in the slitting method. An approach based on the eigenstrain concept was exploited to ascertain the optimum region of interest (ROI) for the analysis. After that, a robust procedure was implemented to utilize the DIC method while excluding the rigid body motion and rotation artifacts from the obtained displacements. Different slitting steps may cause dissimilar rigid body motions and rotations of the specimen. The proposed method was able to eliminate all of these different shears and stretches in the images simultaneously. The slitting experiment was conducted on a symmetric cross-ply composite specimen, and the slit progressed down to half the thickness. Although some rigid body motions were large, the method managed to exclude all of them for eight slitting steps. A comparison made between the results of the current method and those of the strain gage technique shows that they are in acceptable agreement with each other, and this full-field method can be extended to smaller scales or other destructive techniques.
Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts

Accurate solar forecasts is one of the most effective solution to enhance grid operations. As the solar resource is intrinsically uncertain, a growing interest for solar probabilistic forecasts is observed in the solar research community. In this work, we compare two approaches for the generation of day-ahead solar irradiance probabilistic forecasts. The first class of models termed as deterministic-based models generates probabilistic forecasts from a deterministic value of the irradiance predicted by a Numerical Weather Prediction (NWP) model. The second type of models denoted by ensemble-based models issues probabilistic forecasts through the calibration of an Ensemble Prediction System (EPS) or from information (such as mean and variance) derived from the ensemble. The verification of the probabilistic forecasts is made using a sound framework. A numerical score, the Continuous Ranked Probability Score (CRPS), is used to assess the overall performance of the different models. The decomposition of the CRPS into reliability and resolution provides a further detailed insight into the quality of the probabilistic forecasts. In addition, a new diagnostic tool which evaluates the contribution of the statistical moments of the forecast distributions to the CRPS is proposed. This tool denoted by MC-CRPS allows identifying the characteristics of an ensemble that have an impact on the quality of the probabilistic forecasts. The assessment of the different models is done on several sites experiencing very different climatic conditions. Results show a general superior performance of ensemble-based models as the gain in forecast quality measured by the CRPS ranges from 4% to 16% depending on the site.
Recognition and Repetition Counting for Local Muscular Endurance Exercises in Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models

Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data, is an important technology to enable patients to perform exercises independently in remote settings, e.g., their own home. In this paper, we first report on a comparison of traditional approaches to exercise recognition and repetition counting (supervised ML and peak detection) with Convolutional Neural Networks (CNNs). We investigated CNN models based on the AlexNet architecture and found that the performance was better than the traditional approaches, for exercise recognition (overall F1-score of 97.18%) and repetition counting (±1 error among 90% observed sets). To the best of our knowledge, our approach of using a single CNN method for both recognition and repetition counting is novel. Also, we make the INSIGHT-LME dataset publicly available to encourage further research.
The Impact of E-Learning Strategy on Students' Academic Achievement. Case Study: Al- Quds Open University.

The study examines the effect of e-learning on the students’ academic performance at Al-Quds Open University. 382 students have been randomly selected from the university's official records where a statistical analysis of the study variables was performed using the Statistical Package of Social Sciences program (SPSS.20). The study hypotheses were tested by Paired Samples T-tests.The study has revealed that there are statistically significant differences in the students’ academic performance over the implementation of the e-learning strategy with regard to the level of the vast majority of students, gender, educational program, as well as their academic level.The study recommends the necessity of enhancing the e-learning strategy in the higher education institutions to foster the students’ academic performance taking into account the specificity of some learning programs such as the Arabic language programs and the social studies.
Computing square-free polarized abelian varieties over finite fields

We give algorithms to compute isomorphism classes of ordinary abelian varieties defined over a finite field $\mathbb {F}_q$ whose characteristic polynomial (of Frobenius) is square-free and of abelian varieties defined over the prime field $\mathbb {F}_p$ whose characteristic polynomial is square-free and does not have real roots. In the ordinary case we are also able to compute the polarizations and the group of automorphisms (of the polarized variety) and, when the polarization is principal, the period matrix.
Deep Hash Remote Sensing Image Retrieval with Hard Probability Sampling

As satellite observation technology improves, the number of remote sensing images significantly and rapidly increases. Therefore, a growing number of studies are focusing on remote sensing image retrieval. However, having a large number of remote sensing images considerably slows the retrieval time and takes up a great deal of memory space. The hash method is being increasingly used for rapid image retrieval because of its remarkably fast performance. At the same time, selecting samples that contain more information and greater stability to train the network has gradually become the key to improving retrieval performance. Given the above considerations, we propose a deep hash remote sensing image retrieval method, called the hard probability sampling hash retrieval method (HPSH), which combines hash code learning with hard probability sampling in a deep network. Specifically, we used a probability sampling method to select training samples, and we designed one novel hash loss function to better train the network parameters and reduce the hashing accuracy loss due to quantization. Our experimental results demonstrate that HPSH could yield an excellent representation compared with other state-of-the-art hash approaches. For the university of California, merced (UCMD) dataset, HPSH+S resulted in a mean average precision (mAP) of up to 90.9% on 16 hash bits, 92.2% on 24 hash bits, and 92.8% on 32 hash bits. For the aerial image dataset (AID), HPSH+S achieved a mAP of up to 89.8% on 16 hash bits, 93.6% on 24 hash bits, and 95.5% on 32 hash bits. For the UCMD dataset, with the use of data augmentation, our proposed approach achieved a mAP of up to 99.6% on 32 hash bits and 99.7% on 64 hash bits.
Minerva: The curse of ECDSA nonces

We present our discovery of a group of side-channel vulnerabilities in implementations of the ECDSA signature algorithm in a widely used Atmel AT90SC FIPS 140-2 certified smartcard chip and five cryptographic libraries (libgcrypt, wolfSSL, MatrixSSL, SunEC/OpenJDK/Oracle JDK, Crypto++). Vulnerable implementations leak the bit-length of the scalar used in scalar multiplication via timing. Using leaked bit-length, we mount a lattice attack on a 256-bit curve, after observing enough signing operations. We propose two new methods to recover the full private key requiring just 500 signatures for simulated leakage data, 1200 for real cryptographic library data, and 2100 for smartcard data. The number of signatures needed for a successful attack depends on the chosen method and its parameters as well as on the noise profile, influenced by the type of leakage and used computation platform. We use the set of vulnerabilities reported in this paper, together with the recently published TPM-FAIL vulnerability [MSE+20] as a basis for real-world benchmark datasets to systematically compare our newly proposed methods and all previously published applicable lattice-based key recovery methods. The resulting exhaustive comparison highlights the methods’ sensitivity to its proper parametrization and demonstrates that our methods are more efficient in most cases. For the TPM-FAIL dataset, we decreased the number of required signatures from approximately 40 000 to mere 900.
Demonstrating NISQ era challenges in algorithm design on IBM’s 20 qubit quantum computer

As superconducting qubits continue to advance technologically, the realization of quantum algorithms from theoretical abstraction to physical implementation requires knowledge of both quantum circuit construction as well as hardware limitations. In this study, we present results from experiments run on IBM’s 20-qubit “Poughkeepsie” architecture, with the goal of demonstrating various qubit qualities and challenges that arise in designing quantum algorithms. These include experimentally measuring T1 and T2 coherence times, gate fidelities, sequential CNOT gates, techniques for handling ancilla qubits, and finally CCNOT and QFT† circuits implemented on several different qubit geometries. Our results demonstrate various techniques for improving quantum circuits, which must compensate for limited connectivity, either through the use of SWAP gates or additional ancilla qubits.
Intuitionistic Fuzzy Set and Its Application in Corona Covid-19

Intuitionistic Fuzzy set (IFS) theory plays an important role in real life and engineering problems.There are many model involving fuzzy matrices to deal with different complicated aspects.Intuitionistic fuzzy set (IFS) is useful in providing a flexible model for developing the uncertainty and vagueness involved in making decisions where the theories of uncertainty are very useful to treat with mathematics that needs to address.In other words, the application of intuitionistic fuzzy sets instead of fuzzy sets means the introduction of another degree of freedom into a set description.Intuitionistic fuzzy set (IFS) called the generalization of fuzzy sets was proposed in K. T. Atanassov.So, we can use it in decision making.We examined the definition of IFS and puts new definitions of IFS (Intuitionistic fuzzy set) in this paper and suggested its implementation in the Corona Covid-19.For several similar real-life cases the suggested approach can be applied.
Face recognition using viola-jones depending on python

&lt;p&gt;In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition, The proposed software system, depending on using OpenCV (Open Source Computer Vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are:&lt;/p&gt;&lt;p&gt;Eigenfaces → createEigenFaceRecognizer()&lt;/p&gt;&lt;p&gt;Fisherfaces → createFisherFaceRecognizer()&lt;/p&gt;&lt;p&gt;Local Binary Patterns Histograms → createLBPHFaceRecognizer()&lt;/p&gt;Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.&lt;p&gt; &lt;/p&gt;
A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous optimisation

Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in practice, it is difficult to choose one appropriately. Moreover, it is necessary to determine a good enough set of parameters for the selected approach. Hence, this work proposes a strategy based on a hyper-heuristic for tailoring population-based metaheuristics. Besides, our approach considers search operators from well-known techniques as building blocks for new ones. We test this strategy through four benchmark functions and by varying their dimensions. We obtain metaheuristics with diverse configurations. We observe a possible performance boost when two or more search operators are considered. This could be due to previously unexplored interactions between such operators.
Learning to Plan with Uncertain Topological Maps

We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a probabilistic structure. Whereas classical symbolic algorithms achieve optimal results on noise-less topologies, or optimal results in a probabilistic sense on graphs with probabilistic structure, we aim to show that machine learning can overcome missing information in the graph by taking into account rich high-dimensional node features, for instance visual information available at each location of the map. Compared to purely learned neural white box algorithms, we structure our neural model with an inductive bias for dynamic programming based shortest path algorithms, and we show that a particular parameterization of our neural model corresponds to the Bellman-Ford algorithm. By performing an empirical analysis of our method in simulated photo-realistic 3D environments, we demonstrate that the inclusion of visual features in the learned neural planner outperforms classical symbolic solutions for graph based planning.
Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model

Mobile and pervasive computing is one of the recent paradigms available in the area of information technology. The role of pervasive computing is foremost in the field where it provides the ability to distribute computational services to the surroundings where people work and leads to issues such as trust, privacy, and identity. To provide an optimal solution to these generic problems, the proposed research work aims to implement a deep learning-based pervasive computing architecture to address these problems. Long short-term memory architecture is used during the development of the proposed trusted model. The applicability of the proposed model is validated by comparing its performance with the generic back-propagation neural network. This model results with an accuracy rate of 93.87% for the LSTM-based model much better than 85.88% for the back-propagation-based deep model. The obtained results reflect the usefulness and applicability of such an approach and the competitiveness against other existing ones.
Block-Aware Item Similarity Models for Top- <i>N</i> Recommendation

Top- N recommendations have been studied extensively. Promising results have been achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in the estimation of item similarities. Observing the block-diagonal structure of the item similarities in practice, we propose a block-diagonal regularization (BDR) over item similarities for ICF. The intuitions behind BDR are as follows: (1) with BDR, item clustering is embedded into the learning of ICF methods; (2) BDR induces sparsity of item similarities, which guarantees recommendation efficiency; and (3) BDR captures in-block transitivity to overcome rating sparsity. By regularizing the item similarity matrix of item similarity models with BDR, we obtain a block-aware item similarity model. Our experimental evaluations on a large number of datasets show that the block-diagonal structure is crucial to the performance of top- N recommendation.
Multi Scale-Adaptive Super-Resolution Person Re-Identification Using GAN

In real-world surveillance systems, the person images captured by the camera network consists of various low-resolution (LR) images. It creates a resolution mismatching problem when compared against high-resolution images of a targeted person. It significantly affects the performance of person re-Identification. This problem is known as Low-Resolution Person re-identification (LR PREID). An efficient strategy would be to exploit image super-resolution (SR) with person re-identification as a mutual learning approach. In this paper, we propose a novel method MSA-SR-PREID to solve this problem. The model takes low-resolution images on different resolutions and resized them to pre-defined fixed resolution. The design of the super-resolution network consists of ESRGAN and the de-Noising module to generate super-resolution images. The SR images are later passed to the re-identification network to learn the unique descriptors to recognize a person identity. The performance of this model is evaluated on four competitive benchmarks, MLR-VIPeR, MLR-DukeMTMC-reID, VR-MSMT17, and VR-Market1501. The comparison with similar state-of-the-art demonstrates the superiority of our model.