Reviewerly/exHarmony huggingface dataset:
Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test

The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were applied in sequence. Comparative models were Xception, InceptionV3, DenseNet201, NASNetMobile, DenseNet121, VGG16, VGG19, and ResNet101, and fine tuning was performed. They were trained in a high-performance computing environment, and validation and loss were measured as comparative indicators of performance. Relatively low validation loss and high validation accuracy were obtained from Xception, InceptionV3, and DenseNet201 models, which is evaluated as an excellent model compared with other models. On the other hand, from VGG16, VGG19, and ResNet101, relatively high validation loss and low validation accuracy were obtained compared with other models. There was a narrow range of difference between the validation accuracy and the validation loss of the Xception, InceptionV3, and DensNet201 models. This study suggests that training applied with transfer learning can classify ULTT videos, and that there is a difference in performance between models.
A diagrammatic approach to variational quantum ansatz construction

Variational quantum eigensolvers (VQEs) are a promising class of quantum algorithms for preparing approximate ground states in near-term quantum devices. Minimizing the error in such an approximation requires designing ansatzes using physical considerations that target the studied system. One such consideration is size-extensivity, meaning that the ground state quantum correlations are to be compactly represented in the ansatz. On digital quantum computers, however, the size-extensive ansatzes usually require expansion via Trotter-Suzuki methods. These introduce additional costs and errors to the approximation. In this work, we present a diagrammatic scheme for the digital VQE ansatzes, which is size-extensive but does not rely on Trotterization. We start by designing a family of digital ansatzes that explore the entire Hilbert space with the minimum number of free parameters. We then demonstrate how one may compress an arbitrary digital ansatz, by enforcing symmetry constraints of the target system, or by using them as parent ansatzes for a hierarchy of increasingly long but increasingly accurate sub-ansatzes. We apply a perturbative analysis and develop a diagrammatic formalism that ensures the size-extensivity of generated hierarchies. We test our methods on a short spin chain, finding good convergence to the ground state in the paramagnetic and the ferromagnetic phase of the transverse-field Ising model.
Multi-Task Fusion for Improving Mammography Screening Data Classification

Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive patient-level prediction. We train and evaluate our full pipeline on public mammography data, i.e., DDSM and its curated version CBIS-DDSM, and report an AUC score of 0.962 for predicting the presence of any lesion and 0.791 for predicting the presence of malignant lesions on patient level. Overall, our fusion approaches improve AUC scores significantly by up to 0.04 compared to standard model ensembling. Moreover, by providing not only global patient-level predictions but also task-specific model results that are related to radiological features, our pipeline aims to closely support the reading workflow of radiologists.
Edge Tracing Using Gaussian Process Regression

We introduce a novel edge tracing algorithm using Gaussian process regression. Our edge-based segmentation algorithm models an edge of interest using Gaussian process regression and iteratively searches the image for edge pixels in a recursive Bayesian scheme. This procedure combines local edge information from the image gradient and global structural information from posterior curves, sampled from the model's posterior predictive distribution, to sequentially build and refine an observation set of edge pixels. This accumulation of pixels converges the distribution to the edge of interest. Hyperparameters can be tuned by the user at initialisation and optimised given the refined observation set. This tunable approach does not require any prior training and is not restricted to any particular type of imaging domain. Due to the model's uncertainty quantification, the algorithm is robust to artefacts and occlusions which degrade the quality and continuity of edges in images. Our approach also has the ability to efficiently trace edges in image sequences by using previous-image edge traces as a priori information for consecutive images. Various applications to medical imaging and satellite imaging are used to validate the technique and comparisons are made with two commonly used edge tracing algorithms.
Spatial-temporal motion control via composite cam-follower mechanisms

Motion control, both on the trajectory and timing, is crucial for mechanical automata to perform functionalities such as walking and entertaining. We present composite cam-follower mechanisms that can control their spatial-temporal motions to exactly follow trajectories and timings specified by users, and propose a computational technique to model, design, and optimize these mechanisms. The building blocks of our mechanisms are a new kind of cam-follower mechanism with a modified joint, in which the follower can perform spatial motion on a planar, cylindrical, or spherical surface controlled by the 3D cam's profile. We parameterize the geometry of these cam-follower mechanisms, formulate analytical equations to model their kinematics and dynamics, and present a method to combine multiple cam-follower mechanisms into a working mechanism. Taking this modeling as a foundation, we propose a computational approach to designing and optimizing the geometry and layout of composite cam-follower mechanisms, with an objective of performing target spatial-temporal motions driving by a small motor torque. We demonstrate the effectiveness of our technique by designing different kinds of personalized automata and showing results not attainable by conventional mechanisms.
Hand Pose Recognition Using Parallel Multi Stream CNN

Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance. Recently, depth data have become popular due to better understating of posture attributes. In this study, a multiple parallel stream 2D CNN (two-dimensional convolution neural network) model is proposed to recognize the hand postures. The proposed model comprises multiple steps and layers to detect hand poses from image maps obtained from depth data. The hyper parameters of the proposed model are tuned through experimental analysis. Three publicly available benchmark datasets: Kaggle, First Person, and Dexter, are used independently to train and test the proposed approach. The accuracy of the proposed method is 99.99%, 99.48%, and 98% using the Kaggle hand posture dataset, First Person hand posture dataset, and Dexter dataset, respectively. Further, the results obtained for F1 and AUC scores are also near-optimal. Comparative analysis with state-of-the-art shows that the proposed model outperforms the previous methods.
Ability of unbounded pairs of observers to achieve quantum advantage in random access codes with a single pair of qubits

Complications in preparing and preserving quantum correlations stimulate recycling of a single quantum resource in information processing and communication tasks multiple times. Here, we consider a scenario involving multiple independent pairs of observers acting with unbiased inputs on a single pair of spatially separated qubits sequentially. In this scenario, we address whether more than one pair of observers can demonstrate quantum advantage in some specific $2\ensuremath{\rightarrow}1$ and $3\ensuremath{\rightarrow}1$ random access codes. Interestingly, we not only address these in the affirmative but also illustrate that unbounded pairs can exhibit quantum advantage. Furthermore, these results remain valid even when all observers perform suitable projective measurements and an appropriate separable state is initially shared.
Application of region-based video surveillance in smart cities using deep learning

Smart video surveillance helps to build more robust smart city environment. The varied angle cameras act as smart sensors and collect visual data from smart city environment and transmit it for further visual analysis. The transmitted visual data is required to be in high quality for efficient analysis which is a challenging task while transmitting videos on low capacity bandwidth communication channels. In latest smart surveillance cameras, high quality of video transmission is maintained through various video encoding techniques such as high efficiency video coding. However, these video coding techniques still provide limited capabilities and the demand of high-quality based encoding for salient regions such as pedestrians, vehicles, cyclist/motorcyclist and road in video surveillance systems is still not met. This work is a contribution towards building an efficient salient region-based surveillance framework for smart cities. The proposed framework integrates a deep learning-based video surveillance technique that extracts salient regions from a video frame without information loss, and then encodes it in reduced size. We have applied this approach in diverse case studies environments of smart city to test the applicability of the framework. The successful result in terms of bitrate 56.92%, peak signal to noise ratio 5.35 bd and SR based segmentation accuracy of 92% and 96% for two different benchmark datasets is the outcome of proposed work. Consequently, the generation of less computational region-based video data makes it adaptable to improve surveillance solution in Smart Cities.
Ecuadorian and Uruguayan teachers' perceptions and experiences of teaching online during COVID

Purpose The COVID-19 pandemic is a unique event that forced K-12 schools to rethink the delivery of instruction to protect the well-being of school system stakeholders. Teachers, school administrators and parents had to adapt to and embrace new ways of teaching and learning by utilizing available technology. The purpose of this study is to examine the challenges encountered by in-service teachers when moving from face-to-face to online teaching. Design/methodology/approach This study utilized a qualitative phenomenological research methodology to examine Ecuadorian and Uruguayan teachers' perceptions and experiences transitioning from face-to-face to online teaching during the COVID-19 pandemic. This comparative study used convenience sampling to include 12 K-12 teachers from Ecuador and Uruguay. Findings The results of this study produced two themes that evidenced the demands placed on educators. The first theme was job demands, relating to teachers' perceptions about workload, preparation time and curriculum issues. The second theme related to available support provided by the school administrators and technology issues faced by teachers and students. Even though the teachers demonstrated adaptability for educating students during the pandemic, the experiences from both countries should be considered by teacher training programs and in post-graduate professional development. Originality/value This article examined how COVID-19 affected teachers in Uruguay and Ecuador. Data analysis documented the challenges encountered by teachers transitioning to online learning during the pandemic. The findings inform a larger audience about the needs of teachers working online.
An improved X-means and isolation forest based methodology for network traffic anomaly detection

Anomaly detection in network traffic is becoming a challenging task due to the complexity of large-scale networks and the proliferation of various social network applications. In the actual industrial environment, only recently obtained unlabelled data can be used as the training set. The accuracy of the abnormal ratio in the training set as prior knowledge has a great influence on the performance of the commonly used unsupervised algorithms. In this study, an anomaly detection algorithm based on X-means and iForest is proposed, named X-iForest, which clusters the standard Euclidean distance between the abnormal points and the normal cluster centre to achieve secondary filtering by using X-means. We compared X-iForest with seven mainstream unsupervised algorithms in terms of the AUC and anomaly detection rates. A large number of experiments showed that X-iForest has notable advantages over other algorithms and can be well applied to anomaly detection of large-scale network traffic data.
HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders

Head pose estimation (HPE) is the task of estimating head pose given an RGB image, video, or RGB-D data. In this paper, a network, HeadPosr is proposed to predict the head poses using a single RGB image. HeadPosr uses a novel architecture which includes a transformer encoder. In concrete, it consists of: (1) backbone; (2) connector; (3) transformer encoder; (4) prediction head. The significance of using a transformer encoder for HPE is studied. An extensive ablation study is performed on varying the (1) number of encoders; (2) number of heads; (3) different position embeddings; (4) different activations; (5) input channel size, in a transformer used in HeadPosr. Further studies on using: (1) different backbones, (2) using different learning rates are also shown. The elaborated experiments and ablations studies are conducted using three different open-source widely used datasets for HPE, i.e., 300W-LP, AFLW2000, and BIWI datasets. Experiments illustrate that HeadPosr outperforms all the state-of-art methods including both the landmark-free and the others based on using landmark or depth estimation on the AFLW2000 dataset and BIWI datasets when trained with 300W-LP. It also outperforms when averaging the results from the compared datasets, hence setting a benchmark for the problem of HPE, also demonstrating the effectiveness of using transformers over the state-of-the-art.
On Smart Gaze Based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks

Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process. This work explores the viability and timing comparisons of eye gaze labeling compared to conventional manual labeling for training object detectors. Challenges associated with gaze based labeling and methods to refine the coarse data annotations for subsequent object detection are also discussed. Results demonstrate that gaze tracking based labeling can save valuable pathologist time and delivers good performance when employed for training a deep object detector. Using the task of localization of Keratin Pearls in cases of oral squamous cell carcinoma as a test case, we compare the performance gap between deep object detectors trained using hand-labelled and gaze-labelled data. On average, compared to 'Bounding-box' based hand-labeling, gaze-labeling required 57.6% less time per label and compared to 'Freehand' labeling, gaze-labeling required on average 85% less time per label.
Accellion vulnerabilities lead to breaches of major organisations

Zero-day flaws in Accellion's File Transfer Appliance (FTA) product have resulted in breaches at major government and private organisations, some of which have led to significant compromises of data.
EchoVPR: Echo State Networks for Visual Place Recognition

Recognising previously visited locations is an important, but unsolved, task in autonomous navigation. Current visual place recognition (VPR) benchmarks typically challenge models to recover the position of a query image (or images) from sequential datasets that include both spatial and temporal components. Recently, Echo State Network (ESN) varieties have proven particularly powerful at solving machine learning tasks that require spatio-temporal modelling. These networks are simple, yet powerful neural architectures that&#x2014;exhibiting memory over multiple time-scales and non-linear high-dimensional representations&#x2014;can discover temporal relations in the data while still maintaining linearity in the learning time. In this letter, we present a series of ESNs and analyse their applicability to the VPR problem. We report that the addition of ESNs to pre-processed convolutional neural networks led to a dramatic boost in performance in comparison to non-recurrent networks in five out of six standard benchmarks (GardensPoint, SPEDTest, ESSEX3IN1, Oxford RobotCar, and Nordland), demonstrating that ESNs are able to capture the temporal structure inherent in VPR problems. Moreover, we show that models that include ESNs can outperform class-leading VPR models which also exploit the sequential dynamics of the data. Finally, our results demonstrate that ESNs improve generalisation abilities, robustness, and accuracy further supporting their suitability to VPR applications.
A generalization of Rényi entropy for basic probability assignment

To measure the uncertainty of basic probability assignment (BPA) in the field of evidence theory is an open issue. The Rényi entropy, a continuous family of entropy measures, is extended from the Shannon entropy and has been widely applied in many fields. In this paper, the generalized Rényi entropy for basic probability assignments is proposed. The proposed entropy can degenerate into the Rényi entropy under the condition that the BPA degenerates to probability distributions. Additionally, some desirable properties of the proposed entropy are explored. Finally, the numerical examples are given to show the feasibility and effectiveness of the proposed entropy. Compared with the Shannon entropy and other existing measures, the entropy is efficient to measure the uncertainty of BPA.
Variance of Relative Surprisal as Single-Shot Quantifier

The variance of (relative) surprisal, also known as varentropy, so far mostly plays a role in information theory as quantifying the leading-order corrections to asymptotic independent and identically distributed (IID) limits. Here, we comprehensively study the use of it to derive single-shot results in (quantum) information theory. We show that it gives genuine sufficient and necessary conditions for approximate state transitions between pairs of quantum states in the single-shot setting, without the need for further optimization. We also clarify its relation to smoothed min and max entropies, and construct a monotone for resource theories using only the standard (relative) entropy and variance of (relative) surprisal. This immediately gives rise to enhanced lower bounds for entropy production in random processes. We establish certain properties of the variance of relative surprisal, which will be useful for further investigations, such as uniform continuity and upper bounds on the violation of subadditivity. Motivated by our results, we further derive a simple and physically appealing axiomatic single-shot characterization of (relative) entropy, which we believe to be of independent interest. We illustrate our results with several applications, ranging from interconvertibility of ergodic states, over Landauer erasure to a bound on the necessary dimension of the catalyst for catalytic state transitions and Boltzmann’s H theorem.Received 27 May 2021Accepted 11 January 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010325Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum information processingQuantum thermodynamicsResource theoriesQuantum InformationStatistical Physics
Study of blockchain-based 6G wireless network integration and consensus mechanism

The sixth-generation (6G) system is a future standard of wireless networks and will be expected to deploy with the full support of blockchain. Blockchain technology is a Distributed Ledger Technology (DLT) to store and refer to the data. In this paper, firstly, we discuss the consensus protocol in 6G and then study the applications of blockchain in 6G. As a result, it has been observed that 27.5% of texts rely on PoW, 22.5% of texts are related to PoS, 17.5% of texts are based on DPOS and PBFT consensus protocol. Blockchain technologies in the state-of-the-art results with 6G, offer various applications such as spectrum management, trusted database, service level agreement, fair access through a smart contract. However, this integration suffers resource utilisation problems, computational loads, delays and bandwidth overhead, leading to a new set of issues that can be solved with the integration of blockchain, IoT and AI with 6G.
Spam Filtering of Mobile SMS Using CNN–LSTM Based Deep Learning Model

In our civilizations, SMS continues to be an arguable communication tool despite the rattling development of protocol–predicated messaging approaches. Some businesses consider SMS as superior to e-mails for its representation. Spammers have been drawn to the pertinence of SMS for mobile phone freaks. The attacker can steal secret information by sending SMS primers to the link or directly contacting the victim. Therefore, SMS spam has generally grown in the last numberless spells with new security pitfalls parallel to SMiShing. We propose a deeper model for detecting English SMS spam communications amalgamating Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). We also appraised other machine learning algorithms for the evaluation. The experimental findings presented in this study demonstrate that our model of CNN–LSTM exceeds other approaches with 98.40% accurateness and 98% F1-score.