Reviewerly/exHarmony huggingface dataset:
SENTIMENT ANALYSIS ON YOUTUBE SMART PHONE UNBOXING VIDEO REVIEWS IN SRI LANKA

Product-related reviews are based on users’ experiences that are mostly shared on videos in YouTube. It is the second most popular website globally in 2021. People prefer to watch videos on recently released products prior to purchasing, in order to gather overall feedback and make worthy decisions. These videos are created by vloggers who are enthusiastic about technical materials and feedback is usually placed by experienced users of the product or its brand. Analyzing the sentiment of the user reviews gives useful insights into the product in general. This study is focused on three smartphone reviews, namely, Apple iPhone 13, Google Pixel 6, and Samsung Galaxy S21 which were released in 2021. VADER, which is a lexicon and rule-based sentiment analysis tool was used to classify each comment to its appropriate positive or negative orientation. All three smartphones show a positive sentiment from the users’ perspective and iPhone 13 has the highest number of positive reviews. The resulting models have been tested using Naïve Bayes, Decision Tree, and Support Vector Machine. Among these three classifiers, Support Vector Machine shows higher accuracies and F1-scores.
CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework

Recently, automated co-design of machine learning (ML) models and accelerator architectures has attracted significant attention from both the industry and academia. However, most co-design frameworks either explore a limited search space or employ suboptimal exploration techniques for simultaneous design decision investigations of the ML model and the accelerator. Furthermore, training the ML model and simulating the accelerator performance is computationally expensive. To address these limitations, this work proposes a novel neural architecture and hardware accelerator co-design framework, called CODEBench. It comprises two new benchmarking sub-frameworks, CNNBench and AccelBench, which explore expanded design spaces of convolutional neural networks (CNNs) and CNN accelerators. CNNBench leverages an advanced search technique, Bayesian Optimization using Second-order Gradients and Heteroscedastic Surrogate Model for Neural Architecture Search, to efficiently train a neural heteroscedastic surrogate model to converge to an optimal CNN architecture by employing second-order gradients. AccelBench performs cycle-accurate simulations for diverse accelerator architectures in a vast design space. With the proposed co-design method, called Bayesian Optimization using Second-order Gradients and Heteroscedastic Surrogate Model for Co-Design of CNNs and Accelerators, our best CNN–accelerator pair achieves 1.4% higher accuracy on the CIFAR-10 dataset compared to the state-of-the-art pair while enabling 59.1% lower latency and 60.8% lower energy consumption. On the ImageNet dataset, it achieves 3.7% higher Top1 accuracy at 43.8% lower latency and 11.2% lower energy consumption. CODEBench outperforms the state-of-the-art framework, i.e., Auto-NBA, by achieving 1.5% higher accuracy and 34.7× higher throughput while enabling 11.0× lower energy-delay product and 4.0× lower chip area on CIFAR-10.
Dexterous robotic manipulation using deep reinforcement learning and knowledge transfer for complex sparse reward‐based tasks

Abstract This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipulation task. The RRC consisted of using a TriFinger robot to manipulate a cube along a specified positional trajectory, but with no requirement for the cube to have any specific orientation. We used a relatively simple reward function, a combination of a goal‐based sparse reward and a distance reward, in conjunction with Hindsight Experience Replay (HER) to guide the learning of the DRL agent (Deep Deterministic Policy Gradient [DDPG]). Our approach allowed our agents to acquire dexterous robotic manipulation strategies in simulation. These strategies were then deployed on the real robot and outperformed all other competition submissions, including those using more traditional robotic control techniques, in the final evaluation stage of the RRC. Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory. The requirement to also orient the cube makes the agent less able to learn the task through blind exploration due to increased problem complexity. To circumvent this issue, we make novel use of a Knowledge Transfer (KT) technique that allows the strategies learned by the agent in the original task (which was agnostic to cube orientation) to be transferred to this task (where orientation matters). KT allowed the agent to learn and perform the extended task in the simulator, which improved the average positional deviation from 0.134 to 0.02 m, and average orientation deviation from 142° to 76° during evaluation. This KT concept shows good generalization properties and could be applied to any actor‐critic learning algorithm.
Analysis of an injection-locking-loophole attack from an external source for quantum key distribution

Many security loopholes exist in quantum key distribution (QKD) due to the imperfections of realistic devices. On the source side, the decoy-state method can defend against the most severe photon-number-splitting (PNS) attack and improve the performance of QKD. Measurement-device-independent QKD and twin-field QKD have further closed all loopholes on the detection side. In this paper, we propose a valuable optical injection-locking loophole for the case when the internal isolator inside the laser is removed. We first introduce the effects of the injection locking with different injection intensities on the source frequency. Through the successive responses of adjacent pulses from frequency ports of the dense wavelength division multiplexer, the decoy state and the signal state can be partially distinguished. The specific injection-locking-loophole analysis of the isolatorless decoy phase-encoding Bennet-Brassard 1984 QKD and decoy phase-matching QKD protocols with external optical injection has been deeply studied. Simulation results show that, if we maintain the same observed gain statistics as normal after an external optical injection locking, the loophole cannot be exploited for a PNS attack when the QKD distance is short. As the QKD distance increases, a PNS attack at a medium distance using the optical injection-locking loophole does not threaten the security; the security is still valid. With the further increase of the QKD distance, the lower-bound secure key rate is higher than the upper-bound secure key rate given by a PNS attack at a long distance, some of the keys must be insecure, and information will be leaked.
Benchmarking Analysis of the Panorama of Grid-Connected PV Installations in Spain

Renewable energies play an important role as a solution to the challenge of satisfying the growing global energy demand without jeopardizing the achievements in the fight against climate change. Given this panorama, different countries, including Spain, have developed policies to promote renewable energies. One of the technologies that benefit from these policies is photovoltaics. In Spain, the number of grid-connected photovoltaic installations has increased significantly in recent years. It is interesting to analyze the panorama of these facilities and identify the trends in their design criteria. In this line, in this work, the projects of 70 grid-connected photovoltaic installations distributed across Spain were analyzed. For that purpose, benchmarking techniques were applied, facilitating the systematization of information, the intercomparison of plants and the identification of trends and efficient solutions. A set of characteristic indicators of each installation was defined, and a statistical analysis of them was developed. Likewise, a tool was developed that allows the designers of this type of photovoltaic plant to compare the design parameters chosen for their installations with those of the surrounding area. Therefore, this work provides knowledge about the current panorama of photovoltaic implementation applicable to its future advance.
Assessing contributor features to phishing susceptibility amongst students of petroleum resources varsity in Nigeria

In this observational quasi-experimental study, we recruited 200 participants during the Federal University of Petroleum Resources Effurun’s (FUPRE) orientation, who were exposed to socially engineered (phishing) attacks over nine months. Attacks sought to extract participants’ data and/or entice them to click (compromised) links. The study aims to determine phishing exposure and risks among undergraduates in FUPRE (Nigeria) by observing their responses to socially-engineered attacks and exploring their attitudes to cybercrime risks before and after phishing attacks. The study primed all students in place of cybercrime awareness to remain vigilant to scams and explored the various scam types with their influence on gender, age, status, and their perceived safety on susceptibility to scams. Results show that contrary to public beliefs, these factors have all been found to be associated with scam susceptibility and vulnerability of the participants.
The role of machine learning in robotics

Purpose This paper aims to illustrate the growing role of machine learning techniques in robotics. Design/methodology/approach Following an introduction which includes a brief historical perspective, this paper provides a short introduction to machine learning techniques. It then provides examples of robotic machine learning applications in agriculture, waste management, warehouse automation and exoskeletons. This is followed by a short consideration of applications in future generations of self-driving vehicles. Finally, brief conclusions are drawn. Findings Machine learning is a branch of artificial intelligence and the topic of extensive academic study. Recent years have seen machine learning techniques being applied successfully to a diversity of robotic systems, most of which involve machine vision. They have imparted these with a range of unique or greatly improved operational capabilities, allowing them to satisfy all manner of new applications. Originality/value This provides a detailed insight into how machine learning is being applied to robotics.
A Magic Leap in Tourism: Intended and Realized Experience of Head-Mounted Augmented Reality in a Museum Context

Augmented reality (AR) is an emergent technology in tourism. However, research concerning the AR user experience is relatively scarce and seldom addresses the intentions of designers. Accordingly, we sought to: (a) explore the design intentions underlying a multi-user, purpose-built AR experience; (b) assess the extent to which users’ realized experience aligned with the designers’ intended experience; and (c) examine the relationships between users’ internal states and their associated behavior, in alignment with a Stimulus-Organism-Response framework. In Study 1, designers ( n = 5) took part in a focus group and completed a design intentions survey. In Study 2, users ( n = 48) tested the AR experience, and a range of subjective (e.g., affective responses) and objective (i.e., visual attention) data were recorded. Findings indicated designer–user disparities primarily at the organism and response levels. Additionally, users’ affective responses to the AR experience were strongly associated with visitor engagement.
Convolutional Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening for Vietnamese patients

Nowadays, breast cancer is one of the leading cancers in Vietnam, and it causes approximately 6000 deaths every year. The rate of breast cancer patients was calculated as 26.4/100000 persons in 2018. There are 21,555 new cases reported in 2020. However, these figures can be reduced with early detection and diagnosis of breast cancer disease in women through mammographic imaging. In many hospitals in Vietnam, there is a lack of experienced breast cancer radiologists. Therefore, it is helpful to develop an intelligent system to improve radiologists’ performance in breast cancer screening for Vietnamese patients. Our research aims to develop a convolutional neural network-based system for classifying breast cancer X-Ray images into three classes of BI-RADS categories as BI-RADS 1 (“normal”), BI-RADS 23 (“benign”) and BI-RADS 045 (“incomplete and malignance”). This classification system is developed based on the convolutional neural network with ResNet 50. The system is trained and tested on a breast cancer image dataset of Vietnamese patients containing 7912 images provided by Hanoi Medical University Hospital radiologists. The system accuracy uses the testing set achieved a macAUC (a macro average of the three AUCs) of 0.754. To validate our model, we performed a reader study with the breast cancer radiologists of the Hanoi Medical University Hospital, reading about 500 random images of the test set. We confirmed the efficacy of our model, which achieved performance comparable to a committee of two radiologists when presented with the same data. Additionally, the system takes only 6 seconds to interpret a breast cancer X-Ray image instead of 450 seconds interpreted by a Vietnamese radiologist. Therefore, our system can be considered as a “second radiologist,” which can improve radiologists’ performance in breast cancer screening for Vietnamese patients.
Sports Video Classification Framework Using Enhanced Threshold Based Keyframe Selection Algorithm and Customized CNN on UCF101 and Sports1-M Dataset

The computer vision community has taken a keen interest in recent developments in activity recognition and classification in sports videos. Advancements in sports have a broadened the technical interest of the computer vision community to perform various types of research. Images and videos are the most frequently used components in computer vision. There are numerous models and methods that can be used to classify videos. At the same time, there no specific framework or model for classifying and identifying sports videos. Hence, we proposed a framework based on deep learning to classify sports videos with their appropriate class label. The framework is to perform sports video classification using two different benchmark datasets, UCF101 and the Sports1-M dataset. The objective of the framework is to help sports players and trainers to identify specific sports from the large data source, then analyze and perform well in the future. This framework takes sports video as an input and produces the class label as an output. In between, the framework has numerous intermediary processes. Preprocessing is the first step in the proposed framework, which includes frame extraction and noise reduction. Keyframe selection is carried out by candidate frame extraction and an enhanced threshold-based frame difference algorithm, which is the second step. The final step of the sports video classification framework is feature extraction and classification using CNN. The proposed framework result is compared with pretrained neural networks such as AlexNet and GoogleNet, and then the results are also compared. Three different evaluation metrics are used to measure the accuracy and performance of the framework.
Fast fringe projection profilometry using 3 + 1 phase retrieval strategy and fringe order correction

Fringe projection profilometry (FPP) has attracted wide attention in optical 3D measurement field for its high resolution, high accuracy, and simple system construction. However, the inherent requirements of FPP's measurement speed and accuracy are contradictory. We adopt a 3+1 phase retrieval strategy combining phase shift and the Hilbert transform algorithm to achieve high-speed, high-precision simultaneous measurements with only four binary fringe patterns. Furthermore, when using temporal phase unwrapping, fringe order errors (FOEs) are inevitable. Although these FOEs do not affect neighboring pixels, they are brought into the final point cloud coordinates by the absolute phase value. We propose a fringe order correction method to eliminate FOEs. The feasibility and effectiveness of this method are verified by the experimental results.
Minimizing the effect of white matter lesions on deep learning based tissue segmentation for brain volumetry

Automated methods for segmentation-based brain volumetry may be confounded by the presence of white matter (WM) lesions, which introduce abnormal intensities that can alter the classification of not only neighboring but also distant brain tissue. These lesions are common in pathologies where brain volumetry is also an important prognostic marker, such as in multiple sclerosis (MS), and thus reducing their effects is critical for improving volumetric accuracy and reliability. In this work, we analyze the effect of WM lesions on deep learning based brain tissue segmentation methods for brain volumetry and introduce techniques to reduce the error these lesions produce on the measured volumes. We propose a 3D patch-based deep learning framework for brain tissue segmentation which is trained on the outputs of a reference classical method. To deal more robustly with pathological cases having WM lesions, we use a combination of small patches and a percentile-based input normalization. To minimize the effect of WM lesions, we also propose a multi-task double U-Net architecture performing end-to-end inpainting and segmentation, along with a training data generation procedure. In the evaluation, we first analyze the error introduced by artificial WM lesions on our framework as well as in the reference segmentation method without the use of lesion inpainting techniques. To the best of our knowledge, this is the first analysis of WM lesion effect on a deep learning based tissue segmentation approach for brain volumetry. The proposed framework shows a significantly smaller and more localized error introduced by WM lesions than the reference segmentation method, that displays much larger global differences. We also evaluated the proposed lesion effect minimization technique by comparing the measured volumes before and after introducing artificial WM lesions to healthy images. The proposed approach performing end-to-end inpainting and segmentation effectively reduces the error introduced by small and large WM lesions in the resulting volumetry, obtaining absolute volume differences of 0.01 ± 0.03% for GM and 0.02 ± 0.04% for WM. Increasing the accuracy and reliability of automated brain volumetry methods will reduce the sample size needed to establish meaningful correlations in clinical studies and allow its use in individualized assessments as a diagnostic and prognostic marker for neurodegenerative pathologies.
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The ZpZp2-additive codes are subgroups of Zpα1×Zp2α2, and can be seen as linear codes over Zp when α2=0, Zp2-additive codes when α1=0, or Z2Z4-additive codes when p=2. A ZpZp2-linear generalized Hadamard (GH) code is a GH code over Zp which is the Gray map image of a ZpZp2-additive code. Recursive constructions of ZpZp2-additive GH codes of type (α1,α2;t1,t2) with t1,t2≥1 are known. In this paper, we generalize some known results for ZpZp2-linear GH codes with p=2 to any p≥3 prime when α1≠0, and then we compare them with the ones obtained when α1=0. First, we show for which types the corresponding ZpZp2-linear GH codes are nonlinear over Zp. Then, for these codes, we compute the kernel and its dimension, which allow us to classify them completely. Moreover, by computing the rank of some of these codes, we show that, unlike Z4-linear Hadamard codes, the Zp2-linear GH codes are not included in the family of ZpZp2-linear GH codes with α1≠0 when p≥3 prime. Indeed, there are some families with infinite nonlinear ZpZp2-linear GH codes, where the codes are not equivalent to any Zps-linear GH code with s≥2.
Generic XR game-based approach for industrial training

Over the years several Virtual Reality- (VR) and Augmented Reality- (AR) based learning environments have been proposed. However, they are usually built for a specified scenario and possess a very limited set of configurations. Moreover, the proposed solutions are designed solely to VR- or AR-based learning environments and only a few propose solutions that use both learning environments for training. The present paper proposes an approach composed by a training design methodology and a generic and configurable system for training operators of industrial production processes, which is able to adapt and evolve along time, following changes in the processes and/or machines/tools in an industrial environment. In order to increase the engagement and consequently the development of competences in the operators, a Game Based Learning methodology provided along with VR and AR is used in the training context.
Scheduling Algorithms for Federated Learning With Minimal Energy Consumption

Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private.With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques).Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary objective.In this paper, we investigate the problem of minimizing the energy consumption of FL training on heterogeneous devices by controlling the workload distribution.We model this as the Minimal Cost FL Schedule problem, a total cost minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with arbitrary cost functions.We propose a pseudo-polynomial optimal solution to the problem based on the previously unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem.We also provide four algorithms for scenarios where cost functions are monotonically increasing and follow the same behavior.These solutions are likewise applicable on the minimization of other kinds of costs, and in other one-dimensional data partition problems.
Generalized Scale Factor Calibration Method for an Off-Axis Digital Image Correlation-Based Video Deflectometer

When using off-axis digital image correlation (DIC) for non-contact, remote, and multipoint deflection monitoring of engineering structures, accurate calibration of the scale factor (SF), which converts image displacement to physical displacement for each measurement point, is critical to realize high-quality displacement measurement. In this work, based on the distortion-free pinhole imaging model, a generalized SF calibration model is proposed for an off-axis DIC-based video deflectometer. Then, the transversal relationship between the proposed SF calibration method and three commonly used SF calibration methods was discussed. The accuracy of these SF calibration methods was also compared using indoor rigid body translation experiments. It is proved that the proposed method can be degraded to one of the existing calibration methods in most cases, but will provide more accurate results under the following four conditions: (1) the camera’s pitch angle is more than 20°, (2) the focal length is more than 25 mm, (3) the pixel size of the camera sensor is more than 5 um, and (4) the image y-coordinate corresponding to the measurement point after deformation is far from the image center.