Reviewerly/exHarmony huggingface dataset:
Digital Image Correlation Compatible Mechanoluminescent Skin for Structural Health Monitoring

Monitoring structural health using mechanoluminescent (ML) effects is widely considered as a potential full-field and direct visualizing optical method with high spatial and temporal resolution and simple setup in a noncontact manner. The challenges and uncertainties in the mapping of ML field to effective strain field, however, tend to limit significant commercial ML applications for structural health monitoring systems. Here, however, quantification problems are resolved using the digital image correlation (DIC) method. Specifically, an image containing mechanically induced photon information is processed using a DIC algorithm to measure the strain field components, which enables the establishment of a calibration curve when the ML field is mapped onto the effective strain field using pixel level information. The results show a linear relationship between effective strain and ML intensity despite the plastic flow in ML skin. Furthermore, the calibration curve allows for easy conversion of ML field to effective-strain field at the crack-tip plastic zone of the alloy structure, retaining its spatial resolution. The compatibility of ML skin with the DIC algorithm not only enables the quantification of the ML effects of several organic/inorganic ML materials, but may also be useful in elucidating the fundamentals of the trap-controlled mechanism.
A survey on security challenges and protocols of electric vehicle dynamic charging system

Abstract Electric vehicle (EV) has emerged as the future of transport worldwide, with the rising need for greener and energy‐efficient transportation solutions. The adoption of EVs can accommodate a clean atmosphere, dropping conventional fuel dependence. During the charging process, the EV will have to interact with various entities that are presented in the dynamic charging station to get charged. This emerging technology grabbed the attention of several researchers to propose authentication protocols for the dynamic EV charging system to protect the information exchanged between the EVs and the dynamic charging station. Considering the complexities associated with communications among EV and other involved entities, security researchers have proposed diverse authentication protocols vital for the EV charging system to protect the information exchanged. However, several existing authentication protocols either suffer from prominent security threats or limit themselves to static charging. This review paper discusses security aspects, security threats, the typical network model, and the threat model in the EV dynamic charging system. Next, we studied and analyzed various security protocols that are needed to provide authentication, privacy preservation, secure billing, and payment among the involved entities in the EV dynamic charging system. A comparative analysis of several state‐of‐the‐art security protocols that offer authentication, privacy preservation, and secure billing and payment facilities in EV dynamic charging systems is demonstrated with their efficacy as well as security and functionality aspects. In addition, some future challenges for EV dynamic charging system security protocols are also highlighted.
A YOLOv5 Baseline for Underwater Object Detection

Underwater object detection is harder than that on land. With the rapid development of deep learning, more and more object detection algorithms have been proposed. They have a great performance in object detection on land, but lack ideal performance in underwater object detection. In the line of this observation, we introduce a YOLOv5 baseline for underwater object detection. YOLOv5 is an extremely fast end-to-end algorithm to detect the objects and it has four sizes of models. This makes it easy to choose the right algorithm according to the underwater equipment. Experimental results show that YOLOv5 is a lightweight, fast and accurate object detection algorithm which is suitable for underwater environment. We use these experimental results as a YOLOv5 baseline for underwater object detection. Other researchers who are interested in underwater object detection can conduct research on this baseline.
CNN-Bidirectional LSTM Based Optical Character Recognition of Sanskrit Manuscripts : A Comprehensive Systematic Literature Review

Optical character recognition (OCR) is a technology that allows you to convert different types of documents or images into searchable, editable, and analyzable data. The current work on Sanskrit Character Recognition from Images of Text Documents is one of the most difficult due to similarities in the forms of unique letters, script complexity, non-forte in the representation, and a vast number of symbols. The Devanagari script is used to write the Sanskrit language. There are a variety of approaches for recognizing characters in a scanned image. The present research initiatives highlight the importance and needs of efforts in recognition of printed and handwritten documents written in Sanskrit language. This paper is aims at reviewing the state of various scripts in use including those from medieval to present era and explores the prospective of digital recognition of printed texts and thereby pointing towards futuristic trends in developing restoration software for Sanskrit scripts. Challenge is due to the number of languages and their diverse scripts. The scarcity of digitized linguistic resources makes the task a tougher one. The paper also highlights on the characteristics and challenges of recognition of scripts of Sanskrit origin. Largely the digital recognition is limited to simple numerals and isolated characters. In addition, this review article serves the purpose an optical character recognition (OCR) system that enables to analyse the word recognition and translate various types of Sanskrit documents or images into text using deep learning architectures which include Convolutional Neural Network (CNN) and Bidirectional long-short term memory (Bidirectional LSTM).
Heterogeneous Graph Contrastive Learning Network for Personalized Micro-Video Recommendation

Personalized micro-video recommendation has attracted a lot of research attention with the growing popularity of micro-video sharing platforms. Many efforts have been made to consider micro-video recommendation as a matching task and shown promising performance, while they only focus on simple features or multi-modal attribute information. Recently, Graph Neural Networks (GNNs) have been employed in many recommendation tasks and achieved impressive success. However, these GNN-based methods may suffer from the following limitations: (1) fail to capture the heterogeneity of nodes in user-video bipartite graphs; (2) ignore the non-local (global) semantic correlation information remained in heterogeneous graphs. In this paper, we present a novel approach, Heterogeneous Graph Contrastive Learning Network (HGCL), for personalized micro-video recommendation. To consider heterogeneity in user-video bipartite graphs, we first introduce a heterogeneous graph encoder network for a high-quality representation learning of users and micro-videos. Specifically, we design a random surfing model to generate node-type specific homogeneous graphs to preserve the heterogeneity. Then we propose a graph contrastive learning framework to achieve representation learning on each node-type specific homogeneous graph by maximizing the mutual information between local patches of a graph and the global representation of the entire graph. Finally, a type-crossing objective function is proposed to jointly integrate the node embeddings from different node types to facilitate high-quality representation learning. Experimental results on real-world datasets in the micro-video recommendation task validate the performance of our method, compared with state-of-the-art baseline algorithms.
Effects of AR Picture Books on German Teaching in Universities

In this paper, we discuss the teaching effects of augmented reality (AR) technology in German instruction. We conducted one prestudy and three formal studies on German learners in China's mainland and Taiwan region. In the formal studies, a total of 120 students participated in the survey, allowing us to compare the differences in interest in learning between AR picture books and traditional picture books. A total of 114 students took part in the survey, which enabled us to compare the contribution of AR picture books to teaching when students' satisfaction and German proficiency were different. To improve satisfaction, 514 students participated in the survey regarding the influence of the interactive narrative design effect and peer learning on satisfaction with using AR picture books. The results suggest that when learning German with AR picture books, satisfaction is the key construct that determines students' learning states.
Neural architecture search via standard machine learning methodologies

&lt;abstract&gt;&lt;p&gt;In the context of deep learning, the more expensive computational phase is the full training of the learning methodology. Indeed, its effectiveness depends on the choice of proper values for the so-called hyperparameters, namely the parameters that are not trained during the learning process, and such a selection typically requires an extensive numerical investigation with the execution of a significant number of experimental trials. The aim of the paper is to investigate how to choose the hyperparameters related to both the architecture of a Convolutional Neural Network (CNN), such as the number of filters and the kernel size at each convolutional layer, and the optimisation algorithm employed to train the CNN itself, such as the steplength, the mini-batch size and the potential adoption of variance reduction techniques. The main contribution of the paper consists in introducing an automatic Machine Learning technique to set these hyperparameters in such a way that a measure of the CNN performance can be optimised. In particular, given a set of values for the hyperparameters, we propose a low-cost strategy to predict the performance of the corresponding CNN, based on its behavior after only few steps of the training process. To achieve this goal, we generate a dataset whose input samples are provided by a limited number of hyperparameter configurations together with the corresponding CNN measures of performance obtained with only few steps of the CNN training process, while the label of each input sample is the performance corresponding to a complete training of the CNN. Such dataset is used as training set for a Support Vector Machines for Regression and/or Random Forest techniques to predict the performance of the considered learning methodology, given its performance at the initial iterations of its learning process. Furthermore, by a probabilistic exploration of the hyperparameter space, we are able to find, at a quite low cost, the setting of a CNN hyperparameters which provides the optimal performance. The results of an extensive numerical experimentation, carried out on CNNs, together with the use of our performance predictor with NAS-Bench-101, highlight how the proposed methodology for the hyperparameter setting appears very promising.&lt;/p&gt;&lt;/abstract&gt;
Replies to commentaries on the generalizability crisis

Abstract The 38 commentaries on the target article span a broad range of disciplines and perspectives. I have organized my response to the commentaries around three broad questions: First, how serious are the problems discussed in the target article? Second, are there are other, potentially more productive, ways to think about the issues that the target article framed in terms of generalizability? And third, what, if anything, should we collectively do about these problems?
Balanced Energy-Aware and Fault-Tolerant Data Center Scheduling

Fault tolerance, performance, and throughput have been major areas of research and development since the evolution of large-scale networks. Internet-based applications are rapidly growing, including large-scale computations, search engines, high-definition video streaming, e-commerce, and video on demand. In recent years, energy efficiency and fault tolerance have gained significant importance in data center networks and various studies directed the attention towards green computing. Data centers consume a huge amount of energy and various architectures and techniques have been proposed to improve the energy efficiency of data centers. However, there is a tradeoff between energy efficiency and fault tolerance. The objective of this study is to highlight a better tradeoff between the two extremes: (a) high energy efficiency and (b) ensuring high availability through fault tolerance and redundancy. The main objective of the proposed Energy-Aware Fault-Tolerant (EAFT) approach is to keep one level of redundancy for fault tolerance while scheduling resources for energy efficiency. The resultant energy-efficient data center network provides availability as well as fault tolerance at reduced operating cost. The main contributions of this article are: (a) we propose an Energy-Aware Fault-Tolerant (EAFT) data center network scheduler; (b) we compare EAFT with energy efficient resource scheduling techniques to provide analysis of parameters such as, workload distribution, average task per servers, and energy consumption; and (c) we highlight effects of energy efficiency techniques on the network performance of the data center.
A Novel Hybrid ASO-NM Algorithm and Its Application to Automobile Cruise Control System

A novel hybrid algorithm developed by merging atom search optimization (ASO) and Nelder-Mead (NM) simplex search algorithms is presented. The proposed improved algorithm (ASO-NM) is the first reported work on combining ASO and NM methods for optimization problems. The combination of ASO and NM leads to the construction of the desired metaheuristic approach that has a balanced exploration and exploitation. The proposed hybrid ASO-NM was used for optimizing a proportional-integral-derivative controller design for automobile cruise control systems as well as testing four well-known classical benchmark functions for the first time. The obtained statistical and transient response analyses and comparisons have shown the better capability of the proposed hybrid ASO-NM algorithm which can be used for further optimization problems as an effective approach.
Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning

In collaborative filtering, the quality of recommendations critically relies on how easily a model can find similar users for a target user. Hence, a niche user who prefers items out of the mainstream may receive poor recommendations, while a mainstream user sharing interests with many others will likely receive recommendations of higher quality. In this work, we study this mainstream bias centering around three key thrusts. First, to distinguish mainstream and niche users, we explore four approaches based on outlier detection techniques to identify a mainstream score indicating the mainstream level for each user. Second, we empirically show that severe mainstream bias is produced by conventional recommendation models. Last, we explore both global and local methods to mitigate the bias. Concretely, we propose two global models: Distribution Calibration (DC) and Weighted Loss (WL) methods; and one local method: Local Fine Tuning (LFT) method. Extensive experiments show the effectiveness of the proposed methods to improve utility for niche users and also show that the proposed LFT can improve the utility for mainstream users at the same time.
A neural network aided attuned scheme for gun detection in video surveillance images

Closed Circuit Television (CCTV) cameras are installed and monitored in private and open spaces for security purposes. The video and image footages are used for rapid actions, identity, and object detection in commercial and residential security. Object and human detection require different classifications based on the features exhibited from the static/ mobile footages. This article introduces an Attuned Object Detection Scheme (AODS) for harmful object detection from CCTV inputs. The proposed scheme relies on a convolution neural network (CNN) for object detection and classification. The classification is performed based on the Object's features extracted and analyzed using CNN. The hidden layer processes are split into different feature-constraint-based analyses for identifying the Object. In the classification process, feature attenuation between the dimensional representation and segmented input is performed. Based on this process, the input is classified for hazardous objects detection. The consecutive processing layer of CNN identifies deviations in dimensional feature representation, preventing multi-object errors. The proposed scheme's performance is verified using the metrics accuracy, precision, and F1-Score.External dataset training has improved accuracy by 8.08% and reduced error and complexity by 7.47 and 8.23 percentage points, respectively, in this process. Object classification based on labels is expected to be implemented in the future.
Improving Knowledge Tracing with Collaborative Information

Knowledge tracing, which estimates students' knowledge states by predicting the probability that they correctly answer questions, is an essential task for online learning platforms. It has gained much attention in the decades due to its importance to downstream tasks like learning material arrangement, etc. The previous deep learning-based methods trace students' knowledge states with the explicitly intra-student information, i.e., they only consider the historical information of individuals to make predictions. However, they neglect the inter-student information, which contains the response correctness of other students who have similar question-answering experiences, may offer some valuable clues. Based on this consideration, we propose a method called Collaborative Knowledge Tracing (CoKT) in this paper, which sufficiently exploits the inter-student information in knowledge tracing. It retrieves the sequences of peer students who have similar question-answering experiences to obtain the inter-student information, and integrates the inter-student information with the intra-student information to trace students' knowledge states and predict their correctness in answering questions. We validate the effectiveness of our method on four real-world datasets and compare it with 11 baselines. The experimental results reveal that CoKT achieves the best performance.
Correct and remap solar radiation and photovoltaic power in China based on machine learning models

Accurate estimation of surface solar radiation (SSR) is crucial for photovoltaic (PV) systems design and solar PV power plants site selection. However, the SSR observations often suffer from inhomogeneity issues (e.g., aging equipment and instrument replacement) and low spatial–temporal coverage, which constrained the management and development of PV systems, particularly for countries with huge solar energy investments (e.g., China). To address them, we proposed seven different models (including four machine learning models, two empirical models and a multiple linear regression model) for accurate prediction of daily SSR using conventional meteorological data. Our results showed that the support vector machine (SVM) outperformed other models in estimating daily SSR. Based on SSR data corrected by the SVM model, the sharp downtrend in SSR (-9.64 W m−2 decade-1) due to the sensitivity drift of instrument aging before 1990 was moderated (-2.36 W m−2 decade-1) in China; and the abnormal jump caused by instrument replacement in SSR observations during 1990–1993 was removed. Furthermore, by combining the advantages of large network of conventional meteorological data and SVM model, we extended the limited station-based SSR data (∼100 meteorological stations) to include longer period and larger spatial coverage (2185 meteorological stations, 1971–2016) in China. By coupling the grid-based method and PV power model, we created reliable SSR and PV power maps with higher temporal and spatial resolution over China. Spatially, the main distribution of high SSR and PV power were in the northwestern China, Tibetan Plateau and some coastal areas in the southern China. Temporally, the PV power experienced a significant drop during 1971–2016 (-1.94 kWh m−2 decade-1) due to the significant decline in SSR (-0.91 W m−2 decade-1). Above all, the new maps of SSR and PV power prepared here should support PV investments within the Chinese territory.
The Direction of Updatable Encryption Does Matter

We introduce a new definition for key updates, called backward-leak uni-directional key updates, in updatable encryption (UE). This notion is a variant of uni-directional key updates for UE. We show that existing secure UE schemes in the bi-directional key updates setting are not secure in the backward-leak uni-directional key updates setting. Thus, security in the backward-leak uni-directional key updates setting is strictly stronger than security in the bi-directional key updates setting. This result is in sharp contrast to the equivalence theorem by Jiang (Asiacrypt 2020), which says security in the bi-directional key updates setting is equivalent to security in the existing uni-directional key updates setting. We call the existing uni-directional key updates "forward-leak uni-directional" key updates to distinguish two types of uni-directional key updates in this paper. We also present two UE schemes with the following features.
Assessing the impact of augmented reality application on students’ learning motivation in chemical engineering

Application of augmented reality (AR) in education has recently grown in interest due to distant, online, and self-directed learning. In this study, the impact of implementing an AR application on chemical engineering students' learning motivation and performance was assessed. Two interactive AR lessons on common industrial equipment (i.e., centrifugal pump and shell-and-tube heat exchanger) were developed on the EON-XR platform. A cohort of 50 undergraduate chemical engineering students participated in the AR lessons and evaluated its impact on students' learning motivation and usefulness as a learning resource. The level of students' learning motivation was assessed with a 16-item questionnaire based on the Instructional Materials Motivation Survey (IMMS) from Keller's ARCS model, and qualitative questions related to the future of AR technology in chemical engineering education. Results show that 82% of respondents found AR lessons helpful compared to conventional lesson delivery modes, while 92% were supportive for AR lessons to be an additional resource to existing learning materials. These findings demonstrated that AR technology impacted students' learning motivation positively across multiple constructs, namely 'Attention', 'Relevance', 'Confidence' and 'Satisfaction' and showed great potential as an innovative pedagogical advancement in chemical engineering education.