Reviewerly/exHarmony huggingface dataset:
Effects of e-learning technologies on university librarians and libraries: a systematic literature review

Purpose The purpose of this study was to identify the effects of emerging technologies in e-learning on university librarians and libraries. The study also intended to identify emerging technologies in e-learning and challenges being faced to implement e-learning technologies. Design/methodology/approach The preferred reporting items for the systematic review and meta-analysis method was applied as the research methodology for conducting the study. A total of 25 core studies published in peer-reviewed journals were selected to conduct a systematic literature review. The data were analysed through a thematic approach. Findings The results of the study revealed that e-learning technologies assist library professionals to strengthen their expertise and support them in initiating smart library services to deliver customer-focused services. A shortage of skilled manpower, the unavailability of adequate IT infrastructure, a lack of technical support, copyright issues, poor planning and ineffective library leadership are major challenges to implementing emerging technologies in e-learning. Originality/value The study offers theoretical implications by adding valuable literature to the existing body of knowledge. It has a societal impact as it has offered recommendations and practical solutions for the successful adoption of emerging technologies in e-learning. The study also offers managerial implications to develop fruitful policies for implementing e-learning technologies for the innovative sustainable competence development of library manpower and implementation of smart library services.
Automatic and Robust Infrared-Visible Image Sequence Registration via Spatio-Temporal Association

To solve the problems of the large differences in gray value and inaccurate positioning of feature information during infrared-visible image registration, we propose an automatic and robust algorithm for registering planar infrared-visible image sequences through spatio-temporal association. In particular, we first create motion vector distribution descriptors which represent the temporal motion information of foreground contours in adjacent frames to complete coarse registration without feature extraction. Then, for precise registration, we extracted FAST corners of the foreground, which are described by the spatial location distribution of contour points based on connected blob detection, and match these corners using bidirectional optimal maximum strategy. Finally, a reservoir updated by Better-In, Worse-Out (BIWO) strategy is established to save matched point pairs and obtain the optimal global transformation matrix. Extensive evaluations on the LITIV dataset well demonstrate the effectiveness of the proposed algorithm. Particularly, our algorithm achieves lower registration overlapping errors than the other two state-of-the-arts.
Evaluating the user experience in interactive installations

Currently, digital technologies are present in many areas of our lives and are used for various purposes. The ubiquitous technology landscape through ubiquitous and pervasive technologies has brought new forms of interaction. In this context, Human-Computer Interaction (HCI) community has been directing efforts towards its methods. Among the challenges are the types of evaluations commonly performed to improve the quality of systems.
Disentangling and Learning Robust Representations with Natural Clustering

Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We propose N-VAE, a model which is capable of separating factors of variation which are exclusive to certain classes from factors that are shared among classes. This model implements an explicitly compositional latent variable structure by defining a class-conditioned latent space and a shared latent space. We show its usefulness for detecting and disentangling class-dependent generative factors as well as its capacity to generate artificial samples which contain characteristics unseen in the training data.
Learning entity type structured embeddings with trustworthiness on noisy knowledge graphs

Knowledge graphs (KGs) automatic construction generally involves fine-grained entity typing, i.e., assigning the types to given entities as (entity, entity type). Since the non-negligible inaccuracy of entity typing systems and lack of sufficient human supervision, KGs inevitably face entity type noises. However, most conventional entity type embedding models unreasonably assume that all entity type instances in existing KGs are completely correct, which ignore noises and could lead to potential errors for down-stream tasks. To address this issue, we propose TrustE to build trustworthiness-aware entity type structured embeddings, which takes possible entity type noises into consideration for learning better representations. Specifically, since entities and entity types are completely distinct objects, we encode them in separate entity space and entity type space with a structural projecting matrix, and learn entity type embeddings with tuple trustworthiness. To make the trustworthiness more universal, we only utilize the internal structural knowledge in existing KGs and build two tuple trustworthiness considering the local tuple and global triple information respectively, which correspondingly makes it more challenging due to the limited knowledge. We evaluate our models on three tasks: entity type noise detection, entity type prediction and classification. Experimental results on real-world datasets (FB15kET and YAGO43kET) show that our models outperform all baselines on all tasks, which verify the capability of TrustE in learning better entity type structural embeddings on noisy KGs. The source code and data of this paper can be obtained from https://github.com/Quan-SWUFE/TrustE
On-site or Remote Working?: An Initial Solution on How COVID-19 Pandemic May Impact Augmented Reality Users

As a cutting edge technique requiring high-precision equipment, augmented reality (AR) and its users are influenced by the ambient environment. With the tremendous effect brought by COVID-19 pandemic, most people have shifted from on-site working to remote working. In this study, we propose an initial solution to explore the impact of COVID-19 pandemic on AR users working in these two situations. We develop a prototype application facilitated with gamification process in which users are requested to play an AR game in headset both in on-site and remote working environments. This game, which is highly dependent on the ambient environment, enables people to memorize, distinguish, and place virtual objects when immersing themselves into different surroundings with distinct distractors. We envision to conduct more user studies investigating how COVID-19 affects AR users, which could lead to more in-depth studies in the future.
Image splicing forgery detection using simplified generalized noise model

To deal with the problem of image forgery detection, many forensic tools have been proposed. Most existing tools perform efficiently in a supervised scenario with a large training set while their statistical performances cannot be analytically established. Also, limited research proposes statistical model-based detectors for image forensics, especially for image splicing forgery detection. Therefore, in this paper, we propose a training-free forensic detector with analytically statistical performance for splicing forgery detection. The detector is designed based on a simplified noise model of JPEG image which assumes the variance of pixels as a quadratic function of the expectation of pixels. The proposed simplified noise model is characterized by two parameters which can serve as camera fingerprints for image forgery detection. By employing the framework of hypothesis testing theory, a training-free Generalized Likelihood Ratio Test (GLRT) is designed, which ensures the high detection performance for a prescribed false alarm rate. Besides, detection threshold can be configured in the fashion independent of image content. Numerical results validate the accuracy of the simplified noise model and the effectiveness of the proposed detector. • A simplified generalized noise model characterizing a natural JPEG image is proposed. • A model-based forensic detector is devised to detect image splicing forgery. • The statistical performance of detector can be analytically established. • An effective fusion strategy is proposed to improve the performance of detector.
The Development and Validation of an Augmented and Mixed Reality Usability Heuristic Checklist

Augmented Reality (AR) and Mixed Reality (MR) are new emerging technologies that are becoming increasingly popular. Because these technologies are so new, there is a lack of standards and consistency in application and hardware design. This can make it difficult to learn and frustrating for users. One way to standardized design and enhance the usability of a product is through the use of heuristic evaluations. General heuristics, such as Nielsen’s 10 or Schneiderman’s 8 usability heuristics have been used to evaluate these technologies. These heuristics are a useful starting point because they bring attention to many crucial aspects of the usability of a product. However, additional aspects that could alter the users’ experience may not be assessed due to the uniqueness of AR and MR. There are very few validated AR and MR heuristics in the literature for practitioners to use. The purpose of this study was to create and validate a heuristic checklist that can be used to assess and inform design changes that influence the user experience of an AR or MR application and/or device. We followed an established and comprehensive 8-stage methodology developed by Quiñones, Rusu, & Rusu to create and validate our AR and MR usability heuristic checklist [4]. This included a search and summary of the current literature, formally defining heuristics based on this literature search, and validating through heuristic evaluations, expert reviews, and user testing. Our final revised heuristic checklist included 11 heuristics and 94 checklist items that encompasses usability aspects for AR and MR technologies.
Privacy-preserving certificateless public auditing supporting different auditing frequencies

Public auditing enables a verifier, say a third party auditor (TPA), to verify whether the cloud correctly stores the user’s cloud data. To date, a lot of public auditing schemes have been proposed. However, none of them consider the problem of auditing frequency. In practice, to reduce the cost of auditing service and avoid waste of resources, the users prefer to frequently verify the integrity of high-value data and check the integrity of low-value data with low frequency. In this paper, we propose a privacy-preserving certificateless public auditing scheme supporting different auditing frequencies. In this scheme, a novel auditing strategy is provided, in which the TPA is allowed to complete auditing tasks on high-value and low-value files with different auditing frequencies. The user’s privacy can be achieved by utilizing permutation technology to confuse the real file index. Hence, the TPA cannot distinguish the files with high value. We also use the random masking technology to mask the auditing proof, which guarantees the privacy of user data. The proposed scheme avoids the complicated certificate management and key escrow since it is designed in the certificateless cryptography. We prove that the proposed scheme is secure. The experimental results are shown to validate the efficiency of the proposed scheme.
The Mediating Effect of Students Engagement on Leadership Skills of Full-Time Teachers' Teaching Strategies on Students' Achievement with Financial Difficulties

Purpose: This study sought to investigate the impact of full-time teacher teaching approach and the corporation between university and family in enhancing the students’ performance with the moderating effect of student’s engagement in Guangdong University Of Petrochemical Technology, China. Theoretical framework: In order to conduct an in-depth investigation into the connections between these factors, the research makes use of a quantitative methods methodology, which incorporates quantitative surveys, and an examination of academic performance data. Design/ methodology/approach: Through the use of a questionnaire, this study seeks to acquire an all-encompassing knowledge of the variables that influence academic accomplishment among students who are experiencing financial challenges. Participants in the survey came from both leadership positions and regular teaching positions at Guangdong University of Petrochemical Technology in Maoming city. Findings: The instructional approach of a full-time teacher is crucial for improving the academic performance of students with financial difficulties. Their ability to create an inclusive, empathetic, and adaptable learning environment, coupled with individualised teaching strategies, enables these students to surmount obstacles, realise their maximum potential, and positively impact their education and society. Research, Practical &amp; Social Implications: In addition to imparting knowledge, the duty of a full-time teacher is to inspire and empower students to attain their maximum potential, regardless of their socioeconomic situation. This partnership ensures that students have the financial resources, emotional support, and direction they need to overcome financial obstacles and achieve academic success.
Learning to map source code to software vulnerability using code-as-a-graph.

We explore the applicability of Graph Neural Networks in learning the nuances of source code from a security perspective. Specifically, whether signatures of vulnerabilities in source code can be learned from its graph representation, in terms of relationships between nodes and edges. We create a pipeline we call AI4VA, which first encodes a sample source code into a Code Property Graph. The extracted graph is then vectorized in a manner which preserves its semantic information. A Gated Graph Neural Network is then trained using several such graphs to automatically extract templates differentiating the graph of a vulnerable sample from a healthy one. Our model outperforms static analyzers, classic machine learning, as well as CNN and RNN-based deep learning models on two of the three datasets we experiment with. We thus show that a code-as-graph encoding is more meaningful for vulnerability detection than existing code-as-photo and linear sequence encoding approaches. (Submitted Oct 2019, Paper #28, ICST)
Learning Adaptive Regularization for Image Labeling Using Geometric Assignment

We introduce and study the inverse problem of model parameter learning for image labeling, based on the linear assignment flow. This flow parametrizes the assignment of labels to feature data on the assignment manifold through a linear ODE on the tangent space. We show that both common approaches are equivalent: either differentiating the continuous system and numerical integration of the state and the adjoint system, or discretizing the problem followed by constrained parameter optimization. Experiments demonstrate how a parameter prediction map based on kernel regression and optimal parameter values, enables the assignment flow to perform adaptive regularization that can be directly applied to novel data.
Personal Devices for Contact Tracing: Smartphones and Wearables to Fight Covid-19

Digital contact tracing has emerged as a viable tool supplementing manual contact tracing. To date, more than 100 contact tracing applications have been published to slow down the spread of highly contagious Covid-19. Despite subtle variabilities among these applications, all of them achieve contact tracing by manipulating the following three components: use of a personal device to identify the user while designing a secure protocol to anonymize the user's identity; leverage networking technologies to analyze and store the data; and exploit rich sensing features on the user device to detect the interaction among users and thus estimate the exposure risk. This article reviews the current digital contact tracing based on these three components. We focus on two personal devices that are intimate to the user: smartphones and wearables. We discuss the centralized and decentralized networking approaches that are used to facilitate the data flow. Lastly, we investigate the sensing feature available on smartphones and wearables to detect the proximity between any two users and present experiments comparing the proximity sensing performance between these two personal devices.
Automated Concept Drift Handling for Fault Prediction in Edge Clouds Using Reinforcement Learning

Fault management systems that use real-time analytics based on Machine Learning (ML) help provide the reliability required in edge clouds, though they suffer from frequent changes in data distribution (concept drift) caused by highly dynamic traffic in edge clouds, requiring frequent adaptations of the ML model. We propose an automated concept drift handling framework for fault prediction in edge clouds using Reinforcement Learning (RL) to select the most appropriate drift adaptation method as well as the amount of data needed for adaptation, while considering edge cloud operator’s requirements. We implemented an edge cloud testbed and introduced infrastructure and network faults to it in the presence of abrupt and incremental concept drift. According to the obtained results, our proposed framework achieves up to 40% higher accuracy compared to a system without drift handling, and up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30\times $ </tex-math></inline-formula> less regret for selecting adaptation methods and amount of data, respectively, compared to other approaches.
Matchgate Shadows for Fermionic Quantum Simulation

"Classical shadows" are estimators of an unknown quantum state, constructed from suitably distributed random measurements on copies of that state [Nature Physics 16, 1050-1057]. Here, we analyze classical shadows obtained using random matchgate circuits, which correspond to fermionic Gaussian unitaries. We prove that the first three moments of the Haar distribution over the continuous group of matchgate circuits are equal to those of the discrete uniform distribution over only the matchgate circuits that are also Clifford unitaries; thus, the latter forms a "matchgate 3-design." This implies that the classical shadows resulting from the two ensembles are functionally equivalent. We show how one can use these matchgate shadows to efficiently estimate inner products between an arbitrary quantum state and fermionic Gaussian states, as well as the expectation values of local fermionic operators and various other quantities, thus surpassing the capabilities of prior work. As a concrete application, this enables us to apply wavefunction constraints that control the fermion sign problem in the quantum-classical auxiliary-field quantum Monte Carlo algorithm (QC-AFQMC) [Nature 603, 416-420], without the exponential post-processing cost incurred by the original approach.
Sentiment Analysis Twitter Based Lexicon and Multilayer Perceptron Algorithm

As the COVID-19 pandemic begins, the perception of online lectures according to students needs to be researched, to find out whether students have positive or negative sentiments regarding online lectures so far. Therefore, it is necessary to conduct research on sentiment analysis about online lectures taken according to student comments via tweets on the Twitter platform. The extracted tweets data will then be analyzed using machine learning to predict student sentiment about online lectures. The multilayer perceptron algorithm is used in research because it can solve non-linear problems well and is easy to implement without complicated parameter settings. However, multilayer perceptron is a supervised learning algorithm so it requires data that has been labeled/classified. So that to label the data of online lecture tweets, lexicon-based sentiment analysis is used. A total of 2,391 Indonesian-language tweets were successfully extracted. The results of the study using lexicon-based showed that as many as 63.9% gave negative sentiments towards online lectures, and 29% gave positive sentiments while the remaining 7.1% gave neutral sentiments. Meanwhile, the prediction ability of the multilayer perceptron algorithm for tweets data in this online lecture produces an accuracy of 71%.
PVDet: Towards pedestrian and vehicle detection on gigapixel-level images

Recently, gigapixel photography has been developed considerably and gradually put into remote sensing, video surveillance, etc. Gigapixel images have a visible field of view area at the square-kilometer level (containing thousands of targets) and up to 100 times the scale variation. Among them, the differences in target pose, scale, and occlusion are huge, and most existing target detection algorithms cannot directly process them. To solve these problems, we propose a new multi-target pedestrian and vehicle detector PVDet (Towards Pedestrian and Vehicle Detection on Gigapixel-level images) for gigapixel-level images. First, the DPRNet (Deformable deeP Residual Network) is designed as the backbone network to enhance the effective perceptual field and improve the feature representation of pose varying and occluded targets. Then, the PAFPN (Path Aggregation Feature Pyramid Network) is adopted to process the multi-scale features extracted by the backbone, boosting the multi-scale target modeling capability and the localization of small targets. Finally, the DyHead module is introduced to enhance the detection head’s scale, spatial and task awareness, further optimizing pedestrian and vehicle classification and localization. Compared with other State-of-the-Art methods on the PANDA dataset, the experimental results show that the proposed method dramatically improves AP of pedestrian and vehicle detection in gigapixel-level images by 10.4 AP over baseline, which is better than the existing target detection algorithms. We also conducted experiments on the PASCAL VOC 2012 dataset to further demonstrate the generalization capability and effectiveness of the proposed method.
Policy Consolidation for Continual Reinforcement Learning

We propose a method for tackling catastrophic forgetting in deep reinforcement learning that is \textit{agnostic} to the timescale of changes in the distribution of experiences, does not require knowledge of task boundaries, and can adapt in \textit{continuously} changing environments. In our \textit{policy consolidation} model, the policy network interacts with a cascade of hidden networks that simultaneously remember the agent's policy at a range of timescales and regularise the current policy by its own history, thereby improving its ability to learn without forgetting. We find that the model improves continual learning relative to baselines on a number of continuous control tasks in single-task, alternating two-task, and multi-agent competitive self-play settings.