Reviewerly/exHarmony huggingface dataset:
Location-Based Augmented Reality Games Through Immersive Experiences

Augmented reality (AR) is an immersive experience of a physical world environment which is enhanced by virtual objects, offers interesting ways of human-computer interacting. Because location-based augmented reality allows people to enhance virtual information to geographical points of interest at specific predefined geolocations, it can help researchers to create amazing applications and augmented reality games in a different way. More recently, due to the technology's capacity and recent innovation, location-based augmented reality has been extremely popular by using geo-based features to create a new immersive experience. This paper presents a novel summary of pioneering location-based augmented reality systems and games through immersive experiences, including our own research, in an interdisciplinary augmented cognition perspective. To begin with, we explore location-based augmented reality researches, starting from non-game works such as SPIRIT and IntelligShop. Then, we discuss pioneering augmented reality games, particularly well-known games, since the early of the twentieth century until now, including Human Pacman, ARCarGame, Pokémon Go, and AR Mario Kart Live. We finally give a recommendation and predict a future scenario for location-based augmented reality systems and games.
Designing Efficient DNNs via Hardware-Aware Neural Architecture Search and Beyond

Hardware systems integrated with deep neural networks (DNNs) are deemed to pave the way for future artificial intelligence (AI). However, manually designing efficient DNNs involves nontrivial computation resources since significant trial-and-errors are required to finalize the network configuration. To this end, we, in this article, introduce a novel hardware-aware neural architecture search (NAS) framework, namely, GoldenNAS, to automate the design of efficient DNNs. To begin with, we present a novel technique, called dynamic channel scaling, to enable the channel-level search since the number of channels has non-negligible impacts on both accuracy and efficiency. Besides, we introduce an efficient progressive space shrinking method to raise the awareness of the search space toward target hardware and alleviate the search overheads as well. Moreover, we propose an effective hardware performance modeling method to approximate the runtime latency of DNNs upon target hardware, which is further integrated into GoldenNAS to avoid the tedious on-device measurements. Then, we employ the evolutionary algorithm (EA) to search for the optimal operator/channel configurations of DNNs, denoted as GoldenNets. Finally, to enable the depthwise adaptiveness of GoldenNets under dynamic environments, we propose the adaptive batch normalization (ABN) technique, followed by the self-knowledge distillation (SKD) approach to improve the accuracy of adaptive subnetworks. We conduct extensive experiments directly on ImageNet, which clearly demonstrate the advantages of GoldenNAS over existing state-of-the-art approaches.
Automatic Extraction of Photovoltaic Panels from UAV Imagery with Object-Based Image Analysis and Machine Learning

We develop an automatic pipeline for photovoltaic panels extraction based on Object-Based Image Analysis (OBIA) and machine learning (ML). Automatic optimization of segmentation parameters, statistical and morphological feature engineering, and ML segment-based classification are the main building blocks of the proposed pipeline. Experimentation was conducted on a dataset comprising RGB and thermal orthomosaics generated from UAV data. An F-factor of 98.7% was scored with a recall rate above 98%. Obtained results were also compared to a developed solution under the software eCognition.
A Wheelchair Locomotion Interface in a VR Disability Simulation Reduces Implicit Bias

This research investigates how experiencing virtual embodiment in a wheelchair affects implicit bias towards people who use wheelchairs. We also investigate how receiving information from a virtual instructor who uses a wheelchair affects implicit bias towards people who use wheelchairs. Implicit biases are actions or judgments of people towards various concepts or stereotypes (e.g., races). We hypothesized that experiencing a Disability Simulation (DS) through an avatar in a wheelchair and receiving information from an instructor with a disability will have a significant effect on participants’ ability to recall disability-related information and will reduce implicit biases towards people who use wheelchairs. To investigate this hypothesis, a 2x2 between-subjects user study was conducted where participants experienced an immersive VR DS that presents information about Multiple Sclerosis (MS) with factors of instructor (i.e., instructor with a disability versus instructor without a disability) and locomotion interface (i.e., without a disability – locomotion through in-place-walking, with a disability – locomotion in a wheelchair). Participants took a disability-focused Implicit Association Test two times, once before and once after experiencing the DS. They also took a test of knowledge retention about MS. The primary result is: experiencing the DS through locomotion in a wheelchair was better for both the disability-related information recall task and reducing implicit bias towards people who use wheelchairs.
ACAE-REMIND for online continual learning with compressed feature replay

Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset.
Indonesian Lexicon-Based Sentiment Analysis of Online Religious Lectures Review

Online videos platforms such as YouTube is the most popular social media platform in terms of user numbers in Indonesia. YouTube is also one of the most popular online platforms for accessing religious lectures. Users can provide feedback on the videos through comments, likes, and shares. On the other hand, sentiment analysis in the Indonesian language is getting popular, but few have tapped the vast unstructured data source on YouTube. Comments and reviews from viewers are valuable feedbacks for improvements. The review on YouTube is an essential resource to be analyzed by a preacher. However, manual analysis of YouTube reviews is complicated due to a large amount of review data. Therefore, this study aims to analyze sentiment on YouTube video reviews. In this paper, we employed the Lexicon and Latent Dirichlet Allocation (LDA) to analyze a total of 2575 review data. In this case study, we mined YouTube user’s review to understand the netizen's opinion on a famous Islamic Preacher in South East Asia, namely Ustadz Abdul Somad (UAS). We employed the Google Apps Script (GAS) with Javascript coding language to crawl YouTube review data. Based on the results, the lexicon method successfully analyzed sentiments with an accuracy of 70%. Furthermore, 98% of YouTube users gave positive reviews on the UAS videos lecture. This study is a stepping stone for more complex sentiment analysis regarding text pre-processing and algorithm robustness.
Delta: A Modular Ontology Evaluation System

Ontologies are widely used nowadays. However, the plethora of ontologies currently available online, makes it really difficult to identify which ontologies are appropriate for a given task and to decide on their quality characteristics. This is further complicated by the fact that multiple quality criteria have been proposed for ontologies, making it even more difficult to decide which ontology to adopt. In this context, in this paper we present Delta, a modular online tool for analyzing and evaluating ontologies. The interested user can upload an ontology to the tool, which then automatically analyzes it and graphically visualizes numerous statistics, metrics, and pitfalls. Those visuals presented include a diverse set of quality dimensions, further guiding users to understand the benefits and the drawbacks of each individual ontology and how to properly develop and extend it.
Social Media and Experiences of Nature

The chapter focuses on people’s experiences of natural places and changes in their sense of place through the use of social media. It explores how social media are linked to senses of place and experiences of nature from a social–ecological–technological systems perspective. This is illustrated through four empirical cases representing specific people–place–tech systems, i.e. systems where different social, ecological and tech contexts interact. From a system perspective, those couplings are integrated parts of people’s experiences of nature that bridge virtual and physical worlds, thereby facilitating and communicating cognitive, affective and behavioural social-ecological interactions. These interactions foster novel individual and co-constructed meanings of place and thus plural senses of place; they can also mobilise people around shared meanings of place that are used to question dominant views. Thus, it is argued that social media can mediate and proliferate plural meanings of place, leading to new conceptualisations of senses of place.
Analysis of college martial arts teaching posture based on 3D image reconstruction and wavelet transform

Martial arts is a traditional sports event of the Chinese nation, which carries history and culture. With the development of “Martial Arts on Campus Activities” in recent years, more and more schools have opened general martial arts courses. However, due to the more complex technical movements of martial arts, there are often varying degrees of gaps between the movements and standard movements. Based on this, this research introduces three-dimensional imaging technology on the basis of traditional physical education teaching methods, aiming to explore new martial arts teaching models through image reconstruction and posture analysis. First of all, in order to obtain a three-dimensional point cloud and three-dimensional line, this paper extracts and matches feature points and feature lines on the input image. Secondly, on the basis of obtaining dense matching and straight-line matching, this paper selects the image with the most feature line matching for reprojection. Wavelet transform is used in the process of image compression and coding, including signal decomposition and reconstruction steps. Finally, through the experimental test of martial arts teaching posture images in colleges and universities, it shows that the method of combining three-dimensional image reconstruction and wavelet transform proposed in this paper has good applicability and efficiency, and can provide a scientific reference for college martial arts teaching.
Shaping Blockchain Technology for Securing Supply Chains

Purchases in supply chains involve a network of suppliers, manufacturers, logistics or even customers needed for the procurement of goods or services. These are needed to operate a supply chain efficiently and allow timely deliverables to consumers. In our work, we identify and map a typical business process to demonstrate how we can securely allow participants to interact with smart contracts and discover potential use cases for supply chains.
A deep semantic segmentation correction network for multi-model tiny lesion areas detection

Abstract Background Semantic segmentation of white matter hyperintensities related to focal cerebral ischemia (FCI) and lacunar infarction (LACI) is of significant importance for the automatic screening of tiny cerebral lesions and early prevention of LACI. However, existing studies on brain magnetic resonance imaging lesion segmentation focus on large lesions with obvious features, such as glioma and acute cerebral infarction. Owing to the multi-model tiny lesion areas of FCI and LACI, reliable and precise segmentation and/or detection of these lesion areas is still a significant challenge task. Methods We propose a novel segmentation correction algorithm for estimating the lesion areas via segmentation and correction processes, in which we design two sub-models simultaneously: a segmentation network and a correction network. The segmentation network was first used to extract and segment diseased areas on T2 fluid-attenuated inversion recovery (FLAIR) images. Consequently, the correction network was used to classify these areas at the corresponding locations on T1 FLAIR images to distinguish between FCI and LACI. Finally, the results of the correction network were used to correct the segmentation results and achieve segmentation and recognition of the lesion areas. Results In our experiment on magnetic resonance images of 113 clinical patients, our method achieved a precision of 91.76% for detection and 92.89% for classification, indicating a powerful method to distinguish between small lesions, such as FCI and LACI. Conclusions Overall, we developed a complete method for segmentation and detection of WMHs related to FCI and LACI. The experimental results show that it has potential clinical application potential. In the future, we will collect more clinical data and test more types of tiny lesions at the same time.
Low-Light Image Enhancement Based on Multi-Path Interaction

Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image and further adjusted in detail. In the multi-path interaction network, the feature maps in several encoding–decoding subnetworks are used to exchange information across paths, while a high-resolution path is retained to enrich the feature representation. Meanwhile, in order to avoid the possible unnatural results caused by the separation of the R, G, B channels, the output of the multi-path interaction network is corrected in detail to obtain the final enhancement results. Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.
Leading Attacks in Privacy Protection Domain

In this chapter, we discuss seven leading attacks in privacy domain built upon the major privacy concerns in general cases. There are background knowledge attack, collusion attack, linkage attack, structural attack, forgery attack, eavesdropping attack, and Sybil attack. There are also some other forms of attacks such as tracking attacks [1] and inference attacks [2] [3], but these attacks fall in the range of the seven illustrated attacks.
HSiPu<sup>2</sup> - A New Human Physical Fitness Action Dataset for Recognition and 3D Reconstruction Evaluation

In this paper a human physical fitness action feature dataset named HSiPu <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is introduced, which contains 8,044 action data sequences and 80,440 images. The dataset is built for three physical fitness actions, which are sit-up, push-up and pull-up, and each action data has two categories corresponding to standard and non-standard actions. Two cameras work as sensors to capture features from different views. HSiPu <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> data set facilitates the evaluation of the performance of machine learning algorithms used in recognition and evaluation problems related to human behaviours recognition. The dataset is freely and publicly available online. A new recognition method based on deep learning techniques, including the two-branch multi-stage convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with attention, are employed to learn the long-term dependencies from videos for human physical fitness action recognition on the HSiPu <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . For comparison purpose, traditional machine learning models, including Decision Tree, Random Forest, SVM, Bagging, GBDT, AdaBoost, XGBoost and Voting, are utilized for the action recolonization. Furhtermore, HSiPu <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> also can serve as a dataset to evaluating a human action 3D model.
Improving ENIGMA-style Clause Selection while Learning From History

Abstract We re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a recursive neural network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41 % improvement on a relevant subset of SMT-LIB in a real time evaluation.
Privacy Challenges and Enhanced Protection in Blockchain Using Erasable Ledger Mechanism

Blockchain is a decentralized network that has a distributed ledger which is one of the emerging invention in recent years. It is immutable, persistent and peer to peer in nature which make it a more secure technology. Beyond cryptocurrencies, blockchain has many potential applications such as smart cities, Internet of Things, online polling system and so on. In addition, the transparent ledger technology of the blockchain makes it more vulnerable to privacy and security issues. Privacy leakage issue in the blockchain is a major limitation which slows down the growth of this technology. In this paper, privacy challenges and existing mechanisms to overcome the privacy issues such as zero-knowledge proof, coin mixing services, ring signatures and commitment scheme are surveyed. The limitations of these existing privacy mechanisms are analyzed. Finally, based on the analyzed limitations and open issues, the future research directions are proposed. In this, combining blockchain with trusted computing and the development of content erasure mechanism can improve the current privacy issues. This idea of privacy improvement is proposed as new direction of development in blockchain domain.
Leading dissemination of digital, science-based innovation in school – a case study

Digitalizing school is a process that comes with challenges. It requires strategic leadership and transformational change to work processes. Nevertheless, some succeed, and it is useful to understand what makes for success. This paper studies the challenges of leading digitalization of education in a city which decided to implement an IT-supported method for teaching literacy in primary schools. The method required transformational change; teachers not only had to learn to use technologies, they also had to review their pedagogy. This study is guided by the Diffusion of Innovation theory and draws on interviews with school principals. We find that the critical factors relate to the communication channel, the social system within and across schools, and the time perspective.
Self-Supervised Video Object Segmentation by Motion-Aware Mask Propagation

We propose a self-supervised spatio-temporal matching method, coined Motion-Aware Mask Propagation (MAMP), for video object segmentation. MAMP leverages the frame reconstruction task for training without the need for annotations. During inference, MAMP builds a dynamic memory bank and propagates masks according to our proposed motion-aware spatio-temporal matching module, which is able to handle fast motion and long-term matching scenarios. Evaluation on DAVIS-2017 and YouTube-VOS datasets show that MAMP achieves state-of-the-art performance with stronger generalization ability compared to existing self-supervised methods, i.e., 4.2% higher mean <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{J}$</tex> & <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{F}$</tex> on DAVIS-2017 and 4.85% higher mean <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{J}$</tex> & <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{F}$</tex> on the unseen categories of YouTube-VOS than the nearest competitor. Moreover, MAMP performs at par with many supervised video object segmentation methods. Our code is available at: https://github.com/bo-miao/MAMP.
Particle Swarm Optimization-Based approaches for Cloud-Based Task and Workflow scheduling: A Systematic Literature Review

Cloud computing is a novel perspective that provides effective methods to distribute independent and dependent tasks to virtual resources. Task and workflow scheduling play a significant role in optimizing makespan, energy consumption, load balancing, and cost in the cloud environment. Various metaheuristics solutions are proposed to solve scheduling problems. Optimizing the scheduling problem is NP-hard. In this paper, we are presenting an in-depth review of the current landscape of Particle Swarm Optimization(PSO)-based methods to schedule tasks and workflows. We adopt a systematic literature review(SLR) procedure to select studies based on framed Research Questions and predefined inclusion/exclusion criteria from online electronic databases. 39 articles were selected out of 364 articles. Those articles that answer our framed research questions are selected for analysis. The study considers various advantages, drawbacks, properties, simulation and evaluation tools, function objectives of PSO and prepares classification based on PSO modified and PSO hybrid. Finally, a future research direction is presented.
Linear Cryptanalysis of FF3-1 and FEA

Improved attacks on generic small-domain Feistel ciphers with alternating round tweaks are obtained using linear cryptanalysis. This results in practical distinguishing and message-recovery attacks on the United States format-preserving encryption standard FF3-1 and the South-Korean standards FEA-1 and FEA-2. The data complexity of the proposed attacks on FF3-1 and FEA-1 is $$\widetilde{\mathcal {O}}(N^{r/2 - 1.5})$$ , where $$N^2$$ is the domain size and r is the number of rounds. For example, FF3-1 with $$N = 10^3$$ can be distinguished from an ideal tweakable block cipher with advantage $$\ge 1/10$$ using $$2^{23}$$ encryption queries. Recovering the left half of a message with similar advantage requires $$2^{24}$$ data. The analysis of FF3-1 serves as an interesting real-world application of (generalized) linear cryptanalysis over the group $$\mathbb {Z}/N\mathbb {Z}$$ .