Reviewerly/exHarmony huggingface dataset:
Revolutionizing elementary disaster prevention education and training via augmented reality-enhanced collaborative learning

In light of a recent spike in natural and man-made disasters, there has been an increase in interest in disaster prevention education and training. The effectiveness of both publicly-funded and voluntarily organized disaster education (DE) has attracted wide attention. More studies are needed to understand the innovative pedagogical practice and the impact of technological advances on disaster learning content development, effectiveness and motivation. This study investigates the application of augmented reality (AR) in DE and training. An AR-enhanced tool named ‘disaster-proof warrior’ was developed and tested to evaluate its enhancement effect on learning under two collaborative learning modes. A series of quasi-experiments involving 85 elementary school subjects was carried out to assess the learning effectiveness and the subjective reaction in learning motivation. The results showed the AR embedded learning tool is effective in engaging and motivating collaborative team knowledge building. This study adds to the existing literature of AR applications in education and training as well as providing a useful reference for future development and improvement of national DE and training.
Electron and muon $(g-2)$ in the 2HDM

Recent precise determination of the electron anomalous magnetic moment (AMM) adds to the longstanding tension of the muon AMM and together strongly point towards physics beyond the Standard Model (BSM). Here we present a solution to both anomalies via a light scalar that emerges from a second Higgs doublet and resides in the $\mathcal{O}(10)$-MeV to $\mathcal{O}(1)$-GeV mass range. A scalar of this type is subject to a number of various experimental constraints, however, as we show, it can remain sufficiently light by evading all experimental bounds and has the great potential to be discovered in the near-future low-energy experiments. In addition to the light scalar, our theory predicts the existence of a nearly degenerate charged scalar and a pseudoscalar, which have masses of the order of the electroweak scale. This scenario can be tested at the LHC by looking at the novel process $pp \to H^\pm H^\pm jj \to l^\pm l^\pm j j + {E\!\!\!\!/}_{T}$ via same-sign pair production of charged Higgs bosons. This talk is based on results presented in hep-ph 2003.03386.
Mining challenges in large-scale IoT data framework – a machine learning perspective

With the rapid growth of advanced technologies, the time has come to relook into the data mining tools that are available in the machine learning context. Initially developed machine learning techniques were more suited to the requirements in those times and may need changes to make them more suitable to the present needs. Initial developments of machine learning algorithms focused on brief training and testing datasets to prove their worthiness. Large datasets with a sufficient number of features together with tools and techniques to effectively perform data analysis and prediction in real-time are a more adapted platform. The demand-driven societal requirements of targeted information extraction can easily be met using some data mining analysis techniques. The main aim is to bring artificial intelligence into the business domain. High-level software frameworks help mathematicians, physicists, and computer scientists to reap the benefits of Big Data mining techniques by collaborating with academic researchers and professional data scientists. The rate of data creation is approximately 2.5 quintillion bytes of data per day [1]. Those data are heterogeneous, voluminous, complex, and generated from different autonomous sources with distributed and decentralized control. The driving force is to efficiently mine these data using the latest technologies for research and commercial insights. The present chapter reviews some of the recent machine learning methods used for analyzing data, under some popular frameworks which are capable of handling emerging challenges. The intention is to find appropriate solutions for different use cases.
PriBank: Confidential Blockchain Scaling Using Short Commit-and-Proof NIZK Argument

Decentralized financial applications demand fast, cheap, and privacy-preserving cryptocurrency systems to facilitate high transaction volumes and provide privacy for users. Off-chain Layer-2 scaling solutions such as Plasma, ZK-Rollup, NOCUST are appealing innovations devised to enable the scalability and extensibility account-based blockchains that support smart contracts. The essential idea is simple yet powerful: move expensive computations off-chain and commit the abbreviated transaction data on-chain. Nevertheless, these solutions do not provide privacy for the users' balances and off-chain transaction data. In this paper, we propose PriBank, a novel privacy-preserving cryptocurrency system that enables private balances and transaction values on top of these Layer-2 scaling solutions. To construct PriBank system, we propose a Commit-and-Prove short NIZK argument for quadratic arithmetic programs. The Commit-and-Prove short NIZK argument is built on top of the existing zero-knowledge proof scheme: Bulletproof. It allows a prover to commit to an arbitrary set of witnesses by Pedersen commitments before proving, which may be of independent interest. We construct security models and definitions for Layer-2 privacy-preserving scaling solutions and analyse the security of our scheme under the security model. We also implement and evaluate the system, and present a comparative analysis with the existing solutions.
A View of Security as 20 Subject Areas in Four Themes

We give a detailed example of organizing security knowledge into themes and subject areas. The aim is to stimulate further independent thought on how to distribute security topics into subject areas around which to build courses that support a coherent undergraduate security curriculum.
Parallel Gate Operations Fidelity in a Linear Array of Flip‐Flop Qubits

Abstract Quantum computers based on silicon are promising candidates for long term universal quantum computation due to the long coherence times of electron and nuclear spin states. Furthermore, the continuous progress of micro‐ and nano‐electronics, also related to the scaling of metal–oxide–semiconductor systems, makes it possible to control the displacement of single dopants thus suggesting their exploitation as qubit holders. Flip‐flop qubit is a donor based qubit where interactions between qubits are achievable for distance up to several hundred nanometers. In this work, a linear array of flip‐flop qubits is considered and the unwanted mutual qubit interactions due to the simultaneous application of two one‐qubit and two two‐qubit gates are included in the quantum gate simulations. In particular, by studying the parallel execution of couples of one‐qubit gates, namely and , and of couples of two‐qubit gate, that is, , a safe inter‐qubit distance is found where unwanted qubit interactions are negligible thus leading to parallel gates fidelity up to 99.9%.
On Fingerprinting Attacks and Length-Hiding Encryption

It is well known that already the length of encrypted messages may reveal sensitive information about encrypted data. Fingerprinting attacks enable an adversary to determine web pages visited by a user and even the language and phrases spoken in voice-over-IP conversations. Prior research has established the general perspective that a length-hiding padding which is long enough to improve security significantly incurs an unfeasibly large bandwidth overhead. We argue that this perspective is a consequence of the choice of the security models considered in prior works, which are based on classical indistinguishability of two messages, and that this does not reflect the attacker model of typical fingerprinting attacks well. Therefore we propose a new perspective on length-hiding encryption, which aims to capture security against fingerprinting attacks more accurately. This makes it possible to concretely quantify the security provided by length-hiding padding against fingerprinting attacks, depending on the real message distribution of an application. We find that for many real-world applications (such as webservers with static content, DNS requests, Google search terms, or Wikipedia page visits) and their specific message distributions, even length-hiding padding with relatively small bandwidth overhead of only 2–5% can already significantly improve security against fingerprinting attacks. This gives rise to a new perspective on length-hiding encryption, which helps understanding how and under what conditions length-hiding encryption can be used to improve security.
Privacy-preserving D2D Cooperative Location Verification

Device-to-Device (D2D) cooperative location verification allows a device to verify its location with the help of neighbouring devices. It is especially handy in location-based services where location verification is essential. However, the exposure of device location during verification rises a big privacy risk for participants since they have to send their real-time locations to unknown verifiers holding anonymous identities. Thus, a privacy-preserving solution is urgently needed to provide verification without location disclosure. Traditional solutions based on Paillier and garbled circuits can solve the problem but also introduce high cost. Based on order-preserving encryption, we propose an efficient protocol with high-security guarantee to address this issue. Apart from rigorous security proof, complexity analysis and extensive experiments are also conducted to evaluate the proposed solution. The results compared with related work show that order-preserving encryption based mechanism achieves the best balance with regards to privacy, utility and performance requirements.
Interested Keyframe Extraction of Commodity Video Based on Adaptive Clustering Annotation

Keyframe recognition in video is very important for extracting pivotal information from videos. Numerous studies have been successfully carried out on identifying frames with motion objectives as keyframes. The definition of “keyframe” can be quite different for different requirements. In the field of E-commerce, the keyframes of the products videos should be those interested by a customer and help the customer make correct and quick decisions, which is greatly different from the existing studies. Accordingly, here, we first define the key interested frame of commodity video from the viewpoint of user demand. As there are no annotations on the interested frames, we develop a fast and adaptive clustering strategy to cluster the preprocessed videos into several clusters according to the definition and make an annotation. These annotated samples are utilized to train a deep neural network to obtain the features of key interested frames and achieve the goal of recognition. The performance of the proposed algorithm in effectively recognizing the key interested frames is demonstrated by applying it to some commodity videos fetched from the E-commerce platform.
Supervised Contrastive Learning for Product Classification

Contrastive learning has shown great results in image classification. It is primarily applied for semi-supervised or unsupervised representation learning. Contrastive learning has recently shown high accuracy results in supervised environments where fully labeled images were utilized for image classification. In the e-commerce field, product image datasets tend not to have a large number of instances which make the classification of new products more difficult. Thus, a model that can classify product images needs to utilize all the existing labeled images in a detailed manner. This paper adopts supervised contrastive learning to explicitly classify products based on their images. This is done by using Optical Character Recognition (OCR) as an auxiliary input and by fine-tuning pre-trained models. OCR takes advantage of the fact that product images have distinct features over standard images. Labels or texts that appear on product images are captured and fed into the projection network along with their corresponding images. This simplifies the classification of the images for the classifier. The use of the auxiliary input with the fine-tuning has shown a noticeable and promising increase in classification accuracy. Experiments have demonstrated that our proposed framework has achieved over a 15% top-1 accuracy increase over the existing methods on three different datasets.
A Novel Deep Learning Architecture for Wireless Image Transmission

In this paper, the problem of neural compression based image transmission over wireless channels is studied. Since all procedures are considered over wireless links, the quality of training is affected by wireless factors such as packet errors. In the considered model, compressed data given by the neural source encoder (NSE) are fed into an error-control channel encoder and modulated as discrete symbols sent over a memoryless channel. In the receiving end, the channel decoder and the neural source decoder (NSD) forms an iterative structure to reconstruct the original image. Since all neural compressed data are transmitted over wireless channels, the training of NSD is affected by wireless channel factors such as residual bit errors given by the channel decoder. Meanwhile, during outer-loop iterations, the NSD needs to match the variant of information reliability output by the channel decoder so as to build a global optimal receiver. To this end, a refiner neural network is first attached after the NSD to adjust its output as the format of a priori information sent into the channel decoder. Then, the extrinsic information transfer (EXIT) functions of channel decoder and NSD are derived. At each iteration, the reliability of messages sent into the NSD is explicitly predicted by using the EXIT chart. By this means, the NSD can be trained in a residual bit error aware manner, and we realize a joint learning and iterative decoding framework to ensure the quality of neural image transmission over realistic wireless channels.
Comparison between Support Vector Machine and K-Nearest Neighbor Algorithms for Leukemia Images Classification Using Shape Features

Leukemia occurs when the body produces abnormal white blood cells in amounts exceeding the normal limit, making them misfunctioning. It is highly influential on the human immune system. Currently, medical personnel require a long time to recognize leukemia, and it is difficult to distinguish between acute leukemia cells and normal cells. Hence, this study aims to build a system program using white blood cell images with image processing using feature extraction with the Hu moments invariant and the Support Machine Machine (SVM) and K-Nearest Neighbor (K-NN) classification methods. The samples used were 800 blood images divided into two classes, acute and normal, with each class consisting of 400 sample images. Based on the test results from comparing the average value of accuracy and training time in both methods, the highest accuracy value was in the SVM method, with an accuracy of 87.97% and the K-NN method of 83.96%. The fastest training time was in the K-NN method of 2.43 seconds and the SVM method of 3.73 seconds.
Few-Shot Anomaly Detection Using Deep Generative Models for Grouped Data

There exists a great demand for automatic anomaly detection in industrial world. The anomaly has been defined as a group of samples that rarely or never appears. Given a type of products, one has to collect numerous samples and train an anomaly detector. When one diverts a model trained with old types of products with sufficient inventory to the new type, one can detect anomalies of the new type before a production line is established. However, because of the definition of the anomaly, a typical anomaly detector considers the new type of products anomalous even if it is consistent with the standard. Given the above practical demand, this study propose a novel problem setting, few-shot anomaly detection, where an anomaly detector trained in source domains is adapted to a small set of target samples without full retraining. Then, we tackle this problem using a hierarchical probabilistic model based on deep learning. Our empirical results on toy and real-world datasets demonstrate that the proposed model detects anomalies in a small set of target samples successfully.
Deep Learning for Unsupervised Neural Machine Translation

With the breakthrough of deep learning techniques, many Natural Language Processing tasks have exploited the techniques to enhance performance. Neural Machine Translation, as a sub-field of NLP, also leverages deep learning methods to enhance performance. There are two main categories of NMT, the first one is supervised, and the other one is unsupervised. Supervised NMT uses labelled data and large parallel corpus for training, while unsupervised NMT only uses independent monolingual corpora for training the model. The latter one gives many conveniences in data collection but poses a great difficulty in engineering the architecture. Thus, in this paper, we give a comprehensive review of deep learning techniques for unsupervised neural machine translation.as illustrated by the portions given in this document.
Predicting before acting: improving policy quality by taking a vision of consequence

Deep reinforcement learning has achieved great success in many fields. However, the agent may get trapped during the exploration, lingering around feckless states that pull the agent away from optimal policies. Thus it's worth studying how to improve learning strategies by forecasting future states. To solve the problem, an algorithm that predicts behaviour consequences before taking action, referred to as Thinking about Action Consequence (TAC), based on the combination of the current state and the policy, is proposed. Deep reinforcement learning approaches equipped with TAC take advantage of the intrinsic prediction framework of TAC as well as bad consequence experiences to determine whether to continue exploring the current region or to evade the potential undesired state discriminated by TAC and instead turn to explore other areas. Since TAC will modify the future plan according to the execution of the policy and the changes of the environment, considering the consequences enables the agent to carry out a suitable exploration mechanism, leading to better performance. Compared with traditional Deep Q Learning (DQN), Double DQN, Dueling DQN, Prioritised Experience Replay DQN, and Intrinsic Reward (IR) methods with uncertain stimulus exploration, our TAC method shows better performance in Atari and Box2d environments.
AlphaRA: An AlphaZero based approach to Redundancy Analysis

Manufacturing flaws in memory devices give rise to faulty cells rendering the chips unusable and consequently reducing the wafer yield. To repair faulty memory cells, redundancies are included in the form of spare rows and columns in the memory. Redundancy Analysis is the process of mapping these spare rows and columns to repair faulty lines in the chip. However, Redundancy Analysis is an NP-complete problem, making it difficult to find a trade-off between repair rate and runtime, especially for large chip sizes. In this paper, we introduce AlphaRA, a first-of-its-kind memory repair algorithm based on the Reinforcement Learning algorithm AlphaZero. We explicate AlphaRA as a single agent problem that learns the strategies of Redundancy Analysis through self-play. Starting tabula rasa, AlphaRA achieves an average normalized repair rate of 99.8% on 16×16 chips with only 32 MCTS simulations. It outperforms the next best heuristic algorithm by 5.42% while utilizing 0.29% lesser spares, making it a suitable Redundancy Analysis algorithm for mass production of memory devices.
Open Set Mixed-Reality Human Activity Recognition

Sensor-based human activity recognition (HAR) is a fundamental problem that can have a broad impact on many research/industrial fields. The deep learning methods pave the way for extracting robust and informative features from the non-stationary HAR data, achieving high-accuracy HAR. Most of the works in the literature consider the closed set activity recognition, which assumes all classes of activities are known in both the training and test stages. However, it is a challenging way to apply deep learning models to real-world applications, which can contain unseen activities. These activities will sharply decrease the performance of the deep learning models. To this end, we introduce the problem of Open Set Mixed-Reality (OSM) HAR, which aims to recognize unseen activities while classify seen samples. Furthermore, we propose a novel balanced open set backpropagation method to realize accurate and robust OSM-HAR. Lastly, we verify the effectiveness of the proposed method with a publicly available and our newly collected dataset.
A brief overview of kernel methods with prior information

With the progress of human society and the development of computer technology, more and more experience has been accu¬mulated, which can serve as prior information in model training. With the assistance of prior information, the performance of some machine learning algorithms has been significantly improved. In this article, we mainly discussed four types of prior information, namely knowledge sets, fuzzy sets, universum data, and privilege information. Specifically, (1) the knowledge sets are constraints composed of domain experience; (2) the fuzzy sets provide the importance of each instance through the fuzzy membership function; (3) the universum data belongs to none of the target classes, but reflects the domain information related to the field of interest; (4) the privileged information can provide more relevant information about training examples, but is not available during the testing phase. In order to illustrate how the prior information is integrated into the kernel methods, the cor¬responding representative models are reviewed in detail, including knowledge-based support vector machine(KSVM), fuzzy support vector machine(FSVM), universum support vector machine(USVM) and privileged support vector machine(SVM+). In addition, this article briefly introduced the most related and advanced research, including the deep learning extensions with prior information. Finally, the current challenge and the future trends of incorporating prior information into machine learning are discussed.
Research on Authentication and Key Negotiation Based on Smart Water Environment

More and more countries are exploring smart city related smart facilities to benefit the society. Remote access and control of IOT smart devices is convenient, but there are also some risks. Smart devices may be attacked by criminals who will do serious harm to the production environment. It is particularly important to design a safe and efficient secure communication scheme. Most existing solutions to this problem are usually based on single server architecture, which has some problems in security and efficiency. Aiming at the problem that authentication and key agreement in smart water conservancy are vulnerable to SPOF and DDOS attacks, this paper proposes an authentication key agreement technology based on blockchain. In key agreement, the identity of smart devices can not be tampered with, which can effectively resist man in the middle attack. In the process of protocol execution, the nodes of smart devices are light nodes. The process of key agreement is carried out in the smart contract, which reduces the computational complexity and improves the operation efficiency. This paper also analyzes the security of the scheme.