Reviewerly/exHarmony huggingface dataset:
Heralding the Digitalization of Life in Post-Pandemic East Asian Societies

Following the outbreak of what would become the COVID-19 pandemic, social distancing measures were quickly introduced across East Asia-including drastic shelter-in-place orders in some cities-drawing on experience with the outbreak of severe acute respiratory syndrome (SARS) almost two decades ago. "Smart City" technologies and other digital tools were quickly deployed for infection control purposes, ranging from conventional thermal scanning cameras to digital tracing in the surveillance of at-risk individuals. Chatbots endowed with artificial intelligence have also been deployed to shift part of healthcare provision away from hospitals and to support a number of programmes for self-management of chronic disease in the community. With the closure of schools and adults working from home, digital technologies have also sustained many aspects of both professional and social life at a pace and scale not considered to be practicable before the outbreak. This paper considers how these new experiences with digital technologies in public health surveillance are spurring digitalization in East Asian societies beyond the conventional public health context. It also considers some of the concerns and challenges that are likely to arise with rapid digitalization, particularly in healthcare.
Step-like dependence of memory function on pulse width in spintronics reservoir computing

Abstract Physical reservoir computing is a type of recurrent neural network that applies the dynamical response from physical systems to information processing. However, the relation between computation performance and physical parameters/phenomena still remains unclear. This study reports our progress regarding the role of current-dependent magnetic damping in the computational performance of reservoir computing. The current-dependent relaxation dynamics of a magnetic vortex core results in an asymmetric memory function with respect to binary inputs. A fast relaxation caused by a large input leads to a fast fading of the input memory, whereas a slow relaxation by a small input enables the reservoir to keep the input memory for a relatively long time. As a result, a step-like dependence is found for the short-term memory and parity-check capacities on the pulse width of input data, where the capacities remain at 1.5 for a certain range of the pulse width, and drop to 1.0 for a long pulse-width limit. Both analytical and numerical analyses clarify that the step-like behavior can be attributed to the current-dependent relaxation time of the vortex core to a limit-cycle state.
Knowledge-aware Complementary Product Representation Learning

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness.
The renaissance of augmented reality in construction: history, present status and future directions

Purpose Augmented reality (AR) has become one of the most promising technologies in construction since it can seamlessly connect the physical construction environment and virtual contents. In view of the recent research efforts, this study attempts to summarize the latest research achievements and inform future development of AR in construction. Design/methodology/approach The review was conducted in three steps. First, a keyword search was adopted, and 546 papers were found from Scopus and Web of Science. Second, each paper was screened based on the selection criteria, and a final set of 69 papers was obtained. Third, specific AR applications and the associated technical details were extracted from the 69 papers for further analysis. Findings The review shows that: (1) design assessment, process monitoring and maintenance management and operation were the most frequently cited AR applications in the design, construction, and operation stages, respectively; (2) information browser and tangible interaction were more frequently adopted than collaborative interaction and hybrid interaction; and (3) AR has been integrated with BIM, computer vision, and cloud computing for enhanced functions. Originality/value The contributions of this study to the body of knowledge are twofold. First, this study extends the understanding of AR applications in the construction setting. Second, this study identifies possible improvements in the design and development of AR systems in order to leverage their benefits to construction.
A Survey on Vulnerabilities and Performance Evaluation Criteria in Blockchain Technology

The blockchain technology firstly presented by Haber and Stornetta in the year 1990, and first time blockchain technology used in Bitcoin by Satoshi Nakamoto in 2008. The blockchain technology is truly decentralized technology. In blockchain technology, every block has consisted three main parts that is data, hash block, and the previous hash block. Hash is controls the uniqueness of each block and it is unique for each block. Each block also contains the hash of the previous block; thus, the blocks are connected to each other. A blockchain can divided into three categories public blockchain, consortium blockchain and private blockchain. The proposed paper provided the comparative and analytical review on the blockchain consensus algorithms.
Multi-view Optimization of Local Feature Geometry

In this work, we address the problem of refining the geometry of local image features from multiple views without known scene or camera geometry. Current approaches to local feature detection are inherently limited in their keypoint localization accuracy because they only operate on a single view. This limitation has a negative impact on downstream tasks such as Structure-from-Motion, where inaccurate keypoints lead to large errors in triangulation and camera localization. Our proposed method naturally complements the traditional feature extraction and matching paradigm. We first estimate local geometric transformations between tentative matches and then optimize the keypoint locations over multiple views jointly according to a non-linear least squares formulation. Throughout a variety of experiments, we show that our method consistently improves the triangulation and camera localization performance for both hand-crafted and learned local features.
On a Dual/Hybrid Approach to Small Secret LWE

In this paper, we investigate the security of the Learning With Error (LWE) problem with small secrets by refining and improving the so-called dual lattice attack. More precisely, we use the dual attack on a projected sublattice, which allows generating instances of the LWE problem with a slightly bigger noise that correspond to a fraction of the secret key. Then, we search for the fraction of the secret key by computing the corresponding noise for each candidate using the newly constructed LWE samples. As secrets are small, we can perform the search step very efficiently by exploiting the recursive structure of the search space. This approach offers a trade-off between the cost of lattice reduction and the complexity of the search part which allows to speed up the attack. Besides, we aim at providing a sound and non-asymptotic analysis of the techniques to enable its use for practical selection of security parameters. As an application, we revisit the security estimates of some fully homomorphic encryption schemes, including the Fast Fully Homomorphic Encryption scheme over the Torus (TFHE) which is one of the fastest homomorphic encryption schemes based on the (Ring-)LWE problem. We provide an estimate of the complexity of our method for various parameters under three different cost models for lattice reduction and show that the security level of the TFHE scheme should be re-evaluated according to the proposed improvement (for at least 7 bits for the most recent update of the parameters that are used in the implementation).
Digitalization of teacher education

This paper examines emerging epistemologies in the wake of increased digitalization and the extent to which student teachers are prepared for such epistemologies in their teacher education (TE) programs. Although there are a number of studies on digital epistemologies, few have examined the implications of digitally extended, embedded, and embodied cognition and the ensuing epistemic practices. This study’s analytic focus is partly on a number of project proposals that reflect some of these trends and partly on two surveys of (and some interviews with) student teachers at two universities who were engaged in developing their professional digital competence (PDC) as part of the TE programs. The surveys and interviews indicate that TE tends to focus on the use of digital technologies and less on addressing deeper, epistemological issues in efforts to foster student teachers’ PDC. These findings signify a gap between emerging epistemological trends and how student teachers are prepared for such trends. We conclude by suggesting a focus on digital, epistemic, and transformative agency for TE to cope with the increased digitalization of epistemic practices.
A Two-Stage Fall Recognition Algorithm Based on Human Posture Features

Falls are seriously threatening the health of elderly. In order to reduce the potential danger caused by falls, this paper proposes a two-stage fall recognition algorithm based on human posture features. For preprocessing, we construct the new key features: deflection angles and spine ratio to describe the changes of human posture based on the human skeleton extracted by OpenPose. In the first stage, based on the variables: tendency symbol and steady symbol integrated by the scattered key features, we divide the human body state into three states: stable state, fluctuating state, and disordered state. By analyzing whether the body is in a stable state, the ADL (activities of daily living) actions with high stability can be preliminarily excluded. In the second stage: to further identify the confusing ADL actions and the fall actions, we innovatively design a time-continuous recognition algorithm. When human body is constantly in an unstable state, the human posture features: compare value γ, energy value ε, state score τ are proposed to form a feature vector, and support vector machine (SVM), K nearest neighbors (KNN), decision tree (DT), random forest (RF) are utilized for classification. Experiment results demonstrate that SVM with linear kernel function can distinguish falling actions best and our approach achieved a detection accuracy of 97.34%, precision of 98.50%, and the recall, F1 score are 97.33%, 97.91% respectively. Compared with previous state-of-art algorithms, our algorithm can achieve the highest recognition accuracy. It proves that our fall detection method is effective.
A Generalization of Transformer Networks to Graphs

We propose a generalization of transformer neural network architecture for arbitrary graphs. The original transformer was designed for Natural Language Processing (NLP), which operates on fully connected graphs representing all connections between the words in a sequence. Such architecture does not leverage the graph connectivity inductive bias, and can perform poorly when the graph topology is important and has not been encoded into the node features. We introduce a graph transformer with four new properties compared to the standard model. First, the attention mechanism is a function of the neighborhood connectivity for each node in the graph. Second, the positional encoding is represented by the Laplacian eigenvectors, which naturally generalize the sinusoidal positional encodings often used in NLP. Third, the layer normalization is replaced by a batch normalization layer, which provides faster training and better generalization performance. Finally, the architecture is extended to edge feature representation, which can be critical to tasks s.a. chemistry (bond type) or link prediction (entity relationship in knowledge graphs). Numerical experiments on a graph benchmark demonstrate the performance of the proposed graph transformer architecture. This work closes the gap between the original transformer, which was designed for the limited case of line graphs, and graph neural networks, that can work with arbitrary graphs. As our architecture is simple and generic, we believe it can be used as a black box for future applications that wish to consider transformer and graphs.
Safe contact tracing for COVID-19: A method without privacy breach using functional encryption techniques based-on spatio-temporal trajectory data

The COVID-19 pandemic has spread all over the globe. In the absence of a vaccine, a small number of countries have managed to control the diffusion of viruses by early detection and early quarantine. South Korea, one of the countries which have kept the epidemics well-controlled, has opened the infected patients’ trajectory to the public. Such a reaction has been regarded as an effective method, however, serious privacy breach cases have been issued in South Korea. Furthermore, some suspected contacts have refused to take infection tests because they are afraid of being exposed. To solve this problem, we propose a privacy-preserving contact tracing method based on spatio-temporal trajectory which can be practically used in many quarantine systems. In addition, we develop a system to visualize the contact tracing workflow.
BitJabber: The World’s Fastest Electromagnetic Covert Channel

An air-gapped computer is physically isolated from unsecured networks to guarantee effective protection against data exfiltration. Due to air gaps, unauthorized data transfer seems impossible over legitimate communication channels, but in reality many so-called physical covert channels can be constructed to allow data exfiltration across the air gaps. Most of such covert channels are very slow and often require certain strict conditions to work (e.g., no physical obstacles between the sender and the receiver). In this paper, we introduce a new physical covert channel named BitJabber that is extremely fast and strong enough to even penetrate concrete walls. We show that this covert channel can be easily created by an unprivileged sender running on a victim's computer. Specifically, the sender constructs the channel by using only memory accesses to modulate the electromagnetic (EM) signals generated by the DRAM clock. While possessing a very high bandwidth (up to 300,000 bps), this new covert channel is also very reliable (less than 1% error rate). More importantly, this covert channel can enable data exfiltration from an air-gapped computer enclosed in a room with thick concrete walls up to 15 cm.
Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

Object tracking is still an intriguing task as the target undergoes significant appearance changes due to illumination, fast motion, occlusion and shape deformation. Background clutter and numerous other environmental factors are other major constraints which remain a riveting challenge to develop a robust and effective tracking algorithm. In the present study, an adaptive Spatio-temporal context (STC)-based algorithm for online tracking is proposed by combining the context-aware formulation, Kalman filter, and adaptive model learning rate. For the enhancement of seminal STC-based tracking performance, different contributions were made in the proposed study. Firstly, a context-aware formulation was incorporated in the STC framework to make it computationally less expensive while achieving better performance. Afterwards, accurate tracking was made by employing the Kalman filter when the target undergoes occlusion. Finally, an adaptive update scheme was incorporated in the model to make it more robust by coping with the changes of the environment. The state of an object in the tracking process depends on the maximum value of the response map between consecutive frames. Then, Kalman filter prediction can be updated as an object position in the next frame. The average difference between consecutive frames is used to update the target model adaptively. Experimental results on image sequences taken from Template Color (TC)-128, OTB2013, and OTB2015 datasets indicate that the proposed algorithm performs better than various algorithms, both qualitatively and quantitatively.
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation

Data collection for the knowledge graph-to-text generation is expensive.As a result, research on unsupervised models has emerged as an active field recently.However, most unsupervised models have to use non-parallel versions of existing small supervised datasets, which largely constrain their potential.In this paper, we propose a large-scale, general-domain dataset, GenWiki.Our unsupervised dataset has 1.3M text and graph examples, respectively.With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.
Continuous-Variable Error Correction for General Gaussian Noises

Quantum error correction is essential for robust quantum-information processing with noisy devices. As bosonic quantum systems play a role in quantum sensing, communication, and computation, it is useful to design error-correction codes suitable for these systems against various different types of noises. While most efforts aim at protecting qubits encoded into the infinite-dimensional Hilbert space of a bosonic mode, Ref. [Phys. Rev. Lett. 125, 080503 (2020)] proposed an error-correction code to maintain the infinite-dimensional-Hilbert-space nature of bosonic systems by encoding a single bosonic mode into multiple bosonic modes. Enabled by Gottesman-Kitaev-Preskill states as ancilla, the code overcomes the no-go theorem of Gaussian error correction. In this work, we generalize the error-correction code to the scenario with general correlated and heterogeneous Gaussian noises, including memory effects. We introduce Gaussian preprocessing and postprocessing to convert the general noise model to an independent but heterogeneous collection of additive white Gaussian noise channels and then apply concatenated codes in an optimized manner. To evaluate the performance, we develop a theory framework to enable the efficient calculation of the noise SD after the error correction, despite the non-Gaussian nature of the codes. Our code provides the optimal scaling of the residue-noise SD with the number of modes and can be widely applied to distributed sensor networks, network communication, and composite quantum-memory systems.
Raman Ink for Steganography

Abstract Steganography, also known as “invisible” communication, refers to the technique of hiding information into another medium such as video, audio, image, and text. Surface‐enhanced Raman scattering (SERS) nanotags are well suited to information encoding owing to their nanoscale dimensions, fingerprint optical spectral features, and remarkable multiplexing capability. Herein, Raman ink, fabricated by doping a new type of SERS nanotags (gap‐enhanced Raman tags, GERTs) into commercial ink, is demonstrated as security ink for multiplexing steganography. A stego‐text is written using two types of Raman ink containing different GERTs with diverse Raman signals, imaged via a confocal Raman system, and processed by the classical least squares method to extract the hidden message. The Raman ink generates distinct spectral profiles with low background from pure ink upon near‐infrared laser irradiation. A multiplexing combination of seven kinds of messages extracted from the written stego‐text is demonstrated, which adds enhanced safety and flexibility to information encoding. In addition, Raman ink exhibits good photostability and long‐term stability. Therefore, GERTs‐based Raman ink is promising for steganographic use in information security.
Adaptive Privacy Preserving Deep Learning Algorithms for Medical Data

Deep learning holds a great promise of revolutionizing healthcare and medicine. Unfortunately, various inference attack models demonstrated that deep learning puts sensitive patient information at risk. The high capacity of deep neural networks is the main reason behind the privacy loss. In particular, patient information in the training data can be unintentionally memorized by a deep network. Adversarial parties can extract that information given the ability to access or query the network. In this paper, we propose a novel privacy-preserving mechanism for training deep neural networks. Our approach adds decaying Gaussian noise to the gradients at every training iteration. This is in contrast to the mainstream approach adopted by Google's TensorFlow Privacy, which employs the same noise scale in each step of the whole training process. Compared to existing methods, our proposed approach provides an explicit closed-form mathematical expression to approximately estimate the privacy loss. It is easy to compute and can be useful when the users would like to decide proper training time, noise scale, and sampling ratio during the planning phase. We provide extensive experimental results using one real-world medical dataset (chest radiographs from the CheXpert dataset) to validate the effectiveness of the proposed approach. The proposed differential privacy based deep learning model achieves significantly higher classification accuracy over the existing methods with the same privacy budget.
Interdisciplinary Blockchain Education: Utilizing Blockchain Technology From Various Perspectives

The early development of blockchain technology (BCT) has already demonstrated the technology's potential to serve the needs of different industries. BCT has also become established as a popular research topic in different scientific disciplines. This paper aims at introducing how several relevant scientific disciplines—supply chain management; management, economics and finance; computer science; security engineering—see the research and education perspectives for BCT. A field review is conducted to present challenges and opportunities of BCT, as well as suggestions for future research and education on the topic as seen from the selected different perspectives. The paper also presents methods for combining relevant disciplines in a modular online course to address the stated challenges and promote interdisciplinary blockchain education.
A hierarchical object detection method in large-scale optical remote sensing satellite imagery using saliency detection and CNN

(2021). A hierarchical object detection method in large-scale optical remote sensing satellite imagery using saliency detection and CNN. International Journal of Remote Sensing: Vol. 42, No. 8, pp. 2827-2847.
Semi-Device-Independent Framework Based on Restricted Distrust in Prepare-and-Measure Experiments

A semi-device-independent framework for prepare-and-measure experiments is introduced in which an experimenter can tune the degree of distrust in the performance of the quantum devices. In this framework, a receiver operates an uncharacterized measurement device and a sender operates a preparation device that emits states with a bounded fidelity with respect to a set of target states. No assumption on Hilbert space dimension is required. The set of quantum correlations is investigated and bounded from both the interior and the exterior. Furthermore, the optimal performance of quantum state discrimination with bounded distrust is derived and applied to certification of detection efficiency. Quantum-over-classical advantages are demonstrated and the magnitude of distrust compatible with such advantages is explored. Finally, efficient schemes for semi-device-independent random number generation are developed.