Reviewerly/exHarmony huggingface dataset:
Benefits and Risks of Using Blockchain in Smart Energy: A Literature Review

Being a decentralized system makes Blockchain a suitable technology solution in many cases. The Smart Energy domain has the potential to benefit from the use of Blockchain, by improving data and transaction management, through applications such as peer-to-peer energy trading, grid enhancement, and electric vehicle support. Many researchers have proposed models and prototypes of blockchain-based applications, which are expected to improve the performance of smart energy systems. In this report, a qualitative literature review was performed to look into a sample of these application proposals and to try to identify the impacts of blockchain technology in Smart Energy. This resulted in the discovery of both positive and negative factors. The beneficial factors include increased security, improved data privacy, data transparency and immutability, removal of third-party control and trust, ubiquitous solution, and greater data accessibility. The list of challenges includes limited scalability and speed, off-chain support requirement, high establishment and maintenance cost, and requiring further practical tests. One of the interesting findings is that the impact of Blockchain technology as to whether it may increase or decrease transaction costs is still unclear. Finally, the paper recommends that a future study may focus on economic feasibility and transaction costs, and ascertain which Smart Energy cases and application designs are more appropriate for Blockchain technology.
Ternary Syndrome Decoding with Large Weight

The Syndrome Decoding problem is at the core of many code-based cryptosystems. In this paper, we study ternary Syndrome Decoding in large weight. This problem has been introduced in the Wave signature scheme but has never been thoroughly studied. We perform an algorithmic study of this problem which results in an update of the Wave parameters. On a more fundamental level, we show that ternary Syndrome Decoding with large weight is a really harder problem than the binary Syndrome Decoding problem, which could have several applications for the design of code-based cryptosystems.
White-Box Implementation of the Identity-Based Signature Scheme in the IEEE P1363 Standard for Public Key Cryptography

Unlike black-box cryptography, an adversary in a white-box security model has full access to the implementation of the cryptographic algorithm. Thus, white-box implementation of cryptographic algorithms is more practical. Nevertheless, in recent years, there is no white-box implementation for public key cryptography. In this paper, we propose the first white-box implementation of the identity-based signature scheme in the IEEE P1363 standard. Our main idea is to hide the private key to multiple lookup tables, so that the private key cannot be leaked during the algorithm executed in the untrusted environment. We prove its security in both black-box and white-box models. We also evaluate the performance of our white-box implementations, in order to demonstrate utility for real-world applications.
Distantly Supervised Biomedical Named Entity Recognition with Dictionary Expansion

State-of-the-art biomedical named entity recognition (BioNER) systems apply supervised machine learning models (i.e., relying on human effort for training data annotation) which are not easy to be generalized to new entity types and datasets. We propose a distantly supervised approach, AutoBioNER, that automatically recognizes biomedical entities from massive corpora with user-input dictionaries. AutoBioNER does not need any human annotated data. It relies on incomplete entity dictionaries to provide seeds for each entity type and performs a novel entity set expansion step for corpus-level new entity recognition and dictionary completion. The expanded dictionaries are used as distant supervision to train a neural model for BioNER. Experimental results show that AutoBioNER achieves the best performance among the methods that only use dictionaries with no additional human effort on BioNER benchmark datasets. It is also demonstrated that the dictionary expansion step plays an important role in the great performances.
Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions

The prediction of potential solar energy at candidate sites plays a key role in the search for high solar energy regions to accommodate solar photovoltaic facilities. However, the estimation of solar irradiation can be inaccurate when site conditions are not similar to the ones at observation stations. In particular, regional effects caused by adjacent terrains have rarely been modeled in the estimation process. This study thus presents a digital elevation map-based convolutional neural network modeling method for the prediction of annual solar irradiation under clear-sky conditions. Using map data as an input, the sole impact of neighboring topography on available solar energy can be understood at a large scale. Specifically, with elevation maps and corresponding solar irradiation maps, the mean of solar irradiation values for each map is calculated. This mean value is then used as an output to identify the non-linear and hidden relationship between such values and the input maps in training processes. The proposed network model uses terrain map datasets and hence enables the recognition and learning of complex topographic features and patterns in the datasets. As a result, the network model has a mean absolute percent error of 0.470% for testing datasets. This implies that the topographic patterns on a large scale may coincide with variations of available solar irradiation prior to the effects of weather. Thus, the integration of raster map data with solar energy prediction has emerged to systematically learn the terrain patterns in order to predict the annual amount of solar irradiation in a region of interest. This approach can aid the determination of suitable locations for the installation of solar panels.
Distributed signing protocol for IEEE P1363‐compliant identity‐based signature scheme

The identity-based signature (IBS) scheme is one of the most promising secure and widely used cryptographic primitives for electronic commerce applications. For example, ID-based signing in a multi-party setting, without ever revealing any private and secret information, has received considerable interest in distributed applications such as a global manufacturer. However, there is no practical solution for such a group setting (e.g. more than two parties). Therefore, in this study, the authors present the first distributed identity-based signing protocol for the global electronic commerce system. Specifically, the authors’ designed protocol allows a group of parties to generate the signature in a decentralised and fair manner. They also prove that their proposed protocol is secure against a malicious adversary under the discrete logarithm and decisional Diffie–Hellman assumptions. Moreover, they implement the protocol using the MIRACL libraries on physical computing devices. Findings from the evaluations demonstrate the practical utility of their proposed protocol, in terms of achieving high level of security within a reasonable time framework (e.g. signing time (including communication latency and waiting delay) takes 311.86 ms for three parties, 558.2 ms for five parties, and 707.21 ms for seven parties, under a single-thread implementation).
Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement

Super-resolution (SR) is able to improve the spatial resolution of remote sensing images, which is critical for many practical applications such as fine urban monitoring. In this paper, a new single-image SR method, deep gradient-aware network with image-specific enhancement (DGANet-ISE) was proposed to improve the spatial resolution of remote sensing images. First, DGANet was proposed to model the complex relationship between low- and high-resolution images. A new gradient-aware loss was designed in the training phase to preserve more gradient details in super-resolved remote sensing images. Then, the ISE approach was proposed in the testing phase to further improve the SR performance. By using the specific features of each test image, ISE can further boost the generalization capability and adaptability of our method on inexperienced datasets. Finally, three datasets were used to verify the effectiveness of our method. The results indicate that DGANet-ISE outperforms the other 14 methods in the remote sensing image SR, and the cross-database test results demonstrate that our method exhibits satisfactory generalization performance in adapting to new data.
Defense-PointNet: Protecting PointNet Against Adversarial Attacks

Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks. Many works focus on adversarial attacks and defenses on 2D images, but few focus on 3D point clouds. In this paper, our goal is to enhance the adversarial robustness of PointNet, which is one of the most widely used models for 3D point clouds. We apply the fast gradient sign attack method (FGSM) on 3D point clouds and find that FGSM can be used to generate not only adversarial images but also adversarial point clouds. To minimize the vulnerability of PointNet to adversarial attacks, we propose Defense-PointNet. We compare our model with two baseline approaches and show that Defense-PointNet significantly improves the robustness of the network against adversarial samples.
A Quantum Money Solution to the Blockchain Scalability Problem

We put forward the idea that classical blockchains and smart contracts are potentially useful primitives not only for classical cryptography, but for quantum cryptography as well. Abstractly, a smart contract is a functionality that allows parties to deposit funds, and release them upon fulfillment of algorithmically checkable conditions, and can thus be employed as a formal tool to enforce monetary incentives. In this work, we give the first example of the use of smart contracts in a quantum setting. We describe a simple hybrid classical-quantum payment system whose main ingredients are a classical blockchain capable of handling stateful smart contracts, and quantum lightning, a strengthening of public-key quantum money introduced by Zhandry [55]. Our hybrid payment system employs quantum states as banknotes and a classical blockchain to settle disputes and to keep track of the valid serial numbers. It has several desirable properties: it is decentralized, requiring no trust in any single entity; payments are as quick as quantum communication, regardless of the total number of users; when a quantum banknote is damaged or lost, the rightful owner can recover the lost value.
A Spatial–Spectral Adaptive Haze Removal Method for Visible Remote Sensing Images

Visible remotely sensed images usually suffer from the haze, which contaminates the surface radiation and degrades the data quality in both spatial and spectral dimensions. This study proposes a spatial-spectral adaptive haze removal method for visible remote sensing images to resolve spatial and spectral problems. Spatial adaptation is considered from global and local aspects. A globally nonuniform atmospheric light model is constructed to depict spatially varied atmospheric light. Moreover, a bright pixel index is built to extract local bright surfaces for transmission correction. Spectral adaptation is performed by exploring the relationships between image gradients and transmissions among bands to estimate spectrally varied transmission. Visible remote sensing images featuring different land covers and haze distributions were collected for synthetic and real experiments. Accordingly, four haze removal methods were selected for comparison. Visually, the results of the proposed method are completely free from haze and colored naturally in all experiments. These outcomes are nearly the same as the ground truth in the synthetic experiments. Quantitatively, the mean-absolute-error, root-mean-square-error, and spectral angle are the smallest, and the coefficient-of-determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) is the largest among the five methods in the synthetic experiments. R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , structural similarity index measure, and the correlation coefficient between the result of the proposed method and the reference image are closest to 1 in the real data experiments. All experimental analyses demonstrate that the proposed method is effective in removing haze and recovering ground information faithfully under different scenes.
Rolling Shutter Homography and its Applications

In this article we study the adaptation of the concept of homography to Rolling Shutter (RS) images. This extension has never been clearly adressed despite the many roles played by the homography matrix in multi-view geometry. We first show that a direct point-to-point relationship on a RS pair can be expressed as a set of 3 to 8 atomic 3x3 matrices depending on the kinematic model used for the instantaneous-motion during image acquisition. We call this group of matrices the RS Homography. We then propose linear solvers for the computation of these matrices using point correspondences. Finally, we derive linear and closed form solutions for two famous problems in computer vision in the case of RS images: image stitching and plane-based relative pose computation. Extensive experiments with both synthetic and real data from public benchmarks show that the proposed methods outperform state-of-art techniques.
Architecture Search of Dynamic Cells for Semantic Video Segmentation

In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames. To this end, recent approaches have been reliant on manually arranged operations applied on top of static semantic segmentation networks - with the most prominent building block being the optical flow able to provide information about scene dynamics. Related to that is the line of research concerned with speeding up static networks by approximating expensive parts of them with cheaper alternatives, while propagating information from previous frames. In this work we attempt to come up with generalisation of those methods, and instead of manually designing contextual blocks that connect per-frame outputs, we propose a neural architecture search solution, where the choice of operations together with their sequential arrangement are being predicted by a separate neural network. We showcase that such generalisation leads to stable and accurate results across common benchmarks, such as CityScapes and CamVid datasets. Importantly, the proposed methodology takes only 2 GPU-days, finds high-performing cells and does not rely on the expensive optical flow computation.
Which Body Would You Like to Have? The Impact of Embodied Perspective on Body Perception and Body Evaluation in Immersive Virtual Reality

In this experiment, we wanted aimed to measure the conscious internal representation of one’s body appearance and allow the participants to compare it this to their ideal body appearance and to their real body appearance. We re-created a virtual representation of the internal image participants had of their own body shape. We also re-created a virtual body corresponding to the internal representation they had of their ir ideal body shape, and we built another virtual body based on their real body measures representing their real body shape. Participants saw the three different virtual bodies from an embodied first-person perspective and from a third-person perspective and they had to evaluate the appearance of those virtual bodies. We observed that female participants evaluated their real body as more attractive when they saw it from a third-person perspective, and that their level of body dissatisfaction was lower after the experimental procedure. We believe that third-person perspective allowed female participants to perceive their real body shape without applying the negative prior beliefs usually associated to the “self”, and that this resulted in a more positive evaluation of their body shape. We believespeculate that this method could be applied with patients suffering from eating disorders, by making their body perception more realistic and therefore improve their body satisfaction.
Target transfer Q-learning and its convergence analysis

Reinforcement Learning (RL) technologies are powerful to learn how to interact with environments and have been successfully applied to various important applications. Q-learning is one of the most popular methods in RL, which leverages the Bellman equation to update the Q-function. Considering that data collection in RL is both time and cost consuming and Q-learning converges slowly, different kinds of transfer RL algorithms are designed to improve the sample complexity of the new tasks1. However, most of the previous transfer RL algorithms are similar to the transfer learning methods in deep learning and are heuristic with no theoretical guarantee of the convergence rate. Therefore, it is important for us to clearly understand how and when will transfer learning help RL method and provide the theoretical guarantee for the improvement of the sample complexity. In this paper, we rethink the transfer Rl problems in the RL perspective and propose to transfer the Q-function learned in the old task to the target Q-function in the Q-learning of the new task. We call this new transfer Q-learning method target transfer Q-Learning (abbrev. TTQL). The transfer process is controlled by the error condition which can help to avoid the harm to the new tasks brought by the transferred target. We design the error condition in TTQL as whether the Bellman error of the transferred target Q-function is less than the current Q-function. We show that TTQL with the error condition will achieve a faster convergence rate than Q-learning. Our experiments are consistent with our theoretical results and verify the effectiveness of our proposed target transfer Q-learning method.
Digital Simulation of Topological Matter on Programmable Quantum Processors

Simulating the topological phases of matter in synthetic quantum simulators is a topic of considerable interest. Given the universality of digital quantum simulators, the prospect of digitally simulating exotic topological phases is greatly enhanced. However, it is still an open question how to realize the digital quantum simulation of topological phases of matter. Here, using common single- and two-qubit elementary quantum gates, we propose and demonstrate an approach to design topologically protected quantum circuits on the current generation of noisy quantum processors where spin-orbital coupling and related topological matter can be digitally simulated. In particular, a low-depth topological quantum circuit is performed on both the IBM and Rigetti quantum processors. In the experiments, we not only observe but also distinguish the 0 and $\ensuremath{\pi}$ energy topological edge states by measuring the qubit excitation distribution at the output of the circuits.
HOMEX: Persuasive Technology Acceptance Model and the Moderating Effect of Culture

The number of fitness applications on the market is increasing annually, driven by the increasing awareness of the need to support and motivate physical activity to reduce the incidence of non-communicable diseases worldwide. However, there is limited research on the user-experience (UX) design attributes that drive their adoption and the moderating role culture plays. Consequently, we conducted a study on the Persuasive Technology Acceptance Model (PTAM) for a fitness application aimed at motivating physical activity at home. Using Canada (an individualist culture, n = 189) and Nigeria (a collectivist culture, n = 67) as a case study, we investigated: (1) which of the commonly researched UX design attributes (perceived aesthetics, perceived usability, perceived credibility and perceived usefulness) have the strongest influence on users' intention to use a fitness application; (2) the moderating effect of culture; and (3) how perceived persuasiveness mediates the direct effect of perceived usefulness on the intention to use a fitness application. The results of our path analysis show that, regardless of culture, perceived usefulness and perceived aesthetics are the strongest determinants of users' intention to use a fitness application, with perceived usefulness being stronger in the collectivist culture than in the individualist culture. Secondly, our results show that perceived persuasiveness partially mediates the effect of perceived usefulness on intention to use for the individualist culture, but not for the collectivist culture. Hence, we recommend that designers should invest more in improving functionality (utilitarian benefit) and aesthetics (hedonic benefit) than other UX design attributes such as credibility and usability. However, for the collectivist culture, designers should focus more on usefulness than aesthetics. On the other hand, for the individualist culture, designers should strike a balance between usefulness and aesthetics. This has the potential, regardless of culture, of motivating users to want to try their applications in supporting their behavior change. Our main contribution to knowledge is that, our study, to the best of our knowledge, is the first to investigate the moderating effect of culture using subjects from North America and Africa (an understudied population) as a case study.
Facial Beauty Prediction Based on Lighted Deep Convolution Neural Network with Feature Extraction Strengthened

Convolutionneural network (CNN) has significantly pushed forward machine vision, which has achieved very significant results in face recognition, image classification and objection detection, and provides a new method for facial beauty prediction (FBP). Although the approach is widely applied in FBP, the research progress in FBP is relatively slow compared with face recognition. The first one is that there is less public database for FBP, and experiments for FBP are tested on small-scale database. The second one is that evaluation of facial beauty is subjective and lack of criterion, and CNN model is hard to train. In view of the problems of FBP, we expand Largescale database of Asian women's face database (LSAFBD) with data augmentation. A lighted deep convolution neural network (LDCNN) for FBP including 5650K parameters is constructed by both Inception model of GoogleNet and Max-Feature-Max activation layer, which can extract multi-scale features of an image, get compacted presentation and reduce parameters. Experiments on LSAFBD show that our LDCNN model has advantages of simple structure, small-scale parameters and is suitable for small embedded devices, with the best classification accuracy of 63.5%, which outperforms the other published CNN models for FBP.
The Promise and Pitfalls of Self-report

As a prelude to this special issue on the promise and pitfalls of self-report, this article addresses three issues critical to its current and future use. The development of self-report is framed in Vertical (improvement) and Horizontal (diversification) terms, making clear the role of both paths for continued innovation. The ongoing centrality of research design and analysis in ensuring that self-reported data is employed effectively is reviewed. Finally, the synergistic use of multiple methods is discussed. This article concludes with an overview of the SI's contributions and a summary of the SI's answers to its three central questions: a) In what ways do self-report instruments reflect the conceptualizations of the constructs suggested in theory related to motivation or strategy use? b) How does the use of self-report constrain the analytical choices made with that self-report data? c) How do the interpretations of self-report data influence interpretations of study findings