Reviewerly/exHarmony huggingface dataset:
A secure end-to-end verifiable e-voting system using blockchain and cloud server

We propose a cryptographic technique for an authenticated, end-to-end verifiable and secret ballot election. Currently, almost all verifiable e-voting systems require trusted authorities to perform the tallying process except for the DRE-i and DRE-ip systems. We have shown a weakness of the DRE-ip system and proposed a solution. We propose a secure and verifiable voter registration and authentication mechanism. The proposed scheme prevents ballot stuffing attack. We have modified the DRE-ip system so that no adversary can create and post a valid ballot on the public bulletin board without detection. We propose a method for publishing the final tally without revealing the tally from individual Direct-Recording Electronic (DRE) machines using secure multi-party computation and non-interactive zero-knowledge (NIZK) proof. We propose two methods to store these ballots using blockchain and cloud server. To the best of our knowledge, it is the first end-to-end verifiable DRE based e-voting system using blockchain. We provide security proofs to prove the security properties of the proposed scheme. We prove that the efficient NIZK proof proposed by Lin et al. in APSIPA ASC 2019 is not correct since it does not satisfy the witness indistinguishability property of a zero-knowledge proof. We introduce an improved NIZK proof that boosts the efficiency of the system. The experimental data obtained from our tests show the protocol’s potential for real-world deployment.
An arrival time picker for microseismic rock fracturing waveforms and its quality control for automatic localization in tunnels

The accurate picking of waveform arrival times is fundamental to microseismic (MS) monitoring technology and is related to the location accuracy of MS sources. An urgent requirement is an automatic and accurate method for picking up the MS waveform arrival times to realize automatic MS monitoring. In this work, a new arrival time picker has been designed to pick up the P- and S-wave arrival times of MS waveform automatically in tunnels. A control model for the arrival time quality of the S-wave (ATQS) is proposed to automatically identify waveforms with high ATQS values, and the S-wave arrival time of the waveforms is subsequently used in MS source location, to improve the automatic location accuracy. Many rock fracturing waveforms are used to construct a sample database. The experimental and engineering applications show that the P- and S-wave arrival time accuracies are 94.39% and 91.59%, respectively. The automatic location error of a rockburst event is reduced from 42.2 m to 15.3 m. The arrival time picker proposed in this paper demonstrates high accuracy, which ensures the automatic location accuracy of MS sources and lays a foundation for the realization of automatic MS monitoring.
Gaining-Sharing Knowledge Based Algorithm With Adaptive Parameters for Engineering Optimization

As optimization algorithms have a great power to solve nonlinear, complex, and hard optimization problems, nature-inspired algorithms have been applied extensively in distinct fields in order to solve real life optimization cases. In this paper, modifications for the recently proposed Gaining-Sharing-Knowledge based algorithm (GSK) are presented for enhancing its performance. Gaining-Sharing-Knowledge algorithm is considered as a perfect example of modern nature-inspired algorithm that considered the human life behavior as a source of inspiration in order to solve optimization problems. GSK algorithm mimics the naturalistic phenomenon of gaining knowledge and sharing knowledge back in human being through two major phasis: junior phase and senior phase. The junior phase is based on generating a set of initial solutions (juniors) to start the search process. Subsequently, all individuals move to the senior phase through the interaction with the surrounding environment and the cooperation with other individuals throughout the search process. The main idea in this presented work is extending and improving the original GSK algorithm through proposing a new idea for adaptively obtain the values for the two significant control parameters in GSK: the knowledge factor and the knowledge ratio. Those two significant parameters have the responsibility of controlling the gaining and sharing knowledge during the junior and senior phases throughout the optimization process. Moreover, an adaptive scheme is presented to control the knowledge rate in order to simulate the gaining and sharing experience throughout the human being life span for a specific population by taking into consideration the diverse nature of any population. a non-linear reduction for the population size is used to improve the GSK algorithm’s performance. Finally, a larger initial value of the initial population size (NP) for all dimensions are applied. The presented algorithm (APGSK) is tested on a very recent benchmark testbed on bound constrained numerical optimization that composed of distinct challenging optimization problems with different dimensions. The presented algorithm (APGSK) showed a superior performance comparing with the original GSK and with other four state-of-the-arts algorithms and the winners of CEC2020 competition.
Measuring effectiveness of augmented reality‐based geometry learning assistant on memory retention abilities of the students in 3D geometry

Abstract Mathematics is one of the fundamental subjects for engineering students. While learning mathematics, students often find it difficult or lose interest due to the complexity of the subject. Augmented reality (AR) could aid students to learn certain concepts or theories that were difficult to understand in the classroom and also has the capability to enhance the instructional/learning material. In this study, an AR‐based learning setting named geometry learning assistant (GLA) was developed to teach geometry to students in a mathematics course. GLA provides an interactive and immersive experience to the students that helped them to learn about 3D vectors and direction ratios. The application includes vector addition, cross product, position vector, direction ratios, and dot product with real‐time examples for better learning. Experimental research was organized to investigate the impact of AR intervention on learner's memory retention abilities and learning. Eighty 1st‐year polytechnic students participated in the study and they were divided randomly into two groups: experimental group ( N = 40) and control group ( N = 40). Students of the experimental group were made to learn using AR‐based GLA, whereas the students from the control group were treated using interactive simulation (IS). The experimental results showed that the learners from the experimental group have better memory retention abilities after 2 and 4 weeks of the learning activity. AR‐based GLA provided a 3D immersive learning experience to the students that enhanced their visualization of the core concepts, which additionally improved their memory retention abilities.
YOSO: You Only Speak Once

The inherent difficulty of maintaining stateful environments over long periods of time gave rise to the paradigm of serverless computing, where mostly stateless components are deployed on demand to handle computation tasks, and are torn down once their task is complete. Serverless architecture could offer the added benefit of improved resistance to targeted denial-of-service attacks, by hiding from the attacker the physical machines involved in the protocol until after they complete their work. Realizing such protection, however, requires that the protocol only uses stateless parties, where each party sends only one message and never needs to speaks again. Perhaps the most famous example of this style of protocols is the Nakamoto consensus protocol used in Bitcoin: A peer can win the right to produce the next block by running a local lottery (mining) while staying covert. Once the right has been won, it is executed by sending a single message. After that, the physical entity never needs to send more messages. We refer to this as the You-Only-Speak-Once (YOSO) property, and initiate the formal study of it within a new model that we call the YOSO model. Our model is centered around the notion of roles, which are stateless parties that can only send a single message. Crucially, our modelling separates the protocol design, that only uses roles, from the role-assignment mechanism, that assigns roles to actual physical entities. This separation enables studying these two aspects separately, and our YOSO model in this work only deals with the protocol-design aspect. We describe several techniques for achieving YOSO MPC; both computational and information theoretic. Our protocols are synchronous and provide guaranteed output delivery (which is important for application domains such as blockchains), assuming honest majority of roles in every time step. We describe a practically efficient computationally-secure protocol, as well as a proof-of-concept information theoretically secure protocol.
Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios

In recent years, autonomous vehicle driving technology and advanced driver assistance systems have played a key role in improving road safety. However, weather conditions such as snow pose severe challenges for autonomous driving and are an active research area. Thanks to their superior reliability, the resilience of detection, and improved accuracy, advances in computation and sensor technology have paved the way for deep learning and neural network–based techniques that can replace the classical approaches. In this research, we investigate the semantic segmentation of roads in snowy environments. We propose a multi-modal fused RGB-T semantic segmentation utilizing a color (RGB) image and thermal map (T) as inputs for the network. This paper introduces a novel fusion module that combines the feature map from both inputs. We evaluate the proposed model on a new snow dataset that we collected and on other publicly available datasets. The segmentation results show that the proposed fused RGB-T input can segregate human subjects in snowy environments better than an RGB-only input. The fusion module plays a vital role in improving the efficiency of multiple input neural networks for person detection. Our results show that the proposed network can generate a higher success rate than other state-of-the-art networks. The combination of our fused module and pyramid supervision path generated the best results in both mean accuracy and mean intersection over union in every dataset.
Efficient and robust deep learning architecture for segmentation of kidney and breast histopathology images

Image segmentation is consistently an important task for computer vision and the analysis of medical images. The analysis and diagnosis of histopathology images by using efficient algorithms that separate hematoxylin and eosin-stained nuclei was the purpose of our proposed method. In this paper, we propose a deep learning model that automatically segments the complex nuclei present in histology images by implementing an effective encoder–decoder architecture with a separable convolution pyramid pooling network (SCPP-Net). The SCPP unit focuses on two aspects: first, it increases the receptive field by varying four different dilation rates, keeping the kernel size fixed, and second, it reduces the trainable parameter by using depth-wise separable convolution. Our deep learning model experimented with three publicly available histopathology image datasets. The proposed SCPP-Net provides better experimental segmentation results compared to other existing deep learning models and is evaluated in terms of F1-score and aggregated Jaccard index.
Quantum control landscape for ultrafast generation of single-qubit phase shift quantum gates

In this work, we consider the problem of ultrafast controlled generation of single-qubit phase shift quantum gates. Globally optimal control is a control which realizes the gate with maximal possible fidelity. Trap is a control which is optimal only locally but not globally. It was shown before that traps do not exist for controlled generation of arbitrary single-qubit quantum gates for sufficiently long times, as well as for fast control of quantum gates other than phase shift gates. Ultrafast generation of phase-shift gates was missed in the previous analysis. In this work we show, combining analytical and numerical optimization methods such as Gradient Ascent Pulse Engineering (GRAPE), differential evolution, and dual annealing, that control landscape for ultrafast generation of phase shift gates is also free of traps. Mathematical analysis of quantum control landscapes, which aims to prove either absence or existence of traps for quantum control objective functionals, is an important topic in quantum control. In this work, we provide a rigorous analysis of quantum control landscapes for ultrafast generation of single-qubit quantum gates and show, combining analytical methods based on a sophisticated analysis of spectrum of the Hessian, and numerical optimization methods such as Gradient Ascent Pulse Engineering (GRAPE), differential evolution, and dual annealing, that control landscape for ultrafast generation of phase shift gates is free of traps.
On the intraday return curves of Bitcoin: Predictability and trading opportunities

Motivated by the potential inferences from intraday price data in the controversial Bitcoin market, we apply functional data analysis techniques to study cumulative intraday return (CIDR) curves. First, we indicate that Bitcoin CIDR curves are stationary, non-normal, uncorrelated, but exhibit conditional heteroscedastic, although we find that the projection scores of CIDR curves could be serially correlated during some certain periods. Second, we show the possibility of predicting the CIDR curves of Bitcoins based on the projection scores and then assess the forecasting performance. Finally, we utilize the functional forecasting methods to explore the intraday trading opportunities of Bitcoins and the results provide evidence of profitable trading opportunities based on intraday trading strategies, which confronts the efficient market hypothesis.
Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learning to the industrial context use as inputs extracted features, which are a manually and often arduously obtained compact representation of the original signal. In this paper, we propose a fully unsupervised deep-learning framework that is able to extract a meaningful and sparse representation of raw HF signals. We embed in our architecture important properties of the fast discrete wavelet transform (FDWT) such as 1) the cascade algorithm; 2) the conjugate quadrature filter property that links together the wavelet, the scaling, and transposed filter functions; and 3) the coefficient denoising. Using deep learning, we make this architecture fully learnable: Both the wavelet bases and the wavelet coefficient denoising become learnable. To achieve this objective, we propose an activation function that performs a learnable hard thresholding of the wavelet coefficients. With our framework, the denoising FDWT becomes a fully learnable unsupervised tool that does not require any type of pre- or postprocessing or any prior knowledge on wavelet transform. We demonstrate the benefits of embedding all these properties on three machine-learning tasks performed on open-source sound datasets. We perform an ablation study of the impact of each property on the performance of the architecture, achieve results well above baseline, and outperform other state-of-the-art methods.
Variational quantum tensor networks classifiers

Tensor networks (TN) are a method of decomposing high-rank tensors into tractable lower rank. In this paper, we present a classification algorithm for variational quantum tensor networks (VQTN), which has higher performance on near-term processors. Motivated by the hybrid quantum–classical architecture, the truncated quantum tensor networks (QTN) outputs are fed into a classical neural network. We then utilize kernel encoding, circuit models, multiple readouts, and stochastic gradient descent to achieve shallow quantum circuits. Finally, we deploy the QTN and VQTN algorithms on the TensorFlow Quantum processor by using the Iris and MNIST data sets. Our algorithm experimentally costs only half of the qubits with an average accuracy of 93.72%. Compared with the QTN algorithm, the accuracy is improved by 7.71%.
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models

Image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks. Moreover, they offered the opportunity not only to manipulate a given image, but also to generate a large and diverse set of different outputs from a single natural image. This gave rise to new tasks, which are considered “GAN-only”. However, despite their impressiveness, single-image GANs require long training time (usually hours) for each image and each task and often suffer from visual artifacts. In this paper we revisit the classical patch-based methods, and show that - unlike previously believed - classical methods can be adapted to tackle these novel “GAN-only” tasks. Moreover, they do so better and faster than single-image GAN-based methods. More specifically, we show that: (i) by introducing slight modifications, classical patch-based methods are able to unconditionally generate diverse images based on a single natural image; (ii) the generated output visual quality exceeds that of single-image GANs by a large margin (confirmed both quantitatively and qualitatively); (iii) they are orders of magnitude faster (runtime reduced from hours to seconds). <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 788535), and the Carolito Stiftung. Dr Bagon is a Robin Chemers Neustein AI Fellow.
Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization

Recently, inspired by quantum annealing, many solvers specialized for unconstrained binary quadratic programming problems have been developed. For further improvement and application of these solvers, it is important to clarify the differences in their performance for various types of problems. In this study, the performance of four quadratic unconstrained binary optimization problem solvers, namely D-Wave Hybrid Solver Service (HSS), Toshiba Simulated Bifurcation Machine (SBM), Fujitsu Digital Annealer (DA), and simulated annealing on a personal computer, was benchmarked. The problems used for benchmarking were instances of real problems in MQLib, instances of the SAT-UNSAT phase transition point of random not-all-equal 3-SAT (NAE 3-SAT), and the Ising spin glass Sherrington-Kirkpatrick (SK) model. Concerning MQLib instances, the HSS performance ranked first; for NAE 3-SAT, DA performance ranked first; and regarding the SK model, SBM performance ranked first. These results may help understand the strengths and weaknesses of these solvers.
Collaborative Ontology Engineering Methodologies for the Development of Decision Support Systems: Case Studies in the Healthcare Domain

New models and technological advances are driving the digital transformation of healthcare systems. Ontologies and Semantic Web have been recognized among the most valuable solutions to manage the massive, various, and complex healthcare data deriving from different sources, thus acting as backbones for ontology-based Decision Support Systems (DSSs). Several contributions in the literature propose Ontology engineering methodologies (OEMs) to assist the formalization and development of ontologies, by providing guidelines on tasks, activities, and stakeholders’ participation. Nevertheless, existing OEMs differ widely according to their approach, and often lack of sufficient details to support ontology engineers. This paper performs a meta-review of the main criteria adopted for assessing OEMs, and major issues and shortcomings identified in existing methodologies. The key issues requiring specific attention (i.e., the delivery of a feasibility study, the introduction of project management processes, the support for reuse, and the involvement of stakeholders) are then explored into three use cases of semantic-based DSS in health-related fields. Results contribute to the literature on OEMs by providing insights on specific tools and approaches to be used when tackling these issues in the development of collaborative OEMs supporting DSS.
4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface

Tracking non-rigidly deforming scenes using range sensors has numerous applications including computer vision, AR/VR, and robotics. However, due to occlusions and physical limitations of range sensors, existing methods only handle the visible surface, thus causing discontinuities and in-completeness in the motion field. To this end, we introduce 4DComplete, a novel data-driven approach that estimates the non-rigid motion for the unobserved geometry. 4DComplete takes as input a partial shape and motion observation, extracts 4D time-space embedding, and jointly infers the missing geometry and motion field using a sparse fully-convolutional network. For network training, we constructed a large-scale synthetic dataset called DeformingThings4D, which consists of 1,972 animation sequences spanning 31 different animals or humanoid categories with dense 4D annotation. Experiments show that 4DComplete 1) reconstructs high-resolution volumetric shape and motion field from a partial observation, 2) learns an entangled 4D feature representation that benefits both shape and motion estimation, 3) yields more accurate and natural deformation than classic non-rigid priors such as As-Rigid-As-Possible (ARAP) deformation, and 4) generalizes well to unseen objects in real-world sequences.