Reviewerly/exHarmony huggingface dataset:
Cyclic quantum teleportation via <i>G</i>-states

In 2017, Chen et al. [Quantum Inf. Process. 16 (2017) 201] proposed a scheme for cyclic quantum teleportation (CYQT) of three single-qubit information states among three participants by using six-qubit entangled state as a quantum channel. Following the work of Chen et al., we propose a new scheme for CYQT in which four participants cyclically teleport four arbitrary single-qubit information states among themselves by using two [Formula: see text]-states as a quantum channel. In our scheme, reverse CYQT can also be realized throughout changing the qubit pairs to be measured by each participant. We also generalize our scheme for CYQT of [Formula: see text]-qubit entangled states.
The Security Ingredients for Correct and Byzantine Fault-tolerant Blockchain Consensus Algorithms

The blockchain technology revolution and the use of blockchains in various applications have resulted in many companies and programmers developing and customizing specific fit-for-purpose consensus algorithms. Security and performance are determined by the chosen consensus algorithm; hence, the reliability and security of these algorithms must be assured and tested, which requires an understanding of all the security assumptions that make such algorithms correct and byzantine fault-tolerant.This paper studies the "security ingredients" that enable a given consensus algorithm to achieve safety, liveness, and byzantine fault tolerance (BFT) in both permissioned and permissionless blockchain systems. The key contributions of this paper are the organization of these requirements and a new taxonomy that describes the requirements for security. The CAP Theorem is utilized to explain important tradeoffs between consistency and availability in consensus algorithm design, which are crucial depending on the specific application of a given algorithm. This topic has also been explored previously by De Angelis. However, this paper expands that prior explanation and dilemma of consistency vs. availability and then combines this with Buterin's Trilemma to complete the overall exposition of tradeoffs.
Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data

At present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning. First, the encoders are used to extract the features of the behaviour. At the same time, in order to reduce the computational complexity, the feature extraction is restricted to the space-time volume of the dense trajectory. Second, the neural network model is used to transform features into behavioural visual word representations, and feature fusion is performed using weighted correlation methods to improve feature classification capabilities. Finally, sparse reconstruction errors are used to judge and detect financial fraud. This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. Experimental results show that this method can effectively learn the essential characteristics of the data, and significantly improve the detection rate of fraud detection algorithms.
Chaotic Clock Driven Cryptographic Chip: Towards a DPA Resistant AES Processor

Designing a tamper-resistant microchip for small embedded systems is one of the urgent demands of the computing community nowadays due to the immense security challenges arising particularly in massively connected networks. One of the major threats to secure smart card chips is the ability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Side Channel Attacks (SCA)</i> , such as Correlation Power Analysis (CPA) and Correlation Instantaneous Frequency Analysis (CIFA) to increase the vulnerability of the secured cipher text to attacks even when the state of the art <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Advanced Encryption Standard (AES)</i> is used. In this paper we explore the possibility of using chaotic clocking to protect AES chips against CPA and CIFA attacks. Our findings reveal that chaotic clocks, although not random, can effectively provide this protection with a low power envelope. Chaotic clocks derived from two different chaotic systems were used for testing in order to confirm the findings. Two FPGA boards running AES were driven using these chaotic clocks in order to prove the applicability of the proposed security enhancement technique.
An unsupervised approach to detect review spam using duplicates of images, videos and Chinese texts

Intuitively, image- or video-based recommendations seem to be more reliable than those containing plain text, and these types of recommendations have recently become widely encouraged and commonly seen across opinion sharing platforms. Considering their potential for manipulation, graphs (e.g., images and videos) are more vulnerable to spam than scripts. However, most state-of-the-art solutions for opinion spam detection are exclusively devoted to natural language parsing, and less work has been done concerning photos or videos. After investigating the top two business-to-customer websites, i.e., JD.com and TMALL.com, we propose an unsupervised approach to label suspected spam based on different types of duplication across images, videos and Chinese texts. Experiments verified the effectiveness of this approach and obtained several conclusions: 1) the situation of image spam is more severe than that of video and text spam; 2) for manipulation, borrowing something from a marketing page is less attractive than stealing from other reviewers; 3) in addition to using identical texts, spammers also use fictitious rare incidents to influence customers; and 4) overlapping duplications of images, videos and texts are common.
Ultrafast single-photon detection at high repetition rates based on optical Kerr gates under focusing

The ultrafast detection of single photons is currently restricted by the limited time resolution (a few picoseconds) of the available single-photon detectors. Optical gates offer a faster time resolution, but so far they have been applied mostly to ensembles of emitters. Here, we demonstrate through a semi-analytical model that the ultrafast time-resolved detection of single quantum emitters can be possible using an optical Kerr shutter at gigahertz rates under focused illumination. This technique provides sub-picosecond time resolution, while keeping a gate efficiency at around 85%. These findings lay the ground for future experimental investigations on the ultrafast dynamics of single quantum emitters, with implications for quantum nanophotonics and molecular physics.
Robust control point estimation with an out-of-focus camera calibration pattern

The calibration of a zoom lens camera depends on the precision of the localization of control points. At a long focal length, the narrow depth-of-field (DOF) causes defocused blurring and an inevitable decrease in accuracy in control points localization. In particular, the camera calibration requires multiple control points defined on calibration patterns acquired at various camera angles. However, clear pattern images are difficult to obtain owing to the narrow DOF. We propose a robust and intuitive method to accurately estimate the control points in blurred images. To obtain control points that are less affected by blurring, we dynamically varied the circle size in the patterns and identified the local maximum point using the intensity gradient of accumulated concentric circles. This approach is robust to blurring and can be employed at all zoom levels. In our experiments, the error of the control point estimation was measured while varying the angles of the calibration patterns and the degree of blurring. Compared with the conventional checker pattern method, the performance of the proposed method in the estimation of the control points was better and its related camera parameters with severely defocused images were settled.
Secure and Efficient Protocol for Outsourcing Large-Scale Matrix Multiplication to the Cloud

Outsourcing computing is an application of cloud computing. It is an important method to solve the problem of client resource limitation. However, outsourcing computing brings security and efficiency challenges. Matrix multiplication computation (MMC) outsourcing has attracted many researchers to carry out research and has achieved many admirable results. Unfortunately, there is still a problem with zero elements of information protection for MMC outsourcing. In this paper, we are motivated to design a protocol, which can protect the private information of the zero elements, for outsourcing MMC to the cloud server. Our proposed protocol not only solves the problem of protecting zero elements information but also has no large client-overhead. The main idea is to add a special matrix to the original matrix for hiding zero elements before transforming the computation task. Then, the cloud server computes the received encryption task and returns the result to the client. Finally, the client decrypts and verifies the result. The experiment verifies the efficiency and practicability of our protocol. The comparison with previous schemes shows that the protocol improves security without much client overhead.
Multi-Robot Collision Avoidance with Map-based Deep Reinforcement Learning

Multi-robot collision avoidance in a communication-free environment is one of the key issues for mobile robotics and autonomous driving. In this paper, we propose a map-based deep reinforcement learning (DRL) approach for collision avoidance of multiple robots, where robots do not communicate with each other and only sense other robots' positions and the obstacles around them. We use the egocentric grid map of a robot to represent the environmental information around it, which can be easily generated by using multiple sensors or sensor fusion. The learned policy generated from the DRL model directly maps 3 frames of egocentric grid maps and the robot's relative local goal positions into low-level robot control commands. We first train a convolutional neural network for the navigation policy in a simulator of multiple mobile robots using proximal policy optimization (PPO). Then we deploy the trained model to real robots to perform collision avoidance in their navigation. We evaluate the approach with various scenarios both in the simulator and on three differential-drive mobile robots in the real world. Both qualitative and quantitative experiments show that our approach is efficient with a high success rate. The demonstration video can be found at https://youtu.be/jcLKlEXuFuk.
A computational interpretation of compact closed categories: reversible programming with negative and fractional types

Compact closed categories include objects representing higher-order functions and are well-established as models of linear logic, concurrency, and quantum computing. We show that it is possible to construct such compact closed categories for conventional sum and product types by defining a dual to sum types, a negative type, and a dual to product types, a fractional type. Inspired by the categorical semantics, we define a sound operational semantics for negative and fractional types in which a negative type represents a computational effect that ``reverses execution flow'' and a fractional type represents a computational effect that ``garbage collects'' particular values or throws exceptions. Specifically, we extend a first-order reversible language of type isomorphisms with negative and fractional types, specify an operational semantics for each extension, and prove that each extension forms a compact closed category. We furthermore show that both operational semantics can be merged using the standard combination of backtracking and exceptions resulting in a smooth interoperability of negative and fractional types. We illustrate the expressiveness of this combination by writing a reversible SAT solver that uses backtracking search along freshly allocated and de-allocated locations. The operational semantics, most of its meta-theoretic properties, and all examples are formalized in a supplementary Agda package.
Variational Autoencoder Based Fault Detection and Location Method for Power Distribution Network

With the rapid development of the world economy and the continuous improvement of people's living standards, users put forward higher requirements for the quality and reliability of the power system. As a representative of deep learning, variational autoencoders play an important role in processing high-dimensional big data. In this paper, a novel variational autoencoder based distribution network abnormal monitoring and positioning method is proposed. Through an indepth study of the variational autoencoder, we utilize the real-time dynamic distribution network status information provided by the wide-area measurement system, combined with the abilities of feature extracting and data reconstruction. Finally, the IEEE-33 simulation model was built in Matlab software and the experimental results were used to verify the correctness of this method. The results show that this method can quickly and accurately achieve the fault detection and location for the distribution network with strong robustness.
A novel learning algorithm for Büchi automata based on family of DFAs and classification trees

In this paper, we propose a novel algorithm to learn a Büchi automaton from a teacher who knows an ω-regular language. The learned Büchi automaton can be a nondeterministic Büchi automaton or a limit deterministic Büchi automaton. The learning algorithm is based on learning a formalism called family of DFAs (FDFAs) recently proposed by Angluin and Fisman. The main catch is that we use a classification tree structure instead of the standard observation table structure. The worst case storage space required by our algorithm is quadratically better than that required by the table-based algorithm proposed by Angluin and Fisman. We implement the proposed learning algorithms in the learning library ROLL (Regular Omega Language Learning), which also consists of other complete ω-regular learning algorithms available in the literature. Experimental results show that our tree-based learning algorithms have the best performance among others regarding the number of solved learning tasks.
VMatch: A Matching Theory Based VDC Reconfiguration Strategy

A virtual data center (VDC) mostly encapsulates multiple virtual machines (VMs) with communication dependencies. These VDC requests are dynamic in nature and often experience fluctuating demands across different resources. In this paper, we propose a dynamic resource reconfiguration strategy called VMatch that generates an efficient relocation/remapping plan for already assigned virtual links (VLs) facing bandwidth expansion. The overall problem is formulated as a one-to-one matching game that aims to minimize the relocation cost from the users perspective and at the same time improves resource utilization from a service providers (SPs) perspective. By using the concept of preferences in the matching game, different stakeholders, i.e., end-users and SPs express their priorities. Thorough simulation analysis of the proposed approach shows that the model on an average can reduce the remapping cost by 19% and improve server utilization by 21% in comparison with the baselines.
Scalable Mesh Representation for Depth from Breakpoint-Adaptive Wavelet Coding

A highly scalable and compact representation of depth data is required in many applications, and it is especially critical for plenoptic multiview image compression frameworks that use depth information for novel view synthesis and interview prediction. Efficiently coding depth data can be difficult as it contains sharp discontinuities. Breakpoint-adaptive discrete wavelet transforms (BPA-DWT) currently being standardized as part of JPEG 2000 Part-17 extensions have been found suitable for coding spatial media with hard discontinuities. In this paper, we explore a modification to the original BPA-DWT by replacing the traditional constant extrapolation strategy with the newly proposed affine extrapolation for reconstructing depth data in the vicinity of discontinuities. We also present a depth reconstruction scheme that can directly decode the BPA-DWT coefficients and breakpoints onto a compact and scalable mesh-based representation which has many potential benefits over the sample-based description. For performing depth compensated view prediction, our proposed triangular mesh representation of the depth data is a natural fit for modern graphics architectures.
Efficient and effective exploratory testing of large-scale software systems

Exploratory testing plays an important role in the continuous integration and delivery pipelines of large-scale software systems, but a holistic and structured approach is needed to realize efficient and effective exploratory testing. This paper seeks to address the need for a structured and reliable approach by providing a tangible model, supporting practitioners in the industry to optimize exploratory testing in each individual case. The reported study includes interviews, group interviews and workshops with representatives from six companies, all multi-national organizations with more than 2,000 employees. The ExET model (Excellence in Exploratory Testing) is presented. It is shown that the ExET model allows companies to identify and visualize strengths and improvement areas. The model is based on a set of key factors that have been shown to enable efficient and effective exploratory testing of large-scale software systems, grouped into four themes: "The testers' knowledge, experience and personality", "Purpose and scope", "Ways of working" and "Recording and reporting". The validation of the ExET model showed that the model is novel, actionable and useful in practice, showing companies what they should prioritize in order to enable efficient and effective exploratory testing in their organization.
Classification Based on Brain Storm Optimization With Feature Selection

Classification is one of the most classic problems in machine learning. Due to the global optimization ability, evolutionary computation (EC) techniques have been successfully applied to solve many problems and the evolutionary classification model is one of the methods used to solve classification problems. Recently, some evolutionary algorithms (EAs) such as the fireworks algorithm (FWA) and brain storm optimization (BSO) algorithm have been employed to implement the evolutionary classification model and achieved the desired results. This means that it is feasible to use EC techniques to solve the classification problem directly. However, the existing evolutionary classification model still has some disadvantages. The limited datasets used in the experiment make the experimental results not convincing enough, and more importantly, the structure of the evolutionary classification model is closely related to the dimension of datasets, which may lead to poor classification performance, especially on large-scale datasets. Therefore, this paper aims at improving the structure of the evolutionary classification model to improve classification performance. Feature selection is an effective method to deal with large datasets, firstly, we introduce the concept of feature selection and use the different feature subsets to construct the structure of the evolutionary classification model. Then, the BSO algorithm is employed to implement the evolutionary classification model to search the optimal structure by search for the optimal feature subset. Moreover, the optimal weight parameters corresponding to the different structures are also searched by the BSO algorithm while searching the optimal feature subset. For verification of the classification effectiveness of the proposed method, 11 different datasets are selected for experiments. The results show that it is feasible to optimize the structure of the evolutionary classification model by introducing feature selection. Moreover, the new method has better classification performance than the original method, especially on large-scale or high-dimensional datasets.
Eldercare Robotics - Alexa

This paper seeks to examine the potential and capabilities of the Amazon Alexa for aiding the elderly in their own homes. The Alexa System is expected to work alongside eldercare robots in order to provide healthcare to the increasing elderly population. Amazon Alexa provides tools that allow Alexa Skills to be developed and tested. The Alexa Skills that were developed provided an alarm system to remind an elderly person to take their medications, a diet tracking system to keep track of meals and nutrition, and a Skill that would alert a caregiver if the elderly person has fallen down.
A Multi-Step Approach to Accelerate the Computation of Reachable Sets for Road Vehicles

We propose an approach for the fast computation of reachable sets of road vehicles while considering dynamic obstacles. The obtained reachable sets contain all possible behaviors of vehicles and can be used for motion planning, verification, and criticality assessment. The proposed approach precomputes computationally expensive parts of the reachability analysis. Further, we partition the reachable set into cells and construct a directed graph storing which cells are reachable from which cells at preceding time steps. Using this approach, considering obstacles reduces to deleting nodes from the directed graph. Although this simple idea ensures an efficient computation, the discretization can introduce considerable over-approximations. Thus, the main novelty of this paper is to reduce the over-approximations by intersecting reachable sets propagated from multiple points in time. We demonstrate our approach on a large range of scenarios for automated vehicles showing a faster computation time compared to previous approaches while providing the same level of accuracy.
CMSA: Configurable Multi-directional Systolic Array for Convolutional Neural Networks

The systolic array is one of the most popular choices for convolutional neural network accelerators. However, when computing special convolution, such as small-scale convolution or depthwise convolution, the utilization rate of the array fluctuates or even declines sharply. To address these issues, we design a configurable multi-directional systolic array (CMSA). The array can switch data mapping or dataflow for special convolution by changing the data transmission direction and configuring the array. Meanwhile, it keeps the original systolic array architecture and computing mode. Our design makes the systolic array flexible. Based on our evaluation, CMSA can increase the units utilization rate by up to 1.6× compared to the typical systolic array when running last layers of ResNet. When running depthwise convolution in MobileNet, CMSA can increase the utilization rate by up to 14.8×.