Reviewerly/exHarmony huggingface dataset:
Automated window-based partitioning of quantum circuits

Developing a scalable quantum computer as a single processing unit is challenging due to technology limitations. A solution to deal with this challenge is distributed quantum computing where several distant quantum processing units are used to perform the computation. The main design issue of this approach is costly communication between the processing units. Focused on this issue, in this paper, an efficient partitioning approach is proposed which combines both gate and qubit teleportation concepts in an efficient manner to minimize the communication. Experimental results show the proposed approach on average reduces the communication cost by about 29.5% in comparison with the best approaches in the literature.
Real-Time Data Filling and Automatic Retrieval Algorithm of Road Traffic Based on Deep-Learning Method

In order to enhance the real-time and retrieval performance of road traffic data filling, a real-time data filling and automatic retrieval algorithm based on the deep-learning method is proposed. In image detection, the depth representation is extracted according to the detection target area of a general object. The local invariant feature is extracted to describe local attributes in the region, and it is fused with depth representation to complete the real-time data filling of road traffic. According to the results of the database enhancement, the retrieval results of the deep representation level are reordered. In the index stage, unsupervised feature updating is realized by neighborhood information to improve the performance of a feature retrieval. The experimental results show that the proposed method has high recall and precision, a short retrieval time and a low running cost.
No-Reference Video Quality Assessment Using Space-Time Chips

We propose a new prototype model for no-reference video quality assessment (VQA) based on the natural statistics of space-time chips of videos. Space-time chips (ST-chips) are a new, quality-aware feature space which we define as space-time localized cuts of video data in directions that are determined by the local motion flow. We use parametrized distribution fits to the bandpass histograms of space-time chips to characterize quality, and show that the parameters from these models are affected by distortion and can hence be used to objectively predict the quality of videos. Our prototype method, which we call ChipQA-0, is agnostic to the types of distortion affecting the video, and is based on identifying and quantifying deviations from the expected statistics of natural, undistorted ST-chips in order to predict video quality. We train and test our resulting model on several large VQA databases and show that our model achieves high correlation against human judgments of video quality and is competitive with state-of-the-art models.
Effects of the Number of Hyperparameters on the Performance of GA-CNN

The performance of a machine learning algorithm is highly dependent on its hyperparameters. However, hyperparameter optimization is not a trivial task as it is problem-specific. The difficulty rises when coupled with a larger number of hyperparameters resulting in high search space dimensions. The common understanding seems to be that the optimization is only done on limited hyperparameters. Indeed, a larger number of hyperparameters have not been commonly utilized in hyperparameter optimization. This study investigates the role of hyperparameters by using a genetic algorithm (GA) as the main optimization method for a convolutional neural network (CNN). The novelty of this study is two-fold. Firstly, we defined 20 hyperparameters and their ranges, specifically for text classification. Secondly, we conducted experiments with different numbers of hyperparameters and different numbers of optimized hyperparameters. GA-CNN was evaluated using a disaster tweets dataset and compared to the other methods, i.e., grid search, random search, TPE, TPOT, CNN, LSTM, CNN-LSTM, and BERT. The experimental results demonstrated that the proposed method shows better performance over other methods. The results also showed that a larger number of hyperparameters and layer-specific hyperparameter values are indeed important.
Similarity detection of English text and teaching evaluation based on improved TCUSS clustering algorithm

The semantic similarity calculation task of English text has important influence on other fields of natural language processing and has high research value and application prospect. At present, research on the similarity calculation of short texts has achieved good results, but the research result on long text sets is still poor. This paper proposes a similarity calculation method that combines planar features with structured features and uses support vector regression models. Moreover, this paper uses PST and PDT to represent the syntax, semantics and other information of the text. In addition, through the two structural features suitable for text similarity calculation, this paper proposes a similarity calculation method combining structural features with Tree-LSTM model. Experiments show that this method provides a new idea for interest network extraction.
CS-NLP Team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task

In this paper, we investigate a commonsense inference task that unifies natural language understanding and commonsense reasoning.We describe our attempt at SemEval-2020 Task 4 competition: Commonsense Validation and Explanation (ComVE) challenge.We discuss several state-of-the-art deep learning architectures for this challenge.Our system uses prepared labeled textual datasets that were manually curated for three different natural language inference subtasks.The goal of the first subtask is to test whether a model can distinguish between natural language statements that make sense and those that do not make sense.We compare the performance of several language models and fine-tuned classifiers.Then, we propose a method inspired by question/answering tasks to treat a classification problem as a multiple choice question task to boost the performance of our experimental results (96.06%), which is significantly better than the baseline.For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93.7%) among 27 participants with very competitive results.Our result for last subtask of generating reason against the nonsense statement shows many potentials for future researches as we applied the most powerful generative model of language (GPT-2) with 6.1732 BLEU score among first four teams 1 .
Human action recognition based on Kinect

Abstract With the emergence of Kinect, many research results have emerged in human action recognition based on skeleton information, which has promoted the development of human-computer interaction. In this paper, from the skeleton data obtained by Kinect, static features and dynamic features are extracted, and the two are merged; SVM classifier is used for action recognition. It is verified on the MSR Daily Activity 3D data set, and the experimental results show that the method in this paper improves the accuracy of action recognition.
Application of convolution neural network in medical image processing

Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network.This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball "yan.mat" data set, convenient to use MATLAB software for calculation.In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition.The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.
Boosting AI applications: Labeling format for complex datasets

<h2>Abstract</h2> Data labeling has become a major problem in industries aiming to create and use ground truth labels from massive multi-sensor archives to feed into Artificial Intelligence (AI) applications. Annotation of multi-sensor set-ups with multiple cameras and LIDAR is now particularly relevant for the automotive industry aiming to build Autonomous Driving (AD) functions. In this paper, we present the Video Content Description (VCD), as the first open source metadata structure and set of tools, able to structure annotations for such complex scenes, including unprecedented flexibility to label 2D and 3D objects, pixel-wise labels, actions, events, contexts, semantic relations, odometry, and calibration. Several example cases are reported to demonstrate the flexibility of the VCD.
RoSTAR: ROS-based Telerobotic Control via Augmented Reality

Real world virtual world communication and interaction will be a cornerstone of future intelligent manufacturing ecosystems. Human robotic interaction is considered to be the basic element of factories of the future. Despite the advancement of different technologies such as wearables and Augmented Reality (AR), human-robot interaction (HRI) is still extremely challenging. Whilst progress has been made in the development of different mechanisms to support HRI, there are issues with cost, naturalistic and intuitive interaction, and communication across heterogeneous systems. To mitigate these limitations, RoSTAR is proposed. RoSTAR is a novel open-source HRI system based on the Robot Operating System (ROS) and Augmented Reality. An AR Head Mounted Display (HMD) is deployed. It enables the user to interact and communicate through a ROS powered robotic arm. A model of the robot arm is imported directly into the Unity Game engine, and any interactions with this virtual robotic arm are communicated to the ROS robotic arm. This system has the potential to be used for different process tasks, such as robotic gluing, dispensing and arc welding as part of an interoperable, low cost, portable and naturalistically interactive experience.
A guaranteed collision‐free trajectory planning method for autonomous parking

Planning a feasible, and safe trajectory is a crucial procedure for autonomous parking which remains to be fully solved. Generally, a trajectory is depicted by a sequence of configurations which include positions, and orientations. However, existing approaches usually design parking trajectories without considering the evolution from one configuration to the next. Hence, these trajectories are practically infeasible in some scenarios, and safety risks may exist. In this paper, a guaranteed collision-free trajectory planning method is proposed for autonomous parking. A dynamic optimisation problem is built, which includes vehicle kinematics, collision avoidance constraints, and physical restraints. Then the dynamic optimisation problem is discretised into a finite-dimensional nonlinear programming problem. To describe collision avoidance constraints, the concepts of the virtual protection frame, and the magnification parameter are introduced. It is highlighted that a novel collision detection criterion is raised from the view of geometry. Tests in three common scenarios, and a complex scenario illustrate the effectiveness of the proposed approach. Moreover, the stability of the algorithm is discussed in this paper. Via the proposed approach, an integrated design of planning, and control is possible.
Research Trends in the International Literature on Natural Language Processing, 2000-2019 — A Bibliometric Study

This work presents a review, using bibliometric methods, of the state of research on the whole field of natural language processing (NLP), understanding this as the methods to process human language, including semantic techniques, statistical techniques or a combination of both. Particularly we focus on the trends of research in NLP, since there are not in the literature studies that embrace in an integral way bibliometric studies about natural language processing, its applications and related topics. Our work includes an identification of the main sources where research is published, the most productive and influential countries and research institutions, the main actors involved in research, as well as the main topics that are investigated. We found that research in the field and subfields has increased continuously during the period under study; conference proceedings are the preferred media to communicate results and that biomedical informatics is one relevant field of application of NLP. We conclude with both, a synchronic and a diachronic characterization of research topics carried out internationally on natural language processing and related topics, which showed that several subfields of artificial intelligence are closely related to natural language processing in recent years.
A Model to Enhance Governance Issues through Opinion Extraction

We live in a world where data is expanding exponentially. Most of the data is unstructured when obtained through the web. Many organizations, institutes, and governments worldwide gather public views regarding their products, services, or policies. With thousands of reviews about some product, service, or policy, it is impossible to conclude some kind of final thought from it. To handle this, there is a desperate need for a model that can extract meaningful information from data to make correct and timely decisions for the efficient growth of business and smooth running of an organization or government. Otherwise, the practice of collecting and storing data will be ineffective. In this study, we focused on conducting an extensive public survey on issues of Southern Punjab, carry out appropriate processing on collected data and predict trends in public opinion for decision-making. Natural Language Processing (NLP) and Machine Learning (ML) have dealt with this problem. Different data preprocessing techniques have been utilized to remove the noise from data. Our experiments stated that unemployment, poverty, education, and corruption are the major issues of the targeted region. This study will help government officials and non-governmental organizations to be focused on the extracted issues in the specific region.
Time-bin entangled photon pairs from quantum dots embedded in a self-aligned cavity

We introduce a scalable photonic platform that enables efficient generation of entangled photon pairs from a semiconductor quantum dot. Our system, which is based on a self-aligned quantum dot-micro-cavity structure, erases the need for complex steps of lithography and nanofabrication. We experimentally show collection efficiency of 0.17 combined with a Purcell enhancement of up to 1.7 in the pair emission process. We harness the potential of our device to generate photon pairs entangled in time bin, reaching a fidelity of 0.84(5) with the maximally entangled state. The achieved pair collection efficiency is 4 times larger than the state-of-the art. The device, which theoretically supports pair extraction efficiencies of nearly 0.5 is a promising candidate for the implementation of bright sources of time-bin, polarization- and hyper entangled photon pairs in a straightforward manner.
Human-data interaction design guidelines for visualization systems

The design of interactive visualization systems is challenging due to several aspects involving data interpretation and manipulation mechanisms. Designers need adequate support in the conception of these tools for facilitating the interaction of users with data. In this article, we propose a set of design guidelines on Human-Data Interaction (HDI) with focus on information visualization (IV). Our recommendations were elaborated based on analysis and summary of knowledge from literature, combined with our own design experience with this kind of systems. This study shows to which extent the organized guidelines were understood, used and discussed by Computer Science students in the construction and evaluation of software prototypes for data-driven decision making. Participants were able to understand the guidelines and applied them with success for evaluation of HDI in IV systems.
Coding Analog of Superadditivity Using Entanglement-Assisted Quantum Tensor Product Codes Over $\mathbb {F}_{p^k}$

We provide a procedure to construct entanglement-assisted Calderbank-Shor-Steane (CSS) codes over qudits from the parity check matrices of two classical codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , where q = p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> , p is prime, and k is a positive integer. The construction procedure involves the proposed Euclidean Gram-Schmidt orthogonalization algorithm, followed by a procedure to extend the quantum operators to obtain stabilizers of the code. Using this construction, we provide a construction of entanglement-assisted tensor product codes from classical tensor product codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . We further show that a nonzero rate entanglement-assisted tensor product code can be obtained from a classical tensor product code whose component codes yield zero rate entanglement-assisted CSS codes. We view this result as the coding analog of superadditivity.
Detecting Vulnerabilities in Critical Infrastructures by Classifying Exposed Industrial Control Systems Using Deep Learning

Industrial control systems depend heavily on security and monitoring protocols. Several tools are available for this purpose, which scout vulnerabilities and take screenshots of various control panels for later analysis. However, they do not adequately classify images into specific control groups, which is crucial for security-based tasks performed by manual operators. To solve this problem, we propose a pipeline based on deep learning to classify snapshots of industrial control panels into three categories: internet technologies, operation technologies, and others. More specifically, we compare the use of transfer learning and fine-tuning in convolutional neural networks (CNNs) pre-trained on ImageNet to select the best CNN architecture for classifying the screenshots of industrial control systems. We propose the critical infrastructure dataset (CRINF-300), which is the first publicly available information technology (IT)/operational technology (OT) snapshot dataset, with 337 manually labeled images. We used the CRINF-300 to train and evaluate eighteen different pipelines, registering their performance under CPU and GPU environments. We found out that the Inception-ResNet-V2 and VGG16 architectures obtained the best results on transfer learning and fine-tuning, with F1-scores of 0.9832 and 0.9373, respectively. In systems where time is critical and the GPU is available, we recommend using the MobileNet-V1 architecture, with an average time of 0.03 s to process an image and with an F1-score of 0.9758.
Intention to use an interactive AR app for engineering education

Augmented reality (AR) has been incorporated into educational processes in various subjects to improve academic performance. One of these areas is the field of electronics since students often have difficulty understanding electricity. An interactive AR app on electrical circuits was developed. The app allows the manipulation of circuit elements, computes the voltage and amperage values using the loop method, and applies Kirchhoff's voltage law. This research aims to determine the intention of using the AR app by students. It also looks to determine if it is conditioned by how the survey is applied (online or face-to-face) or students' gender. The results show that the app is well evaluated on the intention of use by students. Regarding how the survey is applied, the attitude towards using does not present significant differences. In contrast, the students who carried out the online survey presented a higher behavioral intention to use than those who participated in the guided laboratory. Regarding gender, women showed a higher attitude toward using and behavioral intention to use this technology than men.
Accelerating Message Propagation in Blockchain Networks

Improving performance of blockchain systems is one of the principal research interests in the field. While many efforts are being made to produce faster and more efficient consensus protocols, they are all constrained by a common factor: the time it takes for a block to propagate through the network. Most blockchain networks use different variations of gossip-like communication protocols to be able to send large messages whilst minimizing latency and bandwidth use. Unfortunately, it still supposes a major bottleneck for state-of-the-art high-performance blockchain systems. In this paper we analyze the most relevant work being done to improve message propagation in blockchain networks. More specifically, we introduce the problem of neighbour selection for gossip protocols, and we propose an alternative method to pick neighbours based on weights derived from self-collected metrics. Our technical performance study shows that the proposed method can suppose an improvement of up to 5 seconds. Additionally, in combination with the use of erasure coding techniques, we are capable of achieving a 75% reduction of the overall block propagation time.
Decentralized Actionable Cyber Threat Intelligence for Networks and the Internet of Things

Security presents itself as one of the biggest threats to the enabling and the deployment of the Internet of Things (IoT). Security challenges are evident in light of recent cybersecurity attacks that targeted major internet service providers and crippled a significant portion of the entire Internet by taking advantage of faulty and ill-protected embedded devices. Many of these devices reside at home networks with user-administrators who are not familiar with network security best practices, making them easy targets for the attackers. Therefore, security solutions are needed to navigate the insecure and untrusted public networks by automating protections through affordable and accessible first-hand network information sharing. This paper proposes and implements a proof of concept (PoC) to secure Internet Service Providers (ISPs), home networks, and home-based IoT devices using blockchain technologies. The results obtained support the idea of a distributed cyber threat intelligence data sharing network capable of protecting various stakeholders.
Security Analysis of SMS as a Second Factor of Authentication

Despite their popularity and ease of use, SMS-based authentication tokens are arguably one of the least secure forms of two-factor authentication. This does not imply, however, that it is an invalid method for securing an online account. The current security landscape is very different from that of two decades ago. Regardless of the critical nature of an online account or the individual who owns it, using a second form of authentication should always be the default option, regardless of the method chosen. In the wake of a large number of leaks and other intrusions, there are many username and password combinations out there in the wrong hands that make password spraying attacks cheap and easy to accomplish.