Reviewerly/exHarmony huggingface dataset:
Multi-Channel Blockchain Scheme for Internet of Vehicles

With the development of advanced information and communication technology, the traditional centralized service model alone no longer meets the increasing demand of data exchange in intelligent transportation systems (ITS). While Internet of Vehicles (IoV) technology has been introduced to achieve more advanced ITS, there are still some unsettled issues such as flexibility and fault tolerance. The conventional centralized approach for ITS is vulnerable to the single point of failure, and lack of flexibility due to its dependence on a trusted third party (TTP). The emergence of blockchain technology provides a potential direction to address these problems. However, due to varying vehicle densities, it is challenging to select the best blockchain parameters to satisfy the application requirements. In this paper, we propose a multi-channel blockchain scheme that can use the best parameters in accordance with the vehicle density. The proposed scheme first defines multiple blockchain channels where each channel is optimized for a certain vehicle density level. Then, the system selects the best channel according to the vehicle density, and the application requirements on the transaction throughput and latency. We use extensive simulations to show that the proposed blockchain scheme achieves a significantly better performance as compared with existing baselines.
Supporting Flipped and Gamified Learning With Augmented Reality in Higher Education

Chemistry education is challenging when many students cannot see the relevance and interest between what they learn at school and their everyday life outside the curriculum. Due to the prevalence of chemicals in real life, students lose interest in those not-so-novel Chemistry problems as they are satisfied with their rudimentary grasp of knowledge. Therefore, it is of paramount importance to draw students’ attention to those day-to-day Chemistry concepts, a task in which augmented reality (AR) can be a competent pedagogical facilitator. Despite its popularity due to the development of smart devices, educators are still averse to adopting AR in teaching because of the doubts about its pedagogical effectiveness and difficulties in implementation. This paper will demonstrate an AR app developed by City University of Hong Kong (CityU) for a year four undergraduate Chemistry course under two UGC’s project funds and CityU’s Teaching Development Grant that aligns with the university’s Discovery and Innovation-enriched Curriculum. The learning theories and technology stack of development and deployment will be shared in this paper. The consideration during preparation, production, and publishing will also be documented. A pilot survey about students’ perception of the AR showed positive feedback for the AR app in terms of enhancing awareness, learning, understanding, and engagement, which addresses the concerns of retaining students’ engagement during teaching and learning real-life Chemistry. We hope that educators who are interested in adopting AR can gain insights from this AR development experience. This research can act as a foundation for further exploration of applying AR in secondary and tertiary Chemistry education.
Containing Malicious Package Updates in npm with a Lightweight Permission System

The large amount of third-party packages available in fast-moving software ecosystems, such as Node.js/npm, enables attackers to compromise applications by pushing malicious updates to their package dependencies. Studying the npm repository, we observed that many packages in the npm repository that are used in Node.js applications perform only simple computations and do not need access to filesystem or network APIs. This offers the opportunity to enforce least-privilege design per package, protecting applications and package dependencies from malicious updates. We propose a lightweight permission system that protects Node.js applications by enforcing package permissions at runtime. We discuss the design space of solutions and show that our system makes a large number of packages much harder to be exploited, almost for free.
The Impact of Cryptocurrency Regulation on Trading Markets

ABSTRACT The meteoric growth of global cryptocurrency markets presents novel challenges to regulators. Some policymakers and scholars warn that regulation will cause trading activity to cross borders into less-regulated jurisdictions—or even smother a promising new financial asset class. Others believe regulatory actions will stimulate activity by providing clarity to market participants. Standing behind this disagreement is a debate about the desirability of either outcome. Some believe that governments should promote development of the cryptocurrency sector within their countries, while others view cryptocurrencies as conduits of illegality and fraud that should be restricted through strict regulation or even outright bans. Yet these debates have, to date, been conducted almost entirely without data concerning the effects of regulation on market activity. As a corrective, in this article we assembled original data on cryptocurrency regulations worldwide and used them to empirically examine movement in trading activity at a number of exchanges following key regulatory announcements. We found that a wide variety of models yielded almost entirely null results. From the creation of bespoke licensing regimes to targeted anti-money-laundering and anti-fraud enforcement actions, as well as many other categories of government activities, we found no systemic evidence that regulatory measures cause traders to flee, or enter into, the affected jurisdictions. These findings at last provide an empirical basis for regulatory decisions concerning cryptocurrency trading. Among other things, they call into question that capital flight or chilling effects should be a first-order concern.
A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain

Neural networks for language modeling have been proven effective on several sub-tasks of natural language processing. Training deep language models, however, is time-consuming and computationally intensive. Pre-trained language models such as BERT are thus appealing since (1) they yielded state-of-the-art performance, and (2) they offload practitioners from the burden of preparing the adequate resources (time, hardware, and data) to train models. Nevertheless, because pre-trained models are generic, they may underperform on specific domains. In this study, we investigate the case of multi-class text classification, a task that is relatively less studied in the literature evaluating pre-trained language models. Our work is further placed under the industrial settings of the financial domain. We thus leverage generic benchmark datasets from the literature and two proprietary datasets from our partners in the financial technological industry. After highlighting a challenge for generic pre-trained models (BERT, DistilBERT, RoBERTa, XLNet, XLM) to classify a portion of the financial document dataset, we investigate the intuition that a specialized pre-trained model for financial documents, such as FinBERT, should be leveraged. Nevertheless, our experiments show that the FinBERT model, even with an adapted vocabulary, does not lead to improvements compared to the generic BERT models.
A Supervised Multi-Head Self-Attention Network for Nested Named Entity Recognition

In recent years, researchers have shown an increased interest in recognizing the overlapping entities that have nested structures. However, most existing models ignore the semantic correlation between words under different entity types. Considering words in sentence play different roles under different entity types, we argue that the correlation intensities of pairwise words in sentence for each entity type should be considered. In this paper, we treat named entity recognition as a multi-class classification of word pairs and design a simple neural model to handle this issue. Our model applies a supervised multi-head self-attention mechanism, where each head corresponds to one entity type, to construct the word-level correlations for each type. Our model can flexibly predict the span type by the correlation intensities of its head and tail under the corresponding type. In addition, we fuse entity boundary detection and entity classification by a multitask learning framework, which can capture the dependencies between these two tasks. To verify the performance of our model, we conduct extensive experiments on both nested and flat datasets. The experimental results show that our model can outperform the previous state-of-the-art methods on multiple tasks without any extra NLP tools or human annotations.
ICU Survival Prediction Incorporating Test-Time Augmentation to Improve the Accuracy of Ensemble-Based Models

This work presents a novel method for applying test-time augmentation (TTA) to tabular data. We used TTA along with an ensemble of 42 models to achieve higher performance on the MIT Global Open Source Severity of Illness Score dataset consisting of 131,051 ICU visits and outcomes. This method achieved an AUC of 0.915 on the private test set (19,669 admissions) and won first place at Stanford University's WiDS Datathon 2020 challenge on Kaggle, while the Acute Physiology and Chronic Health Evaluation (APACHE) IV model (commonly used for ICU survival prediction in the literature) achieved an AUC of 0.868. In addition to increasing the AUC score, our method also reduces “unfair” bias.
VeriDB: An SGX-based Verifiable Database

The emergence of trusted hardwares (such as Intel SGX) provides a new avenue towards verifiable database. Such trust hardwares act as an additional trust anchor, allowing great simplification and, in turn, performance improvement in the design of verifiable databases. In this paper, we introduce the design and implementation of VeriDB, an SGX-based verifiable database that supports relational tables, multiple access methods and general SQL queries. Built on top of write-read consistent memory, VeriDB provides verifiable page-structured storage, where results of storage operations can be efficiently verified with low, constant overhead. VeriDB further provides verifiable query execution that supports general SQL queries. Through a series of evaluation using practical workload, we demonstrate that VeriDB incurs low overhead for achieving verifiability: an overhead of 1-2 microseconds for read/write operations, and a 9% - 39% overhead for representative analytical workloads.
NTIRE 2021 Challenge on Video Super-Resolution

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.
Perceptions towards Cryptocurrency Adoption: A case of Saudi Arabian Citizens

The global financial market is influenced by relatively new technologies such as cryptocurrencies; namely Bitcoin, Ethereum, Litecoin, and others. Cryptocurrencies are a challenging area in finance that requires additional attention from the academic community as they can have a potentially large impact on society and the economy.
Fully Decentralized Trading Games with Evolvable Characters using NFTs and IPFS

We leverage the InterPlanetary File System (IPFS) and Non-Fungible Tokens (NFTs) backed by Distributed Ledger Technologies (DLTs) to build a flexible, decentralized, and fair baseline system for trading games. Our solution creates a fully decentralized system, where new business models are enabled, as the evolvable assets of the games can be resold and priced depending on their rarity, giving also a cut to the digital artist, without the need for a trusted party. The system guarantees that assets will remain online, thus the users do not risk losing control over the artefacts or their value, even if the creator game company loses interest or goes bankrupt.
Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects

Over the past years, the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human life. However, using machine learning in intelligent networks also presents potential security and privacy threats. A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned model. In this survey, we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first time. We emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms, and analyze the strengths and limitations of corresponding defense methods in a compact form. We also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.
Moving-Distance-Minimized PSO for Mobile Robot Swarm

Particle swarm optimizer (PSO) and mobile robot swarm are two typical swarm techniques. Many applications emerge separately along both of them while the similarity between them is rarely considered. When a solution space is a certain region in reality, a robot swarm can replace a particle swarm to explore the optimal solution by performing PSO. In this way, a mobile robot swarm should be able to efficiently explore an area just like the particle swarm and uninterruptedly work even under the shortage of robots or in the case of unexpected failure of robots. Furthermore, the moving distances of robots are highly constrained because energy and time can be costly. Inspired by such requirements, this article proposes a moving-distance-minimized PSO (MPSO) for a mobile robot swarm to minimize the total moving distance of its robots while performing optimization. The distances between the current robot positions and the particle ones in the next generation are utilized to derive paths for robots such that the total distance that robots move is minimized, hence minimizing the energy and time for a robot swarm to locate the optima. Experiments on 28 CEC2013 benchmark functions show the advantage of the proposed method over the standard PSO. By adopting the given algorithm, the moving distance can be reduced by more than 66% and the makespan can be reduced by nearly 70% while offering the same optimization effects.
GraphScope

GraphScope is a system and a set of language extensions that enable a new programming interface for large-scale distributed graph computing. It generalizes previous graph processing frameworks (e.g. , Pregel, GraphX) and distributed graph databases ( e.g ., Janus-Graph, Neptune) in two important ways: by exposing a unified programming interface to a wide variety of graph computations such as graph traversal, pattern matching, iterative algorithms and graph neural networks within a high-level programming language; and by supporting the seamless integration of a highly optimized graph engine in a general purpose data-parallel computing system. A GraphScope program is a sequential program composed of declarative data-parallel operators, and can be written using standard Python development tools. The system automatically handles the parallelization and distributed execution of programs on a cluster of machines. It outperforms current state-of-the-art systems by enabling a separate optimization (or family of optimizations) for each graph operation in one carefully designed coherent framework. We describe the design and implementation of GraphScope and evaluate system performance using several real-world applications.
IoT Based Smart Wheelchair for Disabled People

Smart Wheelchair is known as a Power Wheelchair that is integrated into multiple sensors, assistive technology, and computers that give the user with a disability such as impairment, handicaps, and permanent injury, the required mobility to move freely and safely. These types of wheelchairs are gradually replacing the traditional wheelchairs; however, their expensive costs are preventing a large size of disabled people from having one. According to the organization of World Health (WHO), only 5 to 15% out of 70 million disabled people have access to wheelchairs. Therefore, we need to offer a cost-effective Smart that not only minimized the cost but also provides plenty of features that use the latest components and technologies. In the last years, there have been many pleasant efforts that serve this purpose. They have adopted various technologies such as artificial intelligence, where they have designed an autonomous wheelchair that used machine learning concepts to navigate, and some also used Internet of Thing technology to control the wheelchair-using voice recognition system. This report will present a cost-effective Smart Wheelchair-based Arduino Nano microcontroller and IoT technology that have several features to gain disabled people, especially poor people who cannot afford expensive Smart Wheelchair, the required help to finish daily life tasks without external help. To conclude this project will make the Smart Wheelchair affordable to a wide range of disabled people and will be based on Arduino Nano, ESP-12e module to give Wi-Fi access, MPU6050 to detect fall with Voice message notification using IFTTT platform, obstacle detection with buzzer and LED to work as hazards, voice recognition system, and joysticks to control the wheelchair.
Chaotic Map Optimization for Image Encryption Using Triple Objective Differential Evolution Algorithm

Chaotic maps used to shuffle and manipulate the image pixels are important for image encryption (IE).In this study, a novel 2D optimized chaotic map (OPMAP) using a triple-objective differential evolution (TODE) algorithm is presented for IE.A model for OPMAP with eight decision variables is empirically designed, and then its variables are determined utilizing TODE through minimizing a triple-objective function that involving Lyapunov exponent (LE), entropy and 0-1 test.OPMAP is assessed with respect to credible measurements like bifurcation, 3D phase space, LE, 0-1 test, permutation entropy (PE) and sample entropy (SE).The capability of OPMAP is then verified through an IE scheme including permutation and diffusion through various cryptanalyses: key space 2 298 , mean entropy 7.9995, mean correlation 13.61E-5, number of pixels changing rate (NPCR) 99.6093, unified average changing intensity (UACI) 33.4630 and encryption processing time (EPT) 0.2919 (s).A detailed review of IE schemes reported elsewhere is presented and IE performance of OPMAP is also validated by comparison with those IE schemes with and without optimization used.The 2D-OPMAP-based IE is faster and has low computational complexity.Moreover, the proposed it shows better cryptanalysis results for the most of the comparisons.
On classifiers and affect in the nominal domain

This chapter presents six case studies on the encoding of affective-expressive information in the Dutch nominal domain. Each affective phenomenon that is discussed, displays a formal property that, from a surface perspective, could be qualified as ‘disorganizing.’ It is proposed that disorganization is only apparent and that the affective-expressive nominal expression has an organized structure that falls within the variation space as defined by UG. It is the <italic>different</italic> formal organization, compared to the organization of nominal expressions with descriptive contents, which yields ‘the feel of disorganization.’ The availability of different grammatical routes provided by UG allows the language user to use her language for distinct communicative functions, including the expression of one’s affective feelings. It is proposed that the Classifier Projection plays an important role in the manifestation of those routes.
MBLT: Learning Motion and Background for Vehicle Tracking in Satellite Videos

Recently, satellite videos provide a new way to dynamically monitor the Earth&#x2019;s surface. The interpretation of satellite videos has attracted more and more attentions. In this article, we focus on the problem of the vehicle tracking in satellite videos. Satellite videos usually own a lower resolution, which leads to the following phenomena: 1) the size of a vehicle target usually includes a few pixels and 2) vehicles are usually with similar appearance which easily results in the wrong tracking within the observing region. General popular tracking methods usually focus on the representation of the target and recognize it from background which are limited in this problem. As a consequence, in this article, we propose to learn motion and background of the target in order to help the trackers recognize the target with higher accuracy. A prediction network is proposed to predict the location probability of the target in each pixel in next frame based on fully convolutional network (FCN) which is learned from previous results. In addition, a segmentation method is introduced to generate the feasible region for target in each frame and assign high probability for such a region. For quantitative comparison, we manually annotate 20 representative vehicle targets from nine satellite videos taken by JiLin-1. In addition, we also selected two public satellite video datasets for experiments. Numerous experimental results demonstrate the superior of the proposed method.
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting

Chest radiographs are one of the most common diagnostic modalities in clinical routine. It can be done cheaply, requires minimal equipment, and the image can be diagnosed by every radiologists. However, the number of chest radiographs obtained on a daily basis can easily overwhelm the available clinical capacities. We propose RATCHET: RAdiological Text Captioning for Human Examined Thoraces. RATCHET is a CNN-RNN-based medical transformer that is trained end-to-end. It is capable of extracting image features from chest radiographs, and generates medically accurate text reports that fit seamlessly into clinical work flows. The model is evaluated for its natural language generation ability using common metrics from NLP literature, as well as its medically accuracy through a surrogate report classification task. The model is available for download at: http://www.github.com/farrell236/RATCHET.