Reviewerly/exHarmony huggingface dataset:
Stakeholders’ Viewpoints toward Blockchain Integration within IoT-Based Smart Cities

This study intends to assess the development of IoT-based smart cities industry and the possibilities of blockchain integration from the perspective of industry stakeholders as the vision for a modern, integrated smart city future is predicated upon intelligence and the relationship between data-rich connections and human activities. Although this ideal of an interconnected urban landscape is currently being tested and actively used by consumers spanning a range of connected nodes and service solutions, the scalability, interoperability, and security of this emergent cyber-physical ideal has yet to be adequately resolved. This study used an exploratory study design following a mixed method design approach. A structured questionnaire survey (quantitative) and interviews (qualitative) were conducted for collecting data. IBM SPSS was used for the analysis of the data, which computed descriptive statistics, cross-tabulation, Pearson correlation, and ANOVA for quantitative data and thematic analysis for qualitative data. Through an empirical assessment of the perceptions and expertise of 122 stakeholders from within the worldwide IoT smart city industry, conceptual support for blockchain integration into the IoT solution was acquired, highlighting the solution-oriented, system-centered advantages of a decentralised, autonomous data management backbone that could be applied to future IoT-based smart city solutions. To meet the broad and diversified needs of the smart city and its future evolution, this study has confirmed that a commitment to decentralisation and blockchain intermediary data management is critical to scalable, secure, and autonomous negotiations of the IoT-enabled smart city networks.
Energy-Efficient Device Selection in Federated Edge Learning

Due to the increasing demand from mobile devices for the real-time response of cloud computing services, federated edge learning (FEL) emerges as a new computing paradigm, which utilizes edge devices to achieve efficient machine learning while protecting their data privacy. Implementing efficient FEL suffers from the challenges of devices’ limited computing and communication resources, as well as unevenly distributed datasets, which inspires several existing research focusing on device selection to optimize time consumption and data diversity. However, these studies fail to consider the energy consumption of edge devices given their limited power supply, which can seriously affect the cost-efficiency of FEL with unexpected device dropouts. To fill this gap, we propose a device selection model capturing both energy consumption and data diversity optimization, under the constraints of time consumption and training data amount. Then we solve the optimization problem by reformulating the original model and designing a novel algorithm, named E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DS, to reduce the time complexity greatly. By comparing with two classical FEL schemes, we validate the superiority of our proposed device selection mechanism for FEL with extensive experimental results.
FDI, Stock Market Development and Institutional Quality: An African Perspective

This paper empirically sought to establish the existence of relationships between foreign direct investment, stock market development and institutional quality, respectively. The study adopted a multiple regression analysis, using a panel of nine African countries between 2009 and 2016. Using the random effects model, we find that the relationship between foreign direct investment and stock market development is positive and statistically significant. On the other hand, institutional quality reflected a negative effect on FDI inflows, implying that countries with low quality institutions would struggle to attract inward FDI. The policy implications are that host countries’ policy makers should eliminate or reduce any practices that deter foreign direct investments, such as capital controls and risk of expropriation (institutions), while simultaneously improving the domestic financial infrastructure and related regulations.
On the Training of Deep Neural Networks for Automatic Arabic-Text Diacritization

Automatic Arabic diacritization is one of the most important and challenging problems in Arabic natural language processing (NLP).Recurrent neural networks (RNNs) have proved recently to achieve state-of-the-art results for sequence transcription problems in general, and Arabic diacritization in specific.In this work, we investigate the effect of varying the size of the training corpus on the accuracy of diacritization.We produce a cleaned corpus of approximately 550k sequences extracted from the full dataset of Tashkeela and use subsets of this corpus in our training experiments.Our base model is a deep bidirectional long short-term memory (BiLSTM) RNN that transcribes undiacritized sequences of Arabic letters with fully diacritized sequences.Our experiments show that error rates improve as the size of training corpus increases.Our best performing model achieves average diacritic and word error rates of 1.45% and 3.89%, respectively.When compared with state-of-the-art diacritization systems, we reduce the word error rate by 12% over the best published results.
ECBS with Flex Distribution for Bounded-Suboptimal Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents. CBS is a leading optimal two-level MAPF solver whose low level plans optimal paths for single agents and whose high level runs a best-first search on a Constraint Tree (CT) to resolve the collisions between the paths. ECBS, a bounded-suboptimal variant of CBS, speeds up CBS by reducing the number of collisions that need to be resolved on the high level. It achieves this by generating bounded-suboptimal paths with fewer collisions with the paths of the other agents on the low level and expanding bounded-suboptimal CT nodes that contain fewer collisions on the high level. In this paper, we propose Flexible ECBS (FECBS) that further reduces the number of collisions that need to be resolved on the high level by using looser suboptimal bounds on the low level while still providing bounded-suboptimal solutions. Instead of requiring the cost of each path to be bounded-suboptimal, FECBS requires only the overall cost of the paths to be bounded-suboptimal, which gives us the freedom to distribute the cost leeway among different agents according to their needs. Our empirical results show that FECBS can solve more MAPF instances than state-of-the-art ECBS variants within 5 minutes.
Application of Augmented Reality Technology for Home Healthcare Product Visualization

A user manual is a document which is intended to guide the user about the product usage and its detailed safety precautions. The difficulty in understanding home healthcare products, which is in the form of printed media in user manuals, led to an idea of conceptualizing it into a digital platform and providing better understanding experience to the product users. In order to achieve this, exact 3D model of the product is required. Animation has to be applied to exactly match the real-time working and usage of the model. The existence of a strong technological innovation like Augmented Reality (AR), which blends virtual and real world by making use of computer visual based recognition algorithms to enhance sound, video, graphics, and other sensory inputs embedded within an application, is used. The paper presents the innovative concept pulse oximeter, which will be shown as an augmented user guide for the product.
Modular Self-Supervision for Document-Level Relation Extraction

Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications.Compared to conventional information extraction confined to short text spans, document-level relation extraction faces additional challenges in both inference and learning.Given longer text spans, state-of-the-art neural architectures are less effective and taskspecific self-supervision such as distant supervision becomes very noisy.In this paper, we propose decomposing document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics.This enables us to incorporate explicit discourse modeling and leverage modular self-supervision for each sub-problem, which is less noise-prone and can be further refined end-to-end via variational EM.We conduct a thorough evaluation in biomedical machine reading for precision oncology, where cross-paragraph relation mentions are prevalent.Our method outperforms prior state of the art, such as multi-scale learning and graph neural networks, by over 20 absolute F1 points.The gain is particularly pronounced among the most challenging relation instances whose arguments never co-occur in a paragraph.
Financial Development, Institutions, Gender and Entrepreneurship in the United Arab Emirates

The paper explores the role of financial development on entrepreneurship in the United Arab Emirates by using data on new business registration and new business density over the period 2006-2018. The country is undergoing a rapid transformation phase characterizzed by substantial efforts to boost private sector entepreneurship. The data is drawn from the World Bank’s World Development Indicators. We use the International Monetary Fund’s composite financial development index to capture financial development in the country. The index comprises information on financial depth, access and efficiency by both financial institutions and markets. The paper controls for the impact on the macroeconomic conditions, institutional factors, such as gender equality and the fear of failure, the innovation environment and the business startup environment. The results show that financial development is a robust predictor of entepreneurial activity in the UAE. Macroeconomic, institutional and innovation conditions further mediate the link in more or less significant ways.
Blockchain for Trust and Reputation Management in Cyber-Physical Systems

The salient features of blockchain, such as decentralization and transparency, have allowed the development of Decentralised Trust and Reputation Management Systems (DTRMS), which mainly aim to quantitatively evaluate the trustworthiness of network participants and help to protect the network from adversaries. In the literature, proposals of DTRMS have been applied to various Cyber-physical Systems (CPS) applications, including supply chains, smart cities, and distributed energy trading. In this chapter, we outline the building blocks of a generic DTRMS and discuss how it can benefit from blockchain. To highlight the significance of DTRMS, we present the state-of-the-art DTRMS in various fields of CPS applications. In addition, we also outline challenges and future directions in developing DTRMS for CPS.
A dynamic routing CapsNet based on increment prototype clustering for overcoming catastrophic forgetting

In continual learning, previously learnt knowledge tends to be overlapped by the subsequent training tasks. This bottleneck, known as catastrophic forgetting, has recently been relieved between vision tasks involving pixel shuffles etc. Nevertheless, the challenge lies in the continuous classification of the sequential sets discriminated by global transformations, such as excessively spatial rotations. Aim at this, a novel strategy of dynamic memory routing is proposed to dominate the forward paths of capsule network (CapsNet) according to the current input sets. To recall previous knowledge, a binary routing table is maintained among these sequential tasks. Then, an increment procedure of competitive prototype clustering is integrated to update the routing of the current task. Moreover, a sparsity measurement is employed to decouple the salient routing among the different learnt tasks. The experimental results demonstrate the superiority of the proposed memory network over the state–of–the–art approaches by the recalling evaluations on extended sets of Cifar–100, CelebA and Tiny ImageNet etc.
Lattice-based Cryptosystems on FPGA: Parallelization and Comparison using HLS

This paper deals with hardware implementations for lattice-based cryptography. Various CPA and CCA secure algorithms for LWE, RLWE and MLWE problems have been studied, parallelized, implemented and compared on FPGA using high-level synthesis. The impact of PRNG choices on the implementations performances and costs is also evaluated. HLS allows us to compare various sets of algorithms, architectures and parameters with a reduced design effort. Our results are often similar to state-of-the-art for various speed and cost trade-offs. Sometimes we obtain better results thanks to the exploration of numerous architecture and algorithm optimizations.
Powering Visualization With Deep Learning

The articles in this Special Issue focus on the application of deep learning techniques in visualization. The great success of deep learning techniques in computer vision, natural language processing, and speech recognition offers new opportunities for data visualization and analytics. We can leverage these technologies not only to recognize visual representations but also to understand analytical tasks. However, introducing deep learning techniques into visualization tasks faces new challenges and problems to solve. In this issue, we present six articles that illustrate how deep learning techniques interact with visualization.
The Neural Instantiation of an Abstract Cognitive Map for Economic Choice

Since the discovery of cognitive maps in rodent hippocampus (HC), the cognitive map has evolved from originally referring to spatial representations encoding locations and objects in Euclidean spaces to a general low-dimensional organization of information along selected feature dimensions. A cognitive map includes hypothetical constructs that bridge between environmental stimuli and the final overt behavior. To neuroeconomists, utility and utility functions are such constructs with neurobiological basis that drive choice behavior. Emergence of distinct functional neuron groups in the primate orbitofrontal cortex (OFC) during simple economic choice indicates the formation of an abstract cognitive map for organizing information of goods for value computation. Experimental evidence suggests that organization of neuronal activity in such cognitive map reflects the abstraction of core task features. Thus, such map can be adapted to accommodate economic choices under various task contexts.
Reusing Monolingual Pre-Trained Models by Cross-Connecting Seq2seq Models for Machine Translation

This work uses sequence-to-sequence (seq2seq) models pre-trained on monolingual corpora for machine translation. We pre-train two seq2seq models with monolingual corpora for the source and target languages, then combine the encoder of the source language model and the decoder of the target language model, i.e., the cross-connection. We add an intermediate layer between the pre-trained encoder and the decoder to help the mapping of each other since the modules are pre-trained completely independently. These monolingual pre-trained models can work as a multilingual pre-trained model because one model can be cross-connected with another model pre-trained on any other language, while their capacity is not affected by the number of languages. We will demonstrate that our method improves the translation performance significantly over the random baseline. Moreover, we will analyze the appropriate choice of the intermediate layer, the importance of each part of a pre-trained model, and the performance change along with the size of the bitext.
College Oral English Teaching Reform Driven by Big Data and Deep Neural Network Technology

The ultimate goal of English teaching is to cultivate the students’ ability to communicate information in English, master good language learning methods, and become independent language learners and users. Therefore, successful English language teaching needs to be achieved through language communication training between teachers and students and between students. This article investigates the importance of promoting the reform of oral English teaching in China’s English teaching environment. We believe that to promote the reform of oral English teaching, an oral teaching environment must be available. However, the current common problem in oral English teaching in colleges and universities is that the spoken conversation objects are not standard enough, or there is no person who can talk to. Therefore, an intelligent spoken dialogue system based on big data and neural network technology is particularly important, and the quality of dialogue depends on accurate spoken speech evaluation. We first extracted six features of pronunciation quality, fluency, content richness, topic relevance, grammar, and vocabulary richness. Secondly, we propose an evaluation model that connects specific TDNN layers in a feedforward manner, using the feature representation of target words in different TDNN layers, which can obtain richer context information and greatly reduce the amount of model parameters. Finally, we conducted a simulation experiment. The experimental results show that the proposed model is accurate in evaluating spoken English and can effectively assist the reform of spoken English teaching in colleges and universities, and its performance is better than SVM by 9.2%.
Count Me If You Can: Enumerating QUIC Servers Behind Load Balancers

QUIC is a new transport protocol over UDP which is recently became an IETF RFC. Our security analysis of the Connection ID mechanism in QUIC reveals that the protocol is underspecified. This allows an attacker  to count the number of server instances behind a middlebox, e.g., a  load balancer. We found 4/15 (~25%) implementations vulnerable to  our enumeration attack. We then concretely describe how an attacker  can count the number of instances behind a load balancer that either uses Round Robin or Hashing.
Label-Assisted Memory Autoencoder for Unsupervised Out-of-Distribution Detection

Out-of-Distribution (OoD) detectors based on AutoEncoder (AE) rely on an underlying assumption that an AE network cannot reconstruct OoD data as good as in-distribution (ID) data when it is constructed based on ID data only. However, this assumption may be violated in practice, resulting in a degradation in detection performance. Therefore, alleviating the factors violating this assumption can potentially improve the robustness of OoD performance. Our empirical studies also show that image complexity can be another factor hindering detection performance for AE-based detectors. To cater for these issues, we propose two OoD detectors LAMAE and LAMAE+. Both can be trained without the availability of any OoD-related data. The key idea is to regularize the AE network architecture with a classifier and a label-assisted memory to confine the reconstruction of OoD data while retaining the reconstruction ability for ID data. We also adjust the reconstruction error by taking image complexity into consideration. Experimental studies show that the proposed OoD detectors can perform well on a wider range of OoD scenarios.
Exploiting multi-level parallel metaheuristics and heterogeneous computing to boost phylogenetics

Optimization problems are becoming increasingly difficult challenges as a result of the definition of more realistic formulations and the availability of larger input data. Fortunately, the computing capabilities of state-of-the-art heterogeneous systems represent an opportunity to deal with the main complexity factors of these problems. These platforms open the door to the definition of robust metaheuristic solvers, in which parallel computations of different nature can be efficiently mapped to the most suitable architectures and hardware resources. This work investigates the combination of multi-level parallelism and heterogeneous computing to address an important multiobjective problem in bioinformatics: phylogenetics. A parallel metaheuristic approach, based on the joint exploitation of parallel tasks at the algorithm, iteration, and solution levels, is proposed to tackle computationally intensive inferences on CPU+GPU systems. Different heterogeneous design alternatives are also discussed, in accordance with the way the interactions between CPU and GPU are handled. The experimental evaluation of the proposal on real-world biological datasets points out the benefits of using multi-level, heterogeneous strategies, reporting accelerations up to 396× over the baseline metaheuristic as well as significant energy savings with regard to other parallel approaches, without impacting multiobjective solution quality.
Application Design for Recording Financial Statements

The development of information technology users requires applications to organize the recording and presentation of financial report data more easily and accurately. The current financial statements are not only by companies but also by Micro Small and Medium Enterprises (MSMEs). Besides, the public also uses them to manage their financial records. The purpose of this research is to develop accounting applications that have been circulating today to support financial statements to ensure data security. This research used a descriptive qualitative method to collect data in accordance with field facts. The population of this research is students of Universitas Komputer Indonesia who learn to use accurate applications because accurately studied by accounting students, the sample is students majoring in accounting. Sample techniques used purposive sampling, where respondents will be selected according to the criteria. Data collection through questionnaires were distributed online to obtain data in accordance with field facts. This study shows that the development of accounting applications supports the recording of financial statements in maintaining the security of existing data in financial statements. It impacts MSMEs who want to store their financial transaction data safely because there are security features similar to accuracy, except that before storing data, users must enter a special code as at the beginning when starting data recording. It can ensure security in recording financial statements and preventing data modification fraud.
Applying contextual integrity to digital contact tracing and automated triage for hospitals during COVID-19

To control and minimise the spread of COVID-19, various technological solutions have been proposed. In this research, we focus on digital contact tracing and automated triage for hospitals. We conducted an online survey in Flanders (N = 1708) to investigate the perceived appropriateness of these systems based on the Contextual Integrity framework, as developed by Nissenbaum [1]. For digital contact tracing, significant differences were found between the appropriateness of using various types of data for different goals. Precise individual location data (i.e. GPS) was considered to be least appropriate and much less appropriate than proximity data (i.e. Bluetooth) or coarser location data (i.e. GSM). Goals for digital contact tracing with a high individual impact were considered to be less appropriate than goals with a low individual or societal impact. In addition, the data showed that respondents would find the usage of digital contact tracing to be less appropriate after the pandemic, underlining the temporality of this technological solution. For automated triage, the results indicated that gender is perceived to be significantly less appropriate than the other types of data, including age, to determine the priority of treatment.