Reviewerly/exHarmony huggingface dataset:
Political Instability, Corruption and Enterprise Performance Nexus: Lessons and Policy Implications for Some Selected African Countries

The aim of this study is to investigate the interactions among political instability, corruption and enterprise performance in some selected African countries. Survey data collected by the World Bank from 5,780 enterprises in 2013 and 3,616 enterprises in 2016 from some 18 African countries were used for this study. Descriptive statistics and econometric models were employed for data analysis. We found that covariates such as corruption, power outage, budget and time allotted for R&D, participation in foreign markets, and provision of formal training to employees showed greasing effects on the wheels of enterprise performance in Africa, due to poor functioning of the institutions. On the other hand, political instability and being confined to domestic markets have mediating sanding effects on enterprise performance. The originality and relevance of this study are based on its novelty to establishing a nexus among corruption, political instability and enterprise performance in African context based on survey data collected from 18 African countries and thus providing a new insight on the causal relationships among these key variables.
Knowledge Enhanced Target-Aware Stance Detection on Tweets

Stance detection aims to determine the stance of a text towards a given target. Different from aspect-level sentiment classification, the target may not appear in the text. While existing models have achieved great success in this task using deep neural networks, their performances still drop sharply on cases where targets are not directly mentioned in texts, even with 'target-aware' structures. We argue that the nonalignment between targets and potentially opinioned terms in texts causes such failure and this could be remedied with external knowledge as a bridge. To this end, we propose RelNet, which leverages multiple external knowledge bases as bridges to explicitly link potentially opinioned terms in texts to targets of interest. Experiments on the well-adopted SemEval 2016 task 6 dataset demonstrate the effectiveness of the proposed model, especially on the subset where targets do not appear in texts.
Protecting Secure ICs Against Side-Channel Attacks by Identifying and Quantifying Potential EM and Leakage Hotspots at Simulation Stage

For many years EM Side-Channel Attacks, which exploit the statistical link between the magnetic field radiated by secure ICs and the data they process, are a critical threat. Indeed, attackers need to find only one hotspot (position of the EM probe over the IC surface) where there is an exploitable leakage to compromise the security of the IC and its data. As a result, designing secure ICs robust against these attacks is incredibly difficult because designers must ensure there is no exploitable hotspot over the whole IC surface. This task is all the more difficult as there is no CAD tool to compute the magnetic field radiated by ICs and hence no methodology to detect hotspots at the design stages. In addition, simulations are noise-free and that makes correlation maps useless in identifying potential hotspots. Within this context, this paper introduces a flow allowing predicting the EM radiations of ICs as well as two different methodologies to disclose coordinates of an IC where an attacker can break the security. The first one aims at identifying and quantifying the potential risks of EM hotspots at the surface of ICs, i.e. positions where to place an EM probe to capture a leakage. The second aims at locating leakage hotspots in ICs, i.e. areas in circuits from where these leakages originate.
Socially Distanced: Have user evaluation methods for Immersive Technologies changed during the COVID-19 pandemic?

Since the emergence of COVID-19 in late 2019, there has been a significant disturbance in human-to-human interaction that has changed the way we conduct user studies in the field of Human-Computer Interaction (HCI), especially for extended (augmented, mixed, and virtual) reality (XR). To uncover how XR research has adapted throughout the pandemic, this paper presents a review of user study methodology adaptations from a corpus of 951 papers. This corpus of papers covers CORE 2021 A* published conference submissions, from Q2 2020 through to Q1 2021 (IEEE ISMAR, ACM CHI, IEEE VR). The review highlights how methodologies were changed and reported; sparking discussions surrounding how methods should be conveyed and to what extent research should be contextualised, by drawing on external topical factors such as COVID-19, to maximise usefulness and perspective for future studies. We provide a set of initial guidelines based on our findings, posing key considerations for researchers when reporting on user studies during uncertain and unprecedented times.
Security Analysis of Sharding in the Blockchain System

The design of sharding aims to solve the scalability challenge in a blockchain network. Typically, by splitting the whole blockchain network into smaller shards, the transaction throughput can be significantly improved. However, distributing fewer attesting nodes for transactions in a shard may cause higher security risks. This paper analyzes the security level of transaction verification in different types of shards and transactions. The analyzed result indicates that the size of shards and validating nodes number may influence the transaction security in shards. And the random distribution of attesting nodes can reduce such influence and improve the reliability of consensus in shards.
Permission Blockchain Network based Central Bank Digital Currency

The central bank digital currency (CBDC) is gaining popularity for the last few years in many countries as it will affect the entire ecosystem of the current financial infrastructure. The CBDC utilizes digital tokens to indicate any nation's fiat/stable currency that manages under distributed ledger technology such as blockchain. Compared to existing cryptocurrencies, the CBDC is regulated by the government monetary policies, including the issuance of digital tokens (i.e., digitized central bank money) and circulation of these digital tokens to the commercial banks, then to the public/end-users. This paper proposes a two-layered CBDC architecture after analyzing the recent research and existing CBDC pilot projects. The distribution layer shows wholesale CBDC based on permission blockchain network (PBN) between the central bank and the commercial banks. While, the user layer describes the functioning of retail CBDC, which shows the interaction between the commercial banks and the end-users through the token-based account. We aim to provide the CBDC design architecture and smart-contract functions for PBN.
Prohibited Item Detection on Heterogeneous Risk Graphs

Prohibited item detection, which aims to detect illegal items hidden on e-commerce platforms, plays a significant role in evading risks and preventing crimes for online shopping. While traditional solutions usually focus on mining evidence from independent items, they cannot effectively utilize the rich structural relevance among different items. A naive idea is to directly deploy existing supervised graph neural networks to learn node representations for item classification. However, the very few manually labeled items with various risk patterns introduce two essential challenges: (1) How to enhance the representations of enormous unlabeled items? (2) How to enrich the supervised information in this few-labeled but multiple-pattern business scenario? In this paper, we construct item logs as a Heterogeneous Risk Graph (HRG), and propose the novel Heterogeneous Self-supervised Prohibited item Detection model (HSPD) to overcome these challenges. HSPD first designs the heterogeneous self-supervised learning model, which treats multiple semantics as the supervision to enhance item representations. Then, it presents the directed pairwise labeling to learn the distance from candidates to their most relevant prohibited seeds, which tackles the binary-labeled multi-patterned risks. Finally, HSPD integrates with self-training mechanisms to iteratively expand confident pseudo labels for enriching supervision. The extensive offline and online experimental results on three real-world HRGs demonstrate that HSPD consistently outperforms the state-of-the-art alternatives.
SVEA: A Small-scale Benchmark for Validating the Usability of Post-hoc Explainable AI Solutions in Image and Signal Recognition

Novel solutions in the area of Explainable AI (XAI) have made a significant breakthrough in increasing the trust of end-users in Machine Learning (ML) models. However, validating the performance of these solutions remains a challenging task. In this work, we focus on evaluating the methods that attribute a model’s decision to their input features. The prior metrics on this topic fail to consider multiple properties that a usable explainability solution should satisfy. Also, conducting experiments to assess the concreteness of the explanations provided by these solutions in large-scale datasets consumes excessive time and resources. To overcome these shortcomings, we propose the Small-scale Visual Explanation Analysis (SVEA) benchmark, which comprises the recent minimal MNIST-1D dataset. Our proposed benchmarking tool aids the practitioners and researchers to perform experiments on the Explainable AI methods without the need to access expensive computational devices. Furthermore, we offer a framework to evaluate various characteristics of the state-of-the-art XAI methods and include several widely used interpretability solutions in the SVEA benchmark to perform a thorough analysis of their completeness and understandability. The results obtained from our proposed evaluation metric suggest that specific approaches lack the ability to transfer the chosen model’s understanding to a second interpretable model by the explanations generated. The users can replicate our experiments within few minutes before working extensively on other larger datasets, thereby saving a lot of experimental time and effort.
Detection of evil flies

The aviation community is employing various air traffic control and mobile communication technologies, such as ubiquitous data links, wireless communication architectures and protocols. Recently, software-defined networking (SDN) based architectures (i.e., cockpit network communications environment testing (COMET)) have been proposed for Air-Ground communication. However, an evil can break the communication between a pilot and air traffic control, resulting in a hazardous (or life-threatening) situation up in the air or failure of ground equipment. This paper proposes an efficient evil detection and prevention mechanism (called DoEF) for the COMET architecture. The proposed DoEF utilizes a deep learning-based approach, i.e., long-short term memory (LSTM), to detect the evil flies and provide possible countermeasures. Our preliminary results show that the proposed scheme reduces the detection time and increases the detection accuracy of distributed denial of service (DDoS) attacks for the aviation network.
CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.
A Characterization of Probability-based Dichotomous Belief Revision

Abstract This article investigates the properties of multistate top revision , a dichotomous (AGM-style) model of belief revision that is based on an underlying model of probability revision. A proposition is included in the belief set if and only if its probability is either 1 or infinitesimally close to 1. Infinitesimal probabilities are used to keep track of propositions that are currently considered to have negligible probability, so that they are available if future information makes them more plausible. Multistate top revision satisfies a slightly modified version of the set of basic and supplementary AGM postulates, except the inclusion and success postulates. This result shows that hyperreal probabilities can provide us with efficient tools for overcoming the well known difficulties in combining dichotomous and probabilistic models of belief change.
Research on YOLOv3 pedestrian detection algorithm based on channel attention mechanism

This paper presents YOLOv3 pedestrian identification algorithm based on the channel attention mechanism for addressing the concerns of poor detection accuracy and low placement accuracy of standard pedestrian target recognition methods. To begin with, the structure of the feature extraction network Darknet-53 is enhanced, and the original residual structure is adjusted using the channel attention module ECA module to increase feature extraction capabilities. Secondly, using the spatial pyramid pooling module of three different receptive fields to fuse multi-scale features, thereby inputting features of different sizes, so that the network can learn the target features more comprehensively. Finally, the k-means++ method is used to the Caltech pedestrian data set to perform clustering analysis in order to find a more appropriate prior frame. The experimental results demonstrate that, when compared to the YOLOv3 model, the accuracy of the proposed enhanced YOLOv3 model is raised by 2.4 percent, and the recall rate is increased by 1.9 percent under the assumption of a minor increase in the quantity of model calculation.
Development of a Head-Mounted Mixed Reality Museum Navigation System

Many scholars and exhibition halls in recent years have become engaged in the research and development of augmented reality and virtual reality navigation systems. However, most of these navigation systems are still based on handheld mobile devices. Thus, users are likely to experience intermittent visits owing to repeated viewing of the exhibits, the navigation information on the mobile devices, and limited display and interactive functions. Therefore, this study used the Microsoft HoloLens device to implement a head-mounted museum navigation system by integrating such technologies as mixed reality, gesture recognition, and location awareness. This system presents diversified and highly interactive 3D and multimedia navigation information and supports indoor and outdoor location awareness functions to help visitors understand exhibits and exhibition venues, thus further enhancing the display and interaction functions of the mobile navigation system and increasing the pleasure of visiting.
Detection of Abnormal Motion by Estimating Scene Flows of Point Clouds for Autonomous Driving

Detection of abnormal motion in the environment is one of the most challenging and, at the same time, an important field of research in autonomous driving. Detecting an abnormal motion of objects using video sequences has been widely studied in the last decade. This paper proposes a method to detect abnormal motion by using sequences of LiDARs' point-clouds. Estimation of scene flows enables to compute the motion field in 3D space. Nearest point loss and Cosine-Cycle Consistency loss are proposed to estimate the scene flows of point-clouds in an unsupervised way. The direction of motion of the nearest object is represented in a 3D grid-like structure called VoxCF. VoxCF is used as an input for the extraction of dynamic features that encode the direction of objects' motion. Dynamic features are used to make decisions about the abnormal motion by making predictions from the Long short-term Memory (LSTM) network. The proposed method has been validated by performing real experiments with AV, and the obtained results show the precise detection of abnormal motion.
Emerging Uses of Technology for Development: A New Intelligence Paradigm

With only ten years left to achieve the Sustainable Development Goals (SDGs), development organizations rapidly need to innovate their approach to decision making and problem solving. New lessons and understandings, born from emerging uses of technology, can enable these innovations. This position paper uses the new paradigm of intelligence — which includes data intelligence, artificial intelligence, collective intelligence, and embodied intelligence — to provide development practitioners, policymakers, and decision-makers with an overview of the benefits and risks associated with various emerging uses of technologies. These assessments are illustrated where possible with examples from the field. It recommends the creation of a decision-making framework to help practitioners determine whether to invest in emerging technologies and how such technologies can effectively support development objectives. This early framework iteration focuses on carefully defining the relevant development objectives while taking into account the prevailing environment before addressing the solution by assessing the maturity, challenges, cost implications and risks of the technology’s use as well as the presence of enablers or disablers that could determine its impact and appropriateness.
Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.
Service-oriented knowledge recommender system and performance evaluation in industrial product development

Manufacturing firms today co-exist in a complex collaboration network with their partners, who often seek help from external knowledge services to co-develop more competitive products. Thus, knowing how to accurately and rapidly acquire such knowledge to sustainably improve the quality of knowledge services are critical to maintaining the competitiveness of such firms. There is a need to develop reliable frameworks and tools to support the knowledge services of these firms. This paper proposes a service-oriented knowledge recommender framework focusing on the mechanism of knowledge service transfer and the knowledge recommendation model, which comprises the knowledge service demanders, knowledge service providers, and platform operators. The matching processes and algorithm of the knowledge service demanders and knowledge service providers are designed based on the product development tasks. A fuzzy synthetic evaluation method is proposed, using multi-factor, fuzzy decision making techniques, to improve the quality of the knowledge service. To validate the approach, a case study on gantry crane development is provided to show how to apply the proposed framework and system. An experimental evaluation is designed to demonstrate the benefit and performance of the framework.
Mining for Privacy: How to Bootstrap a Snarky Blockchain

Non-interactive zero-knowledge proofs, and more specifically succinct non-interactive zero-knowledge arguments (zk-SNARKs), have been proven to be the "Swiss army knife" of the blockchain and distributed ledger space, with a variety of applications in privacy, interoperability and scalability. Many commonly used SNARK systems rely on a structured reference string, the secure generation of which turns out to be their Achilles heel: If the randomness used for the generation is known, the soundness of the proof system can be broken with devastating consequences for the underlying blockchain system that utilises them. In this work we describe and analyse, for the first time, a blockchain mechanism that produces a secure SRS with the characteristic that security is shown under comparable conditions to the blockchain protocol itself. Our mechanism makes use of the recent discovery of updateable structured reference strings to perform this secure generation in a fully distributed manner. In this way, the SRS emanates from the normal operation of the blockchain protocol itself without the need of additional security assumptions or off-chain computation and/or verification. We provide concrete guidelines for the parameterisation of this setup which allows for the completion of a secure setup in a reasonable period of time. We also provide an incentive scheme that, when paired with the update mechanism, properly incentivises participants into contributing to secure reference string generation.
Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional feedback control systems to make RL implementation easier for control systems. The RL algorithms are robust and proficient in using trial and error to search for the best strategy. Our proposed algorithm is a deep deterministic policy gradient, in which a large amount of training data trains the agent. Once the system is trained, the agent can automatically adjust the control parameters. The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment. The performance of the proposed RL algorithm is compared with a proportional integral derivative (PID) controller and a linear quadratic regulator (LQR) controller. The simulation results of the proposed scheme are promising for the feedback control problems.