Reviewerly/exHarmony huggingface dataset:
The Rise of China's Digital Economy: An Overview

ABSTRACT To stimulate a debate about the rise of China's digital economy, this essay compares China and the US in one key area of the digital economy – e-commerce and internet-based services. China still lags behind the US in internet penetration, but it distinguishes itself by building a mobile-first, fiber-intensive, and inclusive digital infrastructure. A favorable infrastructure, innovations tailored to the large Chinese market, and local firms’ rapid commercialization of products and services turned the world's largest domestic population into active online consumers, helping China overtake the US by a large margin in retail e-commerce and digital payment. While China translated digital technologies into leading business-to-customer and customer-to-customer businesses, it has not been so successful in business-to-business services. The US is still ahead in the general-purpose technologies underlying the digital economy.
A Quantitative Evaluation of Trust in the Quality of Cyber Threat Intelligence Sources

Threat intelligence sharing has become a cornerstone of cooperative and collaborative cybersecurity. Sources providing such data have become more widespread in recent years, ranging from public entities (driven by legislatorial changes) to commercial companies and open communities that provide threat intelligence in order to help organisations and individuals to better understand and assess the cyber threat landscape putting their systems at risk. Tool support to automatically process this information is emerging concurrently. It has been observed that the quality of information received by the sources varies significantly and that in order to assess the quality of a threat intelligence source it is not sufficient to only consider qualitative indications of the source itself, but it is necessary to monitor the data provided by the source continuously to be able to draw conclusions about the quality of information provided by a source. In this paper, we propose a methodology for evaluating cyber threat information sources based on quantitative parameters. The methodology aims to facilitate trust establishment to threat intelligence sources, based on a weighted evaluation method that allows each entity to adapt it to its own needs and priorities. The approach facilitates automated tools utilising threat intelligence, since information to be considered can be prioritised based on which source is trusted the most at the time the intelligence arrives.
History-based attention in Seq2Seq model for multi-label text classification

Multi-label text classification is an important yet challenging task in natural language processing. It is more complex than single-label text classification in that the labels tend to be correlated. To capture this complex correlations, sequence to sequence model has been widely applied, and achieved impressing performance for multi-label text classification. It encodes each document as contextual representations, and then decodes them to generate labels one by one. At each time step, the decoder usually adopts the attention mechanism to highlight important contextual representations to predict a related label, which has been proved to be effective. Nevertheless, the traditional attention approaches only utilize a hidden state to explore such contextual representations, which may result in prediction errors, or omit several trivial labels. To tackle this problem, in this paper, we propose “history-based attention”, which takes history information into consideration, to effectively explore informative representations for labels’ predictions in multi-label text classification. Our approach consists of two parts: history-based context attention and history-based label attention. History-based context attention considers historical weight trends to highlight important context words, which is helpful to predict trivial labels. History-based label attention explores historical labels to alleviate the error propagation problem. We conduct experiments on two popular text datasets (i.e., Arxiv Academic Paper Dataset and Reuters Corpus Volume I), it is demonstrated that the history-based attention mechanism could boost the performance to a certain extent, and the proposed method consistently outperforms highly competitive approaches.
ProtoPShare

In this work, we introduce an extension to ProtoPNet called ProtoPShare which shares prototypical parts between classes. To obtain prototype sharing we prune prototypical parts using a novel data-dependent similarity. Our approach substantially reduces the number of prototypes needed to preserve baseline accuracy and finds prototypical similarities between classes. We show the effectiveness of ProtoPShare on the CUB-200-2011 and the Stanford Cars datasets and confirm the semantic consistency of its prototypical parts in user-study.
Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems

EXplainable Artificial Intelligence (XAI) approaches are used to bring transparency to machine learning and artificial intelligence models, and hence, improve the decision-making process for their end-users. While these methods aim to improve human understanding and their mental models, cognitive biases can still influence a user’s mental model and decision-making in ways that system designers do not anticipate. This paper presents research on cognitive biases due to ordering effects in intelligent systems. We conducted a controlled user study to understand how the order of observing system weaknesses and strengths can affect the user’s mental model, task performance, and reliance on the intelligent system, and we investigate the role of explanations in addressing this bias. Using an explainable video activity recognition tool in the cooking domain, we asked participants to verify whether a set of kitchen policies are being followed, with each policy focusing on a weakness or a strength. We controlled the order of the policies and the presence of explanations to test our hypotheses. Our main finding shows that those who observed system strengths early-on were more prone to automation bias and made significantly more errors due to positive first impressions of the system, while they built a more accurate mental model of the system competencies. On the other hand, those who encountered weaknesses earlier made significantly fewer errors since they tended to rely more on themselves, while they also underestimated model competencies due to having a more negative first impression of the model. Our work presents strong findings that aim to make intelligent system designers aware of such biases when designing such tools.
No-reference quality metric for contrast-distorted image based on gradient domain and HSV space

Image quality assessment (IQA) plays an important role in digital image forensics. Due to the occurrence of contrast distortion during image acquisition and manipulation, IQA for contrast is a major issue. And it is vital for benchmarking and optimizing the image tampering detection and contrast-enhancement algorithms. In this paper, a new no-reference/blind image quality assessment (IQA) metric is proposed for evaluating image contrast. This research seeks for the inter-relationship between contrast distortion and visual perception quality. The comprehensive quality metric is obtained by combining local binary pattern (LBP) descriptor on gradient domain with color moment on HSV color space. And a prediction model is trained with support vector regression (SVR). Extensive analysis and cross validation are performed on four contrast relevant image databases, which validates the superiority of our proposed blind technique over state-of-the-art no-reference IQA methods.
On equivalence between known families of quadratic APN functions

This paper is dedicated to a question whether the currently known families of quadratic APN polynomials are pairwise different up to CCZ-equivalence. We reduce the list of these families to those CCZ-inequivalent to each other. In particular, we prove that the families of APN trinomials (constructed by Budaghyan and Carlet in 2008) and multinomials (constructed by Bracken et al. 2008) are contained in the APN hexanomial family introduced by Budaghyan and Carlet in 2008. We also prove that a generalization of these trinomial and multinomial families given by Duan et al. (2014) is contained in the family of hexanomials as well.
Target Tracking Using a Mean-Shift Occlusion Aware Particle Filter

Most of the sequential importance resampling tracking algorithms use arbitrarily high number of particles to achieve better performance, with consequently huge computational costs. This article aims to address the problem of occlusion which arises in visual tracking, using fewer number of particles. To this extent, the mean-shift algorithm is incorporated in the probabilistic filtering framework which allows the smaller particle set to maintain multiple modes of the state probability density function. Occlusion is detected based on correlation coefficient between the reference target and the candidate at filtered location. If occlusion is detected, the transition model for particles is switched to a random walk model which enables gradual outward spread of particles in a larger area. This enhances the probability of recapturing the target post-occlusion, even when it has changed its normal course of motion while being occluded. The likelihood model of the target is built using the combination of both color distribution model and edge orientation histogram features, which represent the target appearance and the target structure, respectively. The algorithm is evaluated on three benchmark computer vision datasets: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OTB</i> 100, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VOT</i> 18 and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrackingNet</i> . The performance is compared with fourteen state-of-the-art tracking algorithms. From the quantitative and qualitative results, it is observed that the proposed scheme works in real-time and also performs significantly better than state-of-the-arts for sequences involving challenges of occlusion and fast motions.
Robust Deep Learning Ensemble against Deception

Deep neural network (DNN) models are known to be vulnerable to maliciously crafted adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the training data. How to protect a machine learning model against deception of both types of destructive inputs remains an open challenge. This article presents XEnsemble, a diversity ensemble verification methodology for enhancing the adversarial robustness of DNN models against deception caused by either adversarial examples or out-of-distribution inputs. XEnsemble by design has three unique capabilities. First, XEnsemble builds diverse input denoising verifiers by leveraging different data cleaning techniques. Second, XEnsemble develops a disagreement-diversity ensemble learning methodology for guarding the output of the prediction model against deception. Third, XEnsemble provides a suite of algorithms to combine input verification and output verification to protect the DNN prediction models from both adversarial examples and out of distribution inputs. Evaluated using 11 popular adversarial attacks and two representative out-of-distribution datasets, we show that XEnsemble achieves a high defense success rate against adversarial examples and a high detection success rate against out-of-distribution data inputs, and outperforms existing representative defense methods with respect to robustness and defensibility.
Trojan Attack on Deep Generative Models in Autonomous Driving

Deep generative models (DGMs) have empowered unprecedented innovations in many application domains. However, their security has not been thoroughly assessed when deploying such models in practice, especially in those mission-critical tasks like autonomous driving. In this work, we draw attention to a new attack surface of DGMs, which is the data used in the training phase. We demonstrate that the training data poisoning, the injection of specially-crafted data, are able to teach Trojan behaviors to a DGM without influencing the original training goal. Such Trojan attack will be activated after model deployment only if certain rare triggers are present in an input. For example, a rain-removal DGM after poisoning can, while removing raindrops in input images, change a traffic light from red to green if this traffic light has a specific appearance (i.e. a trigger). Clearly severe consequences can occur if such poisoned model is deployed on vehicle. Our study shows that launching our Trojan attack is feasible on different DGM categories designed for the autonomous driving scenario, and existing defense methods cannot effectively defeat it. We also introduce a concealing technique to make our data poisoning more inconspicuous during the training. In the end, we propose some potential defense strategies inspiring future explorations.
Explainability Methods for Identifying Root-Cause of SLA Violation Prediction in 5G Network

Artificial Intelligence (AI) is implemented in various applications of telecommunication domain, ranging from managing the network, controlling a specific hardware function, preventing a failure, or troubleshooting a problem till automating the network slice management in 5G. The greater levels of autonomy increase the need for explainability of the decisions made by AI so that humans can understand them (e.g. the underlying data evidence and causal reasoning) consequently enabling trust. This paper presents first, the application of multiple global and local explainability methods with the main purpose to analyze the root-cause of Service Level Agreement violation prediction in a 5G network slicing setup by identifying important features contributing to the decision. Second, it performs a comparative analysis of the applied methods to analyze explainability of the predicted violation. Further, the global explainability results are validated using statistical Causal Dataframe method in order to improve the identified cause of the problem and thus validating the explainability.
DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection

This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification.Our system, called Dis-CoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification.Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark.Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pre-trained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification.
Generative Adversarial Multi-Task Learning for Face Sketch Synthesis and Recognition

Face sketch synthesis and recognition have wide range of applications in law enforcement. Despite the impressive progresses have been made in faces sketch and recognition, most existing researches regard them as two separate tasks. In this paper, we propose a generative adversarial multitask learning method in order to deal with face sketch synthesis and recognition simultaneously. Our framework is based on generative adversarial networks (GAN), in which an improved deep network named residual dense U-Net is used as generator to synthesize face sketch image and a multi-task discriminator is designed to not only guide the generator to produce more realistic sketch image, but also extract discriminative face feature. In addition, except the common adversarial loss, the perceptual loss and triplet loss are adopted for the learning of generator and discriminator, respectively. Compared with the state-of-the-art methods, the proposed method obtains better results in terms of face sketch synthesis and recognition.
Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder

In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.
Handling Concept Drifts in Regression Problems – the Error Intersection Approach

Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time—a phenomenon called concept drift. If not handled correctly, a concept drift can lead to significant mispredictions. We explore a novel approach for concept drift handling, which depicts a strategy to switch between the application of simple and complex machine learning models for regression tasks. We assume that the approach plays out the individual strengths of each model, switching to the simpler model if a drift occurs and switching back to the complex model for typical situations. We instantiate the approach on a real-world data set of taxi demand in New York City, which is prone to multiple drifts, e.g. the weather phenomena of blizzards, resulting in a sudden decrease of taxi demand. We are able to show that our suggested approach outperforms all regarded baselines significantly.
Business Process Models of Blockchain and South African Real Estate Transactions

The current real estate purchasing process in South African sector can be described as inefficient due to heavy reliance on multiple third parties which results in high transaction costs and a prolonging of the time in which property transactions are completed in. Additionally, the extensive manual review and verification of financial and legal documents as well as manually updating multiple systems with redundant information not only takes time but is also prone to error and fraudulent activities. Blockchain technology presents an opportunity for the real estate sector as it has the potential to bring about more efficient transactions. This study examines two approaches to executing real estate transactions; the South African case and an international blockchain technology use case. Two conceptual models are presented using Business Process Modelling and Notation. Document review was employed in order to provide sufficient information on the real estate transactions. The findings show that the South African real estate transaction process is inefficient as it is manual, involves paper-based documents and relies heavily on third parties which result in numerous bottlenecks. The study revealed that blockchain-based transactions are more efficient and reduce reliance on third parties and manual processes. The study contributes two conceptual models illustrating how the two different processes are conducted. It also contributes a list of the challenges and opportunities related to blockchain-based real estate transactions.
Effective Throughput Analysis of <i>α</i>-<i>η</i>-<i>κ</i>-<i>μ</i> Fading Channels

The α-η-κ-µ fading model is a very useful instrument to accurately describe various radio wave propagation scenarios.In this paper, we study the effective throughput performance of communication systems over the α-η-κ-µ fading channels.Novel and exact expressions for the effective throughput over α-η-κ-µ channels are derived, and the effective throughput of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) systems over some widely used small-scale fading models are presented based on the derived results.To obtain more understandings on the impact of physical channel characteristics and system configuration on the effective throughput, closed-form expressions for the asymptotic effective throughput at high signal-to-noise ratio (SNR) regimes are also obtained.The results reveal the underlying connections between different physical channel parameters (e.g., scattering level, phase correlation, channel nonlinearity, multipath clustering, and channel imbalance) and the effective throughput.It is found that the effective throughput improves with the increase of channel nonlinearity and number of multipath clusters, and the high-SNR slope is only dependent on the channel nonlinearity and the number of multipath clusters present in the physical channel. INDEX TERMSEffective throughput, quality-of-service (QoS), α-η-κ-µ fading channels, generalized fading, multiple-input single-output (MISO), multiple-input multiple-output (MIMO).
Differential Privacy and Byzantine Resilience in SGD

This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Stochastic Gradient Descent (SGD) learning algorithm is feasible withboth differential privacy (DP) and (α,f)-Byzantine resilience. To the best of our knowledge, this is the first work to tackle this problem from a theoretical point of view. A key finding of our analyses is that the classical approaches to these two (seemingly) orthogonal issues are incompatible. More precisely, we show that a direct composition of these techniques makes the guarantees of the resulting SGD algorithm depend unfavourably upon the number of parameters of the ML model, making the training of large models practically infeasible. We validate our theoretical results through numerical experiments on publicly-available datasets; showing that it is impractical to ensure DP and Byzantine resilience simultaneously.
CI/CD Pipelines Evolution and Restructuring: A Qualitative and Quantitative Study

Continuous Integration and Delivery (CI/CD) pipelines entail the build process automation on dedicated machines, and have been demonstrated to produce several advantages including early defect discovery, increased productivity, and faster release cycles. The effectiveness of CI/CD may depend on the extent to which such pipelines are properly maintained to cope with the system and its underlying technology evolution, as well as to limit bad practices. This paper reports the results of a study combining a qualitative and quantitative evaluation on CI/CD pipeline restructuring actions. First, by manually analyzing and coding 615 pipeline configuration change commits, we have crafted a taxonomy of 34 CI/CD pipeline restructuring actions, either improving extra-functional properties or changing the pipeline's behavior. Based on such actions, we have developed a metric extractor for Travis-CI pipelines, which extracts 16 indicators of how a pipeline evolves. The analysis of the pipeline evolution for 4,644 projects using Travis-CI and developed in 8 programming languages shows how some pipeline components, such as jobs and steps tend to change more often than others, but also the Docker adoption by the projects increases over time.