Reviewerly/exHarmony huggingface dataset:
Automatically Identifying the Quality of Developer Chats for Post Hoc Use

Software engineers are crowdsourcing answers to their everyday challenges on Q&amp;A forums (e.g., Stack Overflow) and more recently in public chat communities such as Slack, IRC, and Gitter. Many software-related chat conversations contain valuable expert knowledge that is useful for both mining to improve programming support tools and for readers who did not participate in the original chat conversations. However, most chat platforms and communities do not contain built-in quality indicators (e.g., accepted answers, vote counts). Therefore, it is difficult to identify conversations that contain useful information for mining or reading, i.e., conversations of post hoc quality. In this article, we investigate automatically detecting developer conversations of post hoc quality from public chat channels. We first describe an analysis of 400 developer conversations that indicate potential characteristics of post hoc quality, followed by a machine learning-based approach for automatically identifying conversations of post hoc quality. Our evaluation of 2,000 annotated Slack conversations in four programming communities (python, clojure, elm, and racket) indicates that our approach can achieve precision of 0.82, recall of 0.90, F-measure of 0.86, and MCC of 0.57. To our knowledge, this is the first automated technique for detecting developer conversations of post hoc quality.
Learning Spatially Variant Linear Representation Models for Joint Filtering

Joint filtering mainly uses an additional guidance image as a prior and transfers its structures to the target image in the filtering process. Different from existing approaches that rely on local linear models or hand-designed objective functions to extract the structural information from the guidance image, we propose a new joint filtering method based on a spatially variant linear representation model (SVLRM), where the target image is linearly represented by the guidance image. However, learning SVLRMs for vision tasks is a highly ill-posed problem. To estimate the spatially variant linear representation coefficients, we develop an effective approach based on a deep convolutional neural network (CNN). As such, the proposed deep CNN (constrained by the SVLRM) is able to model the structural information of both the guidance and input images. We show that the proposed approach can be effectively applied to a variety of applications, including depth/RGB image upsampling and restoration, flash deblurring, natural image denoising, and scale-aware filtering. In addition, we show that the linear representation model can be extended to high-order representation models (e.g., quadratic and cubic polynomial representations). Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods that have been specifically designed for each task.
Cost-Effective Valuable Data Detection Based on the Reliability of Artificial Intelligence

Many previous studies have investigated applying artificial intelligence (AI) to cyber security.Despite considerable performance advantages, AI for cyber security requires final confirmation by an analyst, e.g.malware misdetection can cause significant adverse side effects.Thus, a human analyst must check all AI predictions, which poses a major obstacle to AI expansion.This paper proposes a reliability indicator for AI prediction using explainable artificial intelligence and statistical analysis techniques.This will enable analysts with limited daily workload to focus upon valuable data, and quickly verify AI predictions.Analysts generally make decisions based on several features that they know exactly what they mean, rather than all available features.Since the proposed reliability indicator is calculated using features meaningful to analysts, it can be easily understood and hence speed final decisions.To verify the performance of the proposed method, an experiment was conducted using the IDS dataset and the malware dataset.The AI error was detected better than the existing AI model at about 114% in IDS and 95% in malware.Thus, cyberattack response could be greatly improved by adopting the proposed method.
Secure Computation from One-Way Noisy Communication, or: Anti-correlation via Anti-concentration

Can a sender encode a pair of messages $$(m_0,m_1)$$ jointly, and send their encoding over (say) a binary erasure channel, so that the receiver can decode exactly one of the two messages and the sender does not know which one? Garg et al. (Crypto 2015) showed that this is information-theoretically impossible. We show how to circumvent this impossibility by assuming that the receiver is computationally bounded, settling for an inverse-polynomial security error (which is provably necessary), and relying on ideal obfuscation. Our solution creates a “computational anti-correlation” between the events of receiving $$m_0$$ and receiving $$m_1$$ by exploiting the anti-concentration of the binomial distribution. The ideal obfuscation primitive in our construction can either be directly realized using (stateless) tamper-proof hardware, yielding an unconditional result, or heuristically instantiated in the plain model using existing indistinguishability obfuscation schemes. As a corollary, we get similar feasibility results for general secure computation of sender-receiver functionalities by leveraging the completeness of the above “random oblivious transfer” functionality.
Effects of foreign direct investment on economic growth in Ghana: the role of institutions

The role of institutions in both the inflow and the impact of foreign direct investment is of great im¬portance. The quality of institutions in a country can direct investment towards improving growth. This paper analyzes the individual and combined effect of foreign direct investment and institutions on economic growth in Ghana. The paper used the Auto Regressive Distributed Lag (ARDL) tech¬nique for secondary data obtained from 1995 to 2019. All data series, except for the quality institution index, were drawn from the World Bank Development Indicators. Institutional Quality Index data was obtained from the Heritage Foundation’s Economic Freedom Index website. The results of the ARDL model indicate that foreign direct investment and a quality institutional index together have a significantly positive effect on a country’s economic growth compared to their individual effects in both the short and long run. The study recommends that government policies should be aimed at attracting foreign direct investment while strengthening institutions and regulations to enhance output growth.
Unsupervised learning polarimetric underwater image recovery under nonuniform optical fields

Turbid media will lead to a sharp decline in image quality. Polarization imaging is an effective method to obtain clear images in turbid media. In this paper, we propose an improved method that combines unsupervised learning and polarization imaging theory, which can be applied in a variety of nonuniform optical fields. We treat the background light as a spatially variable parameter, so we designed an end-to-end unsupervised generative network to inpaint the background light, which produces an adversarial loss with the discriminative network to improve the performance. And we use the angle of polarization to estimate the polarization parameters. The experimental results have demonstrated the effectiveness and generalization ability of our method. Compared with other works, our method shows a better real-time performance and has a lower cost in preparing the training dataset.
Digital placement system of a lateral tensioning rod during the formation of flexible-oriented three-dimensional composite preforms

The placement of a lateral tensioning rod is the main event during the formation of a flexible-oriented three-dimensional composite preform. Inaccurate placement can result in defective fiber weaving, degrading the overall quality of the preform. Furthermore, the manual execution of this task is time-consuming and ineffective. In this study, an automatic and digital placement method and mechanism are proposed to address these problems for the first time. In particular, an image processing method is proposed to recognize the placement region, where the geometric features are irregular and the feature differences are neglectable compared to the background. The placement position is then detected using a single industrial endoscope and the similar decision theorem of triangles. Furthermore, the proposed method and mechanism are applied to a digital placement system. The lateral tensioning rod can be accurately placed, as the relative error between the actual and ideal placement positions is within 0.5 %. The proposed method, therefore, automatizes the manual task and is essential in the development of preform formation toward digital manufacturing.
Cryptocurrency: Towards Regulating the Unruly Enigma of Fintech in Nigeria and South Africa

One of the most modern inventions of financial technology (FinTech) since after the global financial crisis of 2008 is the crypto or virtual currency/asset. Since the creation of the first cryptocurrency, the Bitcoin, in 2009, it is estimated that over five thousand variants of the Bitcoin and other cryptocurrencies have emerged. Virtual currencies have become widespread across the globe but their legal status and uses in various countries have remained uncertain. They have been variously classified as currencies, securities, properties, assets, commodities and tokens, and used as means of exchange but are not legally recognised as legal tender. In many jurisdictions their emergence was greeted with scepticism and express or tacit rejection by financial and securities markets regulators, but over time, owing to their increasing popularity, characteristics, positive and negative potentials, there has been a gradual shift towards their formal recognition and regulation. Regulatory authorities in many countries are now grappling with designing appropriate policy and regulatory framework for the crypto phenomenon. This paper interrogates the current legal status and efforts to regulate cryptocurrencies in two leading African nations, Nigeria and South Africa, and highlights the challenges of designing an appropriate regulatory framework for this enigmatic technology. The paper adopts the doctrinal legal research methodology, employing the descriptive, analytical, and comparative approaches. It follows a structured review and analysis of relevant extant legislation on currencies and securities in the countries to ascertain whether they cover cryptocurrencies. It then compares the current position of the law on the subject in the two countries. Bearing in mind that it may not be possible to totally ban dealing in cryptocurrencies, the paper concludes that regulation has become imperative. Drawing from the position on the subject in more developed nations, the United States of America (US) and the European Union (EU), this paper proposes a model of regulation of virtual currency not only for Nigeria and South Africa but also for other African countries.
Cyber-Security Risk Assessment Framework for Blockchains in Smart Mobility

Use of distributed ledger technologies like blockchain is becoming more common in transportation/mobility ecosystems. However, cyber-security failures may occur at places where the blockchain system connects with the real world. In this paper, we propose a novel risk assessment framework for blockchain applications in smart mobility. We aim at systematically quantifying the risk by presenting ordinal values because although vulnerabilities exist in a system, it's the probability that they can be exploited and the impact of this exploitation that determine if in fact, the vulnerability corresponds to a significant risk. As a case study, we carry out an analysis in terms of quantifying the risk associated to a multi-layered Blockchain framework for Smart Mobility Data-markets (BSMD). We first construct an actor-based analysis to determine the impact of the attacks. Then, a scenario-based analysis determines the probability of occurrence of each threat. Finally, a combined analysis is developed to determine which attack outcomes have the highest risk. In the case study of the public permissioned BSMD, the outcomes of the risk analysis highlight the highest risk factors according to their impact on the victims in terms of monetary, privacy, integrity and trust. The analysis uncovers specific blockchain technology security vulnerabilities in the transportation ecosystem by exposing new attack vectors.
Sharing Economy in Future Peer-to-peer Electricity Trading Markets: Security and Privacy Analysis

This paper performs business, security and privacy analysis of emergent (peer-to-peer) electricity trading markets where individual users (via smart meters) can trade electricity with other users and market players in a (semi-)decentralised manner.Firstly, a high-level overview of future electricity markets is presented, and a comprehensive explanation is offered concerning the evolution of the current (and future) actors regarding their roles.Secondly, business model matrix analysis is deployed to develop and discuss in detail four scenarios based on customers' information ownership and citizens' level of involvement.Lastly, an analysis of security and privacy threats is performed which leads to a specification of necessary requirements to mitigate such threats.This paper provides and serves as a benchmark for risk assessment and future design of secure peer-to-peer electricity trading markets.
Approach for improving YOLOv5 network with application to remote sensing target detection

Remote sensing images have the characteristics of a multi-scale, high resolution, and complex background, making the accuracy of small target detection too low. We propose an improved method based on the “you-only-look-once” v5 network model to make it more suitable for small target detection. First, we design a four-scale detection layer that improves the depth of the network and the accuracy of small target detection. Then, we introduce complete intersection over union-non-maximum suppression (NMS) to replace the commonly used NMS as a suppression of the prediction bounding box, and it also considers the overlap area, center distance, and aspect ratio to improve the accuracy of predicting bounding boxes. Furthermore, the remote sensing image is segmented into several fixed-size smaller images, making its size suitable for the input of the model to avoid target loss caused by directly scaling the size. The experimental results show that, compared to the four available detection methods, the improved algorithm significantly improves the detection accuracy of targets from small aircraft and ships in complex environments. The miss detection rate is also greatly reduced, especially for small targets, which verifies the effectiveness and robustness of the algorithm. Moreover, the detection rate has been greatly improved, which can meet the demand for real-time detection.
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation

Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages.In this work, we propose a self-learning framework that further utilizes unlabeled data of target languages, combined with uncertainty estimation in the process to select high-quality silver labels.Three different uncertainties are adapted and analyzed specifically for the cross lingual transfer: Language Heteroscedastic/Homoscedastic Uncertainty (LEU/LOU), Evidential Uncertainty (EVI).We evaluate our framework with uncertainties on two cross-lingual tasks including Named Entity Recognition (NER) and Natural Language Inference (NLI) covering 40 languages in total, which outperforms the baselines significantly by 10 F1 on average for NER and 2.5 accuracy score for NLI.
Exploitation Techniques for Data-oriented Attacks with Existing and Potential Defense Approaches

Data-oriented attacks manipulate non-control data to alter a program’s benign behavior without violating its control-flow integrity. It has been shown that such attacks can cause significant damage even in the presence of control-flow defense mechanisms. However, these threats have not been adequately addressed. In this survey article, we first map data-oriented exploits, including Data-Oriented Programming (DOP) and Block-Oriented Programming (BOP) attacks, to their assumptions/requirements and attack capabilities. Then, we compare known defenses against these attacks, in terms of approach, detection capabilities, overhead, and compatibility. It is generally believed that control flows may not be useful for data-oriented security. However, data-oriented attacks (especially DOP attacks) may generate side effects on control-flow behaviors in multiple dimensions (i.e., incompatible branch behaviors and frequency anomalies). We also characterize control-flow anomalies caused by data-oriented attacks. In the end, we discuss challenges for building deployable data-oriented defenses and open research questions.
A Novel Semi-Supervised Fuzzy C-Means Clustering Algorithm Using Multiple Fuzzification Coefficients

Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common one. To make the FCM algorithm a semi-supervised method, it was proposed in the literature to use an auxiliary matrix to adjust the membership grade of the elements to force them into certain clusters during the computation. In this study, instead of using the auxiliary matrix, we proposed to use multiple fuzzification coefficients to implement the semi-supervision component. After deriving the proposed semi-supervised fuzzy C-means clustering algorithm with multiple fuzzification coefficients (sSMC-FCM), we demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example.
An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems

Spoofing countermeasure (CM) systems are critical in speaker verification; they aim to discern spoofing attacks from bona fide speech trials. In practice, however, acoustic condition variability in speech utterances may significantly degrade the performance of CM systems. In this paper, we conduct a cross-dataset study on several state-of-the-art CM systems and observe significant performance degradation compared with their single-dataset performance. Observing differences of average magnitude spectra of bona fide utterances across the datasets, we hypothesize that channel mismatch among these datasets is one important reason. We then verify it by demonstrating a similar degradation of CM systems trained on original but evaluated on channel-shifted data. Finally, we propose several channel robust strategies (data augmentation, multi-task learning, adversarial learning) for CM systems, and observe a significant performance improvement on cross-dataset experiments.
Distributed and Privacy Preserving Graph Data Collection in Internet of Thing Systems

Internet of Thing (IoT) systems have been treated as a novel platform for graph data acquisition. Contents like dynamic network topology, organization and control flows, and interactions among monitored objects all contribute to the huge volumes of graph data generated in IoT. These data are believed to brought significant benefits to both the operation and functionalities of IoT systems, especially when combined with cutting-edge Artificial Intelligence techniques. However, these graph data are usually locally collected by data contributors with sensing devices, which could be both partially overlapped as they record same environment, and sensitive as they can indicate private physical status of contributors. Considering all challenges, current solutions for graph data collection in IoT are incapable. Therefore, this article proposes a novel framework for privacy-preserving distributed graph data collection for IoT. The framework allows the graphs kept by data contributors to be partially overlapped, and can help the data broker to efficiently derive the universal view by combining these graphs. The differential privacy is applied for privacy preservation during data collection. The proposed problem aims at minimizing the total bandwidth consumption for graph collection, which is proved to be NP-complete. Then three algorithms are proposed for different circumstances, based on the diverse knowledge and purposes held by the data broker. Finally, both theoretical and numerical analysis have demonstrated the advancement of these methods.
Attention-based bidirectional long short-term memory networks for extracting temporal relationships from clinical discharge summaries

Temporal relation extraction between health-related events is a widely studied task in clinical Natural Language Processing (NLP). The current state-of-the-art methods mostly rely on engineered features (i.e., rule-based modelling) and sequence modelling, which often encodes a source sentence into a single fixed-length context. An obvious disadvantage of this fixed-length context design is its incapability to model longer sentences, as important temporal information in the clinical text may appear at different positions. To address this issue, we propose an Attention-based Bidirectional Long Short-Term Memory (Att-BiLSTM) model to enable learning the important semantic information in long source text segments and to better determine which parts of the text are most important. We experimented with two embeddings and compared the performances to traditional state-of-the-art methods that require elaborate linguistic pre-processing and hand-engineered features. The experimental results on the i2b2 2012 temporal relation test corpus show that the proposed method achieves a significant improvement with an F-score of 0.811, which is at least 10% better than state-of-the-art in the field. We show that the model can be remarkably effective at classifying temporal relations when provided with word embeddings trained on corpora in a general domain. Finally, we perform an error analysis to gain insight into the common errors made by the model.
Deep Iterative 2D/3D Registration

Deep Learning-based 2D/3D registration methods are highly robust but often lack the necessary registration accuracy for clinical application. A refinement step using the classical optimization-based 2D/3D registration method applied in combination with Deep Learning-based techniques can provide the required accuracy. However, it also increases the runtime. In this work, we propose a novel Deep Learning driven 2D/3D registration framework that can be used end-to-end for iterative registration tasks without relying on any further refinement step. We accomplish this by learning the update step of the 2D/3D registration framework using Point-to-Plane Correspondences. The update step is learned using iterative residual refinement-based optical flow estimation, in combination with the Point-to-Plane correspondence solver embedded as a known operator. Our proposed method achieves an average runtime of around 8s, a mean re-projection distance error of 0.60 $\pm$ 0.40 mm with a success ratio of 97 percent and a capture range of 60 mm. The combination of high registration accuracy, high robustness, and fast runtime makes our solution ideal for clinical applications.