Reviewerly/exHarmony huggingface dataset:
Low-dose CT image denoising using residual convolutional network with fractional TV loss

In this work, we propose a Fractional-order Residual Convolutional Neural Network (FRCNN) for Low-Dose CT (LDCT) denoising. As increasing the dose of radiation is harmful to the patient, how to trade off between reducing the radiation dose and improving the quality of the CT image has become a challenging problem. To this end, this paper proposes a new approach for LDCT image denoising using Convolutional Neural Network (CNN) with Fractional-order Total Variation (FTV) loss, as well as residual learning. Firstly, this paper introduced the FTV loss function for structural details enhancement. Secondly, skip connections were added to optimize the network. Thirdly, extensive experimental analysis was used to evaluate the capacity of this method in suppressing noise and preserving detailed information. The FTV loss can retain essential structural details while suppressing noise, generating high-quality CT images ready for interpretation by radiologists. Compared with state-of-the-art methods, our method obtained better results visually and numerically, especially in structural details preservation. These promising results will significantly improve the usability of LDCT images.
Investment Opportunities and Strategies in an Era of Coronavirus Pandemic

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Multimodal Dialogue Systems via Capturing Context-aware Dependencies of Semantic Elements

Recently, multimodal dialogue systems have engaged increasing attention in several domains such as retail, travel, etc. In spite of the promising performance of pioneer works, existing studies usually focus on utterance-level semantic representations with hierarchical structures, which ignore the context-aware dependencies of multimodal semantic elements, i.e., words and images. Moreover, when integrating the visual content, they only consider images of the current turn, leaving out ones of previous turns as well as their ordinal information. To address these issues, we propose a Multimodal diAlogue systems with semanTic Elements, MATE for short. Specifically, we unfold the multimodal inputs and devise a Multimodal Element-level Encoder to obtain the semantic representation at element-level. Besides, we take into consideration all images that might be relevant to the current turn and inject the sequential characteristics of images through position encoding. Finally, we make comprehensive experiments on a public multimodal dialogue dataset in the retail domain, and improve the BLUE-4 score by 9.49, and NIST score by 1.8469 compared with state-of-the-art methods.
Feature-oriented defect prediction

Software errors are a major nuisance in software development and can lead not only to reputation damages, but also to considerable financial losses for companies. Therefore, numerous techniques for predicting software defects, largely based on machine learning methods, have been developed over the past decades. These techniques usually rely on code and process metrics in order to predict defects at the granularity of typical software assets, such as subsystems, components, and files. In this paper, we present the first systematic investigation of feature-oriented defect prediction: the prediction of defects at the granularity of features---domain-oriented entities abstractly representing (and often cross-cutting) typical software assets. Feature-oriented prediction can be beneficial, since: (i) particular features might be more error-prone than others, (ii) characteristics of features known as defective might be useful to predict other error-prone features, (iii) feature-specific code might be especially prone to faults arising from feature interactions. We present a dataset derived from 12 software projects and introduce two metric sets for feature-oriented defect prediction. We evaluated seven machine learning classifiers with three different attribute sets each, using our two new metric sets as well as an existing metric set from the literature. We observe precision and recall values of around 85% and better robustness when more diverse metrics sets with richer feature information are used.
A survey of neighborhood construction algorithms for clustering and classifying data points

Clustering and classifying are overriding techniques in machine learning. Neighborhood construction as a key step in these techniques has been extensively used for modeling local relationships between data samples, and constructing global structures from local information. The goal of the neighborhood construction process is to improve the quality of individual data point categorizing. Many applications such as detecting social network communities, bundling related edges, solving location, and routing problems all indicate the importance of this problem. This paper presents theoretical and practical studies of state-of-the-art methods in the context of neighborhood construction which is resulted in a coherent and comprehensive survey to analyze these methods. To this end, significant algorithms of neighborhood construction have been proposed to analyze data points which are very useful for the community of clustering and classifying practitioners since showing the advantages and disadvantages of each algorithm. All of them will be described and discussed deeply in different aspects, which help to select an appropriate solution for problems. A taxonomy of these algorithms is presented and their differences and some important applications are explained. Finally, the future challenges concerning the title of the present paper are outlined.
Intelligent Chatbot for Lab Security and Automation

Artificial intelligence based communicative artefacts are called chatbots. The purpose of a chatterbot or chatbot is to render an interaction between a human and a robot in the form of speech or text. They offer the best services in a variety of areas, such as education, healthcare, transportation, etc. As per the research, nearly 85 % of product offerings will be automated by 2020. The proposed system is a voice-based chatbot that helps to improve the security and automation of a lab. The system makes use of Automatic Speaker Recognition (ASR) algorithm in order to recognize a person and allow him/her inside the lab. This allows only authorized person to access the lab facilities. The same algorithm is used for generating the list of available components in the lab based on the person's keyword input thus automating the lab components dispatch.
Modelling Local and Global Dependencies for Next-Item Recommendations

Session-based recommender systems (SBRSs) aim at predicting the next item by modelling the complex dependencies within and across sessions. Most of the existing SBRSs make recommendations only based on local dependencies (i.e., the dependencies between items within a session), while ignoring global dependencies (i.e., the dependencies across multiple sessions), leading to information loss and thus reducing the recommendation accuracy. Moreover, they are usually not able to recommend cold-start items effectively due to their limited session information. To alleviate these shortcomings of SBRSs, we propose a novel heterogeneous mixed graph learning (HMGL) framework to effectively learn both local and global dependencies for next-item recommendations. The HMGL framework mainly contains a heterogeneous mixed graph (HMG) construction module and an HMG learning module. The HMG construction module map both the session information and the item attribute information into a unified graph to connect items within and across sessions. The HMG learning module learns a unified representation for each item by simultaneously modelling the local and global dependencies over the HMG. The learned representation is then used for next-item recommendations. Results of extensive experiments on real-world datasets show the superiority of HMGL framework over the start-of-the-art methods in terms of recommendation accuracy.
Reweighting and information-guidance networks for Few-Shot Learning

Few-Shot Learning (FSL) aims at recognizing new categories from a few available samples. In this paper, we propose two strategies on the basis of Prototypical Networks [1] to improve the discriminativeness and representativeness of the visual prototypes for few-shot learning task. Firstly, we propose a reweighting mechanism to distribute different weights for accesses the instance representativeness to the class. Secondly, we propose an information-guidance mechanism to encode discriminative knowledge into the class prototypes to compensate for more information across classes. Extensive experimental results on two benchmark datasets empirically show that both the proposed strategies improve the Prototypical Networks and achieve the state-of-the-art performances. Besides, the information-guidance mechanism could be seamlessly combined into some existing approaches to substantially improve their performances on few-shot classification.
Learning Multi-View Interactional Skeleton Graph for Action Recognition

Capturing the interactions of human articulations lies in the center of skeleton-based action recognition. Recent graph-based methods are inherently limited in the weak spatial context modeling capability due to fixed interaction pattern and inflexible shared weights of GCN. To address above problems, we propose the multi-view interactional graph network (MV-IGNet) which can construct, learn and infer multi-level spatial skeleton context, including view-level (global), group-level, joint-level (local) context, in a unified way. MV-IGNet leverages different skeleton topologies as multi-views to cooperatively generate complementary action features. For each view, separable parametric graph convolution (SPG-Conv) enables multiple parameterized graphs to enrich local interaction patterns, which provides strong graph-adaption ability to handle irregular skeleton topologies. We also partition the skeleton into several groups and then the higher-level group contexts including inter-group and intra-group, are hierarchically captured by above SPG-Conv layers. A simple yet effective global context adaption (GCA) module facilitates representative feature extraction by learning the input-dependent skeleton topologies. Compared to the mainstream works, MV-IGNet can be readily implemented while with smaller model size and faster inference. Experimental results show the proposed MV-IGNet achieves impressive performance on large-scale benchmarks: NTU-RGB+D and NTU-RGB+D 120.
Tokenization and Blockchain Tokens Classification: a morphological framework

The work here presented moves from the acknowledgment that, even if blockchain technology has been around for more than ten years, the knowledge about its economic and business implications is fragmented and heterogeneous. In the first place, it is analyzed the shift from economics to tokenomics and the central role of the token within blockchain-based ecosystems. Subsequently, a generalized definition of the token is proposed. Diving into the requirements for a comprehensive description of tokens, that takes into account their wide variety, a comparative assessment of token classification frameworks available in the literature is performed. This analysis is leveraged to propose a new and comprehensive token classification framework, based on a morphological analysis representation. The proposed framework will be further refined with an empirical and iterative approach in future works.
Multiagent Adversarial Collaborative Learning via Mean-Field Theory

Multiagent reinforcement learning (MARL) has recently attracted considerable attention from both academics and practitioners. Core issues, e.g., the curse of dimensionality due to the exponential growth of agent interactions and nonstationary environments due to simultaneous learning, hinder the large-scale proliferation of MARL. These problems deteriorate with an increased number of agents. To address these challenges, we propose an adversarial collaborative learning method in a mixed cooperative-competitive environment, exploiting friend-or-foe Q-learning and mean-field theory. We first treat neighbors of agent i as two coalitions ( i 's friend and opponent coalition, respectively), and convert the Markov game into a two-player zero-sum game with an extended action set. By exploiting mean-field theory, this new game simplifies the interactions as those between a single agent and the mean effects of friends and opponents. A neural network is employed to learn the optimal mean effects of these two coalitions, which are trained via adversarial max and min steps. In the max step, with fixed policies of opponents, we optimize the friends' mean action to maximize their rewards. In the min step, the mean action of opponents is trained to minimize the friends' rewards when the policies of friends are frozen. These two steps are proved to converge to a Nash equilibrium. Then, another neural network is applied to learn the best response of each agent toward the mean effects. Finally, the adversarial max and min steps can jointly optimize the two networks. Experiments on two platforms demonstrate the learning effectiveness and strength of our approach, especially with many agents.
A High Throughput MobileNetV2 FPGA Implementation Based on a Flexible Architecture for Depthwise Separable Convolution

Convolutional Neural Networks are widely applied to various computer vision tasks. For most of these applications, high throughput and energy efficiency are top priorities. MobileNetV2 features very low memory requirements as well as a relatively small model size. On the ILSVRC 2012 classification challenge, it provides a decent prediction accuracy of 71.7 percent at low computational requirements. We present an FPGA based MobileNetV2 accelerator with a high throughput of 1050 frames per second at a power consumption of 34 watt under full load. This equates to a power efficiency of 32 milli-joule per frame. We describe our approach of using stream interfaces and auto-generated control signals to enable fast design of flexible architectures. By using quantization techniques, limiting the accuracy of the used number format to a 16 bit fixed point format, we were able to reduce the memory usage for weights as well as activations by a factor of two. Since the basic building block of MobileNetV2 can be used to build higher performance networks as well, the findings of this paper remain applicable, when higher prediction accuracies are required.
Secure Federated Averaging Algorithm with Differential Privacy

Federated learning (FL), as a recent advance of distributed machine learning, is capable of learning a model over the network without directly accessing the client's raw data. Nevertheless, the clients' sensitive information can still be exposed to adversaries via differential attacks on messages exchanged between the parameter server and clients. In this paper, we consider the widely used federating averaging (FedAvg) algorithm and propose to enhance the data privacy by the differential privacy (DP) technique, which obfuscates the exchanged messages by properly adding Gaussian noise. We analytically show that the proposed secure FedAvg algorithm maintains an O(l/T) convergence rate, where T is the total number of stochastic gradient descent (SGD) updates for local model parameters. Moreover, we demonstrate how various algorithm parameters can impact on the algorithm communication efficiency. Experiment results are presented to justify the obtained analytical results on the performance of the proposed algorithm in terms of testing accuracy.
Investigating Large Scale HTTPS Interception in Kazakhstan

Increased adoption of HTTPS has created a largely encrypted web, but these security gains are on a collision course with governments that desire visibility into and control over user communications. Last year, the government of Kazakhstan conducted an unprecedented large-scale HTTPS interception attack by forcing users to trust a custom root certificate. We were able to detect the interception and monitor its scale and evolution using measurements from in-country vantage points and remote measurement techniques. We find that the attack targeted connections to 37 unique domains, with a focus on social media and communication services, suggesting a surveillance motive, and that it affected a large fraction of connections passing through the country's largest ISP, Kazakhtelecom. Our continuous real-time measurements indicated that the interception system was shut down after being intermittently active for 21 days. Subsequently, supported by our findings, two major browsers (Mozilla Firefox and Google Chrome) completely blocked the use of Kazakhstan's custom root. However, the incident sets a dangerous precedent, not only for Kazakhstan but for other countries that may seek to circumvent encryption online.
Responding to COVID-19 in an African university: the case the National University of Lesotho library

Purpose The purpose of this paper is to discuss the National University of Lesotho Library’s shift to supporting of contact teaching to e-learning because of COVID-19. The university library increased access to digital content and broadened digital services. Design/methodology/approach This case study is based on reflective practice methodology in which practitioners learn from their practice, reflect and adapt to change as they improve on their practice. The library team members were reflective participants in the transformation of library services from on-site to digital services. The main research questions are “How did the digital libraries meet the needs of the digital community?” and “What will be the new normal after COVID-19 experience in digital libraries and their communities?” The literature review juxtaposes reports on transformation of library services to digital services. Findings The library transformed from on-site to digital services library. It also reports on observed increase in databases usage during lockdown. Research limitations/implications The nature of a case study is limited by time and geography. Practical implications This paper has implications and possible applications for other university libraries in African countries that are resources constrained such as Lesotho. Originality/value This paper makes a valuable contribution to lessons on how African universities’ libraries respond to pandemics such as COVID-19. It speaks to the need to strengthen existing infrastructure and digital content, while ensuring the library staff and the university community adapts to the new normal.
A hierarchical fusion framework to integrate homogeneous and heterogeneous classifiers for medical decision-making

Classifier diversity and fusion architecture are two critical characteristics stressed in homogeneous and heterogeneous ensemble learning methods and they are equally important for building a successful multi-classifier system. In this study, we introduced a two-level framework, namely hierarchical fusion of homogeneous and heterogeneous multi-classifiers (HF2HM), to integrate the diversified classification models produced by feeding heterogeneous classifiers with homogeneous random-projected training datasets. The proposed hierarchical fusion scheme was comprehensively validated using fifteen public UCI datasets and three clinical datasets. The experimental results demonstrated the superiority of the proposed HF2HM framework over the base classifiers and the state-of-the-art benchmark ensemble methods, verifying it as a potential tool to assist in medical decision making in practical clinical settings.
Designing a XSS Defensive Framework for Web Servers Deployed in the Existing Smart City Infrastructure

Cross-site scripting is one of the notable exceptions effecting almost every web application. Hence, this article proposed a framework to negate the impact of the XSS attack on web servers deployed in one of the major applications of the Internet of Things (IoT) i.e. the smart city environment. The proposed framework implements 2 approaches: first, it executes vulnerable flow tracking for filtering injected malicious scripting code in dynamic web pages. Second, it accomplished trusted remark generation and validation for unveiling any suspicious activity in static web pages. Finally, the filtered and modified webpage is interfaced to the user. The prototype of the framework has been evaluated on a suite of real-world web applications to detect XSS attack mitigation capability. The performance analysis of the framework has revealed that this framework recognizes the XSS worms with very low false positives, false negatives and acceptable performance overhead as compared to existent XSS defensive methodologies.
Fuzzy Interpretation of Word Polarity Scores for Unsupervised Sentiment Analysis

Sentiment or Opinion Mining aims to determine the polarity of people's opinions, feeling towards any product, service, event or any individual. One of the most popular technique applied in sentiment analysis of textual content is natural language processing. Sentiment can be evaluated using numerous methodologies like machine learning algorithms and statistical tools but the use of the fuzzy concept is not common. In this paper, we analyze the effect of fuzzification of word polarity sentiment scores. These word scores are obtained by deploying two lexicons: SentiWordNet and AFINN. Experiments are conducted on three benchmark datasets: polarity movie dataset by Pang-Lee, IMDB and hotel reviews dataset. The key highlights are: i) proposed an unsupervised fuzzy logic-based approach for sentiment analysis of textual reviews, ii) the proposed model formulated fuzzy cardinality as the measure for the evaluation of word polarity scores, iii) our model has two versions based on the sentiment lexicon deployed in the model, iv) comparison of our fuzzy cardinality approach with other non-fuzzy state-of-the-art methods reveals the superiority of our fuzzy approach.
Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning

Facial expressions of emotion are a major channel in our daily communications, and it has been subject of intense research in recent years. To automatically infer facial expressions, convolutional neural network based approaches has become widely adopted due to their proven applicability to Facial Expression Recognition (FER) task.On the other hand Virtual Reality (VR) has gained popularity as an immersive multimedia platform, where FER can provide enriched media experiences. However, recognizing facial expression while wearing a head-mounted VR headset is a challenging task due to the upper half of the face being completely occluded. In this paper we attempt to overcome these issues and focus on facial expression recognition in presence of a severe occlusion where the user is wearing a head-mounted display in a VR setting. We propose a geometric model to simulate occlusion resulting from a Samsung Gear VR headset that can be applied to existing FER datasets. Then, we adopt a transfer learning approach, starting from two pretrained networks, namely VGG and ResNet. We further fine-tune the networks on FER+ and RAF-DB datasets. Experimental results show that our approach achieves comparable results to existing methods while training on three modified benchmark datasets that adhere to realistic occlusion resulting from wearing a commodity VR headset. Code for this paper is available at: https://github.com/bita-github/MRP-FER.