Reviewerly/exHarmony huggingface dataset:
Analysing Pairwise Logratios Revisited

Even though the logratio methodology provides a range of both generic, mostly exploratory, and purpose-built coordinate representations of compositional data, simple pairwise logratios are preferred by many for multivariate analysis in the geochemical practice, principally because of their simpler interpretation. However, the logratio coordinate systems that incorporate them are predominantly oblique, resulting in both conceptual and practical problems. We propose a new approach, called backwards pivot coordinates, where each pairwise logratio is linked to one orthogonal coordinate system, and these systems are then used together to produce a concise output. In this work, principal component analysis and regression with compositional explanatory variables are used as primary methods to demonstrate the methodological and interpretative advantages of the proposal. In the applied part of this study, sediment compositions from the Jizera River, Czech Republic, were analysed using these techniques through backwards pivot coordinates. This allowed us to discuss grain size control of the element composition of sediments and clearly distinguish anthropogenically contaminated and uncontaminated strata in sediment depth profiles.
Towards a taxonomy of code review smells

Code review is a crucial step of the software development life cycle in order to detect possible problems in source code before merging the changeset to the codebase. Although there is no consensus on a formally defined life cycle of the code review process, many companies and open source software (OSS) communities converge on common rules and best practices. In spite of minor differences in different platforms, the primary purpose of all these rules and practices leads to a faster and more effective code review process. Non-conformance of developers to this process does not only reduce the advantages of the code review but can also introduce waste in later stages of the software development. The aim of this study is to provide an empirical understanding of the bad practices followed in the code review process, that are code review (CR) smells. We first conduct a multivocal literature review in order to gather code review bad practices discussed in white and gray literature. Then, we conduct a targeted survey with 32 experienced software practitioners and perform follow-up interviews in order to get their expert opinion. Based on this process, a taxonomy of code review smells is introduced. To quantitatively demonstrate the existence of these smells, we analyze 226,292 code reviews collected from eight OSS projects. We observe that a considerable number of code review smells exist in all projects with varying degrees of ratios. The empirical results illustrate that 72.2% of the code reviews among eight projects are affected by at least one code review smell. The empirical analysis shows that the OSS projects are substantially affected by the code review smells. The provided taxonomy could provide a foundation for best practices and tool support to detect and avoid code review smells in practice.
Topic Modeling of Short Texts: A Pseudo-Document View with Word Embedding Enhancement

Recent years have witnessed the unprecedented growth of online social media, resulting in short texts being the prevalent format of information on the Internet. Given the sparsity of data, however, short-text topic modeling remains a critical yet much-watched challenge in both academia and industry. Research has been devoted to building different types of probabilistic topic models for short texts, among which self-aggregation methods emerged recently to provide informative cross-text word co-occurrences. However, models along this line are still in their infancy and typically yield overfit results and exhibit high computational costs. In this paper, we propose a novel model called Pseudo-document-based Topic Model (PTM), which introduces the concept of pseudo-document to implicitly aggregate short texts against data sparsity. By modeling the topic distributions of latent pseudo-documents rather than short texts, PTM yields excellent performance in accuracy and efficiency. A word embedding-enhanced PTM (WE-PTM) is also proposed to leverage pre-trained word embeddings, which is essential to further alleviating data sparsity. Extensive experiments with self-aggregation or word embedding-based baselines on four real-world datasets including two online media short texts, demonstrate the high-quality topics learned by our models. Robustness to limited training samples and the explainable semantics of topics are also investigated.
A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images

Visual-based recommender systems (VRSs) enhance recommendation performance by integrating users' feedback with the visual features of items' images. Recently, human-imperceptible image perturbations, defined adversarial samples, have been shown capable of altering the VRSs performance, for example, by pushing (promoting) or nuking (demoting) specific categories of products. One of the most effective adversarial defense methods is adversarial training (AT), which enhances the robustness of the model by incorporating adversarial samples into the training process and minimizing an adversarial risk. The AT effectiveness has been verified on defending DNNs in supervised learning tasks such as image classification. However, the extent to which AT can protect deep VRSs, against adversarial perturbation of images remains mostly under-investigated. This work focuses on the defensive side of VRSs and provides general insights that could be further exploited to broaden the frontier in the field. First, we introduce a suite of adversarial attacks against DNNs on top of VRSs, and defense strategies to counteract them. Next, we present an evaluation framework, named Visual Adversarial Recommender (VAR), to empirically investigate the performance of defended or undefended DNNs in various visually-aware item recommendation tasks. The results of large-scale experiments indicate alarming risks in protecting a VRS through the DNN robustification. Source code and data are available at https://github.com/sisinflab/Visual-Adversarial-Recommendation.
A Few Topical Tweets are Enough for Effective User Stance Detection

User stance detection entails ascertaining the position of a user towards a target, such as an entity, topic, or claim.Recent work that employs unsupervised classification has shown that performing stance detection on vocal Twitter users, who have many tweets on a target, can be highly accurate (+98%).However, such methods perform poorly or fail completely for less vocal users, who may have authored only a few tweets about a target.In this paper, we tackle stance detection for such users using two approaches.In the first approach, we improve user-level stance detection by representing tweets using contextualized embeddings, which capture latent meanings of words in context.We show that this approach outperforms two strong baselines and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics.In the second approach, we expand the tweets of a given user using their Twitter timeline tweets, which may not be topically relevant, and then we perform unsupervised classification of the user, which entails clustering a user with other users in the training set.This approach achieves 95.6% accuracy and 93.1% macro F-measure.
MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion

Knowledge Graphs (KGs) are widely used in various applications of information retrieval. Despite the large scale of KGs, they are still facing incomplete problems. Conventional approaches on Knowledge Graph Completion (KGC) require a large number of training instances for each relation. However, long-tail relations which only have a few related triples are ubiquitous in KGs. Therefore, it is very difficult to complete the long-tail relations. In this paper, we propose a meta pattern learning framework (MetaP) to predict new facts of relations under a challenging setting where there is only one reference for each relation. Patterns in data are representative regularities to classify data. Triples in KGs also conform to relation-specific patterns which can be used to measure the validity of triples. Our model extracts the patterns effectively through a convolutional pattern learner and measures the validity of triples accurately by matching query patterns with reference patterns. Extensive experiments demonstrate the effectiveness of our method. Besides, we build a few-shot KGC dataset of COVID-19 to assist the research process of the new coronavirus.
RetinexGAN:Unsupervised Low-Light Enhancement With Two-Layer Convolutional Decomposition Networks

In the field of image enhancement, using deep learning methods to enhance low-light images is currently mainstream.However, the methods often have complex network structures of a large number of parameters, and their training often uses paired data-sets, which are difficult to obtain in actual practice.To solve these problems, this paper proposes a simple generative adversarial network and Retinex model, dubbed RetinexGAN, that is completely trained using unpaired data-sets.It contains a decomposition network and two discriminator networks.To reduce the parameters of the network, only two convolution layers are used in the decomposition network.We show more challenging testing data where some parts of the image are underexposed and others are normal light.Both quantitative and visual results show that RetinexGAN is largely superior to state-of-the-art methods.
Malicious URL Detection: A Comparative Study

Malicious uniform resource locator (URL), i.e., Malicious websites are one of the most common cybersecurity threats. They host gratuitous content (spam, malware, inappropriate ads, spoofing, etc.) and tempt unwary users to become victims of scams (financial loss, private information disclosure, malware installation, extortion, fake shopping site, unexpected prize etc.) and cause loss of billions of rupees each year. The visit to these sites can be driven by email, advertisements, web search or links from other websites. In each case, the user must click on the malicious URL. The rising cases of phishing, spamming and malware has generated an urgent need for a reliable solution which can classify and identify the malicious URLs. Traditional classification techniques like blacklisting, regular expression, and signature matching approach are challenged because of huge data volume, patterns and technology changing over time, along with complicated relationship among features. In this paper, we address the detection of malicious URLs as a binary classification problem and evaluate the performance of several well-known machine learning classifiers. We adopted a public dataset from Kaggle comprising of 450000 URLs to train the model. The best classifier was used to detect malicious URLs from openphish website. It was found to give better results.
Learning Spatially-Variant MAP Models for Non-blind Image Deblurring

The classical maximum a-posteriori (MAP) framework for non-blind image deblurring requires defining suitable data and regularization terms, whose interplay yields the desired clear image through optimization. The vast majority of prior work focuses on advancing one of these two crucial ingredients, while keeping the other one standard. Considering the indispensable roles and interplay of both data and regularization terms, we propose a simple and effective approach to jointly learn these two terms, embedding deep neural networks within the constraints of the MAP framework, trained in an end-to-end manner. The neural networks not only yield suitable image-adaptive features for both terms, but actually predict per-pixel spatially-variant features instead of the commonly used spatially-uniform ones. The resulting spatially-variant data and regularization terms particularly improve the restoration of fine-scale structures and detail. Quantitative and qualitative results underline the effectiveness of our approach, substantially outperforming the current state of the art.
Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy

The successful recognition of benign and malignant breast nodules using ultrasound images is based mainly on supervised learning that requires a large number of labeled images. However, because high-quality labeling is expensive and time-consuming, we hypothesized that semi-supervised learning could provide a low-cost and powerful alternative approach. This study aimed to develop an accurate semi-supervised recognition method and compared its performance with supervised methods and sonographers.The faster region-based convolutional neural network was used for nodule detection from ultrasound images. A semi-supervised classifier based on the mean teacher model was proposed to recognize benign and malignant nodule images. The general performance of the proposed method on two datasets (8,966 nodules) was reported.The detection accuracy was 0.88±0.03 and 0.86±0.02, respectively, on two testing sets (1,350 and 2,220 nodules). When 800 labeled training nodules were available, the proposed semi-supervised model plus 4,396 unlabeled nodules performed better than the supervised learning model (area under the curve (AUC): 0.934±0.026 vs. 0.83±0.050; 0.916±0.022 vs. 0.815±0.049). The performance of the semi-supervised model trained on 800 labeled and 4,396 unlabeled nodules was close to that of the supervised learning model trained on a massive number of labeled nodules (n=5,196) (AUC: 0.934±0.026 vs. 0.952±0.027; 0.916±0.022 vs. 0.918±0.017). Moreover, the semi-supervised model was better than the average accuracy of five human sonographers (AUC: 0.922 vs. 0.889).The semi-supervised model can achieve excellent performance for nodule recognition and be useful for medical sciences. The method reduced the number of labeled images required for training, thus significantly alleviating the difficulty in data preparation of medical artificial intelligence.
Reversible data hiding in encrypted images with separability and high embedding capacity

In this paper, a high-capacity reversible data hiding (RDH) scheme for encrypted images with separability is proposed. The image is first divided into non-overlapping blocks, and each block is encrypted with the same random value. The advantage is that the correlation between adjacent pixels can be preserved. Utilizing the preserved correlation, the prediction difference in encrypted domain is exactly the same as that of plaintext domain, so that the separability can be achieved. Without accessing the original image content, the data-hider can embed additional data into encrypted image through histogram shifting and difference expansion. At the receiving end, the embedded additional data and the original image can be recovered without any error in separable manner. Experimental results are presented to demonstrate the feasibility and efficiency of the proposed scheme.
The explosion operation of fireworks algorithm boosts the coral reef optimization for multimodal medical image registration

Medical image registration is becoming increasingly important in diagnosis and treatment planning, as it enables to align and integrate different images having a shared content, which obtained under different conditions, into a single representation. The most challenging issues of registration is finding a optimal transformation which settled by optimization methods. With this work, we present a Fireworks Algorithm (FWA) boosts the Coral Reefs Optimization Algorithm (CRO) for medical image registration (FWBCR). Firstly, the Coral Reefs Optimization is easily trapping in local optimum, we enhance the exploration ability of the CRO by integrated with a explosion operator of the FWA. At the Broadcast Spawning stage of CRO, a large fraction of corals are applying the crossover operator which has a good exploitation ability and a small fraction of corals with good solutions are applying the explosion operator which has a good exploration ability. Therefore, the algorithm we proposed is well solved the trades off between exploitation and exploration. Secondly, the explosion amplitude affects the search ability. A larger explosion amplitude benefit for the exploration in the early stages of the algorithm. Contrary, a smaller explosion amplitude benefit for the exploitation in the late stages of the algorithm. To deal with these problems, we design an adaptive explosion amplitude which depends on the fitness of the best solution and the worst solution. Thirdly, the phenomenon of the explosion operator is to generate lots of sparks. It is hard to select a small number of sparks with less computation effort while to guarantee the diversity of the swarm for next generation from a large number of sparks. To overcome this difficulty, we exploring the use of the clustering algorithm on the sparks then get some cluster centers as the new solutions. In this way, the diversity of the swarm can be ensured and the computation effort can be greatly reduced through reducing the number of evaluations. Finally, inspired by the Differential Evolution Algorithm, we construct a differential migration vector(DMV) with the most promising right direction and adaptive length, and to generate elite solutions by adding the DMV to the position of the spark centers. The FWBCR has been tested in numerous experiments on benchmark datasets include six kind of different modality images, from up to eighteen different patients, which can make up 54 multimodal registration scenarios. For registration precision, FWBCR obtained best in 19 scenarios, BBO-EL obtained best in 22 scenarios, while CRO-SL obtained best in 13 scenarios, which demonstrated that FWBCR outperformed the CRO variants like CRO-SL in most cases and as good as the state-of-the-art in the registration field. In addition, compared with BBO-EL, FWBCR achieve fast convergence rate and increase computing performance by 30%.
Augmented Reality with Internet of Things

Today technological changes make the probability of more complex things made into simple tasks with more accuracy in major areas and mostly in Manufacturing Industry. Internet of things contributes its major part in automation which helps human to make life easy by monitoring and directed to a related person with in a fraction of second. Continuous advances and improvement in computer vision, mobile computing and tablet screens have led to a revived interest in Augmented Reality the Augmented Reality makes the complex automation into an easier task by making more realistic real time animation in monitoring and automation on Internet of Things (eg like temperature, time, object information, installation manual, real time testing).In order to identify and link the augmented content, like object control of home appliances, industrial appliances. The AR-IoT will have a much cozier atmosphere and enhance the overall Interactivity of the IoT environment. Augmented Reality applications use a myriad of data generated by IoT devices and components, AR helps workers become more competitive and productive with the realistic environment in IoT. Augmented Reality and Internet of Things together plays a critical role in the development of next generation technologies. This paper describes the concept of how Augmented Reality can be integrated with industry(AR-IoT)4.0 and how the sensors are used to monitoring objects/things contiguously round the clock, and make the process of converting real-time physical objects into smart things for the upcoming new era with AR-IoT.
BCALS: Blockchain‐based secure log management system for cloud computing

Abstract A computing environment requires a robust and comprehensive process to track and document user activities to uphold confidence in the system. Audit logs are used for this purpose to monitor the actions of administrators and users. However, these logs are vulnerable to multidimensional attacks, including modification of logs, erasability of logs, and privacy of the user. Since administrators have unprecedented access to these logs, they can modify, delete, and even destroy them. Securing these logs against malicious activities is the prime requirement of audit log management. Existing schemes have several limitations, including immutability, computational expensiveness, missing semantics, and are not verifiable. Various schemes have been proposed for this purpose, but a standard method is required to structure heterogeneous logs and their security semantically. To cope with these limitations, in this paper, we propose a Log Management System using blockchain. The proposed system will ensure audit logs' security, which will eventually strengthen users' trust in the computing environment and make it unbreachable even by the administrators. It has been evinced that our model performed better in terms of performance and features already mentioned when compared with existing schemes.
PAN

In practical applications of cross-modal retrieval, test queries of the retrieval system may vary greatly and come from unknown category. Meanwhile, due to the cost and difficulty of data collection as well as other issues, the available data for cross-modal retrieval are often imbalanced over different modalities. In this paper, we address two important issues to increase the robustness of cross-modal retrieval system for real-world applications: handling test queries from unknown category and modality-imbalanced training data. The first issue has not been addressed by existing methods and the second issue was not well addressed in the related research. To tackle the above issues, we take the advantage of prototype learning, and propose a prototype-based adaptive network (PAN) for robust cross-modal retrieval. Our method leverages a unified prototype to represent each semantic category across modalities, which provides discriminative information of different categories and takes unified prototypes as anchors to learn cross-modal representations adaptively. Moreover, we propose a novel prototype propagation strategy to reconstruct balanced representations which preserves the semantic consistency and modality heterogeneity. Experimental results on the benchmark datasets demonstrate the effectiveness of our method compared to the SOTA methods, and further robustness tests show the superiority of our method in solving the above issues.
Collusion-resistant identity-based Proxy Re-encryption: Lattice-based constructions in Standard Model

The concept of proxy re-encryption (PRE) dates back to the work of Blaze, Bleumer, and Strauss in 1998. PRE offers delegation of decryption rights, i.e., it securely enables the re-encryption of ciphertexts from one key to another, without relying on trusted parties. PRE allows a semi-trusted third party termed as a “proxy” to securely divert encrypted files of user A (delegator) to user B (delegatee) without revealing any information about the underlying files to the proxy. To eliminate the necessity of having a costly certificate verification process, Green and Ateniese introduced an identity-based PRE (IB-PRE). The potential applicability of IB-PRE sprung up a long line of intensive research from its first instantiation. Unfortunately, till today, there is no collusion-resistant unidirectional IB-PRE secure in the standard model, which can withstand quantum attack. In this paper, we present the first concrete constructions of collusion-resistant unidirectional IB-PRE, for both selective and adaptive identity, which are secure in standard model based on the hardness of learning with errors problem.