Reviewerly/exHarmony huggingface dataset:
Cast-Gan: Learning To Remove Colour Cast From Underwater Images

Underwater images are degraded by blur and colour cast caused by the attenuation of light in water. To remove the colour cast with neural networks, images of the scene taken under white illumination are needed as reference for training, but are generally unavailable. As an alternative, one can use surrogate reference images taken close to the water surface or degraded images synthesised from reference datasets. However, the former still suffer from colour cast and the latter generally have limited colour diversity. To address these problems, we exploit open data and typical colour distributions of objects to create a synthetic image dataset that reflects degradations naturally occurring in underwater photography. We use this dataset to train Cast-GAN, a Generative Adversarial Network whose loss function includes terms that eliminate artefacts that are typical of underwater images enhanced with neural networks. We compare the enhancement results of Cast-GAN with four state-of-the-art methods and validate the cast removal with a subjective evaluation.
Moderated Redactable Blockchains: A Definitional Framework with an Efficient Construct

Blockchain is a multiparty protocol to reach agreement on the order of events, and to record them consistently and immutably without centralized trust. In some cases, however, the blockchain can benefit from some controlled mutability. Examples include removing private information or unlawful content, and correcting protocol vulnerabilities which would otherwise require a hard fork. Two approaches to control the mutability are: moderation, where one or more designated administrators can use their private keys to approve a redaction, and voting, where miners can vote to endorse a suggested redaction. In this paper, we first present several attacks against existing redactable blockchain solutions. Next, we provide a definitional framework for moderated redactable blockchains. Finally, we propose a provable and efficient construct, which applies a single digital signature per redaction, achieving a much simpler and secure result compared to the prior art in the moderated setting.
What Do Linguistic Expressions Tell Us about Learners’ Confusion? A Domain-Independent Analysis in MOOCs

The substantial growth of online learning, and in particular, through massively open online courses (MOOCs), supports research into nontraditional learning contexts. Learners' confusion is one of the identified aspects which impact the overall learning process, and ultimately, course attrition. Confusion for a learner is an individual state of bewilderment and uncertainty as to how to move forward. The majority of recent works in MOOCs neglects the individual factor and measure the influence of community-related aspects (e.g., votes, views) for confusion classification. While these are useful measures, these models neglect the personalized context, such as an individual's affect or emotions. Toward this, and within the MOOC context, we propose machine learning models based solely on language and discourse features extracted from learners' discussion posts. With over 83% F1-score in all domains, it is evident that linguistic-only features are highly effective in confusion classification, with our models outperforming previous models developed for confusion classification. In this article, we also obtain good performance for cross-domain confusion classification between 70.7% and 84.5% F1-score for all domain pairs (i.e., training on one domain and testing on another domain). Primarily, this article contributes through the development of a novel linguistic feature set that is predictive for effective confusion classification.
K-memory-embedded insertion mechanism for opacity enforcement

Opacity is a privacy property which aims to determine whether the “secret” of a system can be deduced by an outside intruder. In this paper, we investigate the enforcement of opacity using insertion functions which insert additional events if necessary to modify the output of the system. Inspired by the existing insertion mechanisms, we propose a mechanism named k-memory-embedded insertion mechanism for the enforcement of opacity. In our k-memory-embedded insertion mechanism, the insertion functions are determined based on the knowledge of system’s exact states and the stored next sequence of events with length k. Especially, we define the property of ik-E-enforceability that a k-memory-embedded insertion function needs to satisfy for opacity enforcement, and further construct a verifier to determine whether a given opacity notion of the system is ik-E-enforceable or not. Our mechanism improves the embedded insertion mechanism by applying to a broader class of systems. Also, we improve the k-memory insertion mechanism by requiring a smaller number of consecutive events which need to be stored.
Twitter Sentiment Analysis Using Lexical or Rule Based Approach: A Case Study

Opinion analysis or sentiment analysis is one of the most effective techniques these days in order to determine the sentiments or emotions of people regarding any event. This technique has come to the fore because of extensive use of social media platforms like Facebook, Twitter etc. by people to express their emotions regarding any event that has occurred or any event that is most likely to happen, be that the release of a movie or a political rally that is about to take place. People make sure to express their sentiments. Sentiment analysis proves very beneficial for any company selling a product to know how their product was received by people or by any political party to determine how people are reacting towards their running candidate. In order to analyze these sentiments two approaches of machine learning can be used - unsupervised or supervised. In this paper we have worked with Unsupervised Approach which is a rule based or lexical approach and can be done using the pre-built open source libraries like TextBlob, VADER.
Assessment of the image-based atmospheric correction of multispectral satellite images for geological mapping in arid and semi-arid regions

This paper aims to assess the image-based and physical atmospheric corrections of Landsat 8 images for geological mapping. This study was carried out in the central Jebilet inlier in Moroccan anti atlas, which is characterized by its geological diversity, its mining potential and an arid climate. Two physical atmospheric corrections FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) and ATCOR (Atmospheric & Topographic Correction) were compared to the DOS1 (Dark Object Subtraction) image-based method. The assessment of the results was based on the ASD (Analytical Spectral Devices) spectroradiometric data as well as the SVM (Support Vector Machine) classifications issued from the atmospherically corrected images. The FLAASH provided the most accurate Bottom Of Atmosphere BOA reflectance estimation (R2 = 0.95 and RMSE = 0.033) and slightly outperformed the DOS1 method (R2 = 0.94 and RMSE = 0.034). For the SVM classification outputs, DOS1 gave results almost similar to those obtained by FLAASH and ATCOR. The similarity between the FLAASH and DOS1 SVM classifications was 97.77%. The results of this work demonstrate the effectiveness of the image-based method in atmospheric correction of multispectral data for geological and mineral mapping over the arid and semi-arid regions. The image-based atmospheric correction method is proved as an accurate, simple way and very straightforward to apply.
An Experimental Study on Sentiment Classification of Algerian Dialect Texts

The aim of this paper is to study and compare some well-known and commonly used methods for sentiment analysis to evaluate the opinion and emotion expressed in Algerian texts. The classification task herein is a ternary sentiment classification. By using several combinations of text preprocessing and data representation techniques, we aim to compare the precise modelling results of Deep Learning models with other commonly used algorithms (random forest, maximum entropy, SVM, and the lexicon-based method for which we tested several lexicons). Based on the experiments carried out, Deep Learning models clearly outperform the baseline and offer better accuracy especially for CNN. In order to improve modelling results, we set a new baseline for future works. This is the integrated embeddings in the training model. We experimented with different models and data representations, including a recent approach, the ” contextual embedding” which appeared in 2018 and gained popularity in the NLP community in 2019. Our results give openings for further research in this domain.
Hypergraph-Based Image Processing

Learning and processing of signals over hypergraph models have gained substantial traction owing to the ability of hypergraphs in characterizing multilateral interactions. In this work, we explore hypergraph spectral analysis and provide alternative definitions of frequency domain operations that are practically useful in image processing. We analyze hypergraph spectral properties and present several application examples, including compression, edge detection and segmentation. Successful experiment results demonstrate the effectiveness and the future prospect of the proposed hypergraph frequency operations in image processing.
Generating Use Case Scenarios from User Stories

Textual user stories capture interactions of users with the system as high-level requirements. However, user stories are typically rather short and backlogs can include many stories. This makes it hard to (a) maintain user stories and backlogs, (b) fully understand the scope of a software project without a detailed analysis of the backlog, and (c) analyse how user stories impact design decisions during sprint planning and implementation. This paper proposes a technique to automatically transform textual user stories into visual use case scenarios in the form of robustness diagrams (a semi-formal scenario-based visualisation of workflows). In addition to creating diagrams for individual stories, the technique allows combining diagrams of multiple stories into one diagram to visualise workflows within sets of stories (e.g., a backlog). Moreover, the technique supports "viewpoint-based" diagrams, i.e., diagrams that show relationships between actors, domain entities and user interfaces starting from a diagram element (e.g., an actor) selected by the analyst. The technique utilises natural language processing and rule-based transformations. We evaluated the technique with more than 1,400 user stories from 22 backlogs and show that (a) the technique generates syntactically valid robustness diagrams, and (b) the quality of automatically generated robustness diagrams compares to the quality of diagrams created by human experts, but depends on the quality of the textual user stories.
Wildfish++: A Comprehensive Fish Benchmark for Multimedia Research

In this paper, we develop a large-scale visionlanguage fish benchmark, namely WildFish++, for comprehensive studies in multimedia research. Concretely, WildFish++ consists of 2,348 fish categories with 103,034 images in the wild, and 3,817 fish descriptions with 213,858 words. Based on these distinct characteristics, we mainly introduce four challenging research tasks on WildFish++. (1) Fine-Grained Recognition with Comparison Texts. WildFish++ naturally contains subtle difference among fish categories, which leads to fine-grained classification. Most approaches resort to tackle this problem by capturing discriminative regions in the view of each image. However, this paradigm may be still far way from extracting the most distinct features when the context on visual difference is not available. In this case, we propose to introduce comparison fish descriptions, a unique corpus that can directly point out subtle difference between highly-confused species and naturally serve as a kind of valuable context information. With such texts, we creatively elaborate a multi-modal fish network, aiming at incorporating those comparison textual information as prior knowledge and consequently leveraging it to guide CNNs to find subtle yet distinct regions in the context of comparison texts. (2) Open-Set Classification. We often confront with unknown categories in practice, e.g., there may still exist unknown fishes in our planet. Hence, we creatively adapt WildFish++ for a novel open-set classification task, which aims at correctly assigning each test image into the unknown class or one of known classes. More importantly, we investigate a number of practical designs to boost accuracy of deep learning models in open-set scenarios. (3) Cross-Modal Retrieval. WildFish++ not only contains diversified fish images in the wild but also has rich fish descriptions about morphology diagnosis, biology information, etc. Hence, we design a challenging cross-modal retrieval task, which leverages three subtasks such as text-to-text, text-to-image, image-to-text retrieval in a unified end-to-end framework. (4) Automatic Fish Classification. Automatic fish classification is a long-term research in marine biology, while current studies are unsatisfactory due to the lack of large-scale data. In this case, we train a number of CNNs with WildFish++, and use its pre-trained models to boost fish classification on most existing benchmarks of wild fishes. We will release WildFish++ with codes/protocols (https://github.com/PeiqinZhuang/WildFish++). We believe it can promote relevant studies in multimedia and beyond.
AxMM: Area and Power Efficient Approximate Modular Multiplier for R-LWE Cryptosystem

Amongst various Post-Quantum Cryptographic (PQC) schemes, Lattice-Based Cryptography (LBC) stands out as the most viable substitute to the classical cryptographic schemes due to its efficiency, versatility and solid foundations on hard mathematical problems. Ring Learning With Errors (R-LWE) is a Public Key Encryption (PKE) scheme of LBC, in which the modular polynomial multiplication in a ring is the main bottleneck in the realization of a practical resource-constraint design for the embedded IoT devices. This work explores novel Approximate Computing (AC) technique for the design of area/power efficient modular multiplier (so called AxMM) for R-LWE, exploiting the inherent approximate structure of the scheme. The proposed AxMM on 45nm ASIC library achieved an area and power reduction of 36% and 23%, respectively, along with a speed increase of 1.34× as compared to state-of-art smallest exact R-LWE modular multiplier.
Knowledge Augmented Dialogue Generation with Divergent Facts Selection

The end-to-end open-domain dialogue system is a challenging task since the existing neural models suffer from the issue of trivial responses. Employing background knowledge as a major solution, has been proven to be effective to improve the responses quality. However, less attention was paid to the selection of the appropriate knowledge in scenarios where the utterance subject drifts between two partners, which could prohibit the model from learning to access knowledge correctly. In this paper, we propose a novel Knowledge Augmented Dialogue Generation (KADG) model to facilitate both knowledge selection and incorporation in open-domain dialogue systems. The core components of KADG consist of Divergent Knowledge Selector (DKS) and Knowledge Aware Decoder (KAD). DKS performs a one-hop subject reasoning over knowledge by pre-optimizing each knowledge candidate with inferred drift clue. Drift clue implies the potential subjects association of the current conversation and is served to bridge the subject gap in the knowledge selection. Thereafter, KAD makes full use of this selected knowledge to generate responses contextual coherently as well as knowledgeably. Comprehensive experiments on a newly released knowledge-grounded conversation dataset Wizard-of-Wikipedia have verified the superiority of our model than previous baselines and shown that our method can refer to the knowledge properly and generate diverse and informative responses.
Fast Local Planning and Mapping in Unknown Off-Road Terrain

In this paper, we present a fast, on-line mapping and planning solution for operation in unknown, off-road, environments. We combine obstacle detection along with a terrain gradient map to make simple and adaptable cost map. This map can be created and updated at 10 Hz. An A* planner finds optimal paths over the map. Finally, we take multiple samples over the control input space and do a kinematic forward simulation to generated feasible trajectories. Then the most optimal trajectory, as determined by the cost map and proximity to A* path, is chosen and sent to the controller. Our method allows real time operation at rates of 30 Hz. We demonstrate the efficiency of our method in various off-road terrain at high speed.
A Feature Selection Comparative Study for Web Phishing Datasets

Among the plethora of cybercrime techniques employed by criminals, Phishing is by far the most extensively implemented technique. Phishing attacks are performed with the motive of monetary gains or theft of sensitive or intellectual data leading to major losses to both organizations and individuals. In this paper, we talk about the detection of Web Phishing attacks using Machine Learning. A comparative study is made between different Machine Learning Algorithms (that can be used for binary classification) and Feature Selection Techniques when applied to Phishing datasets. The goal of this experiment is to obtain similar/comparable accuracies while achieving a significant reduction in the number of features. We use the F1_Score and Time of execution as metrics to evaluate improvements in the overall system. Results are then rendered in the form of tables and graphs that demonstrate the same.
Unsupervised Conditional Disentangle Network For Image Dehazing

Image dehazing aims to restore the blurry image information caused by the ambiguities of unknown scene radiance and transmission. Instead of using paired images or depth information, we propose an Unsupervised Conditional Disentangle Network (UCDN) using unpaired dataset. Our approach enforces the constraint by introducing physical-based disentanglement. Unlike other unsupervised dehazing models, our approach adapts the multi-concentration of fog and outperforms on the dataset with different concentrations. Extensive experiments on synthesized dataset demonstrate that our approach can surpass state-of-the-arts. Meanwhile, through benchmarking on our collected natural hazy dataset, our approach can generate more perceptually appealing dehazing results.
Using Siamese Graph Neural Networks for Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning

Similarity-based retrieval of semantic graphs is widely used in real-world scenarios, e. g., in the domain of business workflows. To tackle the problem of complex and time-consuming graph similarity computations during retrieval, the MAC/FAC approach is used in Process-Oriented Case-Based Reasoning (POCBR), where similar graphs are extracted from a preselected set of candidate graphs. These graphs result from a similarity computation with a computationally inexpensive similarity measure. The contribution of this paper is a novel similarity measure where vector space embeddings generated by two siamese Graph Neural Networks (GNNs) are used to approximate the similarities of a precise but therefore computationally complex graph similarity measure. Our approach includes a specific encoding scheme for semantic graphs that enables their usage in neural networks. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of the graph similarity measure for two workflow domains. The results show great potential of the approach for being used in a MAC/FAC scenario, either as a preselection model or as an approximation of the graph similarity measure.
Incentive analysis of Bitcoin-NG, revisited

Bitcoin-NG is among the first blockchain protocols to approach the near-optimal throughput by decoupling blockchain operation into two planes: leader election and transaction serialization. Its decoupling idea has inspired a new generation of high-performance blockchain protocols. However, the existing incentive analysis of Bitcoin-NG has several limitations. First, the impact of network capacity is ignored. Second, an integrated incentive analysis that jointly considers both key blocks and microblocks is still missing. In this paper, we aim to address these two limitations. First, we propose a new incentive analysis that takes the network capacity into account, showing that Bitcoin-NG can still maintain incentive compatibility against the microblock mining attack even under limited network capacity. Second, we leverage a Markov decision process (MDP) to jointly analyze the incentive of both key blocks and microblocks, showing that the selfish mining revenue of Bitcoin-NG is a little higher than that in Bitcoin only when the selfish miner controls more than 35% of the mining power. We hope that our in-depth incentive analysis for Bitcoin-NG can shed some light on the mechanism design and incentive analysis of next-generation blockchain protocols.
Blockchain-Based Smart Advertising Network With Privacy-Preserving Accountability

In a smart advertising network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAN</i> ), a broker builds user profiles from its wealth of user data, manages advertisements for retailers, and disseminates the advertisements through multiple channels. However, the broker sometimes provides insufficient transparency explanations of advertising activities, which may result in the increasing popularity of ad-blocking software and lower advertising investments from retailers. In this paper, we propose a blockchain-based Smart Advertising Network with Privacy-preserving Accountability ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> ). Specifically, we design a composite Succinct Non-interactive Argument ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SNARG</i> ) system, that commits advertising policies as cryptographic authenticators in a smart contract. By doing so, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> is compatible with the existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAN</i> without posing prohibitive implementation cost over the blockchain architecture. Users or retailers can require explanations of an advertising activity by sending a challenge to the smart contract. With the succinctness and privacy preservation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SNARG</i> system, the smart contract can efficiently verify whether the challenged advertising activity follows committed advertising policies without exposing user profile privacy. If any misconduct is identified, the contract enforces public accountability on the misbehaving party by confiscating its cryptocurrency deposits. We conduct extensive experiments to provide both on-chain and off-chain benchmarks, which demonstrates the application feasibility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> .
Fourier neural networks: A comparative study

We review neural network architectures which were motivated by Fourier series and integrals and which are referred to as Fourier neural networks. These networks are empirically evaluated in synthetic and real-world tasks. Neither of them outperforms the standard neural network with sigmoid activation function in the real-world tasks. All neural networks, both Fourier and the standard one, empirically demonstrate lower approximation error than the truncated Fourier series when it comes to approximation of a known function of multiple variables.