Reviewerly/exHarmony huggingface dataset:
Episodic Memory in Lifelong Language Learning

We introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier. We propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on text classification and question answering demonstrate the complementary benefits of sparse experience replay and local adaptation to allow the model to continuously learn from new datasets. We also show that the space complexity of the episodic memory module can be reduced significantly (~50-90%) by randomly choosing which examples to store in memory with a minimal decrease in performance. We consider an episodic memory component as a crucial building block of general linguistic intelligence and see our model as a first step in that direction.
An efficient and improved scheme for handwritten digit recognition based on convolutional neural network

Character recognition from handwritten images has received greater attention in research community of pattern recognition due to vast applications and ambiguity in learning methods. Primarily, two steps including character recognition and feature extraction are required based on some classification algorithm for handwritten digit recognition. Former schemes exhibit lack of high accuracy and low computational speed for handwritten digit recognition process. The aim of the proposed endeavor was to make the path toward digitalization clearer by providing high accuracy and faster computational for recognizing the handwritten digits. The present research employed convolutional neural network as classifier, MNIST as dataset with suitable parameters for training and testing and DL4J framework for hand written digit recognition. The aforementioned system successfully imparts accuracy up to 99.21% which is higher than formerly proposed schemes. In addition, the proposed system reduces computational time significantly for training and testing due to which algorithm becomes efficient.
In-memory database acceleration on FPGAs: a survey

Abstract While FPGAs have seen prior use in database systems, in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons. First, specifically for in-memory databases, FPGAs integrated with conventional I/O provide insufficient bandwidth, limiting performance. Second, GPUs, which can also provide high throughput, and are easier to program, have emerged as a strong accelerator alternative. Third, programming FPGAs required developers to have full-stack skills, from high-level algorithm design to low-level circuit implementations. The good news is that these challenges are being addressed. New interface technologies connect FPGAs into the system at main-memory bandwidth and the latest FPGAs provide local memory competitive in capacity and bandwidth with GPUs. Ease of programming is improving through support of shared coherent virtual memory between the host and the accelerator, support for higher-level languages, and domain-specific tools to generate FPGA designs automatically. Therefore, this paper surveys using FPGAs to accelerate in-memory database systems targeting designs that can operate at the speed of main memory.
Using Blockchain for Provenance and Traceability in Internet of Things-Integrated Food Logistics

Provenance and traceability are crucial in the food distribution and transportation network for ensuring integrity of food labeling and efficient management of quality and contamination issues. Significant issues in terms of cost, quality, and health issues can result without sufficient visibility. This article elaborates on the use of emerging blockchain technology to improve Internet of Things-enabled food logistics.
Serverless Computing: A Survey of Opportunities, Challenges, and Applications

The emerging serverless computing paradigm has attracted attention from both academia and industry. This paradigm brings benefits such as less operational complexity, a pay-as-you-go pricing model, and an auto-scaling feature. The paradigm opens up new opportunities and challenges for cloud application developers. In this article, we present a comprehensive overview of the past development as well as the recent advances in research areas related to serverless computing. First, we survey serverless applications introduced in the literature. We categorize applications in eight domains and separately discuss the objectives and the viability of the serverless paradigm along with challenges in each of those domains. We then classify those challenges into nine topics and survey the proposed solutions. Finally, we present the areas that need further attention from the research community and identify open problems.
Does investor sentiment on social media provide robust information for Bitcoin returns predictability?

We use a dataset of approximately one million messages sent on StockTwits to explore the relationship between investor sentiment on social media and intraday Bitcoin returns. We find a statistically significant relationship between investor sentiment and Bitcoin returns for frequencies of up to 15 minutes. For lower frequencies, the relation disappears. We also find that the impact of sentiment on returns is concentrated on the period around the Bitcoin bubble. However, the magnitude of the effect is rather small making it impossible for a trader to make economic profits by trading on the information published on social media.
Magpylib: A free Python package for magnetic field computation

This paper introduces Magpylib, a light-weight and free Python package used to compute magnetic fields of permanent magnets, currents and moments based on analytical models. The package features a user-friendly interface to create, manipulate, group and visualize such magnetic sources, and to compute the field they generate. We present an overview of structure and functionality of Magpylib, followed by a discussion of possible applications and examples of magnetic system design. Special attention is given to computational performance possibilities in this context.
Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine

Papermaking wastewater accounts for a large proportion of industrial wastewater, and it is essential to obtain accurate and reliable effluent indices in real-time. Considering the complexity, nonlinearity, and time variability of wastewater treatment processes, a dynamic kernel extreme learning machine (DKELM) method is proposed to predict the key quality indices of effluent chemical oxygen demand (COD). A time lag coefficient is introduced and a kernel function is embedded into the extreme learning machine (ELM) to extract dynamic information and obtain better prediction accuracy. A case study for modeling a wastewater treatment process is demonstrated to evaluate the performance of the proposed DKELM. The results illustrate that both training and prediction accuracy of the DKELM model is superior to other models. For the prediction of the quality indices of effluent COD, the determinate coefficient of the DKELM model is increased by 27.52 %, 21.36 %, 10.42 %, and 10.81 %, compared with partial least squares, ELM, dynamic ELM, and kernel ELM, respectively.
Secure Electronic Medical Records Storage and Sharing Using Blockchain Technology

Electronic medical records (EMRs) are electronically-stored highly sensitive and private information related to the diagnosis and treatment of patient, and needs to be frequently shared among peers. Sharing of medical records between participants is very challenging because the data might be revealed or tampered during the operational process. To address these challenges, a blockchain-based electronic medical records system is the key solution. In this research, we have discussed how blockchain technology can help in better healthcare data management. We have proposed a blockchain-based records management system for efficient management and sharing of electronic medical records (EMRs). We have implemented a prototype of Electronic Medical Records Management System using permissioned blockchain platform “Hyperledger”. This system ensures privacy, security and easy accessibility and availability of medical records.
The ethics of COVID-19 tracking apps – challenges and voluntariness

As COVID-19 continues to spread, a variety of COVID-19 tracking apps (CTAs) have been introduced to help contain the pandemic. Deployment of this technology poses serious challenges of effectiveness, technological problems and risks to privacy and equity. The ethical use of CTAs depends heavily on the protection of voluntariness. Voluntary use of CTAs implies not only the absence of a legal obligation to employ the app but also the absence of more subtle forms of coercion such as enforced exclusion from certain social and work activities. The protection of individual rights to voluntary use can be enhanced through an ethics by design approach in the development of CTAs that treat the introduction of CTAs for what it is: a complete novelty that is being tested for the first time in democracies.
A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment

Credit risk assessment is usually regarded as an imbalanced classification task solved by static ensemble classifiers. However, the dynamic ensemble selection (DES) strategy that can select different ensemble classifiers for each query sample is rarely used. Deficiency of the existing DES algorithm in dealing with imbalanced data is the major challenge. In this paper, a novel combined DES model is developed for imbalanced learning problems. To handle the imbalanced data sets, the synthetic minority over-sampling technique is initially used to balance a training set before generating a candidate classifier pool; then, the weighting mechanism of DES-MI (multi-class imbalance) is used to highlight the importance of minority instances when evaluating classifier competences. To further ensure the comprehensive evaluation and right selection of the ensemble classifier, the meta-learning framework of META-DES is used to account for multiple criteria, and the two-step selection strategy of DES-KNN (k-nearest neighbours) is employed to perform a trade-off between the competence and diversity of the classifiers. Our experiments on 15 imbalanced data sets from the KEEL repository show that the proposed model improves the performance of seven known and popular DES algorithms in terms of the area under the curve. Moreover, the type I error rate of the proposed method is lower than that of XGBoost and LightGBM in a real P2P loan data set indicating the efficiency of the proposed method for credit risk assessment.
A Chaotic-Based Encryption/Decryption Framework for Secure Multimedia Communications

Chaos-based encryption has shown an increasingly important and dominant role in modern multimedia cryptography compared with traditional algorithms. This work proposes novel chaotic-based multimedia encryption schemes utilizing 2D alteration models for high secure data transmission. A novel perturbation-based data encryption for both confusion and diffusion rounds is proposed. Our chaotification structure is hybrid, in which multiple maps are combined combines for media encryption. Blended chaotic maps are used to generate the control parameters for the permutation (shuffling) and diffusion (substitution) structures. The proposed schemes not only maintain great encryption quality reproduced by chaotic, but also possess other advantages, including key sensitivity and low residual clarity. Extensive security and differential analyses documented that the proposed schemes are efficient for secure multimedia transmission as well as the encrypted media possesses resistance to attacks. Additionally, statistical evaluations using well-known metrics for specific media types, show that proposed encryption schemes can acquire low residual intelligibility with excessive nice recovered statistics. Finally, the advantages of the proposed schemes have been highlighted by comparing it against different state-of-the-art algorithms from literature. The comparative performance results documented that our schemes are extra efficacious than their data-specific counterpart methods.
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images

Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwriting. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images.
Learning End-to-End Lossy Image Compression: A Benchmark

Image compression is one of the most fundamental techniques and commonly used applications in the image and video processing field. Earlier methods built a well-designed pipeline, and efforts were made to improve all modules of the pipeline by handcrafted tuning. Later, tremendous contributions were made, especially when data-driven methods revitalized the domain with their excellent modeling capacities and flexibility in incorporating newly designed modules and constraints. Despite great progress, a systematic benchmark and comprehensive analysis of end-to-end learned image compression methods are lacking. In this paper, we first conduct a comprehensive literature survey of learned image compression methods. The literature is organized based on several aspects to jointly optimize the rate-distortion performance with a neural network, i.e., network architecture, entropy model and rate control. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. With this survey, the main challenges of image compression methods are revealed, along with opportunities to address the related issues with recent advanced learning methods. This analysis provides an opportunity to take a further step towards higher-efficiency image compression. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance, especially on high-resolution images. Extensive benchmark experiments demonstrate the superiority of our model in rate-distortion performance and time complexity on multi-core CPUs and GPUs.
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss

Snow is a highly complicated atmospheric phenomenon that usually contains snowflake, snow streak, and veiling effect (similar to the haze or the mist). In this literature, we propose a single image desnowing algorithm to address the diversity of snow particles in shape and size. First, to better represent the complex snow shape, we apply the dual-tree wavelet transform and propose a complex wavelet loss in the network. Second, we propose a hierarchical decomposition paradigm in our network for better under-standing the different sizes of snow particles. Last, we propose a novel feature called the contradict channel (CC) for the snow scenes. We find that the regions containing the snow particles tend to have higher intensity in the CC than that in the snow-free regions. We leverage this discriminative feature to construct the contradict channel loss for improving the performance of snow removal. Moreover, due to the limitation of existing snow datasets, to simulate the snow scenarios comprehensively, we propose a large-scale dataset called Comprehensive Snow Dataset (CSD). Experimental results show that the proposed method can favorably outperform existing methods in three synthetic datasets and real-world datasets. The code and dataset are released in https://github.com/weitingchen83/ICCV2021-Single-Image-Desnowing-HDCWNet.
Regulation by Blockchain: the Emerging Battle for Supremacy between the Code <i>of</i> Law and Code <i>as</i> Law

Abstract This paper critically examines the intersection and interactions between conventional law produced and enforced by national legal systems (ie the ‘code of law’) and the internal rules of blockchain systems, which take the form of executable software code and cryptographic algorithms operating across a distributed computing network (‘code as law’). In so doing, it seeks to identify whether, and to what extent, ‘regulation by blockchain’ will successfully avoid governance by conventional law. It identifies three different ways in which the code of law is likely to interact with code as law, based primarily on the intended motives and purposes of those engaged in activities in developing, maintaining or undertaking transactions upon the network. It argues that these different classes of case are likely to generate different kinds of dynamic interaction between the blockchain code and conventional legal systems, and critically examines the normative foundations of these emerging and anticipated interactions.
All-Or-Nothing data protection for ubiquitous communication: Challenges and perspectives

As we are in the age of ubiquitous computing and communication, the need to provide safety and security for such systems is more urgent than ever. Research works of cryptography methods aim to protect the data in the ubiquitous computing and communication systems by encryption. However, more and more recent incidents threatening data security have shown that sole encryption schemes cannot meet the safety needs in today’s complicated ubiquitous computing systems. Thus, the topic of providing additional protection such as All-Or-Nothing (AON) data protection scheme has drawn interest from a large number of researchers and practitioners. Many data protection methods based on this AON data protection concepts are developed aiming to enhance the classical encryption schemes. In this paper, to the best of our knowledge, we collate research works across a number of topics in AON, from historical developments to the recent research proposal. By introducing new definitions and category methods, we hope to provide useful reference and perspectives for the state of AON research and development.
Document image binarization with cascaded generators of conditional generative adversarial networks

Binarization is often the first step in many document analysis tasks and plays a key role in the subsequent steps. In this paper, we formulate binarization as an image-to-image generation task and introduce the conditional generative adversarial networks (cGANs) to solve the core problem of multi-scale information combination in binarization task. Our generator consists of two stages: In the first stage, sub-generator G1 learns to extract text pixels from an input image. Different scales of the input image are processed by G1 and corresponding binary images are generated. In the second stage, our sub-generator G2 learns a combination of results at different scales from the first stage and produces the final binary result. We conduct comprehensive experiments of the proposed method on nine public document image binarization datasets. Experimental results show that compared with many classical and state-of-the-art approaches, our method gains promising performance in the accuracy and robustness of binarization.
Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning

Background/Aims To develop a deep learning system (DLS) that can automatically detect malignant melanoma (MM) in the eyelid from histopathological sections with colossal information density. Methods Setting: Double institutional study. Study population: We retrospectively reviewed 225 230 pathological patches (small section cut from pathologist-labelled area from an H&amp;E image), cut from 155 H&amp;E-stained whole-slide images (WSI). Observation procedures: Labelled gigapixel pathological WSIs were used to train and test a model designed to assign patch-level classification. Using malignant probability from a convolutional neural network, the patches were embedded back into each WSI to generate a visualisation heatmap and leveraged a random forest model to establish a WSI-level diagnosis. Main outcome measure(s): For classification, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the efficacy of the DLS in detecting MM. Results For patch diagnosis, the model achieved an AUC of 0.989 (95% CI 0.989 to 0.991), with an accuracy, sensitivity and specificity of 94.9%, 94.7% and 95.3%, respectively. We displayed the lesion area on the WSIs as graded by malignant potential. For WSI, the obtained sensitivity, specificity and accuracy were 100%, 96.5% and 98.2%, respectively, with an AUC of 0.998 (95% CI 0.994 to 1.000). Conclusion Our DLS, which uses artificial intelligence, can automatically detect MM in histopathological slides and highlight the lesion area on WSIs using a probabilistic heatmap. In addition, our approach has the potential to be applied to the histopathological sections of other tumour types.
Real-time visual detection and tracking system for traffic monitoring

Computer vision systems for traffic monitoring represent an essential tool for a broad range of traffic surveillance applications. Two of the most noteworthy challenges for these systems are the real-time operation with hundreds of vehicles and the total occlusions which hinder the tracking of the vehicles. In this paper, we present a traffic monitoring approach that deals with these two challenges based on three modules: detection, tracking and data association. First, vehicles are identified through a deep learning based detector. Second, tracking is performed with a combination of a Discriminative Correlation Filter and a Kalman Filter. This permits to estimate the tracking error in order to make tracking more robust and reliable. Finally, the data association through the Hungarian algorithm combines the information of the previous steps. The contributions are: (i) a real-time traffic monitoring system robust to occlusions that can process more than four hundred vehicles simultaneously; and (ii) the application of the system to anomaly detection in traffic and roundabout input/output analysis. The system has been evaluated with more than two thousand vehicles in real-life videos.