Reviewerly/exHarmony huggingface dataset:
A self-organizing incremental neural network for continual supervised learning

Continual learning algorithms can adapt to changes of data distributions, new classes, and even completely new tasks without catastrophically forgetting previously acquired knowledge. Here, we present a novel self-organizing incremental neural network, GSOINN+, for continual supervised learning. GSOINN+ learns a topological mapping of the input data to an undirected network and uses a weighted nearest-neighbor rule with fractional distance for classification. GSOINN+ learns incrementally—new classification tasks do not need to be specified a priori, and no rehearsal of previously learned tasks with stored training sets is required. In a series of sequential learning experiments, we show that GSOINN+ can mitigate catastrophic forgetting, even when completely new tasks are to be learned.
Application of Hermitian self-orthogonal GRS codes to some quantum MDS codes

The construction of quantum maximum distance separable (abbreviated to MDS) error-correcting codes has become one of the major concerns in quantum coding theory. In this paper, we further generalize the approach developed in the previous paper, and construct several new classes of Hermitian self-orthogonal generalized Reed-Solomon (GRS) codes. By employing these classical MDS codes, we obtain several classes of quantum MDS codes with large minimum distance. It turns out that our quantum MDS codes exhibited here have less constraints on the selection of code length and some of them have not been constructed before, and in some cases, have larger minimum distance than previous literature. Meanwhile, about half of the distance parameters of our codes are greater than q2+1.
APER: AdaPtive Evidence-driven Reasoning Network for machine reading comprehension with unanswerable questions

Machine Reading Comprehension with unanswerable questions requires that systems not only answer questions when possible, but also output an unanswerable prediction when there is no answer with the given passage. This task encourages systems for true language understanding instead of just selecting the span that seems most related to the question in the conventional extractive reading comprehension. Previous methods have two weaknesses. First, most of them utilize a simple classifier or a verifiable module to determine whether a question is unanswerable. However, they predict the probability of unanswerable questions directly, which lacks the explicit process of explanation. Second, these methods treat the answer extraction task and the unanswerable MRC task as two independent tasks without considering the logical consistency of their results, which leads to the contradiction between two tasks with opposite results on the same question. In this paper, we propose an AdaPtive Evidence-driven Reasoning Network (APER) which can adaptively choose to extract an answer span or output an unanswerable prediction based on the evidence which is refined by Evidence Refining Reasoner. Furthermore, the APER directly correlates the two tasks and guarantees the logical consistency of their results with the proposed novel logical consistency training objective. Experiments on the SQuAD 2.0 and DuReader demonstrate the superiority and effectiveness of our proposed APER model.
SUNet: Symmetric Undistortion Network for Rolling Shutter Correction

The vast majority of modern consumer-grade cameras employ a rolling shutter mechanism, leading to image distortions if the camera moves during image acquisition. In this paper, we present a novel deep network to solve the generic rolling shutter correction problem with two consecutive frames. Our pipeline is symmetrically designed to predict the global shutter image corresponding to the intermediate time of these two frames, which is difficult for existing methods because it corresponds to a camera pose that differs most from the two frames. First, two time-symmetric dense undistortion flows are estimated by using well-established principles: pyramidal construction, warping, and cost volume processing. Then, both rolling shutter images are warped into a common global shutter one in the feature space, respectively. Finally, a symmetric consistency constraint is constructed in the image decoder to effectively aggregate the contextual cues of two rolling shutter images, thereby recovering the high-quality global shutter image. Extensive experiments with both synthetic and real data from public benchmarks demonstrate the superiority of our proposed approach over the state-of-the-art methods.
Characterization, geostatistical modeling and health risk assessment of potentially toxic elements in groundwater resources of northeastern Iran

Northeastern Iran. In northeastern Iran, water needed for municipal and agricultural activities mainly comes from groundwater resources. However, it is subject to substantial anthropogenic and geogenic contamination. We characterize the sources of groundwater contamination by employing an integrated approach that can be applied to the identification of large-scale contamination sources in other regions. An existing dataset of georeferenced water quality parameters from 676 locations in northeast of Iran was analyzed to investigate the geochemical properties of groundwater. Gridding of the parameters graphically illustrates the areas affected by high concentrations of As, Cl−, Cr, Fe, Mg2+, Na+, NO3−, Se, and SO42-. We then identified potential anthropogenic and geogenic contamination sources by employing random forest (RF) regression modeling. Random forest (RF) models show that the major ions, As, Cr, Fe, and Se content of groundwater are mainly determined by geology in the study area. Modeling also links groundwater NO3− contamination with sewage discharge into aquifers as well as the application of nitrogenous and animal-waste fertilizers. Areas of high salinity result from evaporate deposits and irrigation return flow. Medium to high non-carcinogenic health risk is found in areas with high concentrations of geogenic As and Cr in groundwater. Our approach can be applied elsewhere to analyze regional groundwater quality and associated health risks as well as identify potential sources of contamination.
Imbalanced classification: A paradigm‐based review

Abstract A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads to unsatisfactory prediction results on test data. Multiple resampling techniques have been proposed to address the class imbalance issues. Yet, there is no general guidance on when to use each technique. In this article, we provide a paradigm‐based review of the common resampling techniques for binary classification under imbalanced class sizes. The paradigms we consider include the classical paradigm that minimizes the overall classification error, the cost‐sensitive learning paradigm that minimizes a cost‐adjusted weighted type I and type II errors, and the Neyman–Pearson paradigm that minimizes the type II error subject to a type I error constraint. Under each paradigm, we investigate the combination of the resampling techniques and a few state‐of‐the‐art classification methods. For each pair of resampling techniques and classification methods, we use simulation studies and a real dataset on credit card fraud to study the performance under different evaluation metrics. From these extensive numerical experiments, we demonstrate under each classification paradigm, the complex dynamics among resampling techniques, base classification methods, evaluation metrics, and imbalance ratios. We also summarize a few takeaway messages regarding the choices of resampling techniques and base classification methods, which could be helpful for practitioners.
A bibliometric analysis of quantum computing literature: mapping and evidences from scopus

Quantum computing is the next-generation technology for future human society. This article aims to give a comprehensive and in-depth analysis of the global development status of quantum computing. First, the latest research hotspots of quantum computing are reviewed. Then multiple bibliometric metrics were used to analyse the current development status, research hotspots, technological trends, and the contribution of authors, institutions, and countries in quantum computing from 2016 to 2020. The results confirm that quantum computing is still under development, with the United States and China in the leading position, from where most critical scholars came. The most important institutions are scattered in the United States, China, and France. The characteristics of international cooperation, research impact, topics with the highest Prominence percentile, distinctive keyphrases, and leading source journals within the field were also analysed. The results may provide a quantitative basis for scientific research and decision-making in quantum computing.
Iterative Text-Based Editing of Talking-Heads Using Neural Retargeting

We present a text-based tool for editing talking-head video that enables an iterative editing workflow. On each iteration users can edit the wording of the speech, further refine mouth motions if necessary to reduce artifacts, and manipulate non-verbal aspects of the performance by inserting mouth gestures (e.g., a smile) or changing the overall performance style (e.g., energetic, mumble). Our tool requires only 2 to 3 minutes of the target actor video and it synthesizes the video for each iteration in about 40 seconds, allowing users to quickly explore many editing possibilities as they iterate. Our approach is based on two key ideas. (1) We develop a fast phoneme search algorithm that can quickly identify phoneme-level subsequences of the source repository video that best match a desired edit. This enables our fast iteration loop. (2) We leverage a large repository of video of a source actor and develop a new self-supervised neural retargeting technique for transferring the mouth motions of the source actor to the target actor. This allows us to work with relatively short target actor videos, making our approach applicable in many real-world editing scenarios. Finally, our, refinement and performance controls give users the ability to further fine-tune the synthesized results.
Novel implicit-trust-network-based recommendation methodology

Recommender systems are known for helping e-commerce and entertainment sits to provide excellent services into their clients by finding those interests in a short time as possible. Recently, researchers have studied on trust relationships of people for improving user-based recommender systems. Hence, this study proposed an implicit approach for constructing a trust-network, which contains two parts. i. constructing an incipient trust-network by examining Similarity, Confidence and Identical Opinion of users; ii. Reconstructing the incipient trust-network as a telic trust-network by appraising its precision for predicting unknown-items rating. The study also presented methods for determining each of the similarity, the confidence, and the identical opinion. Furthermore, this study presented a recommendation strategy by merging two ways of ranking items based on their predicted rating and users' score to them, considering the trust-network. The study was evaluated on datasets of Ciao and FilmTrust for the trust-network correctness, the predicted-ratings error, and the quality of the recommendations. The results have shown, the scheme of this study has significantly improved the performance compared to some state-of-the-art techniques.
Domain and View-Point Agnostic Hand Action Recognition

Hand action recognition is a special case of action recognition with applications in human-robot interaction, virtual reality or life-logging systems. Building action classifiers able to work for such heterogeneous action domains is very challenging. There are very subtle changes across different actions from a given application but also large variations across domains (e.g. virtual reality vs life-logging). This work introduces a novel skeleton-based hand motion representation model that tackles this problem. The framework we propose is agnostic to the application domain or camera recording view-point. When working on a single domain (intra-domain action classification) our approach performs better or similar to current state-of-the-art methods on well-known hand action recognition benchmarks. And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods. These experiments show the robustness and generalization capabilities of our framework.
Conversational AI: Chatbots

The growth of technologies like Artificial Intelligence (AI), Big Data & Internet of Things (IoT), etc. has marked many advancements in the technological world since the last decade. These technologies have a wide range of applications. One such application is “Chatterbot or “Chatbot”. Chatbots are conversational AIs, which mimics the human while conversing. This technology is a combination of AI & Natural Language Processing (NLP). Chatbots have been a part of technological advancement as it eliminates the need of human & automates boring tasks. Chatbots are used in various domains like education, healthcare, business, etc. In the study undertaken, we reviewed several papers & discussed types of chatbots, their advantages & disadvantages. The review suggested that chatbots can be used everywhere because of its accuracy, lack of dependability on human resources & 24x7 accessibility.
Deep Multiview Collaborative Clustering

The clustering methods have absorbed even-increasing attention in machine learning and computer vision communities in recent years. In this article, we focus on the real-world applications where a sample can be represented by multiple views. Traditional methods learn a common latent space for multiview samples without considering the diversity of multiview representations and use K -means to obtain the final results, which are time and space consuming. On the contrary, we propose a novel end-to-end deep multiview clustering model with collaborative learning to predict the clustering results directly. Specifically, multiple autoencoder networks are utilized to embed multi-view data into various latent spaces and a heterogeneous graph learning module is employed to fuse the latent representations adaptively, which can learn specific weights for different views of each sample. In addition, intraview collaborative learning is framed to optimize each single-view clustering task and provide more discriminative latent representations. Simultaneously, interview collaborative learning is employed to obtain complementary information and promote consistent cluster structure for a better clustering solution. Experimental results on several datasets show that our method significantly outperforms several state-of-the-art clustering approaches.
Construction of binary LCD codes, ternary LCD codes and quaternary Hermitian LCD codes

We give two methods for constructing many linear complementary dual (LCD for short) codes from a given LCD code, by modifying some known methods for constructing self-dual codes. Using the methods, we construct binary LCD codes and quaternary Hermitian LCD codes, which improve the previously known lower bounds on the largest minimum weights.
A throughput-centric view of the performance of datacenter topologies

While prior work has explored many proposed datacenter designs, only two designs, Clos-based and expander-based, are generally considered practical because they can scale using commodity switching chips. Prior work has used two different metrics, bisection bandwidth and throughput, for evaluating these topologies at scale. Little is known, theoretically or practically, how these metrics relate to each other. Exploiting characteristics of these topologies, we prove an upper bound on their throughput, then show that this upper bound better estimates worst-case throughput than all previously proposed throughput estimators and scales better than most of them. Using this upper bound, we show that for expander-based topologies, unlike Clos, beyond a certain size of the network, no topology can have full throughput, even if it has full bisection bandwidth; in fact, even relatively small expander-based topologies fail to achieve full throughput. We conclude by showing that using throughput to evaluate datacenter performance instead of bisection bandwidth can alter conclusions in prior work about datacenter cost, manageability, and reliability.
Blockchain for the Management of Internet of Things Devices in the Medical Industry

With the recent emergence of the smart healthcare era, and patients relying more on personalized health monitoring based on Internet of Medical Things (IoMT) devices; patients’ lives are becoming highly threatened in case they fall victim to counterfeit devices. Thus, verifying whether these body sensors utilized are authentic and reliable in an unimpeachable, credible, and auditable manner without any centralized management is of crucial importance. Furthermore, manipulating data and hijacking in an IoMT context are also of tremendous criticality. Motivated by the aforementioned challenges, in this article, a smart contract-based scalable authentication scheme dedicated for IoMT devices is proposed. The scheme mitigates the deficiencies of the traditional established systems that are extensively built on centralized approaches, vulnerable to distributed denial of service attacks, by leveraging blockchain’s decentralization and security properties. The scheme ensures confidentiality, anonymity, and privacy as it is built on a consortium blockchain and integrity by offering secure firmware updates and protects patients from counterfeit devices by leveraging the physical unclonable function. The authentication approach was implemented on Ethereum and evaluated with regard to its computation and communication costs to prove its feasibility and effectiveness as well as its security by presenting a formal analysis using ProVerif.
Internal Video Inpainting by Implicit Long-range Propagation

We propose a novel framework for video inpainting by adopting an internal learning strategy. Unlike previous methods that use optical flow for cross-frame context propagation to inpaint unknown regions, we show that this can be achieved implicitly by fitting a convolutional neural network to known regions. Moreover, to handle challenging sequences with ambiguous backgrounds or long-term occlusion, we design two regularization terms to preserve high-frequency details and long-term temporal consistency. Extensive experiments on the DAVIS dataset demonstrate that the proposed method achieves state-of-the-art inpainting quality quantitatively and qualitatively. We further extend the proposed method to another challenging task: learning to remove an object from a video giving a single object mask in only one frame in a 4K video. Our source code is available at https://tengfei-wang.github.io/Implicit-Internal-Video-Inpainting/.
Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset

Abstract Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.
An Approach for Peer-to-Peer Federated Learning

We present a novel approach for the collaborative training of neural network models in decentralized federated environments. In the iterative process a group of autonomous peers run multiple training rounds to train a common model. Thereby, participants perform all model training steps locally, such as stochastic gradient descent optimization, using their private, e.g. mission-critical, training datasets. Based on locally updated models, participants can jointly determine a common model by averaging all associated model weights without sharing the actual weight values. For this purpose we introduce a simple n-out-of-n secret sharing schema and an algorithm to calculate average values in a peer-to-peer manner. Our experimental results with deep neural networks on well-known sample datasets prove the generic applicability of the approach, with regard to model quality parameters. Since there is no need to involve a central service provider in model training, the approach can help establish trustworthy collaboration platforms for businesses with high security and data protection requirements.
Block6Tel: Blockchain-based Spectrum Allocation Scheme in 6G-envisioned Communications

The 6G-based spectrum bands allocation to telecom providers would guarantee ultra peak rates, high availability, and extremely low-latency for various user applications. However, the spectrum allocation still suffers from the limitations of fair allocation process, delays in auction process, and collusive bidding due to inherent centralization. Thus, this paper proposes a scheme, Block6Tel, that integrates blockchain (BC) in 6G-envisioned spectrum allocation to ensure secure and trusted band allocation among telecom providers, and ensure transparency among telecom stakeholders. The scheme operates in two phases. First, a 6G-based protocol stack model is proposed that leverages a cell-free communication infrastructure. Then, in the second phase, a BC-based auction algorithm is proposed for inter-operator spectrum allocation, and resource allocations among service providers are finalized. Finally, smart contracts (SC) are executed among telecom providers as bidders, and government authorities (GA) as auctioneers. Through extensive simulations, we prove the superiority of Block6Tel compared with traditional static allocation approaches, in terms of parameters like- resource utilization, requests overhead, and allocation fairness. The results demonstrate that the proposed scheme outperforms the traditional schemes using various parameters.