Reviewerly/exHarmony huggingface dataset:
Literature Survey on Multi-Camera System and Its Application

A multi-camera system combines features from different cameras to exploit a scene of an event to increase the output image quality.The combination of two or more cameras requires prior settings in terms of calibration and architecture.Therefore, this paper surveys the available literature in terms of multi-camera systems' physical arrangements, calibrations, algorithms, and their advantages and disadvantages.We also survey the recent developments and advancements in four areas of multi-camera system applications, which are surveillance, sports, education, and mobile phones.In the surveillance system, the combination of multiple heterogeneous cameras and the discovery of Pan-Tilt-Zoom (PTZ) and smart cameras have brought tremendous achievements in the area of multi-camera control and coordination.Different approaches have been proposed to facilitate effective collaboration and monitoring among the camera network.Furthermore, the application of multi-cameras in sports has made the games more interesting in the aspect of analyses and transparency.The application of the multi-camera system in education has taken education beyond the four walls of the class.The method of teaching, student attendance enrollment, determination of students' attention, teacher and student assessment can now be determined with ease, and all forms of proxy and manipulation in education can be reduced by using a multi-camera system.Besides, the number of cameras featuring on smartphones is gaining noticeable recognition.However, most of these cameras serve different purposes, from zooming, telephoto, and wider Field of View (FOV).Therefore, future smartphones should be expecting more cameras or the development would be in a different direction.
SEML: A Semi-Supervised Multi-Task Learning Framework for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) involves two sub-tasks, namely Aspect Mining (AM) and Aspect Sentiment Classification (ASC), which aims to extract the words describing aspects of a reviewed entity (e.g., a product or service) and analyze the expressed sentiments on the aspects.As AM and ASC can be formulated as a sequence labeling problem to predict the aspect or sentiment labels of each word in the review, supervised deep sequence learning models have recently achieved the best performance.However, these supervised models require a large number of labeled reviews which are very costly or unavailable, and they usually perform only one of the two sub-tasks, which limits their practical use.To this end, this paper proposes a SEmi-supervised Multi-task Learning framework (called SEML) for ABSA.SEML has three key features.(1) SEML applies Cross-View Training (CVT) to enable semi-supervised sequence learning over a small set of labeled reviews and a large set of unlabeled reviews from the same domain in a unified end-to-end architecture.(2) SEML solves the two sub-tasks simultaneously by employing three stacked bidirectional recurrent neural layers to learn the representations of reviews, in which the representations learned from different layers are fed into CVT, AM and ASC, respectively.(3) SEML develops a Moving-window Attentive Gated Recurrent Unit (MAGRU) for the three recurrent neural layers to enhance representation learning and prediction accuracy, as nearby contexts within a moving-window in a review can provide important semantic information for the prediction task in ABSA.Finally, we conduct extensive experiments on ABSA over four review datasets from the SemEval workshops.Experimental results show that SEML significantly outperforms the state-of-the-art models.
Deep Neural Models for Medical Concept Normalization in User-Generated Texts

In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).This is a challenging task since medical terminology is very different when coming from health care professionals or from the general public in the form of social media texts.We approach it as a sequence learning problem with powerful neural networks such as recurrent neural networks and contextualized word representation models trained to obtain semantic representations of social media expressions.Our experimental evaluation over three different benchmarks shows that neural architectures leverage the semantic meaning of the entity mention and significantly outperform an existing state of the art models.
Binary Sine Cosine Algorithms for Feature Selection from Medical Data

A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features.This challenge can be worse while dealing with medical datasets.The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones.In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets.In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data.In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution.The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository.The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Stochastic Client Selection for Federated Learning With Volatile Clients

Federated learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a fraction of available clients are chosen to participate, and the selection decision might have a significant effect on the training efficiency, as well as the final model performance. In this article, we investigate the client selection problem under a volatile context, in which the local training of heterogeneous clients is likely to fail due to various kinds of reasons and in different levels of frequency. Intuitively, too much training failure might potentially reduce the training efficiency, while too much selection on clients with greater stability might introduce bias, thereby resulting in degradation of the training effectiveness. To tackle this tradeoff, we, in this article, formulate the client selection problem under joint consideration of effective participation and fairness. Furthermore, we propose E3CS, a stochastic client selection scheme as a solution. According to our experimental results over a public data set, the proposed selection scheme is able to achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> faster convergence to a fixed model accuracy while maintaining the same level of final model accuracy, compared with the state-of-the-art selection schemes.
A New Method With Swapping of Peers and Fogs to Protect User Privacy in IoT Applications

Internet of Things (IoT) is now serving as a vehicle to a huge number of applications, resulting in innovative and smart solutions in many fields. While providing abundant benefits which improve the quality of our lives, the IoT environment has also created new challenges, especially to users' privacy. Many IoT applications use location-based services (LBS), where service provider (SP) trust cannot be taken for granted. Sensitive information available to SPs could be used to cause considerable loss or damage to users' property or even endanger their lives. There are several methods to preserve the privacy of users' data from SPs but they all suffer from one or more anomalies. This research presents a new method, known as the Swapping of Peers and Fogs (SPF), to protect users' privacy from SPs. By exploiting the features of fogs and smart dummies, the SPF approach offers remarkable improvements to the level of protection of users' identity, which can be used to extract personal information. The SPF method does not compromise accuracy and, by using a pair of caches and fogs, provides greater efficiency for applications as compared to the existing approaches. To demonstrate the effectiveness and efficiency of the proposed method, detailed comparisons with current methods are presented via simulations based on different scenarios. Finally, an application of the SPF method to connected street systems in smart cities is also discussed.
A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization Under Linear Constraints

This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES (lcCMSA-ES). It uses a specially built mutation operator together with repair by projection to satisfy the constraints. The lcCMSA-ES evolves itself on a linear manifold defined by the constraints. The objective function is only evaluated at feasible search points (interior point method). This is a property often required in application domains such as simulation optimization and finite element methods. The algorithm is tested on a variety of different test problems revealing considerable results.
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis

The supervised models for aspect-based sentiment analysis (ABSA) rely heavily on labeled data.However, fine-grained labeled data are scarce for the ABSA task.To alleviate the dependence on labeled data, prior works mainly focused on feature-based adaptation, which used the domain-shared knowledge to construct auxiliary tasks or domain adversarial learning to bridge the gap between domains, while ignored the attribute of instance-based adaptation.To resolve this limitation, we propose an end-to-end framework to jointly perform feature and instance based adaptation for the ABSA task in this paper.Based on BERT, we learn domain-invariant feature representations by using part-of-speech features and syntactic dependency relations to construct auxiliary tasks, and jointly perform word-level instance weighting in the framework of sequence labeling.Experiment results on four benchmarks show that the proposed method can achieve significant improvements in comparison with the state-of-the-arts in both tasks of cross-domain End2End ABSA and crossdomain aspect extraction.
SLM: Learning a Discourse Language Representation with Sentence Unshuffling

We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner.Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other.However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural languages such as sentences and the relationships among them.To that end, we propose a new approach to encourage learning of a contextualized sentence-level representation by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering.Through experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show that this feature of our model improves the performance of the original BERT by large margins.
The Seventh Answer Set Programming Competition: Design and Results

Abstract Answer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017).
Grafit: Learning fine-grained image representations with coarse labels

This paper tackles the problem of learning a finer representation than the one provided by training labels. This enables fine-grained category retrieval of images in a collection annotated with coarse labels only. Our network is learned with a nearest-neighbor classifier objective, and an instance loss inspired by self-supervised learning. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods. Our strategy outperforms all competing methods for retrieving or classifying images at a finer granularity than that available at train time. It also improves the accuracy for transfer learning tasks to fine-grained datasets, thereby establishing the new state of the art on five public benchmarks, like iNaturalist-2018.
Segmentation of osteosarcoma in MRI images by K‐means clustering, Chan‐Vese segmentation, and iterative Gaussian filtering

Unlike other types of tumours, automated osteosarcoma segmentation in magnetic resonance images (MRI) is a challenging task due to its different and unique intensity and texture. This paper presents a technique for segmenting osteosarcoma in MRI images using a combination of image processing techniques which include K-means clustering, Chan-Vese segmentation, iterative Gaussian filtering, and Canny edge detection. In addition, the proposed technique involves iterative morphological operations and object counting. The technique was tested using 50 MRI scan images that contain osteosarcoma tumours. The proposed technique was able to segment the osteosarcoma regardless of the variations in their intensities, textures and locations. The performance of the technique was measured by calculating the values for precision, recall, specificity, Dice score coefficient, accuracy and the running time (RT) for all tested cases. The proposed technique achieved 95.96% precision, 86.15% recall, 99.51% specificity, 89.84% Dice score coefficient, 98.02% accuracy, and 191.62 s average running time. This technique can assist clinicians in making treatment plans for patients with osteosarcoma.
Explain and Predict, and then Predict Again

A desirable property of learning systems is to be both effective and interpretable. Towards this goal, recent models have been proposed that first generate an extractive explanation from the input text and then generate a prediction on just the explanation called explain-then-predict models. These models primarily consider the task input as a supervision signal in learning an extractive explanation and do not effectively integrate rationales data as an additional inductive bias to improve task performance. We propose a novel yet simple approach ExPred, which uses multi-task learning in the explanation generation phase effectively trading-off explanation and prediction losses. Next, we use another prediction network on just the extracted explanations for optimizing the task performance. We conduct an extensive evaluation of our approach on three diverse language datasets -- sentiment classification, fact-checking, and question answering -- and find that we substantially outperform existing approaches.
Requirements, Politics, or Individualism: What Drives the Success of COVID-19 Contact-Tracing Apps?

The year 2020 brought us the global pandemic of COVID-19, which is not just a health crisis but a disruption to the fabric of society around the world. With no vaccine yet approved, other measures have been taken all over the world related to lockdowns, social distancing, and contact tracing to quarantine the infected individuals and suppress community transmission. The numerous challenges presented by this novel coronavirus, such as the incubation period, various symptoms, and asymptomatic superspreaders, have exacerbated the challenges of manual contact tracing.
RPT: Learning Point Set Representation for Siamese Visual Tracking

While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem. In this paper, we argue that this issue is closely related to the prevalent bounding box representation, which provides only a coarse spatial extent of object. Thus an efficient visual tracking framework is proposed to accurately estimate the target state with a finer representation as a set of representative points. The point set is trained to indicate the semantically and geometrically significant positions of target region, enabling more fine-grained localization and modeling of object appearance. We further propose a multi-level aggregation strategy to obtain detailed structure information by fusing hierarchical convolution layers. Extensive experiments on several challenging benchmarks including OTB2015, VOT2018, VOT2019 and GOT-10k demonstrate that our method achieves new state-of-the-art performance while running at over 20 FPS.
A Smart Contract-Based P2P Energy Trading System with Dynamic Pricing on Ethereum Blockchain

We implement a peer-to-peer (P2P) energy trading system between prosumers and consumers using a smart contract on Ethereum blockchain. The smart contract resides on a blockchain shared by participants and hence guarantees exact execution of trade and keeps immutable transaction records. It removes high cost and overheads needed against hacking or tampering in traditional server-based P2P energy trade systems. The salient features of our implementation include: 1. Dynamic pricing for automatic balancing of total supply and total demand within a microgrid, 2. prevention of double sale, 3. automatic and autonomous operation, 4. experiment on a testbed (Node.js and web3.js API to access Ethereum Virtual Machine on Raspberry Pis with MATLAB interface), and 5. simulation via personas (virtual consumers and prosumers generated from benchmark). Detailed description of our implementation is provided along with state diagrams and core procedures.
The ISO/IEC 27001 information security management standard: literature review and theory-based research agenda

Purpose After 15 years of research, this paper aims to present a review of the academic literature on the ISO/IEC 27001, the most renowned standard for information security and the third most widespread ISO certification. Emerging issues are reframed through the lenses of social systems thinking, deriving a theory-based research agenda to inspire interdisciplinary studies in the field. Design/methodology/approach The study is structured as a systematic literature review. Findings Research themes and sub-themes are identified on five broad research foci: relation with other standards, motivations, issues in the implementation, possible outcomes and contextual factors. Originality/value The study presents a structured overview of the academic body of knowledge on ISO/IEC 27001, providing solid foundations for future research on the topic. A set of research opportunities is outlined, with the aim to inspire future interdisciplinary studies at the crossroad between information security and quality management. Managers interested in the implementation of the standard and policymakers can find an overview of academic knowledge useful to inform their decisions related to implementation and regulatory activities.
Quantum repeaters in space

Long-distance entanglement is a very precious resource, but its distribution is very difficult due to the exponential losses of light in optical fibres. A possible solution consists in the use of quantum repeaters, based on entanglement swapping or quantum error correction. Alternatively, satellite-based free-space optical links can be exploited, achieving better loss-distance scaling. We propose to combine these two ingredients, quantum repeaters and satellite-based links, into a scheme that allows to achieve entanglement distribution over global distances with a small number of intermediate untrusted nodes. The entanglement sources, placed on satellites, send quantum states encoded in photons towards orbiting quantum repeater stations, where entanglement swapping is performed. The performance of this repeater chain is assessed in terms of the secret key rate achievable by the BB-84 cryptographic protocol. We perform a comparison with other repeater chain architectures and show that our scheme is superior in almost every situation, achieving higher key rates, reliability and flexibility. Finally, we analyse the feasibility of the implementation in the mid-term future and discuss exemplary orbital configurations. The integration of satellite-based links with ground repeater networks can be envisaged to represent the backbone of the future Quantum Internet.
Span-based Semantic Parsing for Compositional Generalization

Despite the success of sequence-to-sequence (seq2seq) models in semantic parsing, recent work has shown that they fail in compositional generalization, i.e., the ability to generalize to new structures built of components observed during training.In this work, we posit that a span-based parser should lead to better compositional generalization.we propose SPANBASEDSP, a parser that predicts a span tree over an input utterance, explicitly encoding how partial programs compose over spans in the input.SPANBASEDSP extends Pasupat et al. ( 2019) to be comparable to seq2seq models by (i) training from programs, without access to gold trees, treating trees as latent variables, (ii) parsing a class of non-projective trees through an extension to standard CKY.On GEOQUERY, SCAN and CLOSURE datasets, SPANBASEDSP performs similarly to strong seq2seq baselines on random splits, but dramatically improves performance compared to baselines on splits that require compositional generalization: from 61.0 → 88.9 average accuracy.
LCD codes from tridiagonal Toeplitz matrices

Double Toeplitz (DT) codes are codes with a generator matrix of the form (I,T) with T a Toeplitz matrix, that is to say constant on the diagonals parallel to the main. When T is tridiagonal and symmetric we determine its spectrum explicitly by using Dickson polynomials, and deduce from there conditions for the code to be LCD. Using a special concatenation process, we construct optimal or quasi-optimal examples of binary and ternary LCD codes from DT codes over extension fields.