Reviewerly/exHarmony huggingface dataset:
The State of the Art in Map‐Like Visualization

Abstract Cartographic maps have been shown to provide cognitive benefits when interpreting data in relation to a geographic location. In visualization, the term map‐like describes techniques that incorporate characteristics of cartographic maps in their representation of abstract data. However, the field of map‐like visualization is vast and currently lacks a clear classification of the existing techniques. Moreover, choosing the right technique to support a particular visualization task is further complicated, as techniques are scattered across different domains, with each considering different characteristics as map‐like. In this paper, we give an overview of the literature on map‐like visualization and provide a hierarchical classification of existing techniques along two general perspectives: imitation and schematization of cartographic maps. Each perspective is further divided into four principal categories that group common map‐like techniques along the visual primitives they affect. We further discuss this classification from a task‐centered view and highlight open research questions.
AutoSTR: Efficient Backbone Search for Scene Text Recognition

Scene text recognition (STR) is challenging due to the diversity of text instances and the complexity of scenes. However, no STR methods can adapt backbones to different diversities and complexities. In this work, inspired by the success of neural architecture search (NAS), we propose automated STR (AutoSTR), which can address the above issue by searching data-dependent backbones. Specifically, we show both choices on operations and the downsampling path are very important in the search space design of NAS. Besides, since no existing NAS algorithms can handle the spatial constraint on the path, we propose a two-step search algorithm, which decouples operations and downsampling path, for an efficient search in the given space. Experiments demonstrate that, by searching data-dependent backbones, AutoSTR can outperform the state-of-the-art approaches on standard benchmarks with much fewer FLOPS and model parameters. (Code is available at https://github.com/AutoML-4Paradigm/AutoSTR.git ).
Malaria Detection using Deep Learning

Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the examination. Deep Learning algorithms have been applied to malaria blood smears for diagnosis before. However, practical performance has not been sufficient so far. This paper proposes a new and highly robust machine learning model based on a convolutional neural network (CNN) which automatically classifies and predicts infected cells in thin blood smears on standard microscope slides. A ten-fold cross-validation layer of the convolutional neural network on 27,558 single-cell images is used to understand the parameter of the cell. Three types of CNN models are compared based on their accuracy and select the precise accurate - Basic CNN, VGG-19 Frozen CNN, and VGG-19 Fine Tuned CNN. Then by comparing the accuracy of the three models, the model with a higher rate of accuracy is acquired.
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

Robotic vision is a field where continual learning can play a significant role. An embodied agent operating in a complex environment subject to frequent and unpredictable changes is required to learn and adapt continuously. In the context of object recognition, for example, a robot should be able to learn (without forgetting) objects of never before seen classes as well as improving its recognition capabilities as new instances of already known classes are discovered. Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates. In this paper, we introduce a novel continual learning protocol based on the CORe50 benchmark and propose two rehearsal-free continual learning techniques, CWR* and AR1*, that can learn effectively even in the challenging case of nearly 400 small non-i.i.d. incremental batches. In particular, our experiments show that AR1* can outperform other state-of-the-art rehearsal-free techniques by more than 15% accuracy in some cases, with a very light and constant computational and memory overhead across training batches.
Architecting Noisy Intermediate-Scale Trapped Ion Quantum Computers

Trapped ions (TI) are a leading candidate for building Noisy Intermediate-Scale Quantum (NISQ) hardware. TI qubits have fundamental advantages over other technologies such as superconducting qubits, including high qubit quality, coherence and connectivity. However, current TI systems are small in size, with 5-20 qubits and typically use a single trap architecture which has fundamental scalability limitations. To progress towards the next major milestone of 50-100 qubit TI devices, a modular architecture termed the Quantum Charge Coupled Device (QCCD) has been proposed. In a QCCD-based TI device, small traps are connected through ion shuttling. While the basic hardware components for such devices have been demonstrated, building a 50-100 qubit system is challenging because of a wide range of design possibilities for trap sizing, communication topology and gate implementations and the need to match diverse application resource requirements.Towards realizing QCCD-based TI systems with 50-100 qubits, we perform an extensive application-driven architectural study evaluating the key design choices of trap sizing, communication topology and operation implementation methods. To enable our study, we built a design toolflow which takes a QCCD architecture's parameters as input, along with a set of applications and realistic hardware performance models. Our toolflow maps the applications onto the target device and simulates their execution to compute metrics such as application run time, reliability and device noise rates. Using six applications and several hardware design points, we show that trap sizing and communication topology choices can impact application reliability by up to three orders of magnitude. Microarchitectural gate implementation choices influence reliability by another order of magnitude. From these studies, we provide concrete recommendations to tune these choices to achieve highly reliable and performant application executions. With industry and academic efforts underway to build TI devices with 50-100 qubits, our insights have the potential to influence QC hardware in the near-future and accelerate the progress towards practical QC systems.
Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments

Online coverage planning can be useful in applications like field monitoring and search and rescue. Without prior information of the environment, achieving resolution-complete coverage considering the non-holonomic mobility constraints in commonly-used vehicles (e.g., wheeled robots) remains a challenge. In this letter, we propose a hierarchical, hex-decomposition-based coverage planning algorithm for unknown, obstacle-cluttered environments. The proposed approach ensures resolution-complete coverage, can be tuned to achieve fast exploration, and plans smooth paths for Dubins vehicles to follow at constant velocity in real-time. Gazebo simulations and hardware experiments with a non-holonomic wheeled robot show that our approach can successfully tradeoff between coverage and exploration speed and can outperform existing online coverage algorithms in terms of total covered area or exploration speed according to how it is tuned.
FedMed: A Federated Learning Framework for Language Modeling

Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next word or phrase and facilitating the human-machine interaction in a virtual keyboard of the smartphone or laptop. Mobile keyboard prediction with FL hopes to satisfy the growing demand that high-level data privacy be preserved in artificial intelligence applications even with the distributed models training. However, there are two major problems in the federated optimization for the prediction: (1) aggregating model parameters on the server-side and (2) reducing communication costs caused by model weights collection. To address the above issues, traditional FL methods simply use averaging aggregation or ignore communication costs. We propose a novel Federated Mediation (FedMed) framework with the adaptive aggregation, mediation incentive scheme, and topK strategy to address the model aggregation and communication costs. The performance is evaluated in terms of perplexity and communication rounds. Experiments are conducted on three datasets (i.e., Penn Treebank, WikiText-2, and Yelp) and the results demonstrate that our FedMed framework achieves robust performance and outperforms baseline approaches.
Deep Learning Methods for Modeling Bitcoin Price

A precise prediction of Bitcoin price is an important aspect of digital financial markets because it improves the valuation of an asset belonging to a decentralized control market. Numerous studies have studied the accuracy of models from a set of factors. Hence, previous literature shows how models for the prediction of Bitcoin suffer from poor performance capacity and, therefore, more progress is needed on predictive models, and they do not select the most significant variables. This paper presents a comparison of deep learning methodologies for forecasting Bitcoin price and, therefore, a new prediction model with the ability to estimate accurately. A sample of 29 initial factors was used, which has made possible the application of explanatory factors of different aspects related to the formation of the price of Bitcoin. To the sample under study, different methods have been applied to achieve a robust model, namely, deep recurrent convolutional neural networks, which have shown the importance of transaction costs and difficulty in Bitcoin price, among others. Our results have a great potential impact on the adequacy of asset pricing against the uncertainties derived from digital currencies, providing tools that help to achieve stability in cryptocurrency markets. Our models offer high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.
A DNN-Based Cross-Domain Recommender System for Alleviating Cold-Start Problem in E-Commerce

Many applications use recommender systems to predict user preferences, improve user experience, and increase the amount of sales. However, because of the cold-start problem, it is not easy to recommend items to new users accurately. Recommendation performance degrades in the case of users with little interaction, in particular latent users who have never used the service. To alleviate the cold-start problem, we develop a framework that combines an online shopping domain with information from an Ads platform. Our framework employs deep learning to build a cross-domain recommender system based on shared users in these two domains. This is the first attempt that models users based on shared users in online shopping and Ads domains for solving the user-cold start problem. We apply Word2Vec to turn textual information on users and items into latent vectors as their representations. The experimental results show the effectiveness of deep neural approaches with knowledge transferred from another domain for the cold-start problem. Textual information may contain useless information, and Word2Vec cannot capture some structural and semantic correlations between different users. Therefore, we propose R-metapath2Vec to enhance user modeling and use the Stacking model to integrate these two kinds of user representations. The experimental results demonstrate the effectiveness of our integration model: our framework can recommend products to users of another domain through Ads distribution in a more accurate level.
A whale optimization system for energy-efficient container placement in data centers

The recent popularity of the container-as-a-service (CaaS) paradigm in data centers and with cloud providers increases the significance of the process of container deployment modeling in cloud environments. Modern data centers face the significant challenge of optimizing two objectives, power consumption and resource utilization. Thus, the task of initial placement has a new dimension, placing the containers on virtual machines (VMs) and placing these host VMs on physical machines (PMs) such that the power consumption is minimized and the resource utilization is maximized. From another perspective, the complexity of this problem increases when the heterogeneity of the containers, VMs and PMs, is considered. Therefore, in this paper, we address the problem of container and VM placement in CaaS environments with consideration of optimizing both power consumption and resource utilization. Existing solutions have addressed this problem by applying simple heuristics to the container placement problem and then applying a more sophisticated approach to the VM placement problem. In other words, the existing methods separate the two search spaces. In this work, we propose an algorithm based on the Whale Optimization Algorithm (WOA) to solve these two stages of placement as one optimization problem. The proposed algorithm searches for the optimal numbers of VMs and PMs in one search space. The proposed method is evaluated over different levels of heterogeneous environments against recent methods. Experimental results show the superiority of the proposed method over the methods of comparison on the suite of test environments.
Extractive Document Summarization Based on Dynamic Feature Space Mapping

The exponential growth of the Web documents has constituted the need for automatic document summarization.In this context, extractive document summarization, i.e., that task of extracting the most relevant information, removing redundancy and presenting the remained data in a coherent and cohesive structure, is a challenging task.In this article, we propose a novel intelligent approach, namely ExDoS, that harvests benefits of both supervised and unsupervised algorithms simultaneously.To the best of our knowledge, ExDoS is the first approach to combine both supervised and unsupervised algorithms in a single framework and an interpretable manner for document summarization purpose.ExDoS iteratively minimizes the error rate of the classifier in each cluster with the help of dynamic local feature weighting.Moreover, this approach specifies the contribution of features to discriminate each class, which is a challenging issue in the summarization task.Therefore, in addition to summarizing text, ExDoS is also able to measure the importance of each feature in the summarization process.We evaluate our model both automatically (in terms of ROUGE factor) and empirically (human analysis) on the benchmark datasets: the DUC2002 and CNN/DailyMail.Results show that our model obtains higher ROUGE scores comparing to most state-ofthe-art models.The human evaluation also demonstrates that our model is capable of generating informative and readable summaries.
Z-IoT: Passive Device-class Fingerprinting of ZigBee and Z-Wave IoT Devices

In addition to traditional networking devices (e.g., gateways, firewalls), current corporate and industrial networks integrate resource-limited Internet of Things (IoT) devices like smart outlets and smart sensors. In these settings, cyber attackers can bypass traditional security solutions and spoof legitimate IoT devices to gain illegal access to the systems. Thus, IoT device-class identification is crucial to protect critical networks from unauthorized access. In this paper, we propose Z-IoT, the first fingerprinting framework used to identify IoT device classes that utilize ZigBee and Z-Wave protocols. Z-IoT monitors idle network traffic among IoT devices to implement signature-based device-class fingerprinting mechanisms. Utilizing passive packet capturing techniques and optimal selection of filtering criteria and machine learning algorithms, Z-IoT identifies different types of IoT devices while guaranteeing the anonymity of the network data. To test Z-IoT's efficacy, we implemented several testbeds, including a total of 39 commodity IoT devices that communicate over ZigBee and Z-Wave protocols. Our experimental results showed an excellent performance in identifying different classes of IoT devices with average precision and recall of over 91%. Finally, the proposed framework yields no overhead to the IoT devices or the network traffic.
Unsupervised Anomaly Detection Approach for Time-Series in Multi-Domains Using Deep Reconstruction Error

Automatic anomaly detection for time-series is critical in a variety of real-world domains such as fraud detection, fault diagnosis, and patient monitoring. Current anomaly detection methods detect the remarkably low proportion of the actual abnormalities correctly. Furthermore, most of the datasets do not provide data labels, and require unsupervised approaches. By focusing on these problems, we propose a novel deep learning-based unsupervised anomaly detection approach (RE-ADTS) for time-series data, which can be applicable to batch and real-time anomaly detections. RE-ADTS consists of two modules including the time-series reconstructor and anomaly detector. The time-series reconstructor module uses the autoregressive (AR) model to find an optimal window width and prepares the subsequences for further analysis according to the width. Then, it uses a deep autoencoder (AE) model to learn the data distribution, which is then used to reconstruct a time-series close to the normal. For anomalies, their reconstruction error (RE) was higher than that of the normal data. As a result of this module, RE and compressed representation of the subsequences were estimated. Later, the anomaly detector module defines the corresponding time-series as normal or an anomaly using a RE based anomaly threshold. For batch anomaly detection, the combination of the density-based clustering technique and anomaly threshold is employed. In the case of real-time anomaly detection, only the anomaly threshold is used without the clustering process. We conducted two types of experiments on a total of 52 publicly available time-series benchmark datasets for the batch and real-time anomaly detections. Experimental results show that the proposed RE-ADTS outperformed the state-of-the-art publicly available anomaly detection methods in most cases.
Incorporating Consumer Insights into the UK Food Packaging Supply Chain in the Transition to a Circular Economy

The growth of eating lunch purchased out of the home has led to an increased need for pre-packaged food-to-go products. Single-use plastic packaging is frequently chosen for its food safety and convenience attributes; however, the material format is under scrutiny due to concerns over economic waste and environmental impact. A circular economy could transform linear make-use-dispose supply chains into circular systems, ensuring the cycling of valuable plastic resources. However, there has been limited research into how consumers will behave within circular economic systems. Understanding consumer behaviour with packaging disposed out of the home could aid designers in developing solutions society will adopt in the transition to a circular economy. This study evaluates the application of behaviour research methods, and the behavioural insight outputs, with stakeholders from the UK food-to-go packaging supply chain. A novel co-design workshop and business origami technique allowed multiple stakeholder groups to collaboratively discuss, evaluate, and plan how consumer behaviour techniques could be used within their supply chain packaging development process. Although all stakeholders identified strengths in incorporating behaviour studies into the development process, providing essential knowledge feedback loops, barriers to their application include the cost and time to implement, plus the existing inconsistent UK waste infrastructure.
Least squares KNN-based weighted multiclass twin SVM

K-nearest neighbor (KNN) based weighted multi-class twin support vector machines (KWMTSVM) is a novel multi-class classification method. In this paper, we propose a novel least squares version of KWMTSVM called LS-KWMTSVM by replacing the inequality constraints with equality constraints and minimized the slack variables using squares of 2-norm instead of conventional 1-norm. This simple modification leads to a very fast algorithm with much better results. The modified primal problems in the proposed LS-KWMTSVM solves only two systems of linear equations whereas two quadratic programming problems (QPPs) need to solve in KWMTSVM. The proposed LS-KWMTSVM, same as KWMTSVM, employed the weight matrix in the objective function to exploit the local information of the training samples. To exploit the inter class information, we use weight vectors in the constraints of the proposed LS-KWMTSVM. If any component of vectors is zero then the corresponding constraint is redundant and thus we can avoid it. Elimination of redundant constraints and solving a system of linear equations instead of QPPs makes the proposed LS-KWMTSVM more robust and faster than KWMTSVM. The proposed LS-KWMTSVM, commensurate as the KWMTSVM, all the training data points into a “1-versus-1-versus-rest” structure, and thus our LS-KWMTSVM generate ternary output {-1,0,+1} which helps to deal with imbalance datasets. Numerical experiments on several UCI and KEEL imbalance datasets(with high imbalance ratio) clearly indicate that the proposed LS-KWMTSVM has better classification accuracy compared with other baseline methods but with remarkably less computational time.
Cap-Count Guided Weakly Supervised Insulator Cap Missing Detection in Aerial Images

The vision-based detection of cap missing on insulator in aerial images is an extremely challenging task. Previous approaches have achieved promising performances. However, most of them include two stages of insulator localization and cap detection, which require large-scale insulator and cap annotations. In this article, we propose a cap-count guided weakly supervised approach, which only needs insulator bounding box annotations. The proposed method first generates cap annotations automatically by giving the insulator bounding boxes and the number of caps. Then, we use a deep learning model to detect insulator strings and caps simultaneously. Finally, we design an efficient cap missing detection strategy by using caps geometric constraints to measure the position of caps. We evaluate the proposed approach on our collected large-scale aerial image dataset. The experimental results show that the proposed method can accurately detect the insulator strings and caps with any angle from an aerial image and further recognize the missing cap on insulator. The precision and recall reach 92.86% and 86.67% respectively, which achieves competitive detection results.