Reviewerly/exHarmony huggingface dataset:
Calliope: Automatic Visual Data Story Generation from a Spreadsheet

A full-automatically generated visual data story about COVID-19 virus propagation in March 2020 in China.The whole story consists of six data facts shown in the storyline mode.The story first illustrates an overall trend of the total death followed by the elaborations of data details in Hubei, which is the most influenced province in China.Finally, the story is summarized by the total death and recovered in March, showing the improved situation in China.Abstract-Visual data stories shown in the form of narrative visualizations such as a poster or a data video, are frequently used in data-oriented storytelling to facilitate the understanding and memorization of the story content.Although useful, technique barriers, such as data analysis, visualization, and scripting, make the generation of a visual data story difficult.Existing authoring tools rely on users' skills and experiences, which are usually inefficient and still difficult.In this paper, we introduce a novel visual data story generating system, Calliope, which creates visual data stories from an input spreadsheet through an automatic process and facilities the easy revision of the generated story based on an online story editor.Particularly, Calliope incorporates a new logic-oriented Monte Carlo tree search algorithm that explores the data space given by the input spreadsheet to progressively generate story pieces (i.e., data facts) and organize them in a logical order.The importance of data facts is measured based on information theory, and each data fact is visualized in a chart and captioned by an automatically generated description.We evaluate the proposed technique through three example stories, two controlled experiments, and a series of interviews with 10 domain experts.Our evaluation shows that Calliope is beneficial to efficient visual data story generation.
3D Object Detection Using Scale Invariant and Feature Reweighting Networks

3D object detection plays an important role in a large number of real-world applications. It requires us to estimate the localizations and the orientations of 3D objects in real scenes. In this paper, we present a new network architecture which focuses on utilizing the front view images and frustum point clouds to generate 3D detection results. On the one hand, a PointSIFT module is utilized to improve the performance of 3D segmentation. It can capture the information from different orientations in space and the robustness to different scale shapes. On the other hand, our network obtains the useful features and suppresses the features with less information by a SENet module. This module reweights channel features and estimates the 3D bounding boxes more effectively. Our method is evaluated on both KITTI dataset for outdoor scenes and SUN-RGBD dataset for indoor scenes. The experimental results illustrate that our method achieves better performance than the state-of-the-art methods especially when point clouds are highly sparse.
Blockchain for Modern Applications: A Survey

Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

Sometimes it is not enough for a DNN to produce an outcome. For example, in applications such as healthcare, users need to understand the rationale of the decisions. Therefore, it is imperative to develop algorithms to learn models with good interpretability (Doshi-Velez 2017). An important factor that leads to the lack of interpretability of DNNs is the ambiguity of neurons, where a neuron may fire for various unrelated concepts. This work aims to increase the interpretability of DNNs on the whole image space by reducing the ambiguity of neurons. In this paper, we make the following contributions: 1) We propose a metric to evaluate the consistency level of neurons in a network quantitatively. 2) We find that the learned features of neurons are ambiguous by leveraging adversarial examples. 3) We propose to improve the consistency of neurons on adversarial example subset by an adversarial training algorithm with a consistent loss.
Waiting time in quantum repeaters with probabilistic entanglement swapping

The standard approach to realize a quantum repeater relies upon probabilistic but heralded entangled state manipulations and the storage of quantum states while waiting for successful events. In the literature on this class of repeaters, calculating repeater rates has typically depended on approximations assuming sufficiently small probabilities. Here we propose an exact and systematic approach including an algorithm based on Markov chain theory to compute the average waiting time (and hence the transmission rates) of quantum repeaters with arbitrary numbers of links. For up to four repeater segments, we explicitly give the exact rate formulae for arbitrary entanglement swapping probabilities. Starting with three segments, we explore schemes with arbitrary (not only doubling) and dynamical (not only predetermined) connections. The effect of finite memory times is also considered and the relative influence of the classical communication (of heralded signals) is shown to grow significantly for larger probabilities. Conversely, we demonstrate that for small swapping probabilities the statistical behavior of the waiting time in a quantum repeater cannot be characterized by its average value alone and additional statistical quantifiers are needed. For large repeater systems, we propose a recursive approach based on exactly but still efficiently computable waiting times of sufficiently small sub-repeaters. This approach leads to better lower bounds on repeater rates compared to existing schemes.
Review of Smart Health Monitoring Approaches With Survey Analysis and Proposed Framework

Smart health monitoring system technologies pertaining to the biomedical domain has evinced intense interest, which in turn has led the emphasis on unconventional applications of engineering and internetwork transmission in the field. Throughout the next series of textual and contextual matter, we will be reviewing and exhibiting, such initiatives and established methods which are being used to achieve health monitoring systems approaching toward making smart remote villages and cities. It also collates for the reader the issues and challenges prevalent in the present technical advancements and for societal validation, a survey analysis has also been briefed in this paper to show the demand and requisites of the real world in health monitoring system. Today there is a need for an integrated solution which can diagnose the early cyanotic congenital diseases in newly born babies to monitoring of various geriatric problems in elderly people. The future avenues of maximum life expectancy and uber-prompt medical treatment depend upon it. This paper proposes an approach required to make smart villages and towns with respect to the healthcare domain. This proposal has been made with due consideration and technical backing of survey research including questionnaire-based evaluation and regressive analysis of issues in present technologies.
Ancilla-Free Quantum Error Correction Codes for Quantum Metrology

Quantum error correction has recently emerged as a tool to enhance quantum sensing under Markovian noise. It works by correcting errors in a sensor while letting a signal imprint on the logical state. This approach typically requires a specialized error-correcting code, as most existing codes correct away both the dominant errors and the signal. To date, however, few such specialized codes are known, among which most require noiseless, controllable ancillas. We show here that such ancillas are not needed when the signal Hamiltonian and the error operators commute, a common limiting type of decoherence in quantum sensors. We give a semidefinite program for finding optimal ancilla-free sensing codes in general, as well as closed-form codes for two common sensing scenarios: qubits undergoing dephasing, and a lossy bosonic mode. Finally, we analyze the sensitivity enhancement offered by the qubit code under arbitrary spatial noise correlations, beyond the ideal limit of orthogonal signal and noise operators.
Balanced Sparsity for Efficient DNN Inference on GPU

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that Balanced Sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as finegrained sparsity.
Social Media-Related Cybercrimes and Techniques for Their Prevention

Abstract Since a past decade, social media networking has become an essential part of everyone’s life affecting cultural, economic and social life of the people. According to internetlivestats.com, in March 2019 the Internet users reached 4 168 461 500, i.e., 50.08 % penetration of world population. According to Statista, in 2019 there are 2.22 billion social media networking users worldwide, i.e., 31 % of global social media networking penetration and it is expected that in 2021 this number will reach 3.02 billion. These social networking sites are attracting users from all walks of life and keeping these users’ data in the cloud. Today’s big challenge is related to an increase in volume, velocity, variety and veracity of data in social media networking, and this leads to creating several concerns, including privacy and security; on the other hand, it also proves as a tool to prevent and investigate cybercrime, if intelligently and smartly handled. The law enforcement agencies are putting their utmost efforts to prevent cybercrime by monitoring communications activities over the Internet. In this paper, the authors discuss recommendations and techniques for preventing cybercrime.
Analyzing Compositionality-Sensitivity of NLI Models

Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models’ compositional understanding.
Securing IoT Devices and SecurelyConnecting the Dots Using REST API and Middleware

Internet of Things (IoT) is a fairly disruptive technology with inconceivable growth, impact, and capability. We present the role of REST API in the IoT Systems and some initial concepts of IoT, whose technology is able to record and count everything. We as well highlight the concept of middleware that connects these devices and cloud. The appearance of new IoT applications in the cloud has brought new threats to security and privacy of data. Therefore it is required to introduce a secure IoT system which doesn't allow attackers infiltration in the network through IoT devices and also to secure data in transit from IoT devices to cloud. We provide the details on how Representational State Transfer (REST) API allows to securely expose connected devices to applications on cloud and users. In the proposed model, middleware is primarily used to expose device data through REST and to hide details and act as an interface to the user to interact with sensor data.
Dynamic Path Planning for Unmanned Vehicles Based on Fuzzy Logic and Improved Ant Colony Optimization

Technical advancement has propelled the proliferation of unmanned vehicles. Out of the multiple paths between origin (O) and destination (D), the optimal O-D path should be selected in the light of travel distance, travel time, fuel cost and pollutant emissions. This paper proposes a dynamic path planning strategy based on fuzzy logic (FL) and improved ant colony optimization (ACO). Firstly, the classic ACO was improved into the rank-based ant system. The rank-based ant system works well in static environments, but cannot adapt well to dynamic environments. Considering the difficulty in accurate digitization of dynamic factors, the improved ACO was integrated with the FL into the fuzzy logic ant colony optimization (FLACO) to find the optimal path for unmanned vehicles. Finally, the FLACO, the classic ACO and the improved ACO were separately applied to find the optimal path in a road network, with a novel concept called virtual path length. The results show that the FLACO output the shortest virtual path among the three algorithms, i.e. identified the most cost-effective path. This mean the FLACO can find the most efficient and safe path for unmanned vehicles in a dynamic manner.
Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity Maximization

One-Shot Neural Architecture Search (NAS) significantly improves the computational efficiency through weight sharing. However, this approach also introduces multi-model forgetting during the supernet training (architecture search phase), where the performance of previous architectures degrade when sequentially training new architectures with partially-shared weights. To overcome such catastrophic forgetting, the state-of-the-art method assumes that the shared weights are optimal when jointly optimizing a posterior probability. However, this strict assumption is not necessarily held for One-Shot NAS in practice. In this paper, we formulate the supernet training in the One-Shot NAS as a constrained optimization problem of continual learning that the learning of current architecture should not degrade the performance of previous architectures during the supernet training. We propose a Novelty Search based Architecture Selection (\textbf{NSAS}) loss function and demonstrate that the posterior probability could be calculated without the strict assumption when maximizing the diversity of the selected constraints. A greedy novelty search method is devised to find the most representative subset to regularize the supernet training. We apply our proposed approach to two One-Shot NAS baselines, random sampling NAS (RandomNAS) and gradient-based sampling NAS (GDAS). Extensive experiments demonstrate that our method enhances the predictive ability of the supernet in One-Shot NAS and achieves remarkable performance on CIFAR-10, CIFAR-100, and PTB with efficiency.
Elastic Scheduling for Microservice Applications in Clouds

Microservices are widely used for flexible software development. Recently, containers have become the preferred deployment technology for microservices because of fast start-up and low overhead. However, the container layer complicates task scheduling and auto-scaling in clouds. Existing algorithms do not adapt to the two-layer structure composed of virtual machines and containers, and they often ignore streaming workloads. To this end, this article proposes an Elastic Scheduling for Microservices (ESMS) that integrates task scheduling with auto-scaling. ESMS aims to minimize the cost of virtual machines while meeting deadline constraints. Specifically, we define the task scheduling problem of microservices as a cost optimization problem with deadline constraints and propose a statistics-based strategy to determine the configuration of containers under a streaming workload. Then, we propose an urgency-based workflow scheduling algorithm that assigns tasks and determines the type and quantity of instances for scale-up. Finally, we model the mapping of new containers to virtual machines as a variable-sized bin-packing problem and solve it to achieve integrated scaling of the virtual machines and containers. Via simulation-based experiments with well-known workflow applications, the ability of ESMS to improve the success ratio of meeting deadlines and reduce the cost is verified through comparison with existing algorithms.
Deep transfer learning for reducing health care disparities arising from biomedical data inequality

Abstract As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. However, the biomedical data inequality between different ethnic groups is set to generate new health care disparities through data-driven, algorithm-based biomedical research and clinical decisions. Using an extensive set of machine learning experiments on cancer omics data, we find that current prevalent schemes of multiethnic machine learning are prone to generating significant model performance disparities between ethnic groups. We show that these performance disparities are caused by data inequality and data distribution discrepancies between ethnic groups. We also find that transfer learning can improve machine learning model performance for data-disadvantaged ethnic groups, and thus provides an effective approach to reduce health care disparities arising from data inequality among ethnic groups.
Blockchain-driven supply chain decentralized operations – information sharing perspective

Purpose Information sharing helps improve the efficiency of the supply chain. However, there are some problems in the multi-stage structure supply chain, such as untimely information feedback and distortion. Despite recent progress in ensuring improved collaboration in the past decade or so, the inefficiency status continues to persist. It is also difficult to add value to the supply chain. The information sharing framework and decentralized model designed in this paper are to deal with existing problems. Design/methodology/approach This paper is based on the theoretical research of supply chain information asymmetry and synergetics. We attempt to introduce blockchain technology into supply chain operation management, reconstruct information sharing architecture and provide a new decentralized mode to promote the collaborative operation of all nodes. The information sharing and decentralization operational model are built, which has changed the hierarchical relationship between upstream and downstream enterprises, regarding customers as the center of the whole system and effectively reducing the bullwhip effect. Finally, we selected the home supply chain as an example design and performed system dynamics simulation on the blockchain-based operation process. Findings The model of setting up the scene application mode based on blockchain is helpful to realize the goal of supply chain management to reduce cost, improve quality and enhance the overall efficiency of the system. Originality/value Based on the blockchain technology, this paper constructed a new supply chain operation mode and used the synergetic theory and the concept of the product–service system to explain the process of value increment in detail.
A Fourier Disparity Layer Representation for Light Fields

In this paper, we present a new Light Field representation for efficient Light Field processing and rendering called Fourier Disparity Layers (FDL). The proposed FDL representation samples the Light Field in the depth (or equivalently the disparity) dimension by decomposing the scene as a discrete sum of layers. The layers can be constructed from various types of Light Field inputs, including a set of sub-aperture images, a focal stack, or even a combination of both. From our derivations in the Fourier domain, the layers are simply obtained by a regularized least square regression performed independently at each spatial frequency, which is efficiently parallelized in a GPU implementation. Our model is also used to derive a gradient descent-based calibration step that estimates the input view positions and an optimal set of disparity values required for the layer construction. Once the layers are known, they can be simply shifted and filtered to produce different viewpoints of the scene while controlling the focus and simulating a camera aperture of arbitrary shape and size. Our implementation in the Fourier domain allows real-time Light Field rendering. Finally, direct applications such as view interpolation or extrapolation and denoising are presented and evaluated.
Towards Question-based Recommender Systems

Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless, research in the field is still limited, compared to traditional recommender systems. In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions. Previous conversational recommender systems ask users to express their preferences over items or item facets. Our model, instead, asks users to express their preferences over descriptive item features. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online by a closed-form solution based on the user answers. Meanwhile, our model infers the underlying user belief and preferences over items to learn an optimal question-asking strategy by using Generalized Binary Search, so as to ask a sequence of questions to the user. Our experimental results demonstrate that our proposed matrix factorization model outperforms the traditional Probabilistic Matrix Factorization model. Further, our proposed Qrec model can greatly improve the performance of state-of-the-art baselines, and it is also effective in the case of cold-start user and item recommendations.