Reviewerly/exHarmony huggingface dataset:
Not All Qubits Are Created Equal

Existing and near-term quantum computers are not yet large enough to support fault-tolerance. Such systems with few tens to few hundreds of qubits are termed as Noisy Intermediate Scale Quantum computers (NISQ), and these systems can provide benefits for a class of quantum algorithms. In this paper, we study the problems of Qubit-Allocation (mapping of program qubits to machine qubits) and Qubit-Movement (routing qubits from one location to another for entanglement). We observe that there can be variation in the error rates of different qubits and links, which can impact the decisions for qubit movement and qubit allocation. We analyze publicly available characterization data for the IBM-Q20 to quantify the variation and show that there is indeed significant variability in the error rates of the qubits and the links connecting them. We show that the device variability has a significant impact on the overall system reliability. To exploit the variability in error rate, we propose Variation-Aware Qubit Movement (VQM) and Variation-Aware Qubit Allocation (VQA), policies that optimize the movement and allocation of qubits to avoid the weaker qubits and links, and guide more operations towards the stronger qubits and links. Our evaluations, with a simulation-based model of IBM-Q20, show that Variation-Aware policies can improve the system reliability by up to 1.7x. We also evaluate our policies on the IBM-Q5 machine and demonstrate that our proposal significantly improves the reliability of real systems (up to 1.9X).
BlockSecIoTNet: Blockchain-based decentralized security architecture for IoT network

The exponential growth of the use of insecure stationary and portable devices in the Internet of Things (IoT) network of the smart city has made the security of the smart city against cyber-attacks a vital issue. Various mechanisms for detecting security attacks that rely on centralized and distributed architectures have already been proposed, but they tend to be inefficient due to such problems as storage constraints, the high cost of computation, high latency, and a single point of failure. Moreover, existing security mechanisms are faced with the issue of monitoring and collecting historic data throughout the entire IoT network of the smart city in order to deliver optimal security and defense against cyberattacks. To address the current challenges, this paper proposes a decentralized security architecture based on Software Defined Networking (SDN) coupled with a blockchain technology for IoT network in the smart city that relies on the three core technologies of SDN, Blockchain, and Fog and mobile edge computing in order to detect attacks in the IoT network more effectively. Thus, in the proposed architecture, SDN is liable to continuous monitoring and analysis of traffic data in the entire IoT network in order to provide an optimal attack detection model; Blockchain delivers decentralized attack detection to mitigate the “single point of failure” problem inherent to the existing architecture; and Fog and mobile edge computing supports attack detection at the fog node and, subsequently, attack mitigation at the edge node, thus enabling early detection and mitigation with lesser storage constraints, cheaper computation, and low latency. To validate the performance of the proposed architecture, it was subjected to an experimental evaluation, the results of which show that it outperforms both centralized and distributed architectures in terms of accuracy and detection time.
MVX-Net: Multimodal VoxelNet for 3D Object Detection

Many recent works on 3D object detection have focused on designing neural network architectures that can consume point cloud data. While these approaches demonstrate encouraging performance, they are typically based on a single modality and are unable to leverage information from other modalities, such as a camera. Although a few approaches fuse data from different modalities, these methods either use a complicated pipeline to process the modalities sequentially, or perform late-fusion and are unable to learn interaction between different modalities at early stages. In this work, we present PointFusion and VoxelFusion: two simple yet effective early-fusion approaches to combine the RGB and point cloud modalities, by leveraging the recently introduced VoxelNet architecture. Evaluation on the KITTI dataset demonstrates significant improvements in performance over approaches which only use point cloud data. Furthermore, the proposed method provides results competitive with the state-of-the-art multimodal algorithms, achieving top-2 ranking in five of the six birds eye view and 3D detection categories on the KITTI benchmark, by using a simple single stage network.
Two-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computing

In spin Hall nano-oscillators (SHNOs), pure spin currents drive local regions of magnetic films and nanostructures into auto-oscillating precession. If such regions are placed in close proximity to each other they can interact and may mutually synchronize. Here, we demonstrate robust mutual synchronization of two-dimensional SHNO arrays ranging from 2 × 2 to 8 × 8 nano-constrictions, observed both electrically and using micro-Brillouin light scattering microscopy. On short time scales, where the auto-oscillation linewidth $$\Delta f$$ is governed by white noise, the signal quality factor, $$Q=f/\Delta f$$, increases linearly with the number of mutually synchronized nano-constrictions (N), reaching 170,000 in the largest arrays. We also show that SHNO arrays exposed to two independently tuned microwave frequencies exhibit the same synchronization maps as can be used for neuromorphic vowel recognition. Our demonstrations may hence enable the use of SHNO arrays in two-dimensional oscillator networks for high-quality microwave signal generation and ultra-fast neuromorphic computing. Synchronization of oscillators can be used to carry out cognitive tasks. Large two-dimensional arrays of synchronized spin Hall nano-oscillators have now been demonstrated, and may in future enable neuromorphic computing on the nanoscale.
Measuring the Quality of Explanations: The System Causability Scale (SCS)

Recent success in Artificial Intelligence (AI) and Machine Learning (ML) allow problem solving automatically without any human intervention. Autonomous approaches can be very convenient. However, in certain domains, e.g., in the medical domain, it is necessary to enable a domain expert to understand, why an algorithm came up with a certain result. Consequently, the field of Explainable AI (xAI) rapidly gained interest worldwide in various domains, particularly in medicine. Explainable AI studies transparency and traceability of opaque AI/ML and there are already a huge variety of methods. For example with layer-wise relevance propagation relevant parts of inputs to, and representations in, a neural network which caused a result, can be highlighted. This is a first important step to ensure that end users, e.g., medical professionals, assume responsibility for decision making with AI/ML and of interest to professionals and regulators. Interactive ML adds the component of human expertise to AI/ML processes by enabling them to re-enact and retrace AI/ML results, e.g. let them check it for plausibility. This requires new human-AI interfaces for explainable AI. In order to build effective and efficient interactive human-AI interfaces we have to deal with the question of how to evaluate the quality of explanations given by an explainable AI system. In this paper we introduce our System Causability Scale (SCS) to measure the quality of explanations. It is based on our notion of Causability (Holzinger et al., 2019) combined with concepts adapted from a widely accepted usability scale.
Is COVID-19 the Gateway for Digital Learning in Mathematics Education?

Digital learning has reshaped education in many ways. The purpose of this study is to respond to the question of whether COVID-19 is the gateway for digital-learning in mathematics education. To this end, this study explores some uptakes of social media platforms by prospective secondary school teachers. Data was collected from 102 prospective mathematics teachers from the Copperbelt University (CBU). Cluster analysis approach was used. Results revealed that participants' scores for digital learning in mathematics in cluster 2 were higher than those in both cluster 1 and 3. This is a clear indication that prospective teachers in clusters with low scores are more likely to exhibit low skill levels in the use of mobile technology and the adoption of social media in relation to mathematics pedagogy during the COVID-19 crisis. Results show different patterns. However, overall results show that digital learning could be a positive response to COVID-19 closure period.
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning

In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which selects the device consuming the least updating time obtained by the optimal bandwidth allocation in each step, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius.
Trustworthy artificial intelligence

Abstract Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.
Artificial intelligence in marketing: Systematic review and future research direction

Disruptive technologies such as the internet of things, big data analytics, blockchain, and artificial intelligence have changed the ways businesses operate. Of all the disruptive technologies, artificial intelligence (AI) is the latest technological disruptor and holds immense marketing transformation potential. Practitioners worldwide are trying to figure out the best fit AI solutions for their marketing functions. However, a systematic literature review can highlight the importance of artificial intelligence (AI) in marketing and chart future research directions. The present study aims to offer a comprehensive review of AI in marketing using bibliometric, conceptual and intellectual network analysis of extant literature published between 1982 and 2020. A comprehensive review of one thousand five hundred and eighty papers helped to identify the scientific actors' performance like most relevant authors and most relevant sources. Furthermore, co-citation and co-occurrence analysis offered the conceptual and intellectual network. Data clustering using the Louvain algorithm helped identify research sub-themes and future research directions to expand AI in marketing.
A survey of inverse reinforcement learning: Challenges, methods and progress

Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a problem and as a class of methods. By categorically surveying the extant literature in IRL, this article serves as a comprehensive reference for researchers and practitioners of machine learning as well as those new to it to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges such as the difficulty in performing accurate inference and its generalizability, its sensitivity to prior knowledge, and the disproportionate growth in solution complexity with problem size. The article surveys a vast collection of foundational methods grouped together by the commonality of their objectives, and elaborates how these methods mitigate the challenges. We further discuss extensions to the traditional IRL methods for handling imperfect perception, an incomplete model, learning multiple reward functions and nonlinear reward functions. The article concludes the survey with a discussion of some broad advances in the research area and currently open research questions.
Federated Learning

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches n
Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT

More and more enterprises and communities choose cloud computing platforms to deploy their commercial or scientific workflow applications along with the increasing popularity of pay-as-you-go cloud services. A major task of cloud service providers is to minimize the monetary cost and makespan of executing workflows in the Infrastructure as a Service (IaaS) cloud. Most of the existing techniques for cost and makespan minimization are designed for traditional computing platforms which cannot be applied to the cloud computing platforms with unique service-based resource managing methods and pricing strategies. In this paper, we study the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and propose a novel workflow scheduling scheme. In this scheme, a fuzzy dominance sort based heterogeneous earliest-finish-time (FDHEFT) algorithm is developed which closely integrates the fuzzy dominance sort mechanism with the list scheduling heuristic HEFT. Extensive experiments using the real-world and synthetic workflows demonstrate the efficacy of our scheme. Our scheme can achieve significantly better cost-makespan tradeoff fronts with remarkably higher Hypervolume and can run up to hundreds of times faster than the state-of-the-art algorithms.
Analyzing the potential of Virtual Reality for engineering design review

Virtual Reality (VR) technology still needs to evolve, but as the pace of innovations accelerates, systems allow for more novel modes of visualization and interaction to support engineering design reviews. Currently, the classic design review process is often performed on a PC with the support of CAD software packages. However, CAD on a screen cannot always meet all the requirements in regard to the functional and ergonomic validations of complex 3D models. In this paper, the development and evaluation of a VR-based tool to support engineering design review is described. “VRSmart” visualizes CAD data and allows for an intuitive interaction. In a preliminary user study, the tool was checked for its usability and user experience. VRSmart was then evaluated in a real industrial environment and tested in an authentic design review. The results indicate that a VR-supported design review allows users to see slightly more faults in a 3D model than in a CAD software-based approach on a PC screen. Furthermore, VR reduces the risk of exclusion of certain professional groups from the design review process. In addition, the intuitive interaction with the VR system allowed for a much faster entry into the design review. In summary, VR will not replace the traditional design review process on screen, but it provides a useful addition to engineering companies.
Semi-Supervised Learning With Graph Learning-Convolutional Networks

Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed graph which may not be optimal for semi-supervised learning tasks. In this paper, we propose a novel Graph Learning-Convolutional Network (GLCN) for graph data representation and semi-supervised learning. The aim of GLCN is to learn an optimal graph structure that best serves graph CNNs for semi-supervised learning by integrating both graph learning and graph convolution in a unified network architecture. The main advantage is that in GLCN both given labels and the estimated labels are incorporated and thus can provide useful `weakly' supervised information to refine (or learn) the graph construction and also to facilitate the graph convolution operation for unknown label estimation. Experimental results on seven benchmarks demonstrate that GLCN significantly outperforms the state-of-the-art traditional fixed structure based graph CNNs.
Manifold regularized discriminative feature selection for multi-label learning

In multi-label learning, objects are essentially related to multiple semantic meanings, and the type of data is confronted with the impact of high feature dimensionality simultaneously, such as the bioinformatics and text mining applications. To tackle the learning problem, the key technology, i.e., feature selection, is developed to reduce dimensionality, whereas most of the previous methods for multi-label feature selection are either directly transformed from traditional single-label feature selection methods or half-baked in the label information exploitation, and thus causing the redundant or irrelevant features involved in the selected feature subset. Aimed to seek discriminative features across multiple class labels, we propose an embedded multi-label feature selection method with manifold regularization. To be specific, a low-dimensional embedding is constructed based on the original feature space to fit the label distribution for capturing the label correlations locally, which is also constrained using the label information in consideration of the co-occurrence relationships of label pairs. Following this principle, we design an optimization objective function involving l2,1-norm regularization to achieve multi-label feature selection, and the convergence is guaranteed. Empirical studies on various multi-label data sets reveal that the proposed method can obtain highly competitive performance against some state-of-the-art multi-label feature selection methods.
Stochastic Model-Based Minimization of Weakly Convex Functions

We consider a family of algorithms that successively sample and minimize simple stochastic models of the objective function. We show that under reasonable conditions on approximation quality and regularity of the models, any such algorithm drives a natural stationarity measure to zero at the rate $O(k^{-1/4})$. As a consequence, we obtain the first complexity guarantees for the stochastic proximal point, proximal subgradient, and regularized Gauss--Newton methods for minimizing compositions of convex functions with smooth maps. The guiding principle, underlying the complexity guarantees, is that all algorithms under consideration can be interpreted as approximate descent methods on an implicit smoothing of the problem, given by the Moreau envelope. Specializing to classical circumstances, we obtain the long-sought convergence rate of the stochastic projected gradient method, without batching, for minimizing a smooth function on a closed convex set.
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

BackgroundThe diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25–26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison.MethodsA total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05).FindingsThe CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p = 0.016) superior in classifying the cropped images.InterpretationWith limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses.
Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network

The accumulation of solid waste in the urban area is becoming a great concern, and it would result in environmental pollution and may be hazardous to human health if it is not properly managed. It is important to have an advanced/intelligent waste management system to manage a variety of waste materials. One of the most important steps of waste management is the separation of the waste into the different components and this process is normally done manually by hand-picking. To simplify the process, we propose an intelligent waste material classification system, which is developed by using the 50-layer residual net pre-train (ResNet-50) Convolutional Neural Network model which is a machine learning tool and serves as the extractor, and Support Vector Machine (SVM) which is used to classify the waste into different groups/types such as glass, metal, paper, and plastic etc. The proposed system is tested on the trash image dataset which was developed by Gary Thung and Mindy Yang, and is able to achieve an accuracy of 87% on the dataset. The separation process of the waste will be faster and intelligent using the proposed waste material classification system without or reducing human involvement.