Reviewerly/exHarmony huggingface dataset:
Machine-learning-aided cognitive reconfiguration for flexible-bandwidth HPC and data center networks [Invited]

This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>&lt;<!-- < --></mml:mo> </mml:mrow> <mml:mn>9</mml:mn> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> error) and demonstrate up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>5</mml:mn> <mml:mo>×<!-- × --></mml:mo> </mml:math> throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.
Examining research topics with a dependency-based noun phrase extraction method: a case in accounting

Purpose Examining research topics in a specific area such as accounting is important to both novice and veteran researchers. The present study aims to identify the research topics in the area of accounting and to investigate the research trends by finding hot and cold topics from all those identified ones in the field. Design/methodology/approach A new dependency-based method focusing on noun phrases, which efficiently extracts research topics from a large set of library data, was proposed. An AR(1) autoregressive model was used to identify topics that have received significantly more or less attention from the researchers. The data used in the study included a total of 4,182 abstracts published in six leading (or premier) accounting journals from 2000 to May 2019. Findings The study identified 48 important research topics across the examined period as well as eight hot topics and one cold topic from the 48 topics. Originality/value The research topics identified based on the dependency-based method are similar to those found with the technique of latent Dirichlet allocation latent Dirichlet allocation (LDA) topic modelling. In addition, the method seems highly efficient, and the results are easier to interpret. Last, the research topics and trends found in the study provide reference to the researchers in the area of accounting.
Security Study of Service Worker Cross-Site Scripting.

Nowadays, modern websites are utilizing service workers to provide users with app-like functionalities such as offline mode and push notifications. To handle such features, the service worker is equipped with special privileges including HTTP traffic manipulation. Thus, it is designed with security as a priority. However, we find that many websites introduce a questionable practice that can jeopardize the security of a service worker.
Flexion: A Quantitative Metric for Flexibility in DNN Accelerators

Dataflow and tile size choices, which we collectively refer to as mappings, dictate the efficiency (i.e., latency and energy) of DNN accelerators. Rapidly evolving DNN models is one of the major challenges for DNN accelerators since the optimal mapping heavily depends on the layer shape and size. To maintain high efficiency across multiple DNN models, flexible accelerators that can support multiple mappings have emerged. However, we currently lack a metric to evaluate accelerator flexibility and quantitatively compare their capability to run different mappings. In this letter, we formally define the concept of flexibility in DNN accelerators and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flexion</i> ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flex</u> ibility fract <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ion</u> ), flexion, which is a quantitative metric of mapping flexibility on DNN accelerators. We codify the formalism we construct and evaluate the flexibility of accelerators based on Eyeriss, NVDLA, and TPUv1. We show that Eyeriss-like accelerator is 2.2× and 17.0× more flexible (i.e., capable of running more mappings) than NVDLA and TPUv1-based accelerators on selected ResNet-50 and MobileNetV2 layers. This work is the first work to enable such a quantitative comparison of the flexibility of accelerators.
Deep residual neural network-based classification of loaded and unloaded UAV images

Like any new technology, unmanned aerial vehicles are used not only for good purposes. Nowadays attackers adapted UAVs for drug delivery, transportation of explosives and surveillance. For this reason, UAV detection and classification are the significant problems for researchers of this area. Previous studies in the field of UAV classification have mostly focused on classifying UAV images as UAV and no UAV, or UAV and other flying objects, also classifying different UAV models. This paper proposes a deep residual convolutional neural network based classification of loaded and unloaded UAV images. As the depth of neural network increases it shows a large learning error. In this case it is relatively easy to optimize residual neural network. Also, ResNet makes it easy to increase accuracy by increasing depth, which is more difficult to achieve with other networks. This paper attempts to show that using ResNet-34 for classification of loaded and unloaded UAV images gives superior performance and acceptable accuracy.
Machine Learning Operations

Machine learning operations (MLOps) is DevOps for machine learning processes. MLOps enables data scientists to collaborate and increase the pace of delivery and quality of model development through monitoring, validation, and governance of machine learning models. This is equivalent to how DevOps helps software engineers develop, test, and deploy software quicker and with fewer defects. MLOps supports the data science life cycle just as DevOps supports the application development life cycle. As such, MLOps is based on the principles of DevOps.
Perceived Realism of High-Resolution Generative Adversarial Network–derived Synthetic Mammograms

Purpose To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts. Materials and Methods In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 × 1024 pixels by using images from 90 000 patients (average age, 56 years ± 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra–high-dimensional pixel distributions was used, which was based on moment plots. This method was able to reveal potential sources of misalignment. A total of 117 volunteer participants (55 radiologists and 62 nonradiologists) took part in a study to assess the realism of synthetic images from GANs. Results A quantitative evaluation of distributional alignment shows 60%–78% mutual-information score between the real and synthetic image distributions, and 80%–91% overlap in their support, which are strong indications against mode collapse. It also reveals shape misalignment as the main difference between the two distributions. Obvious artifacts were found by an untrained observer in 13.6% and 6.4% of the synthetic mediolateral oblique and craniocaudal images, respectively. A reader study demonstrated that real and synthetic images are perceptually inseparable by the majority of participants, even by trained breast radiologists. Only one out of the 117 participants was able to reliably distinguish real from synthetic images, and this study discusses the cues they used to do so. Conclusion On the basis of these findings, it appears possible to generate realistic synthetic full-field digital mammograms by using a progressive GAN architecture up to a resolution of 1280 × 1024 pixels. Supplemental material is available for this article. Keywords: Breast, Computer Applications-General (Informatics), Mammography, Statistics © RSNA, 2020
Graph active learning for GCN-based zero-shot classification

In the supervised learning approach, classification models can only categorize objects into seen classes for which labeled data instances are available for training. Zero-shot learning, especially the recent graph neural network-based zero-shot learning, is commonly accepted as an effective approach to address this limitation by exploiting relations between seen classes and unseen classes. However, lots of seen classes are still necessary for most zero-shot learning frameworks to infer the classification model for unseen classes, especially in the image classification task. In this paper, we propose an active learning framework of graph convolutional network (GCN)-based zero-shot learning for image classification and design a new active learning algorithm called GAZL that can enable the zero-shot learning model to achieve a higher performance with a fixed amount of seen classes. Our algorithm extends the k-center algorithm with a new Laplacian energy-based strategy to select the most representative and crucial classes as seen classes for training. Extensive experiments on ImageNet demonstrate that our active learning method is superior to a wide spectrum of active learning methods for GCN-based zero-shot learning.
Binocular vision calibration method for a long-wavelength infrared camera and a visible spectrum camera with different resolutions

We present a calibration plate for the binocular vision system, which is composed of a long-wavelength infrared camera and a visible spectrum camera with different resolutions. The calibration plate mainly consists of a white low-temperature aluminum plate with 7×7 round through-holes, a black high-temperature stainless steel plate, and a heating plate. It can be captured by the long-wavelength infrared camera and visible spectrum camera simultaneously. In order to reduce the influence of thermal crosstalk on the edge and angle sharpness of the thermal image of the chessboard calibration plate, we use the round through-holes to replace the black-white squares in the chessboard calibration plate. Based on the fabricated calibration plate, we also propose a related calibration method. The proposed method can quickly detect the calibration plate by using the YOLO-V4 neural network. The affine transformation is performed to get the front view of the calibration plate, and a novel circular detection strategy based on arc level instead of pixel-level is adopted to detect the edges of the round through-holes in the calibration plate. The centers of round through-holes are detected, and the parameters of the cameras are calculated according to the coordinates of the centers in the image coordinate system. The simulation experiments and error analysis have been done to verify the centers detection method. The simulation results show that the error of center detection is always less than 1.4 (pixel). In order to further verify the performance of the calibration plate and the proposed calibration method, a binocular vision system based on long-wavelength infrared camera and visible spectrum camera is fabricated, and the verification experiments have been done. In experiments, our calibration plate and the proposed method are compared with the famous Zhang’s method. The calibration’s average overall mean errors of the visible spectrum camera and long-wavelength infrared camera are about 0.0126 (pixel) and 0.0238 (pixel), and they are respectively decreased by 78.13% and 81.93% compared with Zhang’s method. The re-projection error of the binocular vision system is about 0.548 (pixel), which is decreased by 24.52% compared with Zhang’s method. The average calibration time of the proposed method is about 0.26s.
Constructing the Impossible

Please keep the original abstract. A number of philosophers have flirted with the idea of impossible worlds and some have even become enamored of it. But it has not met with the same degree of acceptance as the more familiar idea of a possible world. Whereas possible worlds have played a broad role in specifying the semantics for natural language and for a wide range of formal languages, impossible worlds have had a much more limited role; and there has not even been general agreement as to how a reasonable theory of impossible worlds is to be developed or applied. This chapter provides a natural way of introducing impossible states into the framework of truthmaker semantics and shows how their introduction permits a number of useful applications.
Experimental demonstration of quantum advantage for NP verification with limited information

In recent years, many computational tasks have been proposed as candidates for showing a quantum computational advantage, that is an advantage in the time needed to perform the task using a quantum instead of a classical machine. Nevertheless, practical demonstrations of such an advantage remain particularly challenging because of the difficulty in bringing together all necessary theoretical and experimental ingredients. Here, we show an experimental demonstration of a quantum computational advantage in a prover-verifier interactive setting, where the computational task consists in the verification of an NP-complete problem by a verifier who only gets limited information about the proof sent by an untrusted prover in the form of a series of unentangled quantum states. We provide a simple linear optical implementation that can perform this verification task efficiently (within a few seconds), while we also provide strong evidence that, fixing the size of the proof, a classical computer would take much longer time (assuming only that it takes exponential time to solve an NP-complete problem). While our computational advantage concerns a specific task in a scenario of mostly theoretical interest, it brings us a step closer to potential useful applications, such as server-client quantum computing.
Water Flow Optimizer: A Nature-Inspired Evolutionary Algorithm for Global Optimization

Inspired by the shape of water flow in nature, a novel algorithm for global optimization, water flow optimizer (WFO), is proposed. The optimizer simulates the hydraulic phenomena of water particles flowing from highland to lowland through two operators: 1) laminar and 2) turbulent. The mathematical model of the proposed optimizer is first built, and then its implementation is described in detail. Its convergence is strictly proved based on the limit theory. The parametric effect is investigated. The performance of the proposed optimizer is compared with that of the related metaheuristics on an open test suite. The experimental results indicate that the proposed optimizer achieves competitive performance. The proposed optimizer was also successfully applied to solve the spacecraft trajectory optimization problem.
Perbandingan Kualitas 3D Objek Tugu Budaya Saibatin Berdasarkan Posisi Gambar Fotogrametri Jarak Dekat

The Cultural Statistics in 2019 recorded the number of traditional bridal clothing in Lampung province, the Saibatin wedding dress. The wedding dress of Saibatin in the city of Bandar Lampung was made a monument so that it has become a cultural heritage recorded by the Ministry. Therefore, preservation is necessary to protect the monument building. To preserve the steps that will be carried out by this research are reconstruction and conservation, this process is in the form of documentation that is making 3D modeling of objects and shows that the visualization quality of the cultural monument of Saibatin with the technique of making 3D objects with CRF or Close Distance Photogrammetry produces visualization. very good saibatin cultural monument with a record of the number of photos used 400 pictures with a combination of 900 and 450 shots.
Blockchain-based smart contract for international business – a framework

Purpose Cross border trade, involving different business environments between the sellers’ and buyers’ countries, may result in conflicts because of asymmetry in the information structure across the borders. The International Chambers of Commerce (ICC) has laid down ground rules on terms of shipment and payment, enabling harmonization and standardization of business process, and fixing of responsibilities for international trade. The international commercial (INCO) terms by ICC define the duties, obligations and cost borne by the exporter and the importer. An exporter’s uncertainty looms once the goods cross his/her border. Therefore, there is a need for a smart contract that is secured, transparent, legitimate and trustworthy. The authors propose a blockchain technology-based smart global contract (BTGC) framework for international trade. Design/methodology/approach In this paper, the authors develop the framework based on value chain analysis (VCA) of international trade and an ontology-driven-blockchain-design approach. The paper analyzes the sequence of activities in the value chain of global trade, the terms of the contract, the data structure templates, the validation rules and the points-of-failure, and proposes the smart contract blockchain structure. Findings This paper proposes the BTGC framework considering the INCO terms 2020; it provides the validation rules and the probability of failures; and identifies the elements that cause the halting of contracts and conditions of creation of side blockchains. The framework also includes the governance of the BTGC system. Research limitations/implications The proposed framework not only has implications at the firm level as it automates and secures a global sale contract but also is expected to harmonize the global-trade process as well. The developers may use the attributes, data structure templates and the rules identified in this paper for developing the GC software. Future research may consider using case analysis, class diagrams and the related steps for developing the blockchain software. Originality/value This paper proposes a complete value chain of global contract (GC) concerning exports, an ontology of GC and a blockchain-based smart-contract framework based on global standards. Besides, it specifies the elements of fraud (such as the non-integration of side chains) and uncertainty, i.e. the probability of failures. Such a framework will harmonize the global-trade process and build an international standards for smart GC based on blockchain technology (ISSGCBT), which is not yet done.
Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types

Abstract Sentiments associated with assessments and observations recorded in a clinical narrative can often indicate a patient's health status. To perform sentiment analysis on clinical narratives, domain-specific knowledge concerning meanings of medical terms is required. In this study, semantic types in the Unified Medical Language System (UMLS) are exploited to improve lexicon-based sentiment classification methods. For sentiment classification using SentiWordNet, the overall accuracy is improved from 0.582 to 0.710 by using logistic regression to determine appropriate polarity scores for UMLS ‘Disorders’ semantic types. For sentiment classification using a trained lexicon, when disorder terms in a training set are replaced with their semantic types, classification accuracies are improved on some data segments containing specific semantic types. To select an appropriate classification method for a given data segment, classifier combination is proposed. Using classifier combination, classification accuracies are improved on most data segments, with the overall accuracy of 0.882 being obtained.
Weighted Support Tensor Machines for Human Activity Recognition with Smartphone Sensors

Along with the development of the Industrial Internet of Things, human activity recognition (HAR) has received widespread attention in many fields. Support Vector Machine (SVM), is widely used by researchers for human activity recognition. However, the inherent difference of signal properties from different sensors and various orientations is potentially lost when using the vector-based SVM for human activity recognition. What's more, the outlier sensitivity problem of the standard SVM reduces the accuracy of human activity recognition. To tackle this problem, we present a tensor-based feature representation model and a weighted support tensor machine (WSTM) for human activity recognition. Specifically, tensor-based representations are first used to model features from different sensors and various orientations to retain the latent relationship. In addition, the weighted support tensor machine is proposed to classify the human activities in tensor space while avoiding the outlier sensitivity problem. Experimental results demonstrate the proposed WSTM algorithm.