Reviewerly/exHarmony huggingface dataset:
Feasibility Analysis of Machine Learning Optimization on GPU-based Low-cost Edges

Many AI algorithms have been deployed on edge devices as edge computing has the advantages of reducing latency, saving network bandwidth, and protecting data privacy. Whether edge devices can run AI algorithms is an important challenge due to the low-power and low-cost characteristics of edge devices. Therefore, this paper analyzed the performance of optimization techniques by running YOLOv3 on a typical GPU-based low-cost edge device, NVIDIA Jetson Nano. YOLOv3 is a representative object detection algorithm, which is widely used as the benchmark in AI scenarios. We compared latency, memory, and power consumption of three deep learning frameworks, TensorFlow, PyTorch, and TensorRT. Then we squeezed the extreme performance using multiple optimization techniques, including model quantization, model parallelization, and image scaling on TensorRT. The running speed of YOLOv3 increases from 3.9FPS to 13.1FPS on NVIDIA Jetson Nano. It proves that the resource-limited edge device can run AI applications with high computing power requirements in a real-time manner. Moreover, we summarized nine observations and five insights to guide the selection and design of optimization techniques and verified the generalization of these rules on NVIDIA Jetson Xavier NX. We also provided a series of suggestions to help developers choose the appropriate method to deploy AI algorithms on edge devices.
Estimating Video on Demand QoE From Network QoS Through ICMP Probes

With the increasing traffic of Video on Demand (VoD), network providers are seeking to deliver high Quality of Experience (QoE) for their users.Many methods have been proposed to assess VoD-related QoE.Some of them rely on client instrumentation and reporting QoE information to network elements, such as Server and Network Assisted DASH, others are based on statistical methods that make QoE inferences using monitored network conditions, such as throughput and delays.In this article, we present a practical method to estimate QoE for VoD using the widely supported Internet Control Message Protocol (ICMP) probes.Measured network conditions are used as input to a Machine Learning (ML) model that estimates QoE in terms of Mean Opinion Score (MOS), based on the ITU-T P.1203 Recommendation.The estimation encompasses video quality switches and playback stalls.We estimate MOS with an average Root Mean Square Error (RMSE) of 1.05 for a catalog of 25 different videos, training a model with sessions of the shortest video, and evaluating the generalization to the full catalog.We performed experiments using a virtualized setup as well as in a Wide Area Network.
Multi-task Fuzzy Clustering–Based Multi-task TSK Fuzzy System for Text Sentiment Classification

Text sentiment classification is an important technology for natural language processing. A fuzzy system is a strong tool for processing imprecise or ambiguous data, and it can be used for text sentiment analysis. This article proposes a new formulation of a multi-task Takagi-Sugeno-Kang fuzzy system (TSK FS) modeling, which can be used for text sentiment image classification. Using a novel multi-task fuzzy c-means clustering algorithm, the common (public) information among all tasks and the individual (private) information for each task are extracted. The information about clustering, for example, cluster centers, can be used to learn the antecedent parameters of multi-task TSK fuzzy systems. With the common and individual antecedent parameters obtained, a corresponding multi-task learning mechanism for learning consequent parameters is devised. Accordingly, a multi-task fuzzy clustering–based multi-task TSK fuzzy system (MTFCM-MT-TSK-FS) is proposed. When the proposed model is built, the information conveyed by the fuzzy rules formed is two-fold, including (1) common fuzzy rules representing the inter-task correlation information and (2) individual fuzzy rules depicting the independent information of each task. The experimental results on several text sentiment datasets demonstrate the validity of the proposed model.
DVL2021: An ultra high definition video dataset for perceptual quality study

This paper describes an ultra high definition (UHD) video dataset named DVL2021 for the perceptual study of video quality assessment (VQA). To our knowledge, DVL2021 is the first authentically distorted 4K (3840 × 2160) UHD video quality dataset. The dataset contains 206 versatile 4K UHD video sequences, which are all collected in in-the-wild scenarios. Each sequence is captured at 50 frames per second (fps), stored in raw 10-bit 4:2:0 YUV format, and has a duration of 10 s. Following the subjective evaluation method of TV image quality granted by ITU-R BT.500-13, 32 unique participants take part in the manual annotation process, whose ages are from teenage to sixties (32.7 years old on average). DVL2021 has the following merits: (1) enormous variety of video contents, (2) captured by different types of cameras, (3) complex types and multiple levels of authentic distortion, (4) broadly distributed temporal/spatial information, and (5) a wide spectrum of mean opinion scores (MOS) distribution. Furthermore, we conduct a benchmark experiment by evaluating several mainstream VQA methods on DVL2021. The baseline results are higher than 0.75 in Spearman’s rank order correlation coefficient (SROCC) metric. Our study provides a basis for the UHD VQA problem. DVL2021 is publicly available at https://github.com/GZHU-DVL/DVL2021.
Machine learning tool for exploring sentiment analysis on twitter data

In social media, micro-blogging is a common routine of most people around the world. Social media analytics gathering structure and unstructured Big-Data from various social sites and analyzing to make business decisions using Apache Hadoop and Apache Hive respectively. The objective of the research work was to develop big data technology used for gathering and handling large unstructured data from real time social media for sentiment analysis for identifying the brand and services. The methodology devised an algorithm based on sentimental analysis using customers review classification, which dealt with prepossessing the datasets, clustering of the data based on the specific domains, feature vector using n-gram models and tf-idf vectors extracts synonyms and classification sentiment analysis. The result shows that applied sentimental analysis with unsupervised clustering of data into specific domains and supervised machine learning techniques handle large amounts of twitter data in an efficient way. The developed tool 1.5 times faster than that of traditional database to Hadoop cluster and also the accuracy is nearly 80 %, which helps the user in computing, analyzing and interpreting interaction and associations between people, topics and ideas.
One Step Further: Tunable and Explainable Throughput Prediction based on Large-scale Commercial Networks

Throughput prediction remains a relevant and challenging problem in the area of wireless networking. Both users and service providers would benefit from the predictive Quality of Service (QoS) which has the potential to improve perceived service quality by users but also provide valuable insights for the network planning, optimization, and deployment. There are many factors that can hinder the adoption of Machine Learning (ML) models, such as the data collection overhead for the network operator, prediction uncertainties of an ML model and the lack of transparency in the ML inference process. In this paper, we provide results on the instantaneous throughput prediction using ML techniques. The data for the throughput prediction was collected in a live network of one of the biggest operators in Asia which improves confidence of the results. We also discuss how custom loss functions can extract value from prediction errors. Finally, we look at explainability methods providing more transparency on the predictions of ML algorithms. Various explainability methods prove that without being explicitly programmed, ML algorithms can exploit and learn from the underlying physical phenomena and operating principles of wireless networks.
A maximum-margin multisphere approach for binary Multiple Instance Learning

We propose a heuristic approach for solving binary Multiple Instance Learning (MIL) problems, whose objective is to categorize bags of instances. Considering the case with two classes of instances, on the basis of the standard MIL assumption, a bag is classified positive if it contains at least a positive instance and negative if all its instances are negative. Inspired by a well-established MIL Support Vector Machine type approach, our technique is based on iteratively separating the bags by means of successive maximum-margin spheres. Such spheres, whose number is automatically determined, are generated by computing, for each of them, the optimal radius in correspondence to a prefixed center. Numerical results are presented on a set of benchmark test problems, showing the effectiveness of our approach.
Synthesizing high‐resolution magnetic resonance imaging using parallel cycle‐consistent generative adversarial networks for fast magnetic resonance imaging

The common practice in acquiring the magnetic resonance (MR) images is to obtain two-dimensional (2D) slices at coarse locations while keeping the high in-plane resolution in order to ensure enough body coverage while shortening the MR scan time. The aim of this study is to propose a novel method to generate HR MR images from low-resolution MR images along the longitudinal direction. In order to address the difficulty of collecting paired low- and high-resolution MR images in clinical settings and to gain the advantage of parallel cycle consistent generative adversarial networks (CycleGANs) in synthesizing realistic medical images, we developed a parallel CycleGANs based method using a self-supervised strategy.The proposed workflow consists of two parallely trained CycleGANs to independently predict the HR MR images in the two planes along the directions that are orthogonal to the longitudinal MR scan direction. Then, the final synthetic HR MR images are generated by fusing the two predicted images. MR images, including T1-weighted (T1), contrast enhanced T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR), of the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were processed to evaluate the proposed workflow along the cranial-caudal (CC), lateral, and anterior-posterior directions. Institutional collected MR images were also processed for evaluation of the proposed method. The performance of the proposed method was investigated via both qualitative and quantitative evaluations. Metrics of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), edge keeping index (EKI), structural similarity index measurement (SSIM), information fidelity criterion (IFC), and visual information fidelity in pixel domain (VIFP) were calculated.It is shown that the proposed method can generate HR MR images visually indistinguishable from the ground truth in the investigations on the BraTS2020 dataset. In addition, the intensity profiles, difference images and SSIM maps can also confirm the feasibility of the proposed method for synthesizing HR MR images. Quantitative evaluations on the BraTS2020 dataset shows that the calculated metrics of synthetic HR MR images can all be enhanced for the T1, T1CE, T2, and FLAIR images. The enhancements in the numerical metrics over the low-resolution and bi-cubic interpolated MR images, as well as those genearted with a comparative deep learning method, are statistically significant. Qualitative evaluation of the synthetic HR MR images of the clinical collected dataset could also confirm the feasibility of the proposed method.The proposed method is feasible to synthesize HR MR images using self-supervised parallel CycleGANs, which can be expected to shorten MR acquisition time in clinical practices.
Layered and object-based game semantics

Large-scale software verification relies critically on the use of compositional languages, semantic models, specifications, and verification techniques. Recent work on certified abstraction layers synthesizes game semantics, the refinement calculus, and algebraic effects to enable the composition of heterogeneous components into larger certified systems. However, in existing models of certified abstraction layers, compositionality is restricted by the lack of encapsulation of state. In this paper, we present a novel game model for certified abstraction layers where the semantics of layer interfaces and implementations are defined solely based on their observable behaviors. Our key idea is to leverage Reddy's pioneer work on modeling the semantics of imperative languages not as functions on global states but as objects with their observable behaviors. We show that a layer interface can be modeled as an object type (i.e., a layer signature) plus an object strategy. A layer implementation is then essentially a regular map, in the sense of Reddy, from an object with the underlay signature to that with the overlay signature. A layer implementation is certified when its composition with the underlay object strategy implements the overlay object strategy. We also describe an extension that allows for non-determinism in layer interfaces. After formulating layer implementations as regular maps between object spaces, we move to concurrency and design a notion of concurrent object space, where sequential traces may be identified modulo permutation of independent operations. We show how to express protected shared object concurrency, and a ticket lock implementation, in a simple model based on regular maps between concurrent object spaces.
Digital Twin of the Technological Process of the NPP Power Unit

The article presents a flexible modeling system (FMS) of an NPP power unit. The principles of constructing an FMS based on selective programming and its structure are described, and the modeling and experimental data results are compared. This FMS can be considered a digital twin of an NPP power unit, which makes it possible to predict the course of the technological process and simulate the actions of operating personnel and failures of technological equipment.
Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor

Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy diagnosis important. This study used whole-slide images (WSIs) of 187 FA and 100 PT core biopsies, to investigate the potential role of artificial intelligence (AI) in FEL diagnosis. A total of 9228 FA patches and 6443 PT patches was generated from WSIs of the training subset, with each patch being 224 × 224 pixel in size. Our model employed a two-stage architecture comprising a convolutional neural network (CNN) component for feature extraction from the patches, and a recurrent neural network (RNN) component for whole-slide classification using activation values from the global average pooling layer in the CNN model. It achieved an overall slide-level accuracy of 87.5%, with accuracies of 80% and 95% for FA and PT slides respectively. This affirms the potential role of AI in diagnostic discrimination between FA and PT on core biopsies which may be further refined for use in routine practice.
AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text

Adversarial examples are vital to expose vulnerability of machine learning models. Despite the success of the most popular word-level substitution-based attacks which substitute some words in the original examples, only substitution is insufficient to uncover all robustness issues of models. In this paper, we focus on perturbations beyond word-level substitution, and present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdvExpander</b> , a method that crafts new adversarial examples by expanding text. We first utilize linguistic rules to determine which constituents to expand and what types of modifiers to expand with. We then expand each constituent by inserting an adversarial modifier searched from a pre-trained CVAE-based generative model. To ensure that our adversarial examples are label-preserving for text matching, we also constrain the modifications with a heuristic rule. Experiments on three classification tasks verify the effectiveness of AdvExpander and the validity of our adversarial examples. AdvExpander is significantly more effective than sentence-level attack baselines and is complementary to previous word substitution-based attacks, thus promising to reveal new robustness issues.
Bounds on semi-device-independent quantum random-number expansion capabilities

The randomness expansion capabilities of semi-device-independent (SDI) prepare and measure protocols are analyzed under the sole assumption that the Hilbert state dimension is known. It is explicitly proved that the maximum certifiable entropy that can be obtained through this set of protocols is $\ensuremath{-}{log}_{2}[\frac{1}{2}(1+\frac{1}{\sqrt{3}})]$ and the same is independent of the dimension witnesses used to certify the protocol. The minimum number of preparation and measurement settings required to achieve this entropy is also proven. An SDI protocol that generates the maximum output entropy with the least amount of input setting is provided. An analytical relationship between the entropy generated and the witness value is obtained. It is also established that certifiable entropy can be generated as soon as the dimension witness crosses the classical bound, making the protocol noise-robust and useful in practical applications.
Fixation and Creativity in Data Visualization Design: Experiences and Perspectives of Practitioners

Data visualization design often requires creativity, and research is needed to understand its nature and means for promoting it. The current visualization literature on creativity is not well developed, especially with respect to the experiences of professional data visualization designers. We conducted semi-structured interviews with 15 data visualization practitioners, focusing on a specific aspect of creativity known as design fixation. Fixation occurs when designers adhere blindly or prematurely to a set of ideas that limit creative outcomes. We present practitioners’ experiences and perspectives from their own design practice, specifically focusing on their views of (i) the nature of fixation, (ii) factors encouraging fixation, and (iii) factors discouraging fixation. We identify opportunities for future research related to chart recommendations, inspiration, and perspective shifts in data visualization design.
Cyberespionage: Socioeconomic implications on sustainable food security

Sustainable food security is a vision of many governments and international organizations across the globe with its goal directed at preventing hunger, starvation, food shortages, and mortality. These are dreaded indicators that had been long fingered as principal problems that would envelop the world around 2050 when the global population is estimated to hit 9 billion. Global organizations such as the Food and Agricultural Organization (FAO), Heifer International, the International Fund for Agricultural Development, etc., have consistently demonstrated strategies that would help eradicate global hunger by 2030. As sustained efforts are directed at thousands of remarkable projects worldwide, it is important to reemphasize that such projects could only be realized through insights derived from meaningful information and knowledge resources. In agriculture, there has been a rapid rise of late in the modernization and deployment of automated agricultural information systems (agricultural databases, smart systems, Internet of Things (IoT) systems, agribots, etc.) to aid planning, production, and distribution processes. Interestingly, most of these global knowledge and information bases are in the cloud and other Internet infrastructure. The trend in global information storage has sensitive information (government policies, orders, decisions, plans, etc., on agriculture and food production) from public and private domains residing on one or more cyberinfrastructures. That leads to an attendant risk of sabotage and breaches. This is due to the ever-growing attempts on critical infrastructure worldwide by apologists, cyberterrorists, and cybercriminals. This could result in project failures, irrecoverable losses, and governance and policy failures, to name a few. This chapter presents a detailed account of the various cyberthreats, especially cyberespionage and cyberterrorism that are capable of sabotaging the collective strategies of organizations and entities pursuing sustainable food security. In this chapter, we discuss motivations, ideologies, and strategies behind cyberespionage against agriculture and related platforms; the architecture, categorization, propagation, and attack methods; the socioeconomic implications of these attacks; countermeasures; and legal frameworks.
Optimized Deep Learning Object Recognition for Drones using Embedded GPU

Nowadays, drones can be seen in various applications in industry like surveillance and transportation. Industrial drones leverage fully-fledged computer vision techniques, such as object detection based on Deep Learning Neural Networks (DNN), to efficiently perform these objectives. Those techniques come with a high computational effort and are implemented on distributed schemes using ground devices with high performance and power consumption. This limits a drone's operational range since it has to communicate with the ground devices constantly. To alleviate such constraints, an optimized, low-power perception system on the drone is desirable. This work improves a trained DNN architecture to navigate a UAV introduced by the University of Zurich called DroNet. DroNet is computationally expensive and has a high power consumption, making it unsuitable for embedded platforms because of low memory and computational power. In this paper, a ROS-based architecture is first designed to port DroNet on a low-power Jetson Nano board, which conducts the drone's perception and control tasks. Secondly, tuning parameters and various schemes have been carried out to run the inference of the DNN efficiently. To implement the different layers in DNNs, Nvidia's TensorRT SDK is used to compile a high-performance inference engine for the Jetson Nano. Results showed that the Jetson Nano can achieve real-time performance, with 47 frames per second using a Winograd convolution and well-tuned parallelization parameters. The implementation can also achieve a speedup of 2× as compared with the Jetson Nanos ARM CPU while increasing the power consumption by 54%. Finally, the Jetson Nano's usability for drone inference algorithm is shown, achieving real-time response using the DroNet DNN without losing detection accuracy.
Vision-Based Associative Robotic Recognition of Working Status in Autonomous Manufacturing Environment

Recognition of working status and resolving abnormal conditions during the manufacturing process commonly relies on human intervention to visually inspect and adjust, which is boring, repetitive, and sometimes risky. In order to achieve completely autonomous manufacturing, a vision-based robotic associative working status recognition method is proposed. This study aims to recognize the working status of HAAS CNC machine in autonomous manufacturing environment using ‘scene text recognition’, in an effort to develop autonomous machine tending solution. The result of this study based on vision input processing and Convolutional Recurrent Neural Networks (CRNN) has a recognition accuracy of 97.3%, which is a good performance.