Reviewerly/exHarmony huggingface dataset:
Embracing Block Chain Technology in Supply Chain to combat Counterfeiting Luxury and Fashion brands

Many global businesses are facing challenges in genuineness of product as counterfeiting is elevating in supply chain. Luxury industry face huge challenges to combat luxury counterfeiting one of them is that Block chain technology guarantees the traceability and product originality. This study aims to present the implementation of block chain technology adopted by Luxury industry to protect their identity and stop duplication of products. During the study, it is found that counterfeiting is still a continued practice which is often seen in supply chain activity by Luxury companies and customers. Further, due to online buying, counterfeiting is increasing day by day. Lastly, the block chain implementation has huge benefit to luxury companies in terms of intellectual property regulations and privacy concern (https://www.retail-insight-network.com/comment/luxury-blockchain-consortium/) and as its own implications in Luxury landscape.
Access, organize and communicate: the strategic use of browser plugins in libraries

Purpose The present study demonstrates how browser plugins have been adopted by libraries in recent times to empower users in participatory academic and research activities. The study aims to present a brief synthesis of the practical use of browser plugins in libraries and their possible future use. Design/methodology/approach Web-based survey method was applied in the study to search and find browser plugins associated with academic library websites and to explore their area of application. Findings With the descriptive account of the implementation of plugins in academic libraries, supported by numerous examples, the study presents the pertinence of various approaches as appeared on library websites. Research limitations/implications The present investigation is limited to the academic libraries only. Originality/value The good practices enumerated in the study will improve the understanding of the young library professionals about the practical usage of plugins and the steps to enhance accessibility to plugins.
Metareasoning for multi-criteria decision making using complex information sources

The information that decision-makers use for command and control has uncertainty. Previous work has described different types of uncertainties, and the methods for using information to evaluate and rank alternative courses of action vary based on the type of uncertainty that occurs. Thus, when developing ways to generate automated decisions to support Soldier tactical planning, including multi-criteria decision making (MCDM) with different types (“modalities”) of uncertain information, no single method or algorithm will be optimal for all situations. Metareasoning is reasoning about reasoning, which is a type of self-adaptation, and it has been closely studied in AI and in logic due to its relevance to autonomous decision making; it is also of interest in cognitive science under the rubric of executive reasoning. A software agent or autonomous system can use metareasoning to monitor and control the procedures that it uses to process sensor information, evaluate potential courses of action, and plan its actions. This concept paper presents a metareasoning framework that can enhance artificial reasoning about uncertain information in the context of generating and ranking alternative courses of action. In this framework, the decision support agents will use rules to select the MCDM algorithms that are most appropriate for the type of information and the uncertainty modalities that are present. The rules may be curated by experts or generated from machine learning algorithms. We expect that using metareasoning will improve the ability to make complicated decisions with uncertain information.
Scientific and Regulatory Policy Committee Points to Consider: Primary Digital Histopathology Evaluation and Peer Review for Good Laboratory Practice (GLP) Nonclinical Toxicology Studies

The Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee formed a working group to consider the present and future use of digital pathology in toxicologic pathology in general and specifically its use in primary evaluation and peer review in Good Laboratory Practice (GLP) environments. Digital histopathology systems can save costs by reducing travel, enhancing organizational flexibility, decreasing slide handling, improving collaboration, increasing access to historical images, and improving quality and efficiency through integration with laboratory information management systems. However, the resources to implement and operate a digital pathology system can be significant. Given the magnitude and risks involved in the decision to adopt digital histopathology, this working group used pertinent previously published survey results and its members' expertise to create a Points-to-Consider article to assist organizations with building and implementing digital pathology workflows. With the aim of providing a comprehensive perspective, the current publication summarizes aspects of digital whole-slide imaging relevant to nonclinical histopathology evaluations, and then presents points to consider applicable to both primary digital histopathology evaluation and digital peer review in GLP toxicology studies. The Supplemental Appendices provide additional tabulated resources.
The Applications of Machine Learning Algorithms in Multiple Sclerosis: A Systematic Review

Multiple Sclerosis (MS) is a common chronic disease that affects society, especially young people. In recent years, data sciences have been used extensively to deal with the disease. Machine learning is one of the main data sciences types which has been used to deal with chronic diseases such as MS. This study aimed to identify the applications of machine learning algorithms in MS disease. This study is a systematic review that conducted in 2020. The searches were done in PubMed, Scopus, ISI Web of Sciences, Ovid, Science Direct, Embase, and Proquest scientific databases, by combining related keywords. Data extraction was done by using a data extraction form to follow the trends of this field of study. The results of the study showed that diagnosis of MS was the main application of machine learning in MS (33.3 %); also, assessment (24.24%) and prediction (18.18 %) of the disease were other main applications. The most used data type was medical images such as MRI and CT scans (55.17 %). The most used machine learning algorithm type was Support Vector Machine (SVM) (30 %) as a classification algorithm. The most optimized algorithm for the diagnosis and prediction of MS was KNN. It’s suggested to use machine learning algorithms to diagnose, assess, predict lesion classification, treatment, and severity determining of MS disease. Although the most common form of data used for MS is medical images, it is suggested that other types of data are generated to be used in machine learning algorithms. Considering the optimization rate of the algorithms used, it is suggested to pay more attention to the type of data and study objectives in data analysis using machine learning.
Between acceptance and rejection

The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models capable of predicting the score of a review text (RSP) and the final decision of a paper (PDP) were created. But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks, and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model's performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper, however we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected.
ACTA 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials

Evidence-based medicine aims at making decisions about the care of individual patients based on the explicit use of the best available evidence in the patient clinical history and the medical literature results. Argumentation represents a natural way of addressing this task by (i) identifying evidence and claims in text, and (ii) reasoning upon the extracted arguments and their relations to make a decision. ACTA 2.0 is an automated tool which relies on Argument Mining methods to analyse the abstracts of clinical trials to extract argument components and relations to support evidence-based clinical decision making. ACTA 2.0 allows also for the identification of PICO (Patient, Intervention, Comparison, Outcome) elements, and the analysis of the effects of an intervention on the outcomes of the study. A REST API is also provided to exploit the tool’s functionalities.
Deep reinforcement learning to assist command and control

Multi-domain operations drastically increase the scale and speed required to generate, evaluate, and disseminate command and control (C2) directives. In this work we evaluate the effectiveness of using reinforcement learning (RL) within an Army C2 system to design an artificial intelligence (AI) agent that accelerates the commander and staff’s decision making process. Leveraging RL’s superior ability to explore and exploit produces novel strategies that widen a commander’s decision space without increasing cognitive burden. Integrating RL into an efficient course of action war-gaming simulator and training hundreds of thousands of simulated battles using the DoD supercomputing resources generated an AI that produces acceptable strategic actions during a simulated operation. Moreover, this approach played an unexpected but significant role in strengthening the underlying wargame simulation engine by discovering and exploiting weaknesses in its design. This highlights a future role for the use of RL to test and improve DoD systems during their development.
IoT Prototype System of Flood Detection at Housing Pondok Gede

Indonesia is a country with a tropical climate. Entering the rainy season, a number of areas along the river have the potential to be affected by flooding, especially the Pondok Gede Permai residential area. Flooding is a big problem for the affected community, it makes the surrounding community panic, because the flood comes suddenly without knowing the time, seeing conditions like this then a tool is made to give an early warning of the arrival of flooding from the river, so that the community around the Pondok housing complex is made. the big game is more alert to flooding. In the Industrial 4.0 era, the Internet of things has developed rapidly, so flood early warning tools take advantage of IoT technology. The purpose of this study is to provide a programmatic framework for flood early warning. The tool framework that will be created is in the form of an IoT-based programmatic flood location model. By utilizing the NodeMCU ESP8266 as a control, transistors as sensors to identify water levels, Flow meter sensors as water release seekers, LEDs and LCDs as pointers, Buzzer as the highest level warning and the Blynk Application to observe river water levels via Android phones so that the people of Pondok Gede Permai especially those near the riverbanks are already alert. The prototype design of this Automatic Flood Detection System produces information that is sent by the NodeMCU ESP8266 to the Blynk Application as an information receiver. an error rate of 7.85 percent with a time span of 4 minutes 3 seconds when the water reaches its highest level and has notifications set within the Blynk App.
Ensemble Graph Attention Networks

Graph neural networks have demonstrated its success in many applications on graph-structured data. Many efforts have been devoted to elaborating new network architectures and learning algorithms over the past decade. The exploration of applying ensemble learning techniques to enhance existing graph algorithms have been overlooked. In this work, we propose a simple generic bagging-based ensemble learning strategy which is applicable to any backbone graph models. We then propose two ensemble graph neural network models – Ensemble-GAT and Ensemble-HetGAT by applying the ensemble strategy to the graph attention network (GAT), and a heterogeneous graph attention network (HetGAT). We demonstrate the effectiveness of the proposed ensemble strategy on GAT and HetGAT through comprehensive experiments with four real-world homogeneous graph datasets and three real-world heterogeneous graph datasets on node classification tasks. The proposed Ensemble-GAT and Ensemble-HetGAT outperform the state-of-the-art graph neural network and heterogeneous graph neural network models on most of the benchmark datasets. The proposed ensemble strategy also alleviates the over-smoothing problem in GAT and HetGAT.
Circuits resilient to short-circuit errors

Given a Boolean circuit C, we wish to convert it to a circuit C′ that computes the same function as C even if some of its gates suffer from adversarial short circuit errors, i.e., their output is replaced by the value of one of their inputs. Can we design such a resilient circuit C′ whose size is roughly comparable to that of C? Prior work gave a positive answer for the special case where C is a formula.
Supervised Contrastive Representation Embedding Based on Transformer for Few-Shot Classification

Abstract Few-shot learning as an emerging problem has attracted intensive attention in the data-limited situations of Computer Vision. Recently, massive works concentrate on exploiting meta learning to improve performance of recognition. However, most of them overlook the intra-class and inter-class relationships within the support set during episodic training, which are significant and important for the tasks of downstream. To tackle this problem, we present an efficient supervised contrastive representation embedding for few-shot classification. Specially, (1) we employ Swin Transformer as the backbone to replace CNN architecture in order to explore the huge potential of transformer-based backbone for the field of few-shot learning. (2) We introduce supervised contrastive loss to meta learning to take good advantage of extremely limited relations for the first time. Combined the classifier based on metric learning, extensive experiments have demonstrated the efficiency of the representation embedding. We conduct experiments and establish competitive results on two widely-used few-shot classification benchmarks: Fewshot-CIFAR 100 (FC-100) and MiniImageNet.
DEEM'22: Data Management for End-to-End Machine Learning

The DEEM'22 workshop (Data Management for End-to-End Machine Learning) is held on Sunday June 12th, in conjunction with SIGMOD/PODS 2022. DEEM brings together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in ML application scenarios. The workshop solicits regular research papers (10 pages) describing preliminary and ongoing research results, including industrial experience reports of end-to-end ML deployments, related to DEEM topics. In addition, DEEM 2022 establishes a new paper category for reports on applications and tools (4 pages) as a forum for sharing interesting use cases, problems, datasets, benchmarks, visionary ideas, system designs, and descriptions of system components and tools related to end-to-end ML pipelines. DEEM 2022 received 13 high-quality submissions from Africa, Asia, Europe, and North America, with 5 regular research papers, and 8 reports on applications and tools.
Traversability Analysis for Wheeled Robots using Point-Region-Quad-Tree based Elevation Maps

This publication describes a novel approach to generic robot navigation using elevation maps based on point-region-Quadtrees. The described approach plans optimized trajectories in dependency of the robot&#x2019;s morphology by detecting and rating obstacles. This is achieved by tailoring the tree based elevation map to the robot&#x2019;s design. The approach and the related work, it is based on, is described in detail, experiments are provided, which verify the results.
Swiftly Customer App: The Fastest and Safest Way for Grocery Purchase

The activity of smartphones and apps in the world is growing extraordinarily. People are trying to make their lives more innovative and use their smartphones to solve everyday tasks. Customers do not want to wait for checkout lines in groceries or other shops while shopping. In that regard, we developed a mobile application named "Swiftly Customer App" that allows users to scan the product's barcode with their smartphones and pay without waiting in the long checkout lines. The mobile phone's camera scans the barcodes of the products, and online payments are complete with PayPal. It allows the customers to check out quickly and conveniently with less time shopping. Take less time to shop. This research paper discusses the design, implementation, and real-life users' feedback for the application.
Mobile Device Eye Tracking on Dynamic Visual Contents using Edge Computing and Deep Learning

Eye-tracking has been used in various domains, including human-computer interaction, psychology, and many others. Compared to commercial eye trackers, eye tracking using off-the-shelf cameras has many advantages, such as lower cost, pervasiveness, and mobility. Quantifying human attention on the mobile device is invaluable in human-computer interaction. Like videos and mobile games, dynamic visual stimuli require higher attention than static visual stimuli such as web pages and images. This research aims to develop an accurate eye-tracking algorithm using the front-facing camera of mobile devices to identify human attention hotspots when viewing video type contents. The shortage of computational power in mobile devices becomes a challenge to obtain higher user satisfaction. Edge computing moves the processing power closer to the source of the data and reduces the latency introduced by the cloud computing. Therefore, the proposed algorithm will be extended with mobile edge computing to provide a real-time eye tracking experience for users
SatSim: a synthetic data generation engine for electro-optical imagery of resident space objects

The application of machine learning to a task often necessitates the production of synthetic training data. Some tasks involve rare, but important, scenarios that may not yet have been observed; others are difficult to collect or annotate in large volumes. These difficulties are particularly acute in computer vision applications to scientific imagery, in which human annotation is complicated by noise, ambiguity, and interpretation. One such application is the detection of resident space objects (RSOs) in electro-optical images for space domain awareness (SDA). In many cases, the mislabeling of RSOs by an imperfect annotator (human or machine) can be detrimental to machine learning model performance, especially when the signal-to-noise (SNR) is near or below human detection levels. In this work we introduce SatSim, a modular electro-optical synthetic data generation engine designed to procedurally generate representative, annotated synthetic electro-optical imagery of remote space scenes. SatSim enables rapid generation of synthetic data through Graphics Processing Unit (GPU) acceleration with TensorFlow. This paper discusses the use of SatSim to enhance machine learning approaches and reports the performance of models trained with real data, synthetic data, and real data augmented with synthetic RSOs. In addition, we explore using SatSim to evaluate current state-of-the-art RSO detection algorithms with new sensors (such as all-sky and event-based) and rare but critically important scenarios (such as satellite breakups and collisions) for which limited real data are available.
Image contrast enhancement algorithm for X-ray observation of space materials in situ

Abstract Chinese Space Station has planned a high-temperature material science experiment rack, equipped with an X-ray projection imaging module, to support the development of material experiments and research in space. BiFeO3 has been selected as the first batch of experimental materials for Chinese Space Station. The melting and solidification process of BiFeO 3 , an opaque, high-temperature material, is observed by X-ray observation module in situ. X-ray is the dominant way to observe opaque materials due to its penetrability. In-situ observation of materials is the top priority of this study, so we have strict requirements on image quality, and high-quality images can better analyze the properties and properties of materials. Limited by narrow size and high temperature conditions, the X-ray images collected have low contrast, serious noise pollution, and poor imaging quality. To enhance the contrast and improve the edge details of such images, a grayscale weighted histogram equalization combined with high-frequency enhancement (GWHE-HFE) algorithm is proposed. First, we add a mask to the input image to obtain the region of interest (ROI), and then filter out the low-frequency components of the image by Gaussian high-pass filter to preserve high-frequency details. Second, the image obtained in the previous and the X-ray image of ROI are respectively multiplied by a coefficient and added to obtain the edge-emphasized X-ray image. And then, we use grayscale weighted histogram equalization (GWHE) to process the image obtained in the second step to obtain the contrast enhanced X-ray image. The enhanced image shows the crystal grains and the thin bands where the solid and the melt intersect, and it is helpful to accurately locate the solid solution interface. Experiments on X-ray images of BiFeO3 growth demonstrate that this combined method outperforms existing ones both qualitatively and quantitatively, providing an in-depth and effective analysis method for in high-temperature material-science experiments.
The Mechanical and System Design of Finger Training Rehabilitation Device Based on Speech Recognition

Aiming at the problem of long diagnosis and treatment recovery period and low recovery efficiency of traditional stroke patients, a finger rehabilitation training control system based on speech recognition is designed to help patients carry out finger exercise training and obtain the perception ability of finger angle, speed and position, and provide rehabilitation physicians with finger rehabilitation evaluation and training data reference. In order to realize the finger rehabilitation training control system, based on the finger movement perception system, the system is divided into hardware circuits, lower computer control systems, and voice recognition human computer interaction systems. Combining the unique advantages of the HMM algorithm, the HMM algorithm is applied to the voice interaction system for pattern matching, and the simulation test of the finger rehabilitation system is performed. The application results show that the rehabilitation training system based on speech recognition proposed in this study meets the design requirements, has good safety and reliability, and has high application value for future finger rehabilitation training.