Reviewerly/exHarmony huggingface dataset:
A contract among autonomous agents to deal with egalitarian social welfare

Auction security has been a major challenge for researchers in this area. For example, one of the biggest problems has always been the trust in a third party, an intermediary, which is the one who usually conducts the auction and knows the bids made by the participants. Over time, traditional methods have been overtaken by new technologies that eliminate the problems that arise when using traditional methods. Blockchain technology allows us to use its inherent characteristics of privacy, traceability and decentralization to conduct auctions with a much higher level of security and to execute auctions while reducing transaction costs. In addition, the automation of operations provided by smart contracts allows us to eliminate the intermediary, leading to additional cost savings. Furthermore, in contrast to previous technologies, the pseudo-anonymity of blockchain allows us to verify the authenticity of data, mitigating malicious behavior on the part of agents. In this paper we address this challenge; we present a smart contract that allows us to run an auction within the Ethereum blockchain at a relatively low cost, eliminating the intermediary and guaranteeing the trust of the agents involved in the auction. In particular, we concentrate on dealing with egalitarian social welfare, where the goal is to maximize the utility of the agent whose utility turns out to be minimal.
Evaluating the Cognitively-Related Productivity of a Universal Dependency Parser

A key goal of cognitive computing is to correctly model human language. Recently, much has been made of the ability of deep neural nets trained on huge datasets to precisely parse sentences. But do these systems truly incorporate human knowledge of language? In this paper we apply a standard linguistic methodology, transformational analysis, to determine whether this claim is accurate. On this view, if a deep net parser operates properly on one kind of sentence, it should also work correctly on its transformed counterpart. Applying this to a standard set of statement-question transformed sentence pairs, we find that a state of the art neural network system does not replicate human behavior and makes numerous errors. We suggest that this kind of test is more relevant for highlighting what deep neural networks can and cannot do with respect to human language.
Interactive Visualization of Deep Learning for 3D Brain Data Analysis

With multiple hidden layers and massive combinations of features and weights, deep learning models are hard to understand, and even more difficult to interact with. In this paper we describe a visual analytics platform to help with the understanding of and interaction with the deep learning process of human brain image data. A brain connectome network dataset is used to train a classifier for the diagnosis of Alzheimer's Disease (AD). 3D rendering of brain images is integrated into the interactive visualization process of a deep neural network to bring contextual information of the application to the analysis framework. A backpropagation algorithm is applied to track the image features that are captured by each node in the hidden layers. Our results demonstrate that interactive visualization can not only help the understanding of the deep learning process, but also provide a platform for domain experts to interact with and assist in the learning process, which can potentially enhance the interpretability and accuracy of the analysis.
Actual Cause and Chancy Causation in Stit: A Preliminary Account

The paper investigates how actual cause may be treated in ‘seeing to it that’ (‘stit’) logics, that is, determining when the actions of a particular agent, or a particular set of agents collectively, can be said to be the cause of a given outcome in given circumstances. There are two complementary problems: (1) the outcome is brought about by the actions of some set of agents and the task is to identify which of these agents are essential to that bringing about, and (2) the outcome depends partly on chance and the task is to identify which agents could have acted differently and thereby ensured a different outcome. The final part of the paper discusses briefly the need to account for causal and other dependences between the actions of agents, and how that might be done without abandoning the ‘stit’ framework altogether.
The Automatic Operation and Maintenance Scheme for Cloud Data Centers in Multi-site Fusion

With the continuous progress of information technology and power industry, multi-site fusion has become one of the key components of the ubiquitous power Internet of Things. In this article, a new scheme for automatic operation and maintenance is introduced, so as to meet the technical requirements of automatic operation and maintenance. In addition, this scheme can automatically manage resources and improve the efficiency for operation and maintenance in cloud data centers.
Power Quality Data Processing and Distributed Power Quality Evaluation Based on Hadoop

This study offers a classification-based power quality evaluation algorithm and a Hadoop-based distributed power quality evaluation technique. A distributed classification-based power quality comprehensive assessment framework and a distributed Bayes-based power quality comprehensive evaluation algorithm are proposed. This paper introduces the process of power quality analysis based on Hadoop. In this paper, the traditional monitoring system for power quality is improved by using the Hadoop platform, which solves the problems that massive data cannot be stored and the calculation time is too long.
Explainable AI for Car Crash Detection using Multivariate Time Series

The pervasiveness of Artificial Intelligence approaches in effectively supporting the decision process in many applications has raised the need to explain their behaviour. In this context, we present the application and evaluation of three eXplainable Artificial Intelligence methods in a real-world multimodal task of anomaly detection on telematics data. We cope with the challenge of explaining Multivariate Time Series and of translating methods designed for images to this domain.
Web Application Vulnerabilities &amp; Countermeasures

The web applications are evolving with time to provide the communication environment to interact but they are coming up with various vulnerabilities even with just some basic programming bugs. Therefore, security ought to be a significant in web applications. There are categories of vulnerabilities that occur while working with the web applications and countermeasures needs to be provided so as to cope up with these vulnerabilities. This paper discusses the various threats occurring in web applications along with the examples and provides a solution for preventing them. The paper also classifies the different web applications vulnerabilities as per the EDI matrix (exploitability, detection rate, and impact). The research paper is concluded with various countermeasures which deals with the threats over the web applications and lessen the impact by targeting the weak spots that open the web application to the severity.
An Empirical Study of Sentiment Analysis on Movie Review using Machine Learning based Classification Approach

Sentiment analysis is the investigation of feelings and conclusions from any type of text. It is additionally named as assessment. This is extremely helpful to communicate the assessment of the gathering person. The strategy is utilized to discover the estimation of the individual as for a given wellspring of substance. The web-based platform consists of gigantic measure information as tweets, websites. In this paper, we have presented the sentiment analysis approaches on Movie review dataset using different procedures like Naïve Bayes, K-Nearest Neighbour and Random Forest.
Analysis of Classifiers for Gender Identification using Voice Signals

This paper presents analysis of different classifiers to identify gender-based voice signals using MFCC. Gender is an important and critical parameter of individuals. The gender of the human being can be identified with the help of voice signals. To Communicate between human beings, speech is the most convenient and suitable medium. Speaker produces the voice signals with the help of a vocal cord for shouting, singing, talking etc. The vocal cord is the most essential part to produce voice signals. The analysis of the voice signals plays a very important role to identify the gender for authentication in highly secure areas. For the recognition of the speaker, the gender identification has important because it reduces the search space. In this paper, Mel Frequency Cepstral Coefficients (MFCCs) are used as a component of a feature vector to recognition of the speaker's gender. Support Vector Machine (SVM), Vector Quantization (VQ), Multilayer Perceptron (MLP), Gaussian Mixture Model (GMM) and Learning Vector Quantization (LVQ) and k-Nearest Neighbor (KNN) are used as a classifier to classify the speaker with the help of voice signals of the speaker. The highest accuracy for the identification of gender is 99.5% using the Support Vector Machine (SVM).
Comparative analysis of different mining approaches for Blockchain in infant care application on IoT

Ambient means environmental elements, helped living means the kind of houses inserted with innovations that assistance in regular daily existences, in an entire it implies the utilization of environmental elements advancements to improve living and autonomous without hindering other individual life. Ambient Assisted Living (AAL) is a subclass of Ambient understanding, which concerns the use of incorporating savvy strategies, systems and advances to enable people to live uninhibitedly for a long as could be anticipated in light of the current situation, without intrusive practices. In this exploration work has been made to do relative investigation between two distinct agreement calculation verification of work and confirmation of stake in Blockchain utilizing python for information gathered and put away of elements temperature, second, moistness, pressing factor, sound and light. The time taken to mine a square in the verification of stake is less contrasted with the time taken to mine a square in confirmation of work which is putting away information of elements influencing the independence of an individual. This review assists with recognizing the most reasonable agreement calculation to store information in Blockchain.
Research on Ship Relation Graph Analysis Driven by Multi-source Data

In order to improve the application level of maritime supervision and maritime security data, This paper uses scientific knowledge graphs and big data analysis technology to integrate a large number of multi-source, heterogeneous, isolated maritime business and navigation service data for knowledge fusion and knowledge reasoning, and builds a multi-source data-driven ship relationship graph network to make ship data easier to be understood and processed by people and machines, and achieves more accurate and efficient maritime and navigation ship information intelligent retrieval, ship relationship portrait analysis, and visualization and other fusion applications, effectively improve the level of maritime data application and enhance the ability of maritime supervision and analysis decision-making for ship safety.
Insights on Blockchain Frameworks For Decentralized Application Deployment

Blockchain, the digital ownership revolution technology brings together cryptography, peer-to-peer networks, and digital time stamp. It is referred to as blocks that contain digital information stored in a public database. The database is a shared and irreversible archive with no central authority. Hence it can be termed as a transparent technology that distributes the system among all its participants equally and every participant of the system is accountable for its happenings. With limited Blockchain knowledge, end users find it difficult in choosing an appropriate Blockchain framework for deploying their applications. In this paper, we discuss in detail the different frameworks available for the adoption of Blockchain. This work will provide complete insight into various blockchain frameworks based on important parameters like Deployment, Smart Contract, Type of Consensus Algorithm, Privacy, Crypto coin, Anonymity, etc.
Analysis and Evaluation of the Quality of Course Education Open Course Based on Quantitative Analysis

The teaching of public music courses in Chinese colleges and universities has always been in a very weak state. The development of the times has prompted the concept of general education to be gradually valued by modern domestic colleges and universities, and the popularization of music education has promoted major changes in the development of music public education in colleges and universities in my country. Teaching quality is an important criterion for measuring the teaching quality of public music courses in colleges and universities. This article establishes a teaching quality evaluation system for public music courses in colleges and universities, constructs an evaluation index for public music courses in colleges and universities, quantifies the data, and establishes an evaluation of the teaching quality of public music courses in colleges and universities. Model.
FPGA Prototyping of Systolic Array-based Accelerator for Low-Precision Inference of Deep Neural Networks

In this study, we aim to design an energy-efficient computation system for deep neural networks on edge devices. To maximize energy efficiency, we design a novel hardware accelerator that supports low-precision computation and sparsity-aware structured zero-skipping on top of the well-known systolic-array structure. In addition, we introduce a full-stack software platform, including a model optimizer, instruction compiler, and host interface, to translate the pre-trained PyTorch model to the proposed accelerator and orchestrate it automatically. We validate the entire system by prototyping the accelerator on the Xilinx Alveo U250 FPGA board and demonstrating the inference of the 4-bit ResNet-50 model through the software stack. According to our experiment, our platform shows 317 GOPS inference speed and 51.96 GOPS/W energy efficiency for ResNet-50 on Xilinx Alveo U250 FPGA at 108 MHz, which is comparable to the advanced commercial acceleration system in terms of energy efficiency.
BEI-TAB: Enabling Secure and Distributed Airport Baggage Tracking with Hybrid Blockchain-Edge System

Global air transport carries about 7.3 billion pieces of luggage each year and up to 56 percent of travelers prefer obtaining real-time baggage tracking information throughout their trip. However, the traditional baggage tracking scheme is generally based on optical scanning and centralized storage systems, which suffers from low efficiency and information leakage. In this paper, a blockchain and edge computing based IOT system for tracking of airport baggage (BEI-TAB) is proposed. Through the combination of radio frequency identification technology (RFID) and blockchain, real-time baggage processing information is automatically stored in blockchain. In addition, we deploy Interplanetary File System (IPFS) at edge nodes with ciphertext policy attribute-based encryption (CP-ABE) to store basic baggage information. Only hash values returned by the IPFS network are kept in blockchain, enhancing the scalability of the system. Furthermore, a multi-channel scheme is designed to realize the physical isolation of data, and to rapidly process multiple types of data and business requirements in parallel. To the best of our knowledge, it is the first architecture that integrates RFID, IPFS, CP-ABE with blockchain technologies to facilitate secure, decentralized, and real-time characteristics for storing and sharing data for baggage tracking. We have deployed a testbed with both software and hardware to evaluate the proposed system, considering the performances of transaction processing time and speed.
Evaluating Visuospatial Features for Tracking Hazards in Overhead UAS Imagery

Tracking multiple objects as they enter and exit the field of view for a given image sequence has many applications in security, surveillance, traffic control, and additional important fields. To track objects of interest through videos a computer vision system needs two main components: an object detector, a model which can generate class predictions and bounding boxes for potential objects in a frame, and an object tracker, a model which partitions the nominated image regions (object detections) into groups containing the same real-world object instances. In this work we explore a method for object tracking involving clustering visuospatial features derived from an object detection model's outputs. For each frame, a variety of features are calculated based on the image patches nominated by an object detection model. Periodically, these detections and their associated features are clustered in each feature space. These feature-based partitions are then fused through evidence accumulation, resulting in a single partition of objects where each group corresponds to a single real-world object instance. To evaluate the aptitude of these features for the sake of object tracking, we test our approach on a set of low-altitude overhead UAS imagery for which object labels already exist. This work is meant to contribute to a larger system in which visual information is extracted from multiple airborne sensors in order to aid in mapping novel regions and the navigational hazards within.
Baseline Evaluation Methodology for Adversarial Patterns on Object Detection Models

Physically realizable adversarial patterns have be-come a sophisticated form of adversarial ML across industry and academia, with several algorithms that successfully thwart state-of-the-art ML models. However, there are currently no standard practices for evaluating adversarial patterns. We identify two components that provide insight into an adversarial pattern's performance: the seeding of the algorithm for producing the adversarial pattern and the use of control patterns (patterns that serve as the baseline comparisons to the adversarial pattern). In this study, we implement and compare the performance of a variety of control patterns (solid white, solid gray, solid black, and random noise). We train state-of-the-art DNN object detection models on an open-source dataset. Using the trained models, we evaluate performance on the various control patterns in order to establish performance baselines for current and future adversarial pattern algorithms.
A Deep Learning Approach for Contour Interpolation

Contour interpolation is an important tool to expedite manual segmentation of anatomical structures. The conventional shape-based interpolation (SBI) algorithm, based on distance map calculation then interpolation, often performs sub-optimally when the two adjacent to-be-interpolated manual contours differ dramatically, especially near the superior and inferior borders of organs and for the gastrointestinal structures. In this study, we present a deep learning solution to improve the robustness and accuracy for contour interpolation, especially for these historically difficult cases. The deep contour interpolation model achieved a dice score of 0.95±0.06 and a mean DTA value of 1.08±2.31 mm, averaged on 3167 testing cases of all 16 organs. In a comparison, the results by the conventional SBI method were 0.94±0.08 and 1.50±3.63mm, respectively. For the difficult cases, the dice score and DTA value were 0.91±0.09 and 1.71±2.25 mm by the deep model, compared to 0. 86± 0. 13 and 3.42±5.88 mm by the conventional SBI method. A student t-test was applied to confirm that the performance improvements were statistically significant (p< 0. 05) for all cases. It could be useful to expedite the tasks of manual segmentation of organs and structures in the medical images.
Brain Intracranial Hemorrhage Segmentation using Unsupervised Learning on Volume CT Images

In this paper we propose a novel unsupervised learning approach pipeline for brain intraparenchymal and subdural hematoma segmentation using brain volume computed tomography (CT) modality, specifically for instance when there is low contrast and noisy brain image dataset. The proposed method was analyzed using a private CT brain image dataset from a collaborator. Finally, the performance of the proposed approach was evaluated by comparing constructed segmentation results with ground truth generated by manual contour segmentation.
Objects Detection Deep Learning System Based on 2-D Winograd Convolutional Neural Network

In recent years, artificial intelligence (AI) grows exponentially. Traditional chips cannot satisfy modern applications of AI requirements because AI chips must provide significant computation speed on deep learning. In order to solve this problem, 2-D Winograd Convolutional Neural Network integrated circuit that decreases the amount of multiplier and reduces the complexity of computation is proposed to speed up convolution operations. This chip is designed as the core circuit of Yolov4 object detection system. To improve the performance of Yolov4, the Yolov4 structure based on Modified Spatial Attention Module (MSAM) is designed in this paper, and batch size 4 is selected to achieve high-precision object recognition. In our experiment, the presented system can reach exceed of mean Average Precision (mAP) 73.01&#x0025;.
Do Marketing strategies, COVID 19 Pandemic and Consumer Location Affect Consumer Buying Behaviour? Empirical Study on Oil and Gas Lubricant Industries in United Arab Emirates

For UAE end-users, consumer behavior and purchasing power faced many swings period after Covid 19 pandemic. Recently, changes in sales capacity and customers' preferences for eco-friendly products highlighted the industrial gap for producers before and after the pandemic. Green marketing practices aim to improve customer knowledge to increase consumer loyalty, preserve wealth, and reduce global environmental degradation. The research examines the relationship between changes in consumer purchasing behavior and green marketing strategies following the COVID-19 pandemic in the UAE's oil and gas lubricants industry. It also assesses the efficacy of green marketing in improving the dynamic of organization performance. Stakeholders and three bottom lines theories formed the theoretical background for this research. The paper's methodology is quantitative with deductive approaches and inferential statistical analysis, while the sampling strategy is a snowball, with 162 respondents, through quantitative methods, most notably surveys. The primary findings of this study established a substantial positive correlation between both study variables and consumer attitudes toward green marketing initiatives. Consumer opinion differs according to geographical ages when developing and advertising environmentally friendly products. Meanwhile, the article examined how consumers’ behavior of economic themes changed prior to and following the Covid-19 pandemic epidemic.