Reviewerly/exHarmony huggingface dataset:
Preliminary Study on Adapting ProtoPNet to Few-Shot Learning Using MAML

ProtoPNet proposed by Chen et al. is able to provide interpretability that conforms to human intuition, but it requires many iterations of training to learn class-specific prototypes and does not support few-shot learning. We propose the few-shot learning version of ProtoPNet by using MAML, enabling it to converge quickly on different classification tasks. We test our model on the Omniglot and MiniImagenet datasets and evaluate their prototype interpretability. Our experiments show that MAML-ProtoPNet is a transparent model that can achieve or even exceed the baseline accuracy, and its prototype can learn class-specific features, which are consistent with our human recognition.
Using a Legal Knowledge Graph for Multilingual Compliance Services in Labor Law, Contract Management, and Geothermal Energy

Abstract This chapter provides insights about the work done and the results achieved by the Horizon 2020-funded Innovation Action “Lynx—Building the Legal Knowledge Graph for Smart Compliance Services in Multilingual Europe.” The main objective of Lynx is to create an ecosystem of multilingual, smart cloud services to manage compliance based on a Legal Knowledge Graph (LKG), which integrates and links heterogeneous compliance data sources including legislation, case law, regulations, standards, and private contracts. The chapter provides a short introduction in regards to the market needs, gives an overview of the Lynx services available on the Lynx Services Platform (LySP), and provides valuable insights into three real-world compliance solutions developed on top of LySP together with Lynx’s industry partners, namely, (1) Labor Law (Cuatrecasas, Spain), (2) Contract Management (Cybly, Austria), and (3) Geothermal Energy (DNV.GL, the Netherlands).
BIAS AND DISCRIMINATION

Chapter 22 relating to Bias and Discrimination says that AI is about people. Again the subject of AI's relationship with hard law (in this case the UK Equality Act) and soft law is discussed. The chapter notes that AI cannot be unpacked from ethics and gives good examples of rules layering on each other, again highlighting a theme of the book. It considers the prospect of new rules and how they would interrelate with existing ones. It also looks at the difficult question of enforcement of rules relating to AI. Finally, the chapter poses questions for society about how AI should be a force for good.
IPARS: An Image-based Personalized Advertisement Recommendation System on Social Networks

Social media has become a primary source of information for decision-makers, organizations, and scientists in today’s fast-paced world. Indeed, because of the large volume of user-generated data available on social media, these online platforms are viewed as computable data sources that potentially mirror reality. It could be an authentic environment for the task of target customers identification for marketing. This paper presents a novel image-based personalized advertisement recommendation system named IPARS to identify target customers in social media using image processing and machine learning techniques for an online advertisement. Assume having a set of advertising images; The problem is identifying a group of social media users who are likely to be the potential target of those images. In IPARS, a given social network is first converted into a weighted bipartite graph where the nodes are the users and keywords. Then, another bipartite graph is formed by decomposing the advertising images into their objects, labels, concepts, and sentiments. We propose a couple of formulas to calculate the weight of edges for both graphs. An algorithm is proposed to search the social graph and identify and rank the best group of users. We have evaluated our proposed model on a set of images and users from the Flickr dataset and Twitter. The results showed that IPARS has a better performance compared with other algorithms.
A new approach for automatic Arabic-text detection and localisation in video frames

Text embedded in video frames provides useful information for semantic indexing and browsing system. In this paper, we propose an efficient approach for automatic Arabic-text detection which combines edge information and maximally stable extremal region (MSER) method in order to extract text region candidates. These regions are, then, grouped and filtered on the basis of geometric properties such as area and orientation. Besides, we introduce a new geometric descriptor of Arabic text called baseline to improve the filtering process. Our proposed approach was tested on a large collection of Arabic TV news and experimental results have been satisfying.
Hybrid Feature Extraction Technique for Tamil Automatic Speech Recognition System in Noisy Environment

Automatic speech recognition (ASR) system is used to recognize the text transcript from the given speech signal. Such speech signal can contain either isolated words or large vocabulary continuous speech (LVCS). Isolated words can be recognized with high accuracy in clean environment, but recognizing continuous words involves various parameters like speech corpus, speaker, environment noise, etc., that affects the accuracy of automatic speech recognition system directly. In the proposed work, hybrid feature extraction technique combines the perceptual linear predictive (PLP) and mel frequency cepstral coefficient (MFCC) to improve the accuracy of ASR in noisy environment. Voice activity and detection (VAD)-based frame dropping is used for improving the phonemes modeling by removing the pauses and distorted elements from the given speech signal. The proposed hybrid model with VAD is implemented by using self-generated speech corpus and shows relatively 12% increase in recognition rate compared with the state-of-the-art methodology.
How AI will transform the cyber security industry

In today’s digital world, one of the most common, as well as concerning, issues is the challenge posed by cyber attacks. Organisations around the world use all types of techniques to combat these threats. And one of the most powerful weapons available, which has the potential to completely transform the cyber security industry, is artificial intelligence (AI).
Motor Fault Diagnosis Method Based on Deep Learning

Motors occupies an important position in the manufacturing industry.Aiming at the problem of low accuracy of motor fault recognition,the temporal convolutional neural network(TCN) and attention mechanism are introduced. The fault diagnosis method based on TCN, by adding the SE-block module, improves the performance of the model at the cost of minimal complexity.Comparative experiments show that compared with the GRU model, LSTM model and the original TCN model,this method can better extract fault features in motor fault diagnosis, with higher accuracy and better generalization ability.
An Ontology Model Approach for Cloud Service Level Agreement (SLAs)

Service Level Agreement (SLA) is established between customers and service providers. Reading SLA in a documented format is a very time consuming task, therefore a platform is required to simplify the reading task, and to integrate various SLA agreements. Semantic Web Ontology model can greatly simplify the problem of understanding SLAs, and comparing SLAs of different Service Providers. Therefore, a Semantic Ontology Model is proposed in the paper. The research work provides an Ontology based platform to help the customer and service provider to better understand the SLA. The integrated Ontology models facilitate users to understand various aspects of SLA agreement. Valuable time of users can be saved by using Integrated Ontology. Models because users don&#x2019;t need to read whole SLA documents, and users can also get information according to their requirements.
Augmented Reality based Monitoring System

Augmented reality is an advanced image processing technique. Using this technique we are able to real time industrial as well as domestic issues. Here In this project we are going to created an augmented reality application which displays the real time sensor values. Using this project we can monitor any dangerous work space safely. While scanning the trigger image the sensor paced in the machine gets the required data and it is processed. Then the data is stored in the cloud that is created. Then the API is called and the data is displayed in the augmented reality. If there is any technical issues in the machine then there will be an indication while scanning the trigger image.
Gaussian Approximation Filter Based on Divergence Minimization for Nonlinear Dynamic Systems

In the Bayesian filtering paradigm, approximation of posterior distribution is an important research topic for nonlinear dynamic systems. In this paper, we aim at obtaining its Gaussian approximation via KL divergence minimization. We formulate the problem as a nonlinear programming problem with linear constraints and resort to the feasible direction method for the solution. Since the gradient of the objective function involves intractable integrals, we adopt a cubature rule to calculate the gradient, which is suitable for real-time filtering for its simplicity, efficiency, and accuracy. Based on the Gaussian approximation, a nonlinear filter is derived, and it is demonstrated to be effective by simulations.
Introduction to Blockchain Basics

The blockchain, invented by a person (or group of people) using the name Satoshi Nakamoto in 2008, serves as the public transaction ledger of bitcoin. Its decentralization, openness, and information that cannot be easily altered or tampered with are likely to have a disruptive impact on a range of industries, including finance and services. In January 2016, the PBOC (PBOC) held a seminar on digital currencies in Beijing to discuss the feasibility of issuing virtual digital currencies using blockchain technology. Although the pilot digital currency did not fully embrace blockchain technology, the “blockchain,” a somewhat mysterious term, suddenly became a hot topic, followed by the rapid rise of blockchain technology in China, and more and more startups and related research groups were established one after another. It has driven the rapid development of blockchain technology, making it one of the most revolutionary emerging technologies in recent years, and was even considered as the fifth disruptive computing paradigm after mainframe, personal computer, the Internet, and mobile/social networking. It has also been hailed as the fourth credit milestone in the history of human credit evolution, after blood relative credit, precious metal credit, and paper money credit.
Fast Depth Estimation of Object via Neural Network Perspective Projection

In autonomous driving and mobile robotic systems, obtaining the depths of objects in real-time is crucial. The current network-based methods usually design complex network to achieve 3D object detection or monocular depth estimation for the whole image, resulting in too slow to be applied to mobile robots. The perspective projection-based method can achieve real-time, which calculates the object depth based on the camera parameters, the object sizes in the world coordinates and in image coordinates. While it relies heavily on the accuracy of object size in images coordinates, and the size is usually obtained with errors through detector network. Combining the perspective projection-based methods and network-based methods, we propose a fast object depth estimation method by designing a neural network to learn perspective projection, called Fast-Depth-NPP: 1) Instead of considering the whole image, we only consider the local depth of the image; 2) Using local image patches as network inputs avoids measurement errors of object size with detector; 3) the use of global information is enhanced by incorporating position encoding. Our method is validated on the mobile robot public dataset Neurons Perception dataset, achieving excellent results and meeting the real-time requirements.
Noise-Tolerant Learning with Silhouette Coefficient for Unsupervised Person Re-Identification

Unsupervised person re-identification (re-ID) attracts growing attention due to its broad prospects in practical applications. State-of-the-art unsupervised re-ID approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, pseudo labels generated directly by clustering are not always reliable and inevitably contain noisy labels. To tackle these challenges, we propose a novel noise inhibition framework to estimate the confidence of each pseudo label and actively correct noisy labels. By introducing the silhouette coefficient, our method can estimate the pseudo-label confidence without any extra model or data, and calculate the correction matrix to correct clustering results directly. However, the silhouette coefficient is usually applied on the hyper-parameters selection of clustering algorithms. In order to make the silhouette coefficient more suitable for estimation and correction tasks, we calibrate the Jaccard distance matrix to alleviate the negative influence of the cluster size on the silhouette coefficient. Our proposed method brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
Weld Seam Extraction of Intersecting Pipelines Based on Point Cloud Entropy

This paper presents a method for extracting intersecting pipelines weld seam based on point cloud. Firstly, the collected point cloud is filtered by PassThrough filter to extract the region of interest, and the intersecting pipelines model is reconstructed by using the camera calibration results and the pose of robot. Then VoxelGrid filtering and Moving Least Squares filtering are performed on the reconstructed model to smooth the error caused by camera and robot. The Fast Point Feature Histograms of point cloud is used to calculate the entropy map, and the weld seam region is extracted by threshold segmentation. Finally, an iterative algorithm for weld point extraction based on entropy is proposed. Through experimental verification, the algorithm can extract the weld points of the actual intersecting pipelines and obtain the real position of the weld points in the robot base coordinate frame, which has great application value in robot automatic welding area.
The results of diagnosing educational competences in undergraduates in the direction of “Pedagogic Educationˮ

The article discusses the methodology for diagnosing the upbringing competences of students in pedagogic areas of training. The authors present the theoretical foundations on which the methodology is based, including the content and structure of the graduate's competency model. The article characterises the criteria for assessing upbringing competences and substantiates the methods that are used to diagnose according to these criteria. The authors describe the general diagnostic procedure and requirements for it, the form for presenting the results and recommendations for students. The article presents in detail the content and procedure of individual methods – testing, solving case tasks, value-oriented game, motivational survey. The paper presents and interprets the results of a comprehensive diagnosis of upbringing competences of future pedagogues, and makes suggestions for the development of diagnostic tools.
Multi-party deterministic secure quantum communication using d-dimension GHZ state

Quantum direct communication (QDC) uses the basic principle of quantum mechanics to realize the secure communication between legal parties. Deterministic secure quantum communication (DSQC), as a branch of QDC, is a way to transmit secret message with the aid of classical bits. In this paper, a multi-party DSQC based on [Formula: see text]-dimension [Formula: see text]-particles GHZ states is proposed, in which unitary operation is not required. On the basis of [Formula: see text]-dimension single-photon measurement results, the communication parties encode and decode the message through performing modulo addition and subtraction. In addition, the scheme uses the added [Formula: see text]-dimension single photon for eavesdropping detection. The scheme has expansibility and universality. It can scale to as many parties in any dimension as required. Based on the star-shaped communication structure, it can be extended to a network to achieve the rapid communication of secret message across regions.
A Light Field FDL-HCGH Feature in Scale-Disparity Space

Many computer vision applications rely on feature detection and description, hence the need for computationally efficient and robust 4D light field (LF) feature detectors and descriptors. In this paper, we propose a novel light field feature descriptor based on the Fourier disparity layer representation, for light field imaging applications. After the Harris feature detection in a scale-disparity space, the proposed feature descriptor is then extracted using a circular neighborhood rather than a square neighborhood. It is shown to yield more accurate feature matching, compared with the LiFF LF feature, with a lower computational complexity. In order to evaluate the feature matching performance with the proposed descriptor, we generated a synthetic stereo LF dataset with ground truth matching points. Experimental results with synthetic and real-world dataset show that our solution outperforms existing methods in terms of both feature detection robustness and feature matching accuracy.
Design of Intelligent Information Monitoring System for Distribution Network and Adjustment of Alarm Threshold

In the current distribution network information monitoring system, there are many false alarm information, which forms redundant interference to the fault alarm threshold, and it is difficult to ensure the alarm accuracy of the monitoring system. The distribution network intelligent information monitoring system and the alarm threshold adjustment method based on machine learning are designed, with the physical layer of the system designed to collect the operation status information of each line and equipment of the distribution network according to various sensors, and transfer it to the data layer. The data layer extracts, processes, and classifies the received information, stores it in the database, obtains the abnormal information in the information base, and adjusts the alarm threshold based on the fuzzy clustering method in machine learning, realizing intelligent monitoring of distribution network. The test results show that the detection performance of abnormal information is good, that the abnormal information in the data can be obtained accurately, and that the clustering of the target category of abnormal information can be completed according to the eigenvalue, and has a good threshold adaptive adjustment ability, to maximize the balance between human, machine, and power grid operation state in the process of distribution network monitoring information, ensure real-time and reliable monitoring and alarm results.
Eye-tracking everywhere - software supporting disabled people in interaction with computers

In this paper we present comprehensive system for communication with computer by gaze. One of the main assumptions behind this work was to provide solution that can be used with standard RGB webcam. The proposed comprehensive system included the eye tracking module and user interface for convenient gaze interaction with computer. As a result a fully functional application was developed. The average accuracy of the eye tracking module was 4.1°while precision was 0.042°.
DOLL

Most large-scale ML implementations scale to large amounts of data by utilizing multiple servers or virtual machines (VMs) that iteratively compute model updates on local data that are periodically synchronized. Due to the complexity of managing the resulting computing infrastructure, many companies run their ML jobs on external cloud providers' servers. However, cloud resources can be expensive, particularly for large ML jobs with long runtimes. A particularly popular method to limit the costs of training ML jobs is to utilize preemptible cloud instances. These may be interrupted at the cloud provider's discretion, but they are significantly (up to 90%) cheaper than conventional on-demand instances. Most studies of these ML methods, however, assume the availability of large datasets at training time. In practice, training data may arrive at irregular intervals and models may be trained online as new data samples arrive, e.g., when monitoring data from IoT sensors. While some software frameworks like Apache Kafka can feed online data arrivals to ML algorithms, they provide little insight into the resulting costs of ML training. We extend prior work on provisioning preemptible instances to analyze available pools of data in order to run online ML on incoming datastreams, which presents new challenges due to the need to carefully handle data arrivals. We design, analyze, and optimize DOLL, which to the best of our knowledge is the first system that provides provable performance guarantees for Distributed OnLine Learning over preemptible instances. Research Challenges and Our Contributions: When pools of data are readily available, the bottleneck to distributed ML training often lies in the time required for each VM to compute its model updates. In our scenario, however, the arrival rate of incoming data may also bottleneck data processing. An intuitive strategy would then be for each VM to process each data point as it arrives. However, since arrivals at different VMs may not be coordinated, synchronizing the model parameters at each VM between data arrivals may introduce additional delays, while asynchronous SGD methods can lead to slow convergence [1]. DOLL uses a batching and grouping process to limit the synchronization delay, which naturally realizes traditional mini-batch SGD so as to provide provable model convergence guarantees. Handling online data arrivals becomes particularly challenging when we use preemptible instances to compute model updates. Existing methods utilizing preemptible instances for ML jobs largely focus on mitigating training interruptions [2] and their effects on model convergence [3]. When used on datastreams, we face an additional challenge of interruptions pausing the data arrival process, which impedes the rate at which we can compute model updates and thus model convergence. Thus, one should ensure that preemptions do not happen "too often," e.g., by computing some updates on on-demand instances. Our work is the first to optimize the number of preemptible VMs used and demonstrate that we can meet ML convergence guarantees.
Exploring the Emotions Experienced by Elementary School Students During the COVID-19 Era: By Applying the Photovoice Research Method

Objectives The purpose of this study is to explore the emotions of elementary school students experiencing the COVID-19 era. Methods For this purpose, the researchers applied the Photovoice method to three classes of 6th graders who attended elementary school for nearly two years during the era of COVID-19. and explored their emotions. This study was conducted according to the research procedure of identification, invitation, education, documentation, narration, ideation, presentation, and confirmation as noted by Latz(2017). The analysis of the collected data was carried out according to these three steps: coding, classification, and topic discovery. Results The predominant emotions of elementary school students experiencing COVID-19 were found to be fear, depression, anger, and sadness. If we look at the meaning of each emotion specifically, the following four themes were derived through data analysis; fear of invisible entities(fear), stuffy daily life in a mask(depression), prolongation of daily life that cannot be denied(anger), and isolation created by severed or strained relationships (sadness). Conclusions This study is expected to provide not only a diagnostic function of recognizing the emotions of elementary school students living in Korea during the era of COVID-19, but also the scalability of finding alternatives to problems and contemplating policy connections.&#x0D;