Reviewerly/exHarmony huggingface dataset:
Towards Automatic Creation of Annotations to Foster Development of Named Entity Recognizers

Named Entity Recognition (NER) is an essential step for many natural language processing tasks, including Information Extraction. Despite recent advances, particularly using deep learning techniques, the creation of accurate named entity recognizers continues a complex task, highly dependent on annotated data availability. To foster existence of NER systems for new domains it is crucial to obtain the required large volumes of annotated data with low or no manual labor. In this paper it is proposed a system to create the annotated data automatically, by resorting to a set of existing NERs and information sources (DBpedia). The approach was tested with documents of the Tourism domain. Distinct methods were applied for deciding the final named entities and respective tags. The results show that this approach can increase the confidence on annotations and/or augment the number of categories possible to annotate. This paper also presents examples of new NERs that can be rapidly created with the obtained annotated data. The annotated data, combined with the possibility to apply both the ensemble of NER systems and the new Gazetteer-based NERs to large corpora, create the necessary conditions to explore the recent neural deep learning state-of-art approaches to NER (ex: BERT) in domains with scarce or nonexistent data for training.
Forecasting the Number of Offset Printing Machine Breakdowns Using the Support Vector Machine (SVM) Metdhod

PT. MJT is a company engaged in manufacturing that produces various types of plastic tubes for cosmetic packaging. Production activities at PT. MJT uses an intermittent process, which in the printing division requires a longer total setup time because this process produces various types of specifications of goods to order. This has an effect on the amount of engine breakdown. The purpose of this research is to try the method of forecasting the number of breakdowns for offset printing machines at PT. MJT. One of the methods used in this research is the Support Vector Machine method. Support Vector Machine is a method that can help predict the number of breakdowns that will be experienced by the offset printing machine at PT. MJT. Support vector machine is a method that can reduce the error value in forecasting compared to other methods. From this research, it is hoped that it can produce a forecast of the number of breakdowns for offset printing machines at PT. MJT for a period of one year or twelve periods.
Prospects and risks of digitalization in the XXI century

The article demonstrates that information and digital technologies play a special role in the genesis of a new technological paradigm and acceleration of technological development. The digital reality in which the humanity lives today is largely determined by the ideas that developed in the 1960s and 1970s of the twentieth century: these are the ideas of hypertext, graphical user interface, and the global information network, as well as the deriving from the above mentioned idea of the personal computer as a device with a widest range of applications in almost any area of human life. The author examines the scenarios of positive and negative future presented both in scientific literature and in works of mass culture, and shows that the future of digital civilization is determined only by the human.
Affine Non-Negative Collaborative Representation for Deep Metric Learning

In this paper, we propose a deep metric learning method based on affine non-negative collaborative representation (DML-ANCR) for person and vehicle re-identification. Our method can adaptively generate a non-negative coefficient matrix for support samples per class and fit the query sample with the support samples in the affine subspace. We predict the query sample’s label via the residual between the query sample and optimal fitness. We formulate the affine non-negative collaborative representation learning as a meta-learning problem and present an episode-based approach to learning the best fitness to maximize generalization. Besides, we apply a hard mining strategy to improve the robustness of the metric. In experiments, we also introduce the re-ranking method. Results show our approach has achieved very competitive performance on the widely used person and vehicle re-identification datasets. It surpasses most baseline methods and state-of-the-art methods.
Deep CNN Face Recognition: Looking at the Past and the Future

The need for face recognition has evolved from identifying a few hundred people to identifying hundreds of thousands of people in the last decade. Most of the progress in automatic face recognition has been driven by deep networks in the past few years. In this article, we provide an overview of recent progress in this area and discuss state-of-the-art CNN-based face recognition and verification systems. We also present some open questions and discuss avenues for research in the coming years.
Weak Supervised Behavior Recognition Algorithm Based on Domain Knowledge Graph

The use of artificial intelligence technology for technical and tactical analysis has become an important means and method in karate competitions and training. The integration of artificial intelligence technology and sports technical and tactical analysis is an important way to innovate and improve the technical and tactical level. In this paper, a knowledge graph in the karate domain is designed. In order to effectively use the domain knowledge graph, a new two-flow graph convolutional network is adopted, which includes classifier branch and attribute feature branch. The attribute feature branch maps the attribute embedding and score of each video instance to an attribute feature space. The semantic word embedding vectors of all concepts are used as the input of the classifier branch, and finally a classifier for behavior classification is generated. Finally, the generated classifier is evaluated according to the attribute characteristics of each video, and the whole network is optimized by the classification loss pair. In addition, the time information of the video is modeled by the attention mechanism. This technology effectively solves the problems of high labor cost, serious data loss, long delay, and low accuracy in traditional methods. The results show that the new model has a significant effect on improving the accuracy of behavior recognition, and it also lays a foundation for intelligent technical and tactical analysis. basis.
Entrepreneurship Education as a Narrative Creativity in Digital Technology Coordinates

A model of designing digital narrative in entrepreneurship education is developed and grounded, which involves implementation of such interaction between teachers and students, during which the rational-logical and sensory-emotional characteristics of educational information are transmitted and acquired not only through verbal, but also other information channels through digital narratives. The model of designing digital narratives in education functionally combines five interconnected components: axiological-motivational, analyticalsemantic, program-technological, praxeological-creative, result-reflexive, the content of which is specified by type, form of study, subject area, individual age specifics, material and technological resources, time availability, subjective characteristics of participants in the educational process. The model can be implemented in traditional, combined, distance learning, and it is an important component of open education systems.
Using RFID to identify smart products on a blockchain enabled production network

Industry 4.0 requires the cooperation of several technologies. The intersections of these technologies present us with new challenges. One of these challenges is identification, since we have to identify all the items that are on the network that do work and those that are worked upon. If we fail to identify one of these items the network is presented with an unidentified potentially malicious device or a misidentified product which can cause production to halt. Blockchains or otherwise known as Distributed Ledger Technology, DLT for short is a technology that builds upon the current bookkeeping paradigm and expands it in a decentralized direction. This however can be used in more than just banking since it is essentially a distributed database that has memory of past events not just the current state. By using a blockchain based distributed database to hold processing details and using RFID-s as keys to certain entries in the database it is possible to build a tamper proof production system that can handle the challenges of industry 4.0. It may also be possible to use blockchain technology as a form of digital paper trail that can be used to validate messages sent to the nodes of the system.
Generative adversarial simulator

Knowledge distillation between machine learning models has opened many new avenues for parameter count reduction, performance improvements, or amortizing training time when changing architectures between the teacher and student network.In the case of reinforcement learning, this technique has also been applied to distill teacher policies to students.Until now, policy distillation required access to a simulator or real world trajectories.In this paper we introduce a simulator-free approach to knowledge distillation in the context of reinforcement learning.A key challenge is having the student learn the multiplicity of cases that correspond to a given action.While prior work has shown that data-free knowledge distillation is possible with supervised learning models by generating synthetic examples, these approaches to are vulnerable to only producing a single prototype example for each class.We propose an extension to explicitly handle multiple observations per output class that seeks to find as many exemplars as possible for a given output class by reinitializing our data generator and making use of an adversarial loss.To the best of our knowledge, this is the first demonstration of simulator-free knowledge distillation between a teacher and a student policy.This new approach improves over the state of the art on data-free learning of student networks on benchmark datasets (MNIST, Fashion-MNIST, CIFAR-10), and we also demonstrate that it specifically tackles issues w ith m ultiple inpu t modes.We also identify open problems wh en distillingagents trained in high dimensional environments such as Pong, Breakout, or Seaquest.
Features Selection for Fall Detection Systems Based on Machine Learning and Accelerometer Signals

Fall among older people is a major medical concern. Fall Detection Systems (FDSs) have been actively investigated to solve this problem. In this sense, FDSs must effectively reduce both the rates of false alarms and unnoticed fall. In this work we carry out a systematic evaluation of the performance of one of the most widely used machine learning supervised algorithm (Support Vector Machine) when using different input features. To evaluate the impact of the feature selection, we use Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) Curve as the performance metric. The results showed that with four features it is possible to obtain acceptable values for the detection of falls using accelerometer signals obtained from the user’s waist. In addition, we also investigate if the impact of selecting the features based on the analysis of a dataset different from the final application framework where the detector will be operative.
Two-Layer Reversible Data Hiding for VQ-Compressed Images Based on De-Clustering and Indicator-Free Search-Order Coding

During transmission of digital images, secret messages can be embedded using data hiding techniques. Such techniques can transfer private secrets without drawing the attention of eavesdroppers. To reduce the amount of transmitted data, image compression methods are widely applied. Hiding secret data in compressed images is a hot issue recently. In this paper, we apply the de-clustering concept and the indicator-free search-order coding (IFSOC) technique to hide information into vector quantization (VQ) compressed images. Experimental results show that the proposed two-layer reversible data hiding scheme for IFSOC-encoded VQ index table can hide a large amount of secret data among state-of-the-art methods with a relatively lower bit rate and high security.
Problems and Countermeasures of Emei Wushu Development Based on Computer 3D Simulation Technology

research-article Problems and Countermeasures of Emei Wushu Development Based on Computer 3D Simulation Technology Share on Author: Wang Yahui Leshan Normal University, China Leshan Normal University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 195Pages 1–6https://doi.org/10.1145/3469213.3470402Online:18 August 2021Publication History 0citation12DownloadsMetricsTotal Citations0Total Downloads12Last 12 Months12Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Co-Sounding: Fostering intersubjectivity in electronic music improvisation

Shared music improvisation constitutes a formidable vector for intersubjective connection. Improvisation is a space of non-semantic communication that allows for putting oneself at risk and requires mutual trust and listening, as well as dialogical qualities. This article investigates the intersubjective dimension of improvisation in electronic music praxis, focusing on how the electronic medium can be used to foster mediation between musicians. The article builds on a practice-based enquiry in duo format, conducted in three successive technological settings, with a methodological entanglement of aesthetic and design aims. Systematic video documentation and participant observation provide an analytical counterpoint to an immersion in the improvisatory praxis. A set of design strategies for fostering intersubjective connection in shared musicianship emerges from the research. The findings provide the basis for a dialectical consideration between musical and intersubjective aesthetics. The discussion points to the diversity of social functions of music and their respective aesthetics. Electronic instruments’ inherent plasticity allows for reconfiguring the social space of music-making, and thus opens perspectives for devising synergetic music systems that emphasise an ethos of shared agency over the production of musical objects or performances.
Active Strain-Statistical Models for Reconstructing Multidimensional Images of Lung Tissue Lesions

Coupling augmented reality data with data from previous medical studies is most useful for surgeries on organs with little movement and deformation (e.g., skull, brain, and pancreas), as there is an opportunity to more clearly define the edges of the organ. The proposed coupling methods can be used in other operations. Besides, organ imaging techniques can compensate for the lack of tactile feedback during laparoscopic surgery by providing the surgeon with visual cues, improving hand-eye coordination, including robotic surgery. Using the combined image of MRI, CT-angiography, and ultrasound, individual adjustment of incisions and cutting planes, optimal positioning of paracentesis needles, and position display of the organ's main components are realized.
Research on Face Recognition Based on Improved Convolutional Neural Network Using Raspberry Pi

Abstract In this paper, a face recognition system has been designed using Raspberry Pi based on convolution neural network technology. Convolution neural network that using OpenCV (open source computer vision library) to realize real-time face recognition and feature extraction. The difficult and important problem in deep learning network is that it could be over fitting very easily. In order to alleviate the over fitting phenomenon and improve the accuracy of face recognition, the dropout technology of convolutional neural network had been employed to improve the recognition accuracy and ensure the robustness of face recognition. The results of experiments show that our methods has the advantages in the computer time and recognition rate for the low construction cost, simple structure and strong portability. The accuracy of face detection for static and dynamic images using Raspberry Pi is above 85% in our testament.
The Two Newly Proposed Hybrid Approach Based Conscientious Algorithmic Models for Encryption Mechanisms and its Application in Information Security Systems

Cyber and Information Security are one of the most concernable and challenging issues of today’s strengthen Information age. Cyber attackers and Cyber intruders performs and accomplished a very highly powerful and potential Cyber threat in our most sensitive Cyber Space and threaten our Cyber Security. So the need of the hour is that we have some robust and vigorous Cyber Security algorithms which protects our Cyber Space against these cyber Intruders or Cyber Criminals. Because many algorithms, which are currently runs to protect Cyber Space and responsible for potential data encryption and decryption mechanisms during sensitive transactions of data are worked very slow in manner and consume a large size of memory bandwidth and contains a very expensive computational cost and as the technological advancements in our Cyber Space, Cyber Intruder and Cyber Criminal achieved a very high skills mechanisms to breach the securities bridge of that algorithms, and some algorithms which contains a very typical and complex structure taking a lot of time to perform the mechanisms of desired security tasks and very costly rich for computations. So we design two Cyber Security algorithms models, which also contains a very complex and typical structure but never consume a lot of computation power and as well as memory and also contains a least cost for performing computations, these both two algorithms are a fusion of multiple ciphers and some control mechanisms, which are technically very strong as well as redirect to optimality. Randomization are the main theme point of first algorithm that’s why for Cyber Intruders and Cyber Criminals are very hard to find the actual pattern of data or to accomplished any Cyber Attacks. The detailed mechanisms of these algorithms are covers in the next upcoming sections of this research paper.
Characters, Spirals and Unit Dynamics by training neural network model

Abstract Deep learning is an integral part of machine learning by building a structure like the neural network in the human brain. Amounts of different neural network models have been emerged, improved, and transformed. This article will implement various neural network models for three different specific tasks (Japanese character recognition/double helix task/remote unit dynamics) and analyse the fine-tuning parameters process (different network layers, different types of network layers). The result is that generally, models with more layers would perform better. However, other parameter could have significant effects on the performance. For example, activation function, different numbers of hidden nodes, different initial weights, and different periods, etc.
Scalable Person Re-identification: Beyond Supervised Approaches

Person re-identificationPerson re-identification across cameras is an important problem as it enables associating targets over a wide area, which is likely to be viewed by multiple cameras. It is an extremely active area of research today. Most of the approaches are extensively supervised, in the sense that they require significant labeling effort to train re-identification models, usually based on deep networks. However, as in other problems in computer vision, it raises the question of scalabilityScalability of the approaches as the number of people to be associated grows or the size of the network grows. In this chapter, we focus on two problems that hold the potential for developing highly scalable person re-identificationPerson re-identification approaches. In the first, we focus on the problem of how to limit the labeling effort even as the number of targets in the network grows. On the challenging Market-1501 dataset, we demonstrate that with only 8% labeling, we can achieve performance very close to that with full-set labeling. In the second problem, we focus on the size of the camera networkCamera network and consider how to onboard new camerasCamera on-boarding into an existing network with little to no additional supervision. We leverage upon transfer learningTransfer learning approaches for this purpose and demonstrate the results on a benchmark dataset. Overall, the chapter provides some research directions and initial results in pushing person re-identificationPerson re-identification beyond fully supervised approaches and lays the groundwork for future research in this area.
Application of a Literacy Culture Approach to Foster Student Learning Motivation

Literacy culture is a habit practiced by every individual to use their abilities and potentials. Literacy is applied in schools / madrasas before the learning time starts getting used to students to read non-textbooks. The purpose of this research is to increase students' motivation to learn from habits that are built early on. This research is a type of qualitative research with observational methods, interviews and documentation from various data sources, which will be the respondents to be interviewed are the headmaster of Muhammadiyah 3 Waru Middle School, some teaching teachers and some of the junior high school students in the school. The results of this study are 1) the existence of literacy culture is able to increase student motivation, in SMP Muhammadiyah 3 Waru with a culture of reading before the lesson starts approximately 15 minutes, 2) there are supporting and inhibiting factors in implementing this literacy culture as for supporting factors from the literacy culture in the school with adequate facilities and infrastructure and the absence of students who arrive late for school to take part in literacy program activities. Lack of motivation from students themselves or outside students, so the lack of supporting facilities and infrastructure. 3) solving the existing problems is increasing the motivation of various parties, especially the school and parents of students as well as supporting facilities and infrastructure.
A Step Towards More Eco-responsible Computing

On a global scale, the environmental footprint of digital technology represents a continent two to three times the size of France and five times the size of the French car fleet. In order to limit the negative impact of this overabundance and to optimize the emerging computing potential of our environment, we propose an architecture model and its implementation in order to allow the mutualization of the components embedded in our connected devices.
Neural Variational Learning for Grounded Language Acquisition

We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that enables the learning of language about a wide range of real-world objects. We evaluate the efficacy of this learning by predicting the semantics of objects and comparing the performance with neural and non-neural inputs. We show that this generative approach exhibits promising results in language grounding without pre-specifying visual categories under low resource settings. Our experiments demonstrate that this approach is generalizable to multilingual, highly varied datasets.