Reviewerly/exHarmony huggingface dataset:
Spatial–temporal graph neural network based on node attention

Abstract Recently, the method of using graph neural network based on skeletons for action recognition has become more and more popular, due to the fact that a skeleton can carry very intuitive and rich action information, without being affected by background, light and other factors. The spatial–temporal graph convolutional neural network (ST-GCN) is a dynamic skeleton model that automatically learns spatial–temporal model from data, which not only has stronger expression ability, but also has stronger generalisation ability, showing remarkable results on public data sets. However, the ST-GCN network directly learns the information of adjacent nodes (local information), and is insufficient in learning the relations of non-adjacent nodes (global information), such as clapping action that requires learning the related information of non-adjacent nodes. Therefore, this paper proposes an ST-GCN based on node attention (NA-STGCN), so as to solve the problem of insufficient global information in ST-GCN by introducing node attention module to explicitly model the interdependence between global nodes. The experimental results on the NTU-RGB+D set show that the node attention module can effectively improve the accuracy and feature representation ability of the existing algorithms, and obviously improve the recognition effect of the actions that need global information.
Review on Image Caption Generation

With the rapid development of Deep learning, AI along with Computer Vision and Natural Language processing Image caption has become an interesting and complex task. Image caption generation is the process of generating textual description of the given image and it is a challenging task because it consists of apprehension of objects. If the machine will be programmed to accurately describe an image or environment like human vision, it will be highly beneficial for robotic vision, business and many more. In order to generate an effective description of the image, the machine needs to detect, recognize objects as well as understand the scene type or location, object properties, their relationships and their interactions with each other. In this paper, we focus on advanced image captioning techniques such as CNN (Convolutional Neural Network)-LSTM(Long Short Term Memory) to generate meaningful captions. and the advantages and limitations of each method are discussed.
ANALYSIS AND DESIGN OF HUMAN RESOURCES INFORMATION SYSTEM (SISDM) AT THE OFFICE OF THE ELECTION SUPERVISORY AGENCY OF SOUTH SULAWESI PROVINCE

The purpose of this research is to find out about the design of human resource information systems in bawaslu of south sulawesi province. This type of qualitative research uses observation and interview data collection techniques. The data analysis technique is data reduction. The results showed that the analysis of employee training needs serves to classify what types of training are needed by employees in an institution. Given the one hand bawaslu which has a wide scope of work, namely in the south sulawesi province area, it has many employees who work and must understand different knowledge and skills by facing a place of supervision which also consists of various characteristics of people. With the HR design, it can provide convenience in the process of calculating employee performance and assist the HR departement in evaluating the performance of employees within the organization as well as employee transfer statistics
Growth of the Internet in Polish Libraries

This article highlights essential aspects of development of the Internet in Polish libraries, both in public and academic libraries. An author describes what influenced the equipment of libraries with computers, laptops, scanners and cameras. Important roles in Internet access were projects supported by ministries, foundations, NGOs and telecommunication companies. The article also presents some statistics related to computers and Internet access in libraries, as well as wifi and broadband Internet networks. Add to this, training initiatives developing digital skills organized for librarians and by librarians for readers were shortly explained.
HoloCMDS: Investigating Around Field of View Glanceable Commands Selection in AR-HMDs

Augmented reality merges the real and virtual worlds seamlessly in real-time. However, we need contextual menus to manipulate virtual objects rendered in our physical space. Unfortunately, designing a menu for augmented reality head-mounted displays (AR-HMDs) is challenging because of their limited display field of view (FOV). In this paper, we propose HoloCMDS (see Section 2) to support quick access of contextual commands in AR-HMDs and conduct an initial experiment to get users&#x0027; feedback about this technique.
A Study on Types, Classification and Mechanism for Optimization in Search Engine

This paper is a over-all valuation of different tools and practices used for optimization in Search Engines, these are used to discover any material across internet. The core purpose of any website is to list at the top of all the links. Search Engine Optimization is the art of civilizing a website’s visibility in the main body of search engine results. This art of improving a websites visibility in the body of search engines outcome is promising using the techniques and applications accessible in the market.
Discussion on improvement of training quality and using English for Math — Applied Economics — International Relation — History Education for students following career path linkage

The study purpose is to analyze real situation and propose solutions to strengthen the connection between advanced math - math for applied economics - international relation and history and the practice of vocational training for students of Vietnam universities, which students can apply mathematics and esp. using English to various practices: not only in the internal practice of advanced math, but more importantly in the practice of other basic subjects such as applied economics, and especially to solve the problems posed in the practice of vocational training for students.
Primery innovatsionnoy deyatelnostiv bibliotekax

The concept of «innovation» has firmly entered the professional library environment («Innovation» means the use of innovations in its work). Innovation in information and library centres is present in almost all areas of its activities, including mass work, which involves a set of methods and forms of service organisation of a large number of readers or a certain group of users at the same time. Signs of any innovation are novelty, changes, result. Information and library centres are currently expanding their boundaries of impact on the reader, turning into cultural, educational and intellectual and leisure centres, within the walls of which various events are organised for different categories of readers
Research and analysis of big data visualization technology

The development of big data technology has given birth to big data products based on mobile terminals--intelligent teaching tools. Intelligent teaching tools can establish an objective, diversified, and comprehensive evaluation system based on the analysis of users’ massive data in mobile terminals; they are available to quantitatively analyze the characteristics of users’ behaviors and establish intuitive digital chart models, thereby providing effective decision-making services for users. This paper takes Rain Classroom as an example to introduce the use of big data visualization technology in art teaching.
RETRACTED ARTICLE: Einsatz von künstlicher Intelligenz im Management akuter Leukämien

BackgroundArtificial intelligence (AI)-based systems have the potential to revolutionize healthcare worldwide and are already increasing the accuracy and efficiency of diagnosis and treatment in various medical disciplines, including hematology. The systems are equipped to interpret large amounts of data to guide early diagnosis and prognosis assessment, as well as the treatment decision-making process for hematologic diseases. The recent success of AI-based systems has been driven by both the rapid performance improvement in computer technology and the establishment of new learning algorithms. Additionally in the medical field, the availability of large data sets is increasing due to the advancing digitalization and formalization of general clinical documentation, innovations in imaging techniques, and the increasing routine use of high-throughput sequencing tests. These enable the incorporation of molecular data into clinical decision making and consequently personalized interventions that overcome the “one size fits all” paradigm on treatment decisions and improve individualized treatment outcomes.ConclusionsPotential applications of AI-based methods in the field of hematology are numerous: in addition to generating new evidence, machine learning (ML) methods open promising opportunities to assist hematologists in time-consuming routine tasks, so that medical resources can be used for cognitively more challenging tasks, e.g. clarification of rare clinical constellations and communication with patients.
Research of text visualization in 3D environment based on parameter study

Abstract The text visualization in 3D environment is quite complex due to the characteristics of 3D scene. In this paper we propose to study a group of 3D text information visualization based on parameter study. The primary text information visualization position is set as an initial point. Then, several different level-of-details for the texts are designed as the Event LoDs. Firstly, the parameter study for different EventLoDs is conducted, followed by the parameter study for the different size value. Based on the two groups of parameter study result, a use case is applied to test the effect. Finally, the summary and future work is given.
Sentiment Analysis Using Product Based Reviews

Sentiment analysis is opinion mining in which it uses natural language processing and extracts the reviews in positive, negative and neutral categories. This helps users to identify the emotional tone behind the body of a text. Sentiment analysis computes the user opinion, attitudes towards the product, and the emotions to that product. Some machine learning techniques are used to identify the sentiment for the product. This model tests the reviews using various machine learning algorithms. Logistic regression algorithm has given the highest accuracy as compared to other algorithms.
Local earthquake magnitude estimation using ridge regression model

In this paper, a local magnitude estimation model using ridge regression is proposed for the accurate determination of the local magnitude of earthquakes. To increase its accuracy, an estimation model was derived from the hypocentral distance correction term and peak amplitude using ridge regression. The improved performance of the proposed estimation model was evaluated via the mean squared error. It was compared with the least squares model to confirm its superiority in predicting the real magnitude.
Hello, this is Automatic Antigrief: what problem can I solve for you today?

We are here to help. We are here to help.
Leakage-abuse Attacks Against Forward Private Searchable Symmetric Encryption

Dynamic Searchable Symmetric Encryption (DSSE) methods address the problem of securely outsourcing\updating private data into a semi-trusted cloud server.Furthermore, Forward Privacy (FP) notion was introduced to limit data leakage and thwart the related attacks on DSSE approaches.FP schemes ensure previous search queries cannot be linked to future updates and newly added files.Since FP schemes use ephemeral search tokens and one-time use index entries, many scholars conclude that privacy attacks on traditional SSE schemes do not apply to SSE approaches that support forward privacy.However, to obtain efficiency, all FP approaches accept a certain level of data leakage, including access pattern leakage.Here, we introduce two new attacks on forward-private schemes.We demonstrate that it is still plausible to accurately unveil the search pattern by reversing the access pattern.Afterward, the attackers can exploit this information to uncover the search queries and consequently the documents.We also show that the traditional privacy attacks on SSE schemes are still applicable to schemes that support forward privacy.We then construct a new DSSE approach that supports parallelism and obfuscates the search and access pattern to thwart the introduced attacks.Our scheme is cost-efficient and provides secure search and update.Our performance analysis and security proof demonstrate our approach's practicality, efficiency, and security.
SPE Selection Technique: A Projection-based Approach for Precise Object Interaction in Immersive Virtual Environments

Selecting objects has always represented one of the fundamental tasks required by the majority of immersive applications. Despite the key role played by selection techniques in the design of im-mersive experiences, there is not a dominant or a one-size-fits-all approach yet. In this scenario, the current paper proposes a novel selection technique, named Selection among Projected Elements, which focuses on the selection of distant, small and possibly close, objects. The devised approach was assessed in the context of a virtual heritage application that was specifically designed with the aim of stressing the capabilities of the SPE technique.
Environmental Impact of Abattoir Waste Discharge on the Quality of Water Body and Soil in Ado-Ekiti, Ekiti State

Introduction: Wastes generated from abattoirs are an increasing source of pollution of water bodies.&#x0D; Aims: This study emphasizes on identifying environmental impacts caused by the abattoir along Iworoko Road, Ilokun Ado-Ekiti facility on its receiving water body and its users.&#x0D; Methodology: Wastewater, stream water, and soil samples were collected twice from the abattoir over a two-month period (June and August). Heavy metals, physiochemical and microbial analyses were carried out on the samples.&#x0D; Results: The sample collected in June from the study area showed mean bacterial count which ranges from 8.0 – 55.0x103 cfu/ml, and sample obtained in August showed mean bacteria count and this indicated that wastewater sample has the highest bacteria count of 8.0 x 103 cfu/ml. The lowest bacterial count was found in the stream water sample with 1.7 x 103 cfu/ml. The mean bacterial count in the contaminated soil sample was 2.8 x 103 cfu/ml. The results also show the occurrence of potentially pathogenic organisms such as Escherichia coli and Staphylococcus aureus. The heavy metals and physiochemical properties of samples collected from the study area are as follows; Fe (3.806 and 2.812), Cu (1.070 and 0.95), Cd (0.001 and 0.001), Ni (0.0055 and 0.0025), Pb (0.13 and 0.107), Zn (0.37 and 0.406), pH (8.475 and 8.600), temperature (26.250 and 27.750 oC), total dissolved solid (267.05 and 212.8), dissolved oxygen (1.65 and 2.45) for waste water sample; Fe (0.402 and 0.519), Cu (0.277 and 0.3095), Cd (0.0 and 0.001), Ni (0.002 and 0.003), Pb (0.04 and 0.016), Zn (0.126 and 0.16), pH (6.82 and 7.235), temperature (26.600 and 27.700 oC), total dissolved solid (103.4 and 125.95), dissolved oxygen (4.85 and 5.00) for stream water sample; and Fe (122.85 and 98.500), Cu (0.694 and 0.4745), Cd (0.004 and 0.001), Ni (0.02 and 0.010), Pb (0.3675 and 0.2885), Zn (0.3255 and 0.525), pH (6.200 and 6.495), temperature (23.300 and 27.800 oC) for soil sample – each figure representing June and August respectively.&#x0D; Conclusion: The concentration of the heavy metals reported in this study indicates that there is significant difference in the concentration of the pollutants taken at different sample points.
An Unsupervised Machine Learning-based Method for Detection and Explanation of Anomalies in Cloud Environments

To provide the users with ultra reliability, future 6G networks need to be equipped with fully automated anomaly detection systems. 6G is envisioned to be driven by machine learning capabilities to detect anomalies autonomously while dealing with multi-dimensional data with heterogeneous characteristics. In this paper, we propose our hybrid single-/multiple-threshold anomaly detection method, which is tailored for unlabeled and high-dimensional time-series datasets by relying on Long-Short Term Memory (LSTM)-Auto Encoder (AE). Our proposed method is also equipped with an anomaly explanation module. Our performance evaluations have been conducted on a cloud testbed. The evaluation results indicate that our proposed anomaly detection method outperforms the existing methods in terms of F<inf>1</inf>-score.
Stability analysis of visual servo system based on image Jacobian matrix

With the development of computer vision, visual servo control has been widely used in various fields. This article analyzed the stability of the image visual servo system in application, and provided theoretical and practical data support to engineering researchers when using the technology. The Jacobian matrix of the image based on point features was deduced in detail, and a simulation model in an ideal environment was built to show the convergence process. A 6-DOF manipulator was used for practical application experiments, and several measures to improve stability were described for the existing divergence problems, and the reliability of the technology in engineering applications was improved.
AI Based Virtual Assistance on Raspberry PI

Artificial intelligence (AI) technologies are one of the new technologies with new complicated features that are emerging at a fast pace. Although these technologies seem to be extensively adopted, people do not intend to use them in some cases. Technology adoption has been studied for many years, and there are many general models in the literature describing it. However, having more customized models for emerging technologies upon their features seems necessary. In this study, we developed a conceptual model involving a new system quality construct, i.e., interaction quality, which we believe can better describe adoption of AI-based technologies. In order to check our model, we used a voice assistant system (VAS) technology as an example of this technology, and tested a theory-based model using a data set achieved from a field survey. Our results confirm that interaction quality significantly affects individual’s trust and leads to adoption of this technology. Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple's Siri, Amazon's Alexa, Microsoft's Cortana, and Google's Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today's voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.
NYTimes.com Academic Pass Program

The New York Times (NYT) Academic Pass Program is available to all institutions of higher education. It provides convenient and unlimited access to &lt;ext-link ext-link-type="uri" xlink:href="https://www.nytimes.com/"&gt;NYTimes.com&lt;/ext-link&gt; including all articles from 1851 to current. Users simply sign up for an account at a unique institutional registration site and then can log on to &lt;ext-link ext-link-type="uri" xlink:href="https://www.nytimes.com/"&gt;NYTimes.com&lt;/ext-link&gt; on multiple devices with their account. The website is visually appealing, easy to use and navigate, however the search functionality is not the best.
PERAMALAN PRODUKSI KARET INDONESIA MENGGUNAKAN FUZZY TIME SERIES DUA FAKTOR ORDE TINGGI RELASI PANJANG BERDASARKAN RASIO INTERVAL

The fuzzy time series method for forecasting continues to develop over time. This research discusses fuzzy time series, which considers two factors for high order using interval partitioning based on interval ratio with long relation construction for getting different accuracy in forecasting between combination method and existing method. The first step is the formation of the universe of speech. Second, divide the universe of discourse into several intervals using interval ratios. Third, fuzzification. Fourth, build fuzzy logic relations and fuzzy logic relation groups, and fifth, defuzzification. The previous methods would be compared with the fuzzy logic relation construction result. The simulation used Indonesian rubber production data for 2000-2020. The results and errors were tested using the average forecasting error rate (AFER). AFER value of the forecasting method is 1.863% obtained.Keywords: Forecasting, fuzzy time series, long relationMSC2020: 62M10, 62M20, 62M86, 03E72
Comparative Study on Sentiment Analysis on IMDB Dataset

The main part of information gathering is to find out what other people think.In case of movies, the movie reviews can provide an in-depth and detailed understanding of the movie and can help decide whether it is worth watching or not.However, with the growing amount of data in reviews, it is quite prudent to automate the process, saving a lot of time.Sentiment analysis is an important field of study in machine learning that practically deals with extracting useful information of subjects from the textual reviews.The sentiment analysis is closely related to Natural Language Processing (NLP) and text mining.It is used to determine the sentiments of the reviewer in regard to various topics or the overall polarity of the review.In case of movie reviews, along with giving a rating in numeric to a movie, they can give us information on the approval or the disapproval of a movie quantitatively.A collection of those information then gives us a comprehensive qualitative understanding on different facets of the movie.Since human language is complex, we face many kinds of challenges during opinion mining from movie reviews which might leads us to situations where a positive word has a negative connotation and vice versa.
UX for Some and Usability for Others: Issues of Blending Multi-user and Multi-property in Control Centers

When designing an interactive system, considering usability is important in order to ensure that users can perform their tasks with the interactive system and that each information or function they need to perform their tasks is available at most relevant time. Consideration of the user experience is also important in order to take into account how users feel about using the interactive system. In case where users belong to different user profiles, design of the interactive system may have to consider conflicts between target usability and user experience. From study of the documentation and users’ knowledge, we present a multi-user and multi-property control center: Jupiter 2 Control Center at Guiana Space Center of French space studies center (CNES). As public audience and press are allowed to assist to launch in the Jupiter 2 Control Center, they can see operators’ work and information displayed in the control center. Then, specificity of this control center compared to others is that different user profiles with different goals use it at the same time. Some users use it to perform their work, whereas others use it to enjoy the launch. We use the Jupiter 2 Control Center as an example to find potential design issues for a future control center with similar characteristics. Conflicts between the different users’ goals and the related properties are discussed in this paper.