Reviewerly/exHarmony huggingface dataset:
Artificial intelligence and the technological turn of public education privatization: In defence of democratic education

This article considers how the development of for-profit artificial intelligence (AI) technologies fosters the privatization of public education and erodes the values and practices of democratic education. The introduction situates the advent of digital technologies in the context of the structural economic and ideological shifts of the past 40 years. Such changes include neo-liberal restructuring, the repressive school and social turn, changes in the use of positivist ideology in schooling, the role of new technologies in social and cultural reproduction and changing imperatives for capital accumulation. The article illustrates different uses of AI as part of the technological turn of public education privatization. Examples include: (1) adaptive learning technology, and transformations to teacher work and conceptions of knowledge and learning; (2) biometric pedagogy and the cultural politics of locating learning in the body; and (3) the convergence of impact investing and digital surveillance technologies. The article considers how changes in the ownership and control over different aspects of public education relate to the cultural politics of knowledge and learning. It also examines how, under the guise of disinterested objectivity and neutrality, particular class and cultural ideologies and interests are promoted through new technologies, with significant pedagogical, cultural, economic and political implications. The article concludes by arguing that AI education is a site of cultural and political contestation and must be comprehended as a form of representational politics. By showing a critical pedagogical AI project, the article suggests that the anti-democratic tendencies of most AI education is hardly inevitable or determined, but rather represents a replication of long-standing ideologies.
A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX

Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data integrity check module, to achieve hardware-enhanced data privacy protection. Then, we design a smart contract framework to realize distributed data access control management in a big data environment. The crucial role of the smart contract was revealed by designing multiple access control contracts, register contracts, and history contracts. Access control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history behavior. Register contract contains user property information, edge computing node information, the access control and history smart contract information, and provides functions such as registration, update, and deletion. History contract records the historical behavior information of malicious users, receives the report information of malicious requestors from the access control contract, implements a misbehavior check method to determines whether the requestor has misbehavior, and returns the corresponding result. Finally, we design decentralized system architecture, prove the security properties, and analysis to verify the feasibility of the system. Results demonstrate that our method can effectively improve the timeliness of data, reduce network latency, and ensure the security, reliability, and traceability of data.
The usefulness and usability of Moodle LMS as employed by Zarqa University in Jordan

This research investigates the usefulness and usability of Moodle LMS, as employed by a case study university (Zarqa University), based on students’ experience with Moodle LMS on desktop/laptop and mobile/tablet interfaces. A total of 320 students participated in this research in two stages. The results of this research showed that the students at Zarqa University used frequently only two features on Moodle on both its interfaces. The results of this research also showed that a large number of the students required the provision of 15 features on the local instance of Moodle which are not currently provided. Furthermore, the results showed that a large number of the students identified nine usability problems on the interfaces of Moodle, and a large number suggested nine improvements to the design of Moodle to make it more usable. Finally, this research suggests recommendations to improve the usefulness and usability of Moodle LMS as used by the case study university.
Quantum Fisher information-based detection of genuine tripartite entanglement

Genuine multipartite entanglement plays important roles in quantum information processing. The detection of genuine multipartite entanglement has been long time a challenging problem in the theory of quantum entanglement. We propose a criterion for detecting genuine tripartite entanglement of arbitrary dimensional tripartite states based on quantum Fisher information. We show that this criterion is more effective for some states in detecting genuine tripartite entanglement by detailed example.
Penerapan Metode Analytical Hierarchy Process (AHP) Pada Desain Sistem Pendukung Keputusan Pemilihan Calon Penerima Bantuan Pangan Non Tunai (BPNT) Di Kota Palangka Raya

Village/ward party in Palangka Raya is required to collect data of poor people who reserve the right to receive Non-Cash Food Aid (BPNT). Eventually, village/ward party is going to work together with the census officers of Social Protection Program of Central Statistics Body (BPS) in Palangka Raya. The decision process of the recipient candidates of BPNT in Palangka Raya requires a Decision Support System which offers consideration and evaluation to obtain more accurate data. The selection uses ten poor criteria of non-monetary variable include main income, possession status of a house to live, floor material of the house, wall material of the house, facility of drinking water, facility of lighting, fuel for cooking need, facility of defecation, final disposal of feces, and asset possession of household. The research used the Decision Support System with Analytical Hierarchy Process (AHP) method. The system was evaluated by two different methods which were black box testing and turing testing. The application assists to decide the alternative recipient of BPNT, manage data of candidates of recipient of BPNT and manage data of the recipient of BPNT which were selected from the recommendation result
EFFECT OF TRAINING PARAMETERS ON THE ABILITY OF ARTIFICIAL NEURAL NETWORKS TO LEARN: A SIMULATION ON ACCELEROMETER DATA FOR TASK RECOGNITION IN MOTOR-MANUAL FELLING AND PROCESSING

Producing dynamic, real-time reliable data on the performance of timber harvesting operations has gained lately a lot of momentum due to the necessity to proactively manage the fleets of machines and machine allocation and to better monitor them in different operational environments to be able to understand their capability and performance.Techniques of Artificial Intelligence (AI) have been used recently to get accurate data at a low cost in many fields of science.In particular, the use of Artificial Neural Networks (ANN) has been proved to enhance classification accuracy in many applications.This work deals with the effect of factors that are typically used to set up an ANN on the performance of classification, by a case time-and-motion study implemented for motor-manual tasks under an experimental approach.A protocol was designed to vary the number of neurons in a hidden layer, number of iterations to train the ANN and the number of folds for cross-validation of data during training.The protocol was applied to a set of median-filtered vector-magnitude data collected at a 1Hz sampling rate by a triaxial accelerometer which was documented by a video approach to encompass five types of events.The results were promising, showing that it is possible to accurately classify the data by various performance metrics.The overall recall, for instance, may be as high as 98%.However, the number of iterations and neurons used to train the ANN are factors that significantly affect the classification performance.We conclude that implementing the ANN architectures to learn from filtered acceleration data has a lot of potential in longterm monitoring of motor-manual work and that future studies should be implemented to resemble the variation of operational conditions.
Research on Service Discovery Methods Based on Knowledge Graph

Service discovery is one of the main goals of a service-oriented architecture, helping to discover complex applications. Knowledge graph is a cross-cutting research hotspot in the fields of computer, knowledge engineering and information science. This paper makes in-depth research on the key issues of knowledge graph matching for service-oriented discovery. Firstly, construct a knowledge graph for service discovery, and then design a template to match the knowledge graph. By designing a matching template, the service discovery mechanism is transformed into a knowledge graph knowledge query. Finally, the validity of the knowledge graph matching method proposed in this paper is verified by experiments.
Building a Persuasive Virtual Dietitian

This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and of a natural language generation (NLG) system that automatically creates textual messages for explaining the results provided by the reasoner with the aim of persuading users to stick to a healthy diet. In the first part of the paper, we introduce the MADiMan system and, in particular, the basic mechanisms related to reasoning, data interpretation and content selection for a numeric data-to-text NLG system. We also discuss a number of factors influencing the design of the textual messages produced. In particular, we describe in detail the design of the sentence-aggregation procedure, which determines the compactness of the final message by applying two aggregation strategies. In the second part of the paper, we present the app that we developed, CheckYourMeal!, and the results of two human-based quantitative evaluations of the NLG module conducted using CheckYourMeal! in a simulation. The first evaluation, conducted with twenty users, ascertained both the perceived usefulness of graphics/text and the appeal, easiness and persuasiveness of the textual messages. The second evaluation, conducted with thirty-nine users, ascertained their persuasive power. The evaluations were based on the analysis of questionnaires and of logged data of users’ behaviour. Both evaluations showed significant results.
Transformers on Sarcasm Detection with Context

Sarcasm Detection with Context, a shared task of Second Workshop on Figurative Language Processing (co-located with ACL 2020), is study of effect of context on Sarcasm detection in conversations of Social media.We present different techniques and models, mostly based on transformer for Sarcasm Detection with Context.We extended latest pre-trained transformers like BERT, RoBERTa, spanBERT on different task objectives like single sentence classification, sentence pair classification, etc. to understand role of conversation context for sarcasm detection on Twitter conversations and conversation threads from Reddit.We also present our own architecture consisting of LSTM and Transformers to achieve the objective.
Deep Hierarchical Classification for Category Prediction in E-commerce System

In e-commerce system, category prediction is to automatically predict categories of given texts. Different from traditional classification where there are no relations between classes, category prediction is reckoned as a standard hierarchical classification problem since categories are usually organized as a hierarchical tree. In this paper, we address hierarchical category prediction. We propose a Deep Hierarchical Classification framework, which incorporates the multi-scale hierarchical information in neural networks and introduces a representation sharing strategy according to the category tree. We also define a novel combined loss function to punish hierarchical prediction losses. The evaluation shows that the proposed approach outperforms existing approaches in accuracy.
Novel Defense Schemes for Artificial Intelligence Deployed in Edge Computing Environment

The last few years have seen the great potential of artificial intelligence (AI) technology to efficiently and effectively deal with an incredible deluge of data generated by the Internet of Things (IoT) devices. If all the massive data is transferred to the cloud for intelligent processing, it not only brings considerable challenges to the network bandwidth but also cannot meet the needs of AI applications that require fast and real-time response. Therefore, to achieve this requirement, mobile or multiaccess edge computing (MEC) is receiving a substantial amount of interest, and its importance is gradually becoming more prominent. However, with the emerging of edge intelligence, AI also suffers from several tremendous security threats in AI model training, AI model inference, and private data. This paper provides three novel defense strategies to tackle malicious attacks in three aspects. First of all, we introduce a cloud-edge collaborative antiattack scheme to realize a reliable incremental updating of AI by ensuring the data security generated in the training phase. Furthermore, we propose an edge-enhanced defense strategy based on adaptive traceability and punishment mechanism to effectively and radically solve the security problem in the inference stage of the AI model. Finally, we establish a system model based on chaotic encryption with the three-layer architecture of MEC to effectively guarantee the security and privacy of the data during the construction of AI models. The experimental results of these three countermeasures verify the correctness of the conclusion and the feasibility of the methods.
Gaussian Mixture Models Based on Principal Components and Applications

Data scientists use various machine learning algorithms to discover patterns in large data that can lead to actionable insights. In general, high-dimensional data are reduced by obtaining a set of principal components so as to highlight similarities and differences. In this work, we deal with the reduced data using a bivariate mixture model and learning with a bivariate Gaussian mixture model. We discuss a heuristic for detecting important components by choosing the initial values of location parameters using two different techniques: cluster means, k -means and hierarchical clustering, and default values in the “mixtools” R package. The parameters of the model are obtained via an expectation maximization algorithm. The criteria from Bayesian point are evaluated for both techniques, demonstrating that both techniques are efficient with respect to computation capacity. The effectiveness of the discussed techniques is demonstrated through a simulation study and using real data sets from different fields.
Development Of Student’s Creative Abilities In The Fine Arts In The Higher Education System

The article deals with the theoretical and methodological features of the development of creative abilities in the field of fine arts in higher education, analysis of scientific research on this issue, methods of conducting fine arts lessons, the need for students to learn how to work with different visual aids in lessons, the importance of an individual approach to students in the educational process, the use of design methods in high school.
A multi-sensory dataset for the activities of daily living

The article describes a multi-sensory dataset related to the Activities of Daily Living (ADL). These are the activities that contribute to an assessment of the overall status of elderly or people with special needs, possibly suffering from mild cognitive impairments. Typical basic ADLs include walking, such postural transitions as getting up or sitting down, as well as behaviours related to feeding, such as drinking or eating with knife and fork, or personal hygiene, e.g., teeth brushing. The collection process adopted for building this dataset considers nine ADL-related activities, which have been performed in different locations and involving the usage of both left and right arms. The dataset acquisition involved 10 volunteers performing 186 ADL instances, for a grand total of over 1860 examples. The dataset contains data from six 9-axis Inertial Measurement Units (IMUs), worn by each volunteer (two for each arm, one on the back and one on the right thigh). The dataset features an accurate data labelling done via manual annotation performed thanks to videos recorded by an RGB camera. The videos recorded during the experiments have been used only for labelling purposes, and they are not published.
Folk Physics in the Twenty-first Century: Understanding Tooling as Embodied

Povinelli's (2000) studies with chimpanzees (Pan troglodytes) reported in "Folk Physics for Apes" were firmly grounded in a Cartesian view of knowledge, which posits that humans use abstract concepts such as force, gravity, and shape to reason causally about events and plan our actions (with tools in the case of Folk Physics for Apes).Povinelli set out to examine if chimpanzees, like humans, used causal concepts to solve mechanical problems, as the Cartesian view predicts.However, Povinelli's findings uniformly challenged his expectations.Povinelli's book stimulated research and contributed to the development of alternate understandings of how animals (including humans) use tools.We summarize one alternative approach, Ecological psychology, elucidate how predictions drawn from this approach, explain (post hoc) the findings presented in Folk Physics for Apes, and suggest directions for continuing work on this topic from an Ecological approach.Ecological psychology posits direct perception and requires analysis of the animal-task-environment system, thus providing a distinct alternative to the Cartesian approach.Twenty years on, Povinelli's elegant exposition in Folk Physics for Apes of the Cartesian view of how animals use tools, and his efforts to explain the findings of his experiments in this framework, still stimulate those who study animal behavior and cognition from different theoretical perspectives.
IDP: A Privacy Provisioning Framework for TIP Attributes in Trusted Third Party-based Location-based Services Systems

Location-Based Services (LBS) System is rapidly growing due to radio communication services with wireless mobile devices having a positioning component in it.LBS System offers location-based services by knowing the actual user position.A mobile user uses LBS to access services relevant to their locations.In order to provide Point of Interest (POI), LBS confronts numerous privacy related challenges in three different formats including Non-Trusted Third Party (NTTP), Trusted Third Party (TTP), and Mobile Peer-to-Peer (P2P).The current study emphasized the TTP based LBS system where the Location server does not provide full privacy to mobile users.In TTP based LBS system, a user's privacy is concerned with personal identity, location information, and time information.In order to accomplish privacy under these concerns, state-of-the-art existing mechanisms have been reviewed.Hence, the aim to provide a promising roadmap to research and development communities for the right selection of privacy approach has achieved by conducting a comparative survey of the TTP based approaches.Leading to these privacy attributes, the current study addressed the privacy challenge by proposing a new privacy protection model named "Improved Dummy Position" (IDP) that protects TIP (Time, Identity, and Position) attributes under TTP LBS System.In order to validate the privacy level, a comparative analysis has been conducted by implementing the proposed IDP model in the simulation tool, Riverbed Modeler academic edition.The different scenarios of changing query transferring rate evaluate the performance of the proposed model.Simulation results demonstrate that our IDP could be considered as a promising model to protect user's TIP attributes in a TTP based LBS system due to better performance and improved privacy level.Further, the proposed model extensively compared with the existing work.
Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions

Facial Expression Recognition is a human emotion classification problem that attracted much attention from scientific research.Classifying human emotions can be a challenging task for machines.However, more accurate results and less execution time are there still the main issues when extracting features of human emotions.To cope with these challenges, we propose an automatic system that provides users with well-adopted classifier for recognizing facial expressions more accurately.The system consists of two fundamental machine-learning stages, namely, feature selection and feature classification.Feature selection is performed using Active Shape Model (ASM) composed of landmarks while the feature classification has examined seven well-known classifiers.We have used CK+ dataset, implemented and tested seven classifiers to find the best classifier.Experimental results showed that Quadratic classifier provides excellent performance and outperforms other classifiers with the highest accuracy of 92.42% on the same dataset.
Multiscale Gradient Maps Augmented Fisher Information-Based Image Edge Detection

Image edge detection is an important task in image processing and pattern recognition. Edges in digital images signify image discontinuities and traditionally gradient information is utilized in finding possible edge pixels. In this work, we consider a fusion approach using multiscale gradient maps along with non-parametric Fisher information which is recently used in edge detection. By using multiscale gradient maps we obtain better edge localization and robust edge maps and local thresholding with Fisher information helps obtain better detection. Experimental results on a variety of digital images and performance evaluation undertaken in comparison with edge detectors from the literature show the advantage of the proposed approach.