Reviewerly/exHarmony huggingface dataset:
PENGARUH PENGGUNAAN MEDIA AUDIO VISUAL TERHADAP HASIL BELAJAR IPA SISWA KELAS IV SEKOLAH DASAR

Instructional media especially audio visual media is very lacking in science learning causing students to get low learning outcomes. This study aims to determine the effect of the use of audio visual media on science learning outcomes of fourth grade students of SDN Duri Kepa 05. This study used a pre-experimental design type one group pretest-posttest design. The sampling technique that will be used by researchers is the nonprobability sampling Purposive sampling model. The number of samples used were 28 students from 31 students of class IV C. Instrument research was a multiple choice test of 30 questions and questionnaires as many as 20 statements. Before the instrument was used first it was tested on 28 students of class VB in SDN Duri Kepa 05. The results showed that the average pretest value was 59.29 and the posttest value was 75.07, for the significance value at the pretest of 0.126, the significance for posttest is 0.082, and the significance of audio-visual media is 0.200, each significant value of more than 0.05. Hypothesis testing with t test obtained a significant value of 0,000 &lt;0,05, which means that there are significant differences, while the value of t is equal to 12,515 which means that the pretest value is smaller than the posttest of 12,515 so it can be stated that audio visual media have positive significant effect on learning outcomes.
Context-Sensitive Visualization of Deep Learning Natural Language Processing Models

The introduction of Transformer neural networks has changed the landscape of Natural Language Processing (NLP) during the last years. So far, none of the visualization systems has yet managed to examine all the facets of the Transformers. This gave us the motivation of the current work. We propose a novel NLP Transformer context-sensitive visualization method that leverages existing NLP tools to find the most significant groups of tokens (words) that have the greatest effect on the output, thus preserving some context from the original text. The original contribution is a context-aware visualization method of the most influential word combinations with respect to a classifier. This context-sensitive approach leads to heatmaps that include more of the relevant information pertaining to the classification, as well as more accurately highlighting the most important words from the input text. The proposed method uses a dependency parser, a BERT model, and the leave-n-out technique. Experimental results suggest that improved visualizations increase the understanding of the model, and help design models that perform closer to the human level of understanding for these problems.
Semi-supervised extensions of multi-task tree ensembles

Scale inconsistency is a widely encountered issue in multi-output learning problems. Specifically, target sets with multiple real valued or a mixture of categorical and real valued variables require addressing the scale differences to obtain predictive models with sufficiently good performance. Data transformation techniques are often employed to solve that problem. However, these operations are susceptible to different shortcomings such as changing the statistical properties of the data and increase the computational burden. Scale differences also pose problem in semi-supervised learning (SSL) models as they require processing of unsupervised information where distance measures are commonly employed. Classical distance metrics can be criticized as they lose efficiency when variables exhibit type or scale differences, too. Besides, in higher dimensions distance metrics cause problems due to loss of discriminative power. This paper introduces alternative semi-supervised tree-based strategies that are robust to scale differences both in terms of feature and target variables. We propose use of a scale-invariant proximity measure by means of tree-based ensembles to preserve the original characteristics of the data. We update classical tree derivation procedure to a multi-criteria form to resolve scale inconsistencies. We define proximity based clustering indicators and extend the supervised model with unsupervised criteria. Our experiments show that proposed method significantly outperforms its benchmark learning model that is predictive clustering trees.
A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.
OntoPhaco: An Ontology for Virtual Reality Training in Ophthalmology Domain—A Case Study of Cataract Surgery

Performing a safe and successful cataract surgery greatly depends on the effective training programs of the residents that are commonly offered by several public and private healthcare providers. Virtual Reality Training (VRT) as a training tool for example, expresses potential capabilities to improve the learning curve, increasing trainee confidence and acquisition of skills. However, the success of this tool highly depends on how close this tool is to the reality. Ontology is called as a significant modelling tool which can help to provide a shared and common understanding of a domain, and in this scenario, a standardized terminology for representing the training domain, and actions taking place in Virtual Environment (VE). Recent Systematic Literature Review (SLR) findings show that most ontology designs for VRT are not built systematically and there is no ontology describing the domain knowledge for VRT in the ophthalmology field. There is also a lack of a high achievement rate in implementing ontology driven VRT, calls for systematic and comprehensive steps. Therefore, in order to lower the failure rate of ontology applied to VRT, OntoPhaco was designed and developed based on philosophically grounded foundational ontologies, namely, Unified Foundational Ontology (UFO) and Design & Engineering Methodology for Organizations (DEMO). The evaluation results showed that the use of OntoPhaco was able to improve the student’s learning experience, assist effectively in building VR training scenarios, and lead to a successful VRT processes. The lessons learnt from designing OntoPhaco for this domain could generally generate valuable contribution to the theory and practice of VRT, knowledge management and ontology engineering in complex domains.
A Secure and Efficient Federated Learning Framework for NLP

In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks.Existing solutions either involve a trusted aggregator or require heavyweight cryptographic primitives, which degrades performance significantly.Moreover, many existing secure FL designs work only under the restrictive assumption that none of the clients can be dropped out from the training protocol.To tackle these problems, we propose SEFL, a secure and efficient FL framework that (1) eliminates the need for the trusted entities; (2) achieves similar and even better model accuracy compared with existing FL designs; (3) is resilient to client dropouts.Through extensive experimental studies on natural language processing (NLP) tasks, we demonstrate that the SEFL achieves comparable accuracy compared to existing FL solutions, and the proposed pruning technique can improve runtime performance up to 13.7×.
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification

Large-scale multi-label text classification (LMTC) tasks often face long-tailed label distributions, where many labels have few or even no training instances.Although current methods can exploit prior knowledge to handle these few/zero-shot labels, they neglect the metaknowledge contained in the dataset that can guide models to learn with few samples.In this paper, for the first time, this problem is addressed from a meta-learning perspective.However, the simple extension of meta-learning approaches to multi-label classification is suboptimal for LMTC tasks due to long-tailed label distribution and coexisting of few-and zeroshot scenarios.We propose a meta-learning approach named META-LMTC.Specifically, it constructs more faithful and more diverse tasks according to well-designed sampling strategies and directly incorporates the objective of adapting to new low-resource tasks into the metalearning phase.Extensive experiments show that META-LMTC achieves state-of-the-art performance against strong baselines and can still enhance powerful BERTlike models.
PARADe: A low-cost open-source device for photosynthetically active radiation (PAR) measurements

In agriculture, proximal in-field information is crucial for a precise management of crop growth. A low-cost, miniaturized and innovative device, named PARADe (PAR Acquisition Device), is designed for in-field photosynthetically active radiation (PAR) acquisition. It combines an affordable PAR line quantum sensor (PAR/LE, SOLEMS) and an open source development platform (microcontroller) based on Arduino Integrated Development Environment. The quality of the measurement of the PARADe acquisition chain has been validated. The calibration is done by comparison (R2=0.99) with a robust acquisition chain composed of a certified PAR sensor (PQS1, Kipp and Zonen) and a data-logger (CR1000 Campbell Scientific). The accuracy of PARADe measurements is evaluated through three indicators, relative error, RMSE and normalized RMSE. They demonstrate that the PARADe system has certain operating limitations especially for low solar angles (sunset and sunrise) due to the choice of a line quantum sensor. This does not affect the accuracy and reliability of the results, but indicates that the PARADe device is specifically adapted to collect daily cumulative PAR values.
An Improved Smooth Variable Structure Filter for Robust Target Tracking

As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.
Minimax optimal goodness-of-fit testing for densities and multinomials under a local differential privacy constraint

Finding anonymization mechanisms to protect personal data is at the heart of recent machine learning research. Here, we consider the consequences of local differential privacy constraints on goodness-of-fit testing, i.e. the statistical problem assessing whether sample points are generated from a fixed density $f_0$, or not. The observations are kept hidden and replaced by a stochastic transformation satisfying the local differential privacy constraint. In this setting, we propose a testing procedure which is based on an estimation of the quadratic distance between the density $f$ of the unobserved samples and $f_0$. We establish an upper bound on the separation distance associated with this test, and a matching lower bound on the minimax separation rates of testing under non-interactive privacy in the case that $f_0$ is uniform, in discrete and continuous settings. To the best of our knowledge, we provide the first minimax optimal test and associated private transformation under a local differential privacy constraint over Besov balls in the continuous setting, quantifying the price to pay for data privacy. We also present a test that is adaptive to the smoothness parameter of the unknown density and remains minimax optimal up to a logarithmic factor. Finally, we note that our results can be translated to the discrete case, where the treatment of probability vectors is shown to be equivalent to that of piecewise constant densities in our setting. That is why we work with a unified setting for both the continuous and the discrete cases.
How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking

Abstract Explainable artificial intelligence (XAI) systems aim to provide users with information to help them better understand computational models and reason about why outputs were generated. However, there are many different ways an XAI interface might present explanations, which makes designing an appropriate and effective interface an important and challenging task. Our work investigates how different types and amounts of explanatory information affect user ability to utilize explanations to understand system behavior and improve task performance. The presented research employs a system for detecting the truthfulness of news statements. In a controlled experiment, participants were tasked with using the system to assess news statements as well as to learn to predict the output of the AI. Our experiment compares various levels of explanatory information to contribute empirical data about how explanation detail can influence utility. The results show that more explanation information improves participant understanding of AI models, but the benefits come at the cost of time and attention needed to make sense of the explanation.
A Thermal Blended Facial Expression Analysis and Recognition System Using Deformed Thermal Facial Areas

There are many research works in visible as well as thermal facial expression analysis and recognition. Several facial expression databases have been designed in both modalities. However, little attention has been given for analyzing blended facial expressions in the thermal infrared spectrum. In this paper, we have introduced a Visual-Thermal Blended Facial Expression Database (VTBE) that contains visual and thermal face images with both basic and blended facial expressions. The database contains 12 posed blended facial expressions and spontaneous six basic facial expressions in both modalities. In this paper, we have proposed Deformed Thermal Facial Area (DTFA) in thermal expressive face image and make an analysis to differentiate between basic and blended expressions using DTFA. Here, the fusion of DTFA and Deformed Visual Facial Area (DVFA) has been proposed combining the features of both modalities and experiments and has been conducted on this new database. However, to show the effectiveness of our proposed approach, we have compared our method with state-of-the-art methods using USTC-NVIE database. Experiment results reveal that our approach is superior to state-of-the-art methods.
Detecting Aberrant Values and Their Influence on the Time Series Forecast

This article addresses the influence of outliers on building time series models. Two methods for detecting aberrant values are discussed, and models are built for the studied data series in the outliers' presence and absence. Data used consisted in the annual precipitation series recorded at Sulina (Romania). The models have been further employed for generating precipitation fields. This process shows a good concordance of the historical data and the forecast in terms of mean, minimum, maximum values, and minimum recorded precipitation in two, five, seven, and ten successive years. The results show that the field generated after the outliers' removal is better in terms of ten statistical indicators.
Teach Me What to Say and I Will Learn What to Pick: Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models

Knowledge Grounded Conversation Models (KGCM) are usually based on a selection/retrieval module and a generation module, trained separately or simultaneously, with or without having access to a 'gold' knowledge option.With the introduction of large pre-trained generative models, the selection and generation part have become more and more entangled, shifting the focus towards enhancing knowledge incorporation (from multiple sources) instead of trying to pick the best knowledge option.These approaches however depend on knowledge labels and/or a separate dense retriever for their best performance.In this work we study the unsupervised selection abilities of pre-trained generative models (e.g.BART) and show that by adding a scoreand-aggregate module between encoder and decoder, they are capable of learning to pick the proper knowledge through minimising the language modelling loss (i.e.without having access to knowledge labels).Trained as such, our model -K-Mine -shows competitive selection and generation performance against models that benefit from knowledge labels and/or separate dense retriever.
Self-Regulated Particle Swarm Multi-Task Optimization

Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.
Impact of Variation in Number of Channels in CNN Classification model for Cervical Cancer Detection

Development in Computer vision applications, most prominently in Artificial Intelligence (AI) over the past decade, has encouraged researchers to use computational algorithms. Machine Learning (ML) and Deep Learning (DL) algorithms are getting attention in the biomedical area to early diagnosis and prognosis of lethiferous diseases with higher accuracy using statistical analysis. Cervical cancer is one of the predominant gynecological infectious forms of cancer in females ecumenically, generally transpiring in less-developed nations. DL algorithms are more optimized and convenient due to the automatic feature extraction in Cervical lesion detection at an early phase. Here we analyze the convolutional neural network (CNN) models' performance using variation in channels depth comprises in the convolution layers to classify multi-class liquid-based cytology (LBC) Whole slide images (WSI). We propose three CNN models having two convolution layers with the number of channels (4,8), (8,16), and (32,64) and two pooling layers. CNN model with the highest number of channels in the convolution layers gives the leading performance with an accuracy of 96.89%, precision of 93.38%, the sensitivity of 93.75%, and F-score of 94.15%.
Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control

Reinforcement learning (RL) is a booming area in artificial intelligence. The applications of RL are endless nowadays, ranging from fields such as medicine or finance to manufacturing or the gaming industry. Although multiple works argue that RL can be key to a great part of intelligent vehicle control related problems, there are many practical problems that need to be addressed, such as safety related problems that can result from non-optimal training in RL. For instance, for an RL agent to be effective it should first cover all the situations during training that it may face later. This is often difficult when applied to the real-world. In this work we investigate the impact of RL applied to the context of intelligent vehicle control. We analyse the implications of RL in path planning tasks and we discuss two possible approaches to overcome the gap between the theorical developments of RL and its practical applications. Specifically, firstly this paper discusses the role of Curriculum Learning (CL) to structure the learning process of intelligent vehicle control in a gradual way. The results show how CL can play an important role in training agents in such context. Secondly, we discuss a method of transferring RL policies from simulation to reality in order to make the agent experience situations in simulation, so it knows how to react to them in reality. For that, we use Arduino Yún controlled robots as our platforms. The results enhance the effectiveness of the presented approach and show how RL policies can be transferred from simulation to reality even when the platforms are resource limited.
Parallel Electromagnetically Induced Transparency near Ground-State Cooling of a Trapped-Ion Crystal

We theoretically propose and experimentally demonstrate a parallel electromagnetically induced transparency (parallel EIT) cooling technique for ion crystals in the Paul trap. It breaks the stringent cooling requirements of the standard EIT cooling, thus allowing, in principle, to simultaneously cool the motional mode spectrum with an arbitrary range. Experimentally a large cooling range over 5 MHz is proved by using a single trapped ${}^{40}{\mathrm{Ca}}^{+}$ ion. We also observe simultaneous near-ground-state cooling for all motional modes of a 4-ion chain with the best average phonon number of about 0.2. For cooling a large number of modes, we only need to modulate the probe beam of the standard EIT cooling with several rf frequencies. This cooling scheme has no selectivity for the types of ions (e.g., ${}^{171}{\mathrm{Yb}}^{+}$, ${}^{40}{\mathrm{Ca}}^{+}$), therefore it would be a powerful tool for initialization of all kinds of operational trapped-ion quantum computers or simulators.