Reviewerly/exHarmony huggingface dataset:
Mask-Soft Filter Pruning for Lightweight CNN Inference

Pruning is a network compression and acceleration technique to reduce the computation and memory footprint required by Convolutional Neural Networks (CNNs), and various pruning techniques have been proposed by many researchers. Weight pruning (i.e., Unstructured pruning) can reduce many parameters by removing redundant weights, but it requires special software or hardware structure to actually accelerate the neural networks in the GPU environment. On the other hand, filter pruning to remove the filters itself does not require any special software or hardware structure, and consequently, it enables the actual acceleration of CNN in the GPU environment. Inspired by the previous research, soft filter pruning, which prunes the filters in a soft manner, this paper proposes Mask-Soft Filter Pruning(M-SFP) method. The proposed M-SFP is a pruning technique that can preserve weight parameters without zeroing out by masking the output feature maps. By applying the proposed technique to ResNet on CIFAR-10 and CIFAR-100 datasets, more than 40% reduction of BFLOPs can be achieved with only an acceptable accuracy drop of 0.17%.
Treebank Creation and Parser Generation for Tunisian Social Media Text

Tunisian Arabic (TA) is a morphologically and syntactically rich dialect, which presents an interesting challenge for Natural Language Processing (NLP) tasks such as part-of-speech tagging, parsing, semantic analysis, etc. It is classified as a low-resourced language. Tunisians use it in daily life communication, social media exchanges, etc. In this paper, we focus on Tunisian Arabic linguistic resources and tools creation. We present the creation and generation of Tunisian treebank and parser for social media texts. We use an existing state-of-the-art parser to build this treebank. Then, we investigate the effects of different data sizes and different combinations of Tunisian dialect forms in automatic parsing.
Can We Trust Bayesian Uncertainty Quantification from Gaussian Process Priors with Squared Exponential Covariance Kernel?

We investigate the frequentist coverage properties of credible sets resulting from Gaussian process priors with squared exponential covariance kernel.First, we show that by selecting the scaling hyperparameter using the maximum marginal likelihood estimator in the (slightly modified) squared exponential covariance kernel, the corresponding L2-credible sets will provide overconfident, misleading uncertainty statements for a large, representative subclass of the functional parameters in the context of the Gaussian white noise model.Then we show that by either blowing up the credible sets with a logarithmic factor or modifying the maximum marginal likelihood estimator with a logarithmic term, one can get reliable uncertainty statements and adaptive size of the credible sets under some additional restriction.Finally, we demonstrate in a numerical study that the derived negative and positive results extend beyond the Gaussian white noise model to the nonparametric regression and classification models for small sample sizes as well.The performance of the squared exponential covariance kernel is also compared to the Mat\' ern covariance kernel.
Deep Reinforcement Learning for Electric Transmission Voltage Control

Today, human operators primarily perform voltage control of the electric transmission system. As the complexity of the grid increases, so does its operation, suggesting additional automation could be beneficial. A subset of machine learning known as deep reinforcement learning (DRL) has recently shown promise in performing tasks typically performed by humans. This paper applies DRL to the transmission voltage control problem, presents open-source DRL environments for voltage control, proposes a novel modification to the “deep Q network” (DQN) algorithm, and performs experiments at scale with systems up to 500 buses. The promise of applying DRL to voltage control is demonstrated, though more research is needed to enable DRL-based techniques to consistently outperform conventional methods.
Centroid-Tracking-Aided Robust Object Detection for Hospital Objects

COVID-19 outbreak has a big impact to people's daily life in 2020, especially in healthcare sector.As COVID-19 viruses are highly contagious, it is important to take strict measures to ensure all patients got the needed care while taking healthcare workers safety into consideration.Robot-based care is being hurriedly developed recently, and one of the important abilities for such robot is to be able to distinguish object commonly found in hospital thus the robot can make the correct action towards the correct object.For this publication, an object detector is trained to detect the hospital bed, thus it can be an input to the care robot navigation system when it is going to approach patients.As hospital beds vary from one brand to another, and this research has limited time constraint and readily available hardware, the object detector confidence is still low.Thus, a centroid tracking method is implemented to aid the hospital object detection, ensuring the robot can detect the correct bed more robustly with considerable speed for embedded implementation.
No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.
The effect of speaker age on the perception of ironic insults.

We investigated a cue that readers may use in determining whether a remark such as "You are so helpful!" is intended as a compliment or as an ironic insult. The cue was the age of the speaker. Remarks were preceded by a sentence that either invited a literal or ironic interpretation of the remark. Data were collected on the familiarity of the remark as an ironic statement, and the incongruity of the remark with the prior context. In Experiment 1, participants were asked to rate the intent of the speaker as to how ironic, mocking, polite, and funny they intended their remark to be. In Experiment 2, participants read the scenarios as their eye movements were tracked. The results showed that age of the speaker had an impact on first pass reading times when statements were not familiar as ironic statements. Our younger adult participants did not appear to immediately activate a nonliteral interpretation of an ambiguous remark made by an older adult unless they had evidence from past experience that the remark is often used as an insult. However, ratings of the ironic intent of the statements were unaffected by speaker age; the age of the speaker affects the ease of interpretation but not the final outcome. The results are consistent with constraint-based theories of sentence comprehension. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Examining the potential of VR and AR technologies for education

Abstract This paper delves into such technologies as Augmented Reality and Virtual Reality which have lately seen some significant advancements. These technologies are discussed in the context of education which may greatly benefit from them. Firstly, a brief history of these technologies is given and what these technologies are is explained. Then, the paper explains what kind of issues education faces today and how these technologies may help resolve some of these issues. In addition, the paper describes some of the technologies of this sort that are already being implemented to some extent. Finally, some of the main issues and challenges associated with these technologies are discussed.
A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer.

Dense video captioning aims to localize and describe important events in untrimmed videos. Existing methods mainly tackle this task by exploiting only visual features, while completely neglecting the audio track. Only a few prior works have utilized both modalities, yet they show poor results or demonstrate the importance on a dataset with a specific domain. In this paper, we introduce Bi-modal Transformer which generalizes the Transformer architecture for a bi-modal input. We show the effectiveness of the proposed model with audio and visual modalities on the dense video captioning task, yet the module is capable of digesting any two modalities in a sequence-to-sequence task. We also show that the pre-trained bi-modal encoder as a part of the bi-modal transformer can be used as a feature extractor for a simple proposal generation module. The performance is demonstrated on a challenging ActivityNet Captions dataset where our model achieves outstanding performance. The code is available: this http URL
Dialogic-Interactive Media Design for Language Learning To Improve Speaking Activities and Skills

Abstract Abstract. Technology is an unavoidable resource in the education sector, Technology can facilitate learning offline or online. Technology can be used for language learning. Therefore, the purpose of this study is to produce dialogic-interactive media used in Indonesian language learning to improve student learning activities and outcomes. This type of research is development research using Plomp Model (1997) which consists of three phases, namely (1) preliminary research phase; (2) prototype phase; and (3) assessment phase. The study was conducted to develop a product that is dialogic-interactive media to improve learning activities and speaking skills of secondary students in learning Indonesian. Based on the research stages, this article will explain the results of research in the design phase. The data analysis technique used in this study is a descriptive data analysis technique that describes the validity of the learning model. The research instrument was a questionnaire. Based on the results of the questionnaire analysis, it was concluded that the syntax for the development of dialogic-interactive media was categorized as very valid and could be used as a model for the development of instructional media that would be tested in schools.
Blockchain for Emergency Vehicle Routing in Healthcare Services: An Integrated Secure and Trustworthy System

Healthcare is one of the most important sector where security is a major concern in Internet of Things integrated healthcare. The future generation vehicles are enabled with features of autonomous driving and accurate decision making. We can use such features in emergency vehicle routing with the help of Blockchain technology for collecting trustworthy road network information. In this work, we design and develop an emergency vehicle routing solution where each vehicle connects to the Ethereum Blockchain integrated with Open Source Routing Machine (OSRM). Blockchain enables secure information sharing among vehicles and the OSRM provides shortest trustworthy routes. The proposed approach reduces transit delay of the emergency vehicles while providing emergency healthcare services. Furthermore, the efficacy of the proposed approach has been shown through experimentation by implementing a smart contract in Blockchain based trust-worthy vehicle route generation.
Principle of majorization: Application to random quantum circuits

We test the principle of majorization J. I. Latorre and M. A. Mart\'{\i}n-Delgado, Phys. Rev. A 66, 022305 (2002). in random circuits. Three classes of circuits were considered: (i) universal, (ii) classically simulatable, and (iii) neither universal nor classically simulatable. The studied families are: {cnot, H, T}, {cnot, H, not}, {cnot, H, S} (Clifford), matchgates, and IQP (instantaneous quantum polynomial-time). We verified that all the families of circuits satisfy on average the principle of decreasing majorization. In most cases the asymptotic state (number of gates $\ensuremath{\rightarrow}\ensuremath{\infty}$) behaves like a random vector. However, clear differences appear in the fluctuations of the Lorenz curves associated with asymptotic states. The fluctuations of the Lorenz curves discriminate between universal and nonuniversal classes of random quantum circuits, and they also detect the complexity of some nonuniversal but not classically efficiently simulatable quantum random circuits. We conclude that majorization can be used as an indicator of complexity of quantum dynamics, as an alternative to, e.g., entanglement spectrum and out-of-time-order correlators.
Role of Fuzzy Approach towards Fault Detection for Distributed Components

Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment. Among other communication, teamwork, and coordination problems in global software development, the detection of faults is seen as the key challenge. Thus, there is a need to ensure the reliability of component-based applications requirements. Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations. In this study, we propose an approach for fault detection from component-based systems requirements using the fuzzy logic approach and historical information during acceptance testing. This approach identified error-prone components selection for test case extraction and for prioritization of test cases to validate components in acceptance testing. For the evaluation, we used empirical study, and results depicted that the proposed approach significantly outperforms in component selection and acceptance testing. The comparison to the conventional procedures, i.e., requirement criteria, and communication coverage criteria without irrelevancy and redundancy successfully outperform other procedures. Consequently, the F-measures of the proposed approach define the accurate selection of components, and faults identification increases in components using the proposed approach were higher (i.e., more than 80 percent) than requirement criteria, and code coverage criteria procedures (i.e., less than 80 percent), respectively. Similarly, the rate of fault detection in the proposed approach increases, i.e., 92.80 compared to existing methods i.e., less than 80 percent. The proposed approach will provide a comprehensive guideline and roadmap for practitioners and researchers.
The Intrinsic Dimension of Images and Its Impact on Learning

It is widely believed that natural image data exhibits low-dimensional structure despite being embedded in a high-dimensional pixel space. This idea underlies a common intuition for the success of deep learning and has been exploited for enhanced regularization and adversarial robustness. In this work, we apply dimension estimation tools to popular datasets and investigate the role of low dimensional structure in neural network learning. We find that common natural image datasets indeed have very low intrinsic dimension relative to the high number of pixels in the images. Additionally, we find that low dimensional datasets are easier for neural networks to learn. We validate our dimension estimation tools synthetic data generated by GANs in which we can manipulate intrinsic dimension.
Multi-scale spatial distribution of K, Th and U in an Archaean potassic granite: a case study from the Heerenveen batholith, Barberton Granite-Greenstone Terrain, South Africa

Abstract We describe the multi-scale distribution of K, Th and U in the ca. 3.1 Ga Heerenveen batholith of the Barberton Granite-Greenstone Terrain. Data were obtained with a combination of tools, including a portable gamma-ray spectrometer from the scale of the whole batholith to the scale of outcrops, and autoradiography for the thin section scale. U is concentrated preferentially in minor phases in the border shear zones of the batholith and, within these shear zones, in late pegmatites as well as fractures. The processes responsible for the concentration of U in the Heerenveen batholith is discussed in terms of magmatism, hydrothermalism (redistribution of U in fissures associated with magmato-hydrothermal fluids), and supergene alteration. The statistical properties of K, Th and U concentrations are different. K shows spatial correlation over large distance, largely mirroring mappable rock types, with increased variability at larger scales. In contrast, U is dominated by small-scale variations (“nugget effect”) and its variability is, averaged and smoothed by large-scale integration. Spatial and statistical features thus offer useful and complementary insights on petrogenetic and metallogenic processes in granitoids in addition to standard approaches (petrography, geochemistry).
Disruptive Technologies for Enabling Smart Government

This paper presents a review of Blockchain technology and computer protocols called Smart Contracts as a virtual mechanism to expedite the execution of contractual commitments without the intervention of regulatory or verifying entities. Blockchain is considered one of the most important disruptive technologies of recent years and its application can generate profound changes in business models and society in general, even in the public sphere providing potential benefits to citizens and the conformation of a modern e-government. The main contribution of this work is to draw a set of basic considerations that support new e-government processes based on blockchain and smart contracts, and also schematizes an application use case. This is intended to streamline public processes and reduce the problems associated with corruption, lack of transparency and cost overruns, among others; even striving to meet the stated objective of the United Nations regarding the e-government of providing services and information to citizens in a transparent way through technological mechanisms and the use of the Internet.
Towards Understanding Technical Responses to Requirements Changes in Agile Teams

As a part of an extensive study focusing on how agile teams respond to requirements changes, we carried out a pilot study to understand the technical responses shown by agile practitioners to requirements changes. To the best of our knowledge, how agile teams respond technically to such changes has not yet been studied. We used a qualitative approach using Grounded Theory. Analysis of the interview data collected from ten agile practitioners in New Zealand and Australia resulted in identifying three stages where agile teams respond to requirements changes technically -- while receiving, developing, and delivering changes. We found that even though agile practices do not recommend comprehensive documentation, in practice, the product owner defining a requirements change in detail was stated by the participants as the most common technical response. Developers conducting a technical feasibility study and negotiations among product owner and team when receiving a requirements change were the other most common technical responses. These show a tendancy to deviate from some agile practices in some specific situations.
Mechatronics Teaching through Virtual Platforms for Home Confinement due to COVID-19

The pandemic that has been unleashed all over the world has forced us to change our way of life, this includes to avoid face-to-face classes.Therefore, an important question arises: how to carry out the online classes of a subject in which the main focus is on developing projects such as the Mechatronics course?This document presents the platforms used in the online course that include: Factory I/O, Overleaf, Tinkercad and FluidSIM, for replacing face-to-face practices.The course development, its characteristics and even the practices that have been done during the online course on the platforms are explained.The main idea is to show that it is possible to substitute the face-to-face practices, using appropriate online tools for each topic within the course.The use of online platforms and simulation software allows obtaining new skills of teaching regarding laboratory subjects.In general, online platforms allow having an alternative and new tools to transform the most of the course to the online mode.
A robust edge-based template matching algorithm for displacement measurement of compliant mechanisms under scanning electron microscope

This paper develops a robust edge-based template matching algorithm for displacement measurement of compliant mechanisms under a scanning electron microscope (SEM). The algorithm consists of three steps. First, the Sobel gradient operator and a self-adaptive segment strategy are used to establish the shape model in which the gradient directions of the object’s edge points are calculated. Second, a similarity criterion based on image gradients that is robust to illumination change and image noise is utilized for template matching to obtain the coarse results. The third step is to refine the matching results by using an orientation-guided subpixel interpolation strategy. A series of simulations is conducted, and the results show that the proposed algorithm enjoys great robustness against strong image noise and gray-value fluctuation, as well as small rotations and background interferences, and thus is suitable for processing SEM images of compliant mechanisms. Finally, the application of the proposed algorithm in the measurement of the spring constant of the flexure hinges with a straight beam form under a SEM is demonstrated.
Improving Students’ Vocabulary Mastery Through Bingo Games

This research was aimed to improve the vocabulary mastery of students through Bingo Game. This research was an action research study. The place of the research was at SDK Petra Madiun, East Java, Indonesia. The subject of the research was fourth grade students of SDK Petra Madiun, consisting 30 students. It was done in two cycles. The researcher did some steps for each cycle. They were planning, action, observation, and reflection. The data were collected by using test, observation, and interview. Furthermore, the data were analyzed by using descriptive statistics. It was compared between the scores of pre test (before implementing Bingo game) and post test (after implementing Bingo game). The research findings showed that vocabulary mastery of most students improved after Bingo Game was used in the teaching and learning process. It was indicated by the increase of the mean of students’ vocabulary score in the pre-test and post-test (67.22 to 80.57). The students’ involvement and classroom interaction during the implementation of the game improved. It was showed by students’ enthusiasm during the game. They tried to be the first winner and raised their competitiveness among them. In addition, the implementation of Bingo Game and the complementary actions were successful to help the students memorize and understand new words easily. In conclusion, the students’ vocabulary mastery improved through the implementation of Bingo Game.