Reviewerly/exHarmony huggingface dataset:
Assessing the Usability of a Tangible Educational Game for Children

This paper presents the experience and results aimed to evaluate the user experience of children when they interact with a tangible educational application. The main goal was to create a usability study on the Osmo Genius Kit, observe children's experience while playing and assess the overall effectiveness of the game. Twelve children (6 girls and 6 boys) between the ages of 10 and 12 were observed on how they interact with three games (Numbers, Words, and Tangram). Two types of questionnaires (pre- and post-test questionnaires), combined with an interview, were designed. Moreover, smiley faces were used as scales to measure the answers for each participant. Overall, all children enjoyed playing the Osmo game. However, the results confirm that 42% of the participants experienced difficulty in playing with tangible pieces. In addition, boys are slightly better than girls, as they commit fewer errors, and they are generally more skillful than girls in most of the games.
Blockchain-Based Delegation of Rights in Distributed Computing Environment

The paper suggests a new approach based on blockchain technology and smart contracts to delegation of rights within distributed computing systems, which is fault-tolerant, safe and secure. The implementation of the proposed approach is based on the permissioned blockchains and on the Hyperledger Fabric blockchain platform in conjunction with Hyperledger Composer.
Blockchain Mechanisms as Security-Enabler for Industrial IoT Applications

The introduction and enactment of Industrial Internet of Things (IIoT)IIoT have initiated a global revolution. The volume and variety of data that are collected and processed in industries are ever increasing due to the widespread acceptance of modern technologies such as Internet of Things (IoT), big data analytics, machine-to-machine (M2M) communication, edge computing,Bigdata analytics and cloud storageCloud storage. Conventional systems with centralized architecture are not designed to handle the complexity and scale of data that is processed in IIoT operations. Moreover, the threat of security and privacy breaches also increases with the growth of IIoT-connected devices. IoT devices often tend to have poor security defense systems due to low processing power, limited storage capabilities, and poor manufacturingManufacturing standards. In this context, blockchainBlockchain technology can help to eliminate the security vulnerabilities faced by the IIoT systems and provides extensive protection from data thefts, cyberattacksCyber-attacks, and data corruption. Blockchain with its distributed architecture offers peer-to-peer networking and enables auditable and transparent transactionsTransactions. This chapter explores the concept of IIoT and its limitations, and the need for deploying blockchain mechanisms in the IIoT paradigm. We further analyze blockchain technology and how it can reinforce IIoT systems. Existing systems of blockchain in the IIoT ecosystems and relevant use cases are also explored. As conventional systems struggle to handle the scale of data operations handled by the IIoT, blockchain has emerged as a viable solution to reinforce and reform existing systems.
3-D Dimension Measurement of Workpiece Based on Binocular Vision

In this paper, the three-dimensional measurement of workpiece is studied, and the left and right images of the target workpiece are captured by binocular camera. Firstly, the calibration principle and theoretical model of binocular camera are studied in detail, and the calibration of inside and outside parameters of left and right cameras is completed under the environment of Matlab. Secondly, Hough transform is used to detect the contour of the target workpiece after image pre-processing such as filtering, graying and binarization, and to extract its two-dimensional feature point information. Then, on the basis of epipolar rectification, there is only lateral parallax between left and right images, and Block Matching (BM) algorithm is used for stereo matching of relative images. After that, the depth information of the pixels is extracted from the disparity map obtained by stereo matching, and the dimension of the workpiece is measured by combining the two-dimensional feature point information extracted by Hough transform and the three-dimensional Euclidean distance calculation formula. Finally, the experimental results show the validity of the proposed three-dimensional measurement results based on binocular vision.
Improving Adversarial Images Using Activation Maps

Deep Neural Networks are currently gaining a lot of attention for their near human-level performances in tasks such as image classification, object detection, etc. As a result, they are also being deployed in security critical and real time systems such as face recognition and autonomous cars. This requires models to be robust to changes to the input. However, recent literature has showed that they are easily fooled when human imperceptible noise, also known as adversarial noise, is added to the input. By exploiting this adversarial nature, various adversarial attacks and defences against these attacks have been introduced so far. In this paper, we propose a new approach which can be used alongside any existing adversarial attack to further reduce the L2 distance between the generated adversarial image and the original image. Our approach can also be thought of as a new adversarial attack built on top of an existing attack. We evaluated our approach on the ImageNet dataset. Using our approach, we were able to reduce the L2 distance for around 60-70% of the images sampled from the ImageNet dataset.
Guaranteed Scalable Learning of Latent Tree Models

Author(s): Huang, Furong; Niranjan, UN; Perros, Ioakeim; Chen, Robert; Sun, Jimeng; Anandkumar, Anima | Abstract: We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a divide-and-conquer strategy that learns models over small groups of variables and iteratively merges onto a global solution. The structure learning involves combinatorial operations such as minimum spanning tree construction and local recursive grouping; the parameter learning is based on the method of moments and on tensor decompositions. Our method is guaranteed to correctly recover the unknown tree structure and the model parameters with low sample complexity for the class of linear multivariate latent tree models which includes discrete and Gaussian distributions, and Gaussian mixtures. Our bulk asynchronous parallel algorithm is implemented in parallel and the parallel computation complexity increases only logarithmically with the number of variables and linearly with dimensionality of each variable.
Weakly Supervised Object Localization using Min-Max Entropy: an Interpretable Framework.

Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain. In this work, we focus on weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation. Typical convolutional attentions maps are prune to high false positive regions. To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions. Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting. A public reproducible PyTorch implementation is provided in: this https URL .
Statistical Computing with Integrated Photonics System

Artificial Neural Networks (ANN) have dramatically improved performance for many machine learning tasks. Recently, special hardware has been developed to carry out this particular type of algorithms. We demonstrate new architectures based on Photonic Integrated Circuits for carrying out ANN and other statistical processing tasks. We will discuss the challenges and opportunities of such approach, and how it can enable significant enhancement in computational speed, latency and power efficiency over state of the arts electronics.
A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of this work lies in the evaluation of the five machine learning methods for the task of genre identification for classical Arabic text using stylometric features.
A Behavioral Study of Task Scheduling Algorithms in Cloud Computing

All the services offered by cloud computing are bundled into one service know as IT as a Service (ITaaS).The user's processes are executed using these services.The scheduling techniques used in the cloud computing environment execute the tasks at different datacenters considering the needs of the consumers.As the requirements vary from one to one, and so the priorities also change.The jobs are executed either in a preemptive or non-preemptive way.The tasks in cloud computing also migrate from one datacenter to another considering load balancing.This research mainly focused on the study of how the Round Robin (RR) and Throttled (TR) scheduling techniques function subject to different tasks given for processing.An analysis is carried out to measure the performance based on the metrics like response time and service time at different userbases and data centers.The consumers have the option to select the server broker policy as they are the ultimate users and payers.
Sentiment Analysis in Online Reviews Classification using Text Mining Techniques

The growth of social media in recent years has led to an increase in online reviews that reflects consumer opinions.Firms benefit greatly from making this information available in order to respond more effectively to consumer dissatisfaction and to exploit market opportunities by observing standards that may represent unsatisfied needs.The present study aims to address this problem through a survey based on the Yelp platform.To this end, 14,000 comments related to different tourism products were used and text mining techniques and topic models were applied to find the main latent topics discussed in the online comments and their associated sentiments.The study presents 20 latent topics from online discussions and reveals that the topic that discusses "Air Travel" themes is the one with a lower sentiment connotation on average and should therefore be the subject of a deeper evaluation.
TL-GAN: Generative Adversarial Networks with Transfer Learning for Mode Collapse (S)

Image generation based on the generative adversarial network (GAN) has been widely used in the field of computer vision.It helps generate images similar to the given data by learning their distribution.However, in many tasks, training on small datasets of scenes may lead to mode collapse, such that the generated images are often blurred and almost the same.To solve this problem, we propose a generative adversarial network with transfer learning for mode collapse called TL-GAN.Owing to the size of the training dataset, we introduce transfer learning (VGG pre-training network) to extract more useful features from the underlying pixels and add them to the discriminator, which can be used to calculate the distance between samples, and to provide the discriminator with a new training target.The discriminator thus learns the best features that can distinguish between real data and generated data using the proposed model.This also enhances the learning capability of the generator, which learn further about the distribution of real data.Meanwhile, generator can produce new images more realistic.The results of experiments show that the TL-GAN can guarantee the diversity of samples.A qualitative comparison with several prevalent methods confirmed its effectiveness.
Deep slice interpolation via marginal super-resolution, fusion, and refinement

We propose a marginal super-resolution (MSR) approach based on 2D convolutional neural networks (CNNs) for interpolating an anisotropic brain magnetic resonance scan along the highly under-sampled direction, which is assumed to axial without loss of generality. Previous methods for slice interpolation only considered data from pairs of adjacent 2D slices. The possibility of fusing information from the direction orthogonal to the 2D slices remains unexplored. Our approach performs MSR in both sagittal and coronal directions, which provides an initial estimate for slice interpolation. The interpolated slices are then fused and refined in the axial direction for improved consistency. As MSR consists of only 2D operations, it is more feasible in terms of GPU memory consumption and requires fewer training samples compared to 3D CNNs. Our experiments demonstrate that the proposed method outperforms traditional linear interpolation and baseline 2D/3D CNN-based approaches. We conclude by showcasing the method’s practical utility in estimating brain volumes from under-sampled brain MR scans through semantic segmentation.
Determining how diluted bloodstains were derived: Inferring distinctive characteristics and formulating a guideline

In forensic settings, diluted bloodstains are regularly encountered for example when bloodstains are mixed with tap-/rainwater, after deliberate cleaning attempts, or when blood is dropped on a wet surface such as a towel. Such diluted bloodstain scenarios can be subdivided into sequences of events in which a blood drop was either (1) readily diluted (a mixture of blood and water is deposited); (2) deposited on a surface that was readily moistened (first water, then blood) or (3) deposited and subsequently moistened (first blood, then water). Current bloodstain pattern analysis (BPA) lacks data and tools to distinguish these three ways of derivation of a diluted bloodstain that vary in the sequence of deposition of blood and water on textile. In this study, 880 bloodstains were examined for characteristics that can be used to determine the derivation of diluted bloodstains. A guideline to assist BPA-analysts in interpreting diluted bloodstains was extracted. The added value of this guideline was confirmed by conducting two surveys: one survey with and one without the guideline. A third survey confirmed that the characteristics also function on a broader range of textile types that have different weave and knit styles. This guideline can aid BPA-experts to determine, in an objective way, how diluted bloodstains derived which can aid in determining which activities took place at a crime scene.
Estimations of limestone resources using three dimension block kriging method, a case study: limestone sediment at PT Semen Padang

Abstract Making of block model was performed by geostatistic approach using kriging method to estimate and modeling of limestone source quality at Karang Putih Hill. Modeling and counting of source were based on three-dimension block model concept which was divided into block units with grid size 100 100 15 m which was appropriate with the size of mining block at location of the study. Variable used for limestone source estimation were CaO, SiO 2 and H 2 O levels, according to conditions of raw material quality which has been specified.The result from variogram analysis showed that spatial characteristic of CaO, SiO 2 and H 2 O levels in limestone had nugget effect percentage of 47, 36%, 30, 92%, dan 33, 3% with range 145 - 230 m and range for vertical direction 21 - 40 m. Percentage (%) error of limestone estimation with confidence level 95% for CaO level were 54, 74% 0, 04%, SiO 2 : 0, 72% 0, 04% and H 2 O: 0, 36% ± 0, 007% respectively. Limestone resource quantity estimation at Karang Putih Hill up to elevation 214 meters above sea level which fulfilled the standard quality of CaO level ≥ 48 %, SiO 2 level &lt; 10%, and H 2 O level &lt; 6 % was 158, 01 million ton, while tonnase in the form of 799, 94 thousand ton of silica and 1, 17 million ton of basalt.
Multi-criteria Evaluation + Positional Ranking Approach for Candidate Selection in E-voting

E-voting is one of the most important components of e-democracy and includes interesting research topics, such as the mechanisms of participation in elections, technological solutions to e-voting and the efficient application of those in e-voting. Currently, there are numerous voting systems adopted in many countries of the world and each of those has specific advantages and problems. The paper explores the e-voting system as one of the main tools of e-democracy and analyzes its advantages and drawbacks. Voting results always lead to a broad debate in terms of candidate selection and of whether the candidate elected to a position is suitable for that position. At present, the selection of qualified personnel and their appointment to responsible positions in public administration is one of the topical issues. In the paper, multi-criteria decision-making (MCDM) is proposed for the selection of candidates in e-voting. The criteria for candidate selection are determined and the relationship of each candidate with the other candidates is assessed by using a binary matrix. The candidate rank is calculated according to all the criteria. In a numerical experiment, candidate evaluation is enabled based on the selected criteria and ranked by using a positional ranking approach. The proposed model allows for the selection of a candidate with the competencies based on the criteria set out in the e-voting process and the making of more effective decisions as well.
Skipping CNN Convolutions Through Efficient Memoization

Convolutional Neural Networks (CNNs) have become a de-facto standard for image and video recognition. However, current software and hardware implementations targeting convolutional operations still lack embracing energy budget constraints due to the CNN intensive data processing behavior. This paper proposes a software-based memoization technique to skip entire convolution calculations. We demonstrate that, by grouping output values within proximity-based clusters, it is possible to reduce by hundreds of times the amount of memory necessary to store all the tables. Also, we present a table mapping scheme to index the input set of each convolutional layer to its output value. Our experimental results show that for a YOLOv3-tiny CNN, it is possible to achieve a speedup up to 3.5 $$\times $$ while reducing the energy consumption to 22% of the baseline with an accuracy loss of 7.4%.
A Shallow Semantic Parsing Framework for Event Argument Extraction

Currently, many state-of-the-art event argument extraction systems are still based on an unrealistic assumption that gold-standard entity mentions are provided in advance. One popular solution of jointly extracting entities and events is to detect the entity mentions using sequence labeling approaches. However, this methods may ignore the syntactic relationship among triggers and arguments. We find that the constituents in the parse tree structure may help capture the internal relationship between words in an event argument. Besides, the predicate and the corresponding predicate arguments, which are mostly ignored in existing approaches, may provide more potential to represent the close relationship. In this paper, we address the event argument extraction problem in a more actual scene where the entity information is unavailable. Moreover, instead of using word-level sequence labeling approaches, we propose a shallow semantic parsing framework to extract event arguments with the event trigger as the predicate and the event arguments as the predicate arguments. In specific, we design and compare different features for the proposed model. The experimental results show that our approach advances state-of-the-arts with remarkable gains and achieves the best F1 score on the ACE 2005 dataset.
Penggunaan Media Gambar dalam Meningkatkan Kemampuan Membaca Permulaan pada TK PGRI Jatisela

[Title: The Use of Image Media in Improving Initial Reading Ability at Jatisela PGRI Kindergarten]. This study aims to determine the extent to which the use of image media can improve the initial reading ability in Group B of the Jatisela PGRI Kindergarten Students in the 2017/2018 Academic Year. This study is a Classroom Action Research with qualitative-quantitative data analysis techniques. The subjects of the study were 23 students of group B of the Jatisela PGRI Kindergarten. The results of data analysis show that the use of image media can (1) improve students initial reading ability, (2) make learning activities more active and fun, (3) the students initial reading ability after the second cycle increases with a percentage of 95.65%. Based on the results of the study it can be concluded that the use of image media can improve the students initial reading ability of group B Jatisela PGRI Kindergarten Sesela Village, Gunungsari, West Lombok 2017/2018 Academic Year.
Quantum walk as generalized evolution and measurement device

Abstract We show that arbitrary quantum evolution, both unitary and non-unitary ones, can be efficiently realized via one-dimensional quantum walk. This is done based on the equivalence between quantum evolution and positive-operated valued measurements, and an algorithm of properly controlling one-dimensional discrete-time quantum walk to realize arbitrary positive-operated valued measurements. Furthermore, our method based on one-dimensional quantum walk can be easily generalized to other platforms Our method enriches technics for quantum information processes, and the applications of quantum walk.
Generalization Bounds in the Predict-then-Optimize Framework

The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters, in contrast to the prediction error of the parameters. This loss function was recently introduced in [Elmachtoub and Grigas, 2017], which called it the Smart Predict-then-Optimize (SPO) loss. Since the SPO loss is nonconvex and noncontinuous, standard results for deriving generalization bounds do not apply. In this work, we provide an assortment of generalization bounds for the SPO loss function. In particular, we derive bounds based on the Natarajan dimension that, in the case of a polyhedral feasible region, scale at most logarithmically in the number of extreme points, but, in the case of a general convex set, have poor dependence on the dimension. By exploiting the structure of the SPO loss function and an additional strong convexity assumption on the feasible region, we can dramatically improve the dependence on the dimension via an analysis and corresponding bounds that are akin to the margin guarantees in classification problems.