Reviewerly/exHarmony huggingface dataset:
KeyIn: Discovering Subgoal Structure with Keyframe-based Video Prediction.

Real-world image sequences can often be naturally decomposed into a small number of frames depicting interesting, highly stochastic moments (its $\textit{keyframes}$) and the low-variance frames in between them. In image sequences depicting trajectories to a goal, keyframes can be seen as capturing the $\textit{subgoals}$ of the sequence as they depict the high-variance moments of interest that ultimately led to the goal. In this paper, we introduce a video prediction model that discovers the keyframe structure of image sequences in an unsupervised fashion. We do so using a hierarchical Keyframe-Intermediate model (KeyIn) that stochastically predicts keyframes and their offsets in time and then uses these predictions to deterministically predict the intermediate frames. We propose a differentiable formulation of this problem that allows us to train the full hierarchical model using a sequence reconstruction loss. We show that our model is able to find meaningful keyframe structure in a simulated dataset of robotic demonstrations and that these keyframes can serve as subgoals for planning. Our model outperforms other hierarchical prediction approaches for planning on a simulated pushing task.
Proposed algorithm for image classification using regression-based pre-processing and recognition models

&lt;span&gt;Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.&lt;/span&gt;
A comparison of the planning poker and team estimation game: a case study in software development capstone project course

Effort estimation is a crucial part of software development projects. Despite the availability of several assessmenttechniques, accurate assessment still remains an extremely difficult task. Team Estimation Game is a relatively newestimation technique for agile software development methods that has not received significant attention from the scientificcommunity despite its growing popularity between practitioners. In this paper, we attempt to bridge this gap by presentingthe results of an empirical study with undergraduate students in which we compare Team Estimation Game with the moreestablished Planning Poker technique. We mainly focus our analysis of the two techniques on the time needed for user storyestimation and estimation accuracy. The results of the empirical study reveal that Team Estimation Game produces moreaccurate story estimates than Planning Poker. Additionally, we found that for the Team Estimation Game, estimation andplanning skills of the development teams improve from Sprint to Sprint. Team Estimation Game proved to be a usefulestimation method for agile projects within the capstone course. Furthermore, we have shown that the study can besuccessfully incorporated into a software engineering capstone course without hindering the teaching goals while retainingthe validity of research goals.
HYPERSPECTRAL ANALYSIS OF ASTER AND LANDSAT 8 DATA TO CHARACTERIZE EL-MISSIKAT URANIUM BEARING SILICEOUS VEINS AND THEIR ANALOGY TO EL-ERADIYA AND GATTAR GRANITES, EASTERN DESERT, EGYPT

This work aims to apply spectral image processing techniques that have been used often to processhyperspectral data to analyze multispectral ASTER (Advanced Spaceborne Thermal Emission andReflection Radiometer) and Landsat 8 data for mapping uranium-bearing siliceous rocks within El-Missikat granite. These techniques succeeded effectively in recognition of the same features at El-Eradiyaand Gattar granites. Siliceous materials (siliceous sinter), as used here, is a spectral group of SiO2 mineralsincluding opal, chalcedony and cristobalite derived from hydrothermal fluids and/or the decomposition ofgranite. The spectral identification of such minerals depend mainly on the uses a hydration absorptionfeature that characterize some hydrated silica at wavelength region of 2.2- to 2.4-μm.The effectiveness of the hyperspectral analysis techniques used to compare a pixel spectrum with thespectra of known pure materials, extracted from the spectral end member selection procedures, includingminimum noise fraction (MNF), pixel purity index (PPI) and n-dimensional visualization. Among thoseof the spectral analysis algorithms employed, spectral angle mapping (SAM) and matched filtering (MF)produced accurate classifications that were close to the ground reference data. The hyperspectralanalysis of an ASTER and Landsat 8 dataset covering the studied areas, has successfully effective indetecting lithological units than traditional multispectral analysis procedures.Field validation and laboratory investigation were carried out to prove and support the resultsobtained from ASTER and Landsat 8 processing. These results succeeded to delineate additional sitesof siliceous materials that could be a promising areas to host the uranium mineralization.
IoT and Cloud Computing: Specific Security and Data Protection Issues

This chapter will address the specific challenges related to cloud computing and will be directly fed by results from the project CloudWatch2.
Composite Keys and Signatures For Use In Internet PKI

With the widespread adoption of post-quantum cryptography will comethe need for an entity to possess multiple public keys on differentcryptographic algorithms. Since the trustworthiness of individualpost-quantum algorithms is at question, a multi-key cryptographicoperation will need to be performed in such a way that breaking itrequires breaking each of the component algorithms individually. Thisrequires defining new structures for holding composite public keys andcomposite signature data. This document defines the structuresCompositePublicKey, CompositeSignatureValue, and CompositeParams,which are sequences of the respective structure for each componentalgorithm. This document also defines algorithms for generating andverifying composite signatures. This document makes no assumptionsabout what the component algorithms are, provided that their algorithmidentifiers and signature generation and verification algorithms aredefined.
Motion-adapted Three-dimensional Frequency Selective Extrapolation

It has been shown, that high resolution images can be acquired using a low resolution sensor with non-regular sampling. Therefore, post-processing is necessary. In terms of video data, not only the spatial neighborhood can be used to assist the reconstruction, but also the temporal neighbor-hood. A popular and well performing algorithm for this kind of problem is the three-dimensional frequency selective extrapolation (3D-FSE) for which a motion adapted version is introduced in this paper. This proposed extension solves the problem of changing content within the area considered by the 3D-FSE, which is caused by motion within the sequence. Because of this motion, it may happen that regions are emphasized during the reconstruction that are not present in the original signal within the considered area. By that, false content is introduced into the extrapolated sequence, which affects the resulting image quality negatively. The novel extension, presented in the following, incorporates motion data of the sequence in order to adapt the algorithm accordingly, and compensates changing content, resulting in gains of up to 1.75 dB compared to the existing 3D-FSE.
TEACHER TRAINING FOR PROFESSIONAL DEVELOPMENT ON MODERN EDUCATION IN SCIENCE FOR INDUSTRY 4.0

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 4757-4766ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1185Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Synthesizing Database Programs for Schema Refactoring

Many programs that interact with a database need to undergo schema refactoring several times during their life cycle. Since this process typically requires making significant changes to the program's implementation, schema refactoring is often non-trivial and error-prone. Motivated by this problem, we propose a new technique for automatically synthesizing a new version of a database program given its original version and the source and target schemas. Our method does not require manual user guidance and ensures that the synthesized program is equivalent to the original one. Furthermore, our method is quite efficient and can synthesize new versions of database programs (containing up to 263 functions) that are extracted from real-world web applications with an average synthesis time of 69.4 seconds.
ANALISIS INTEGRASI DATA PADA RELASIONAL BASIS DATA DENGAN MENGGUNAKAN METODE SCHEMA MATCHING

University is one of the agencies that use information technology to support various business processes. University requires data integration between the systems so that the data available in one system can be used in other systems to support data management.In forwarding data integration there are several obstacles that occur one of the causes is schema heterogeneity used by each information system. linguistic method is one of the schema matching methods used to overcome the problem of schema heterogeneityBased on the analysis of database schemes with the linguistic method, the values of precision, recall and f measure are 0.75. This value indicates that the application of the matching schema has been quite good. But there are still some of the same data between the schemes so that the integration of the data owned is not maximal. So that optimization is still needed to maximize the data integration
A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.
LMS to Deep Learning: How DSP Analysis Adds Depth to Learning

Difficulty in analyzing deep learning systems is preventing association of its parameters and state outputs with elements that are derived from theoretic considerations. Medical and criminal justice communities are excited by the possibilities nevertheless reluctant to adopt machine learning for fear of errors and bias. There is also concern among educators that mystifying learning may have long-term adverse pedagogical implications on human learning. Deep learning techniques have not received enthusiastic attention in the realm of communication systems, either. This is in part due the effectiveness of traditional analytical solutions that render classification error rates larger that 10-3 unacceptable. Understanding of deep networks for communications, and ability to perform comparison tests can be useful in developing scalable methods to understand them in more complex scenarios. This paper contributes a perspective and analysis with focus on Nyquist and non-Nyquist pulse shapes for bandlimited channels. It is hoped that this fundamental presentation motivates wider consideration of neural networks and deep learning for demodulation.
Modelling of Intelligent Agents Using A–Prolog

Nowadays, research in artificial intelligence has widely grown in areas such as knowledge representation, goal-directed behaviour and knowledge reusability, all of them directly relevant to improving intelligent agents in computer games.In a particular way, we focus on the development of a novel algorithm that allow an agent to combine fundamental theories such as reasoning, learning and simulation.This algorithm combines a system of logical rules and a simulation mechanism based on learning that makes our agent has an infallible mechanism for decision-making into the game "connect four".The logic system is developed in a modelling language known as Answer Set Programming or A-Prolog.This paradigm is the integration of two well-known languages, namely Prolog and ASP.
THE WEBINAR AS A DIGITAL TOOL IN MATHEMATICS TEACHER EDUCATION

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 1821-1825ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0520Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression

This paper attempt to study the effectiveness of text representation schemes on two tasks namely: User Aggression and Fact Detection from the social media contents. In User Aggression detection, The aim is to identify the level of aggression from the contents generated in the Social media and written in the English, Devanagari Hindi and Romanized Hindi. Aggression levels are categorized into three predefined classes namely: `Non-aggressive`, `Overtly Aggressive`, and `Covertly Aggressive`. During the disaster-related incident, Social media like, Twitter is flooded with millions of posts. In such emergency situations, identification of factual posts is important for organizations involved in the relief operation. We anticipated this problem as a combination of classification and Ranking problem. This paper presents a comparison of various text representation scheme based on BoW techniques, distributed word/sentence representation, transfer learning on classifiers. Weighted $F_1$ score is used as a primary evaluation metric. Results show that text representation using BoW performs better than word embedding on machine learning classifiers. While pre-trained Word embedding techniques perform better on classifiers based on deep neural net. Recent transfer learning model like ELMO, ULMFiT are fine-tuned for the Aggression classification task. However, results are not at par with pre-trained word embedding model. Overall, word embedding using fastText produce best weighted $F_1$-score than Word2Vec and Glove. Results are further improved using pre-trained vector model. Statistical significance tests are employed to ensure the significance of the classification results. In the case of lexically different test Dataset, other than training Dataset, deep neural models are more robust and perform substantially better than machine learning classifiers.
Deep Neural Network Ensembles

Current deep neural networks suffer from two problems; first, they are hard to interpret, and second, they suffer from overfitting. There have been many attempts to define interpretability in neural networks, but they typically lack causality or generality. A myriad of regularization techniques have been developed to prevent overfitting, and this has driven deep learning to become the hot topic it is today; however, while most regularization techniques are justified empirically and even intuitively, there is not much underlying theory. This paper argues that to extract the features used in neural networks to make decisions, it's important to look at the paths between clusters existing in the hidden spaces of neural networks. These features are of particular interest because they reflect the true decision making process of the neural network. This analysis is then furthered to present an ensemble algorithm for arbitrary neural networks which has guarantees for test accuracy. Finally, a discussion detailing the aforementioned guarantees is introduced and the implications to neural networks, including an intuitive explanation for all current regularization methods, are presented. The ensemble algorithm has generated state-of-the-art results for Wide-ResNets on CIFAR-10 (top 5 for all models) and has improved test accuracy for all models it has been applied to.
Black-Box Decision based Adversarial Attack with Symmetric $\alpha$-stable Distribution

Developing techniques for adversarial attack and defense is an important research field for establishing reliable machine learning and its applications. Many existing methods employ Gaussian random variables for exploring the data space to find the most adversarial (for attacking) or least adversarial (for defense) point. However, the Gaussian distribution is not necessarily the optimal choice when the exploration is required to follow the complicated structure that most real-world data distributions exhibit. In this paper, we investigate how statistics of random variables affect such random walk exploration. Specifically, we generalize the Boundary Attack, a state-of-the-art black-box decision based attacking strategy, and propose the Levy-Attack, where the random walk is driven by symmetric $\alpha$-stable random variables. Our experiments on MNIST and CIFAR10 datasets show that the Levy-Attack explores the image data space more efficiently, and significantly improves the performance. Our results also give an insight into the recently found fact in the whitebox attacking scenario that the choice of the norm for measuring the amplitude of the adversarial patterns is essential.
On Obtaining Boltzmann-Distributed Configurational Ensembles from Expanded Ensemble Simulations with Fast State Mixing

In Expanded Ensemble (EXE) or Simulated Tempering simulations, the system's (effective) temperature is frequently updated to enhance configurational sampling. We investigated how short the EXE state update interval τ can become before too frequent updates impede Boltzmann sampling. Simulating alanine dipeptide in explicit water, we show that a hybrid MC/MD integrator reliably yields Boltzmann-distributed configurations regardless of τ. However, in MD-driven EXE simulations with short τ, configurational ensembles depend on the thermostat settings.
LEARNING ANALYTICS SOFTWARE FOR MEDICAL STUDENTS REGARDING PREGNANCY COMPLICATIONS

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 3350-3359ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0870Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
COGNITIVE-GENRE APPROACH TO GRAMMAR EXPLANATION IN ESP CLASSROOMS AT TECHNICAL UNIVERSITIES

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 2550-2557ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0694Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference

We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectures, the model scores each word's tags in parallel, with minimal task-specific structure. After scoring, a left-to-right reconciliation phase extracts a tree in (empirically) linear time. Our parser achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies.
HEALTH ASPECTS OF IMMERSION IN VR. VIRTUAL DISEASE – FACT OR MYTH?

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 543-551ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0216Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
PERANCANGAN SISTEM PENSKORAN OLAHRAGA DENGAN TAMPILAN SEVEN SEGMENT

Seven segment models used to showing the decimal numbers from 0 to 9. As shown in the digital direction, the series of counters were distinguished from the up counter and down counter. One of the implementasion of seven segment models were sport's scoreboard. Sport's scoreboard were one of the aids in the sports which is used for announce and record the result of the competition so the the onlooker and athletes knowing its score. it been a based of thinking to make the scoreboard by digital system. By using IC 4062. IC NE555, and sevent segment models would help arbiter to record the result score of a competition. This scoreboard were able to use in permanent sport center like stadium or in the semipermanent sport center like open field.Keywords: Seven Segment, IC 4026, IC NE555
YUVMultiNet: Real-time YUV multi-task CNN for autonomous driving

In this paper, we propose a multi-task convolutional neural network (CNN) architecture optimized for a low power automotive grade SoC. We introduce a network based on a unified architecture where the encoder is shared among the two tasks namely detection and segmentation. The pro-posed network runs at 25FPS for 1280x800 resolution. We briefly discuss the methods used to optimize the network architecture such as using native YUV image directly, optimization of layers &amp; feature maps and applying quantization. We also focus on memory bandwidth in our design as convolutions are data intensives and most SOCs are bandwidth bottlenecked. We then demonstrate the efficiency of our proposed network for a dedicated CNN accelerators presenting the key performance indicators (KPI) for the detection and segmentation tasks obtained from the hardware execution and the corresponding run-time.