Reviewerly/exHarmony huggingface dataset:
On the use of generalized harmonic means in image processing using multiresolution algorithms

In this paper we design a family of cell-average nonlinear prediction operators that make use of the generalized harmonic means and we apply the resulting schemes to image processing. The new family of nonlinear schemes conserve the numerical properties of the linear schemes, such as the L1-stability, the order of accuracy or compression rate but avoiding Gibbs phenomenon close to the discontinuities. The generalized harmonic mean was introduced in the framework of point-values in [A. Guessab, M. Moncayo, and G. Schmeisser, A class of nonlinear four-point subdivision schemes. Properties in terms of conditions, Adv. Comput. Math. 37 (2012), pp. 151–190] in order to improve the results of the harmonic mean. However, in the cell-average setting our conclusion is that, from a numerical point of view, the advantage of using the new mean is not clear.
Implementation and Performance Analysis of Different Hand Gesture Recognition Methods

In recent few years, hand gesture recognition is one of the advanced grooming technologies in the era of human computer interaction and computer vision due to a wide area of application in the real world. But it is a very complicated task to recognize hand gesture easily due to gesture orientation, light condition, complex background, translation and scaling of gesture images. To remove this limitation, several research works have developed which is successfully decrease this complexity. However, the intention of this paper is proposed and compared four different hand gesture recognition system and apply some optimization technique on it which ridiculously increased the existing model accuracy and model running time. After employed the optimization tricks, the adjusted gesture recognition model accuracy was 93.21% and the run time was 224 seconds which was 2.14% and 248 seconds faster than an existing similar hand gesture recognition model. The overall achievement of this paper could be applied for smart home control, camera control, robot control, medical system, natural talk, and many other fields in computer vision and human-computer interaction.
A Novel Text Retrieval Algorithm for Public Crisis Cases

Public crisis has the characteristics of suddenness and uncertainty, and it is necessary to combine the knowledge with the experience of other similar situations to make decisions effectively and quickly. This work combines artificial intelligent theory with information technology and brings case-based reasoning to build models consisting of the features of public crisis. We explore the case-representation approach and build a case-based retrieval algorithm. Combining the specificness of Case-based reasoning (CBR) technology in the monitoring of public crisis events, a new case retrieval algorithm for public crisis cases, named as Combined multi-similarity with set of simi-larity matching algorithm based on sememe (CMSBS), is proposed to analyze the cases with high similarity to current case. The CMSBS algorithm considers the structural and semantic similarities between two public crisis cases comprehensively. Simulation experiments are performed to validate the representation method of the knowledge, and the simulation results demonstrate that the CMSBS algorithm has superior performance in the average number of matching cases and matching accuracy rate and can work well in providing reference cases for subsequent events.
CAF Implementation on FPGA Using Python Tools

The purpose of this project is to provide a real time geolocation solution by generating code for the complex ambiguity function (CAF) in a hardware description language (HDL) and the implementation on FPGA hardware.The CAF has many practical applications, the more traditional being radar or sonar type systems.By using scientific Python tools, this project provides a solution for testing signals and the ability to customize modules to target multiple devices.The processing for this implementation will be done on a PYNQ board designed by Xilinx.The PYNQ board provides a Zynq chip which has both an ARM CPU and FPGA fabric.All required mathematical operations for the CAF are returned to the user through Python classes which produce synthesizable code in the Verilog HDL.The Python classes use Jinja templates integrated into the Verilog code to allow for configuration changes that a user will need to change for investigation and simulation, development, and test.Helper methods are included in the package to help simulation of the HDL such as quantization, complex data reading and writing, and methods to verify the data using quantized values.
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD

We study Stochastic Gradient Descent (SGD) with diminishing step sizes for convex objective functions. We introduce a definitional framework and theory that defines and characterizes a core property, called curvature, of convex objective functions. In terms of curvature we can derive a new inequality that can be used to compute an optimal sequence of diminishing step sizes by solving a differential equation. Our exact solutions confirm known results in literature and allows us to fully characterize a new regularizer with its corresponding expected convergence rates.
Deep learning based automatic software defects detection framework

We present the VulDetect, a source code vulnerability detection system. This system uses deep learning methods to organizate rules for deciding whether a code fragment is vulnerable. This approach is an improvement of the approach proposed in VulDeePecker. The model uses the AST representation of the source code. We compared vulnerability detection results of both systems on the Bitcoin Core project.
Multimodal Self-Assessed Personality Estimation During Crowded Mingle Scenarios Using Wearables Devices and Cameras

This paper focuses on the automatic classification of self-assessed personality traits from the HEXACO inventory during crowded mingle scenarios. These scenarios provide rich study cases for social behavior analysis but are also challenging to analyze automatically as people in them interact dynamically and freely in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-the-wild</i> face-to-face setting. To do so, we leverage the use of wearable sensors recording acceleration and proximity, and video from overhead cameras. We use 3 different behavioral modality types (movement, speech and proximity) coming from 2 sensors (wearable and camera). Unlike other works, we extract an individual’s speaking status from a single body worn triaxial accelerometer instead of audio, which scales easily to large populations. Additionally, we study the effect of different combinations of modality types on the personality estimation, and how this relates to the nature of each trait. We also include an analysis of feature complementarity and an evaluation of feature importance for the classification, showing that combining complementary modality types further improves the classification performance. We estimate the self-assessed personality traits both using a binary classification (community’s standard) and as a regression over the trait scores. Finally, we analyze the impact of the accuracy of the speech detection on the overall performance of the personality estimation.
Devon: Deformable Volume Network for Learning Optical Flow

We propose a new neural network module, Deformable Cost Volume, for learning large displacement optical flow. The module does not distort the original images or their feature maps and therefore avoids the artifacts associated with warping. Based on this module, a new neural network model is proposed. The full version of this paper can be found online ( https://arxiv.org/abs/1802.07351 ).
Superpixel-Based Feature Tracking for Structure from Motion

Feature tracking in image collections significantly affects the efficiency and accuracy of Structure from Motion (SFM). Insufficient correspondences may result in disconnected structures and incomplete components, while the redundant correspondences containing incorrect ones may yield to folded and superimposed structures. In this paper, we present a Superpixel-based feature tracking method for structure from motion. In the proposed method, we first propose to use a joint approach to detect local keypoints and compute descriptors. Second, the superpixel-based approach is used to generate labels for the input image. Third, we combine the Speed Up Robust Feature and binary test in the generated label regions to produce a set of combined descriptors for the detected keypoints. Fourth, the locality-sensitive hash (LSH)-based k nearest neighboring matching (KNN) is utilized to produce feature correspondences, and then the ratio test approach is used to remove outliers from the previous matching collection. Finally, we conduct comprehensive experiments on several challenging benchmarking datasets including highly ambiguous and duplicated scenes. Experimental results show that the proposed method gets better performances with respect to the state of the art methods.
Pemodelan Intrusi Air Asin Pada Akuifer Pantai (Studi Kasus: DKI Jakarta)

Main issues frequently faced by communities living in the city of Jakarta is salt water intrusion. This paper analyzes the extent of the distribution of saltwater contamination to coastal aquifers in Jakarta, the method used is the open source program FREEWAT. modeling is carried out in five scenarios, namely (1) reduction of 50% of groundwater use, (2) reduction of 25% of groundwater use, (3) existing groundwater use in 2012, (4) increase of 25% of groundwater use, and (5) increase of 50% of groundwater use. From the modeling results, the distribution of saltwater contamination on each layer of the aquifer of Jakarta. In unconfined aquifer the existing condition is scenario 3 intrusion as far as 1.604 m, on scenario 1 there is an intrusion decrease of 38%, in scenario 2 there is an intrusion decrease of 13%, in scenario 4 there is an increase in intrusion by 17% and on scenario 5 increase intrusion by 25%. In upper confined aquifer, the existing condition, namely scenario 3, has occurred as much as 1.809 m intrusion, in scenario 1 there is an intrusion decrease of 23%, in scenario 2 there is an intrusion decrease of 8%, in scenario 4 there is an increase in intrusion by 19% and in scenario 5 increase intrusion by 26%. In middle confined aquifer, the existing condition is scenario 3, 1.927 m intrusion occurs, on the scenario 1 there is an intrusion decrease of 23%, in scenario 2 there is an intrusion decrease of 9%, in scenario 4 there is an increase in intrusion by 16% and in the scenario 5 increase intrusion by 22%.
Rank Pruning for Dominance Queries in CP-Nets

&#x0D; &#x0D; &#x0D; Conditional preference networks (CP-nets) are a graphical representation of a person’s (conditional) preferences over a set of discrete features. In this paper, we introduce a novel method of quantifying preference for any given outcome based on a CP-net representation of a user’s preferences. We demonstrate that these values are useful for reasoning about user preferences. In particular, they allow us to order (any subset of) the possible outcomes in accordance with the user’s preferences. Further, these values can be used to improve the efficiency of outcome dominance testing. That is, given a pair of outcomes, we can determine which the user prefers more efficiently. Through experimental results, we show that this method is more effective than existing techniques for improving dominance testing efficiency. We show that the above results also hold for CP-nets that express indifference between variable values.&#x0D; &#x0D; &#x0D;
Errors bounds for finite approximations of coherent lower previsions on finite probability spaces

Coherent lower previsions are general probabilistic models allowing incompletely specified probability distributions. However, for complete description of a coherent lower prevision – even on finite underlying sample spaces – an infinite number of assessments is needed in general. Therefore, they are often only described approximately by some less general models, such as coherent lower probabilities or in terms of some other finite set of constraints. The magnitude of error induced by the approximations has often been neglected in the literature, despite the fact that it can be significant, with substantial impact on consequent decisions. An apparent reason is that no widely used general method for estimating the error seems to be available at the moment. This paper provides a practically applicable method that allows calculating an upper bound for the maximal error induced by approximating a coherent lower probability with its values on a finite set of gambles. An algorithm is also provided with an estimation of its computational complexity.
DEVELOPING ADDIE MODEL-BASED ESP COURSEBOOK

Coursebook plays a substantial role in facilitating teaching and learning process, especially for EFL (English as a Foreign Language) learners of English for Specific Purposes (ESP). However, the widely available coursebooks do not always match with the designed program and the learners� as well as the institution� needs. To ensure that a coursebook can optimally support the goals of an ESP course, developing materials is suggested. This research aimed to develop an ESP coursebook using ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. A Research and Development method was employed in this research in which the data were collected through questionnaire and interview. The questionnaire was distributed to 430 Business English 1 students at Universitas Muhammadiyah Yogyakarta. Meanwhile, the interview was conducted to 4 English teachers. Here, the data were obtained during the first and the last steps of ADDIE model (analysis and evaluation). The data obtained from the analysis phase were used for need analysis, while those obtained from the evaluation phase were used to evaluate the strengths and weaknesses of the coursebook. The findings showed the coursebook�s elements which cover language, content, tasks, learners, and the learning are good, but the visual elements highly need improvement.
Relative pose measurement of binocular vision based on feature circle

In traditional pose measurement algorithms, there are many visual measurement solutions for feature points and lines but few for feature circles. In this paper, the feature circle is described by the rotation characteristic of a point around a rotating axis in dual quaternion algebra. Dual vectors are used to describe the feature points, and Plücker lines are used to describe the rotation axis; that is, the dynamic characteristic of the feature point rotating around the Plücker line is used to describe the feature circle, and then the relative position and attitude information between the leader and follower rigid bodies are calculated by the principle of binocular vision measurement. The simulation results show that the algorithm can meet the requirements of the relative pose measurement accuracy of rigid bodies.
Blockchain Technology applied to Education

The purpose of the present work is offer an implementation of a private blockchain infrastructure for data storage, specifically the certificates of completion or participation issued by the Federal University of Tocantins, Campus Palmas-TO, Brazil, aiming to avoid their falsification by means of validation using hashing.Some institutions, such as the Massachusetts Institute of Technology and Holberton School in San Francisco, use Bitcoin's public blockchain to maintain and validate their certificates, but paying fees for each operation.The blockchain implementation adopted for this project is Multichain, a solution that, because it is open source, reduces deployment costs, facilitates its installation and maintena nce of its infrastructure, and doesn't request the payment of any fees for information storage.Because of its security features and transparency in data storage, Blockchain has been used in a number of technologies, such as the Bitcoin cryptocurrency, sma rt contracts, shared economy, corporate governance, intellectual property protection, Internet of Things (IoT), management of identity, among others, and also in education, focus of this work.
3D Face Synthesis Driven by Personality Impression

Synthesizing 3D faces that give certain personality impressions is commonly needed in computer games, animations, and virtual world applications for producing realistic virtual characters. In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters. Our approach consists of two major steps. In the first step, we train classifiers using deep convolutional neural networks on a dataset of images with personality impression annotations, which are capable of predicting the personality impression of a face. In the second step, given a 3D face and a desired personality impression type as user inputs, our approach optimizes the facial details against the trained classifiers, so as to synthesize a face which gives the desired personality impression. We demonstrate our approach for synthesizing 3D faces giving desired personality impressions on a variety of 3D face models. Perceptual studies show that the perceived personality impressions of the synthesized faces agree with the target personality impressions specified for synthesizing the faces.
Improved Language Modeling by Decoding the Past

Highly regularized LSTMs achieve impressive results on several benchmark datasets in language modeling.We propose a new regularization method based on decoding the last token in the context using the predicted distribution of the next token.This biases the model towards retaining more contextual information, in turn improving its ability to predict the next token.With negligible overhead in the number of parameters and training time, our Past Decode Regularization (PDR) method improves perplexity on the Penn Treebank dataset by up to 1.8 points and by up to 2.3 points on the WikiText-2 dataset, over strong regularized baselines using a single softmax.With a mixture-of-softmax model, we show gains of up to 1.0 perplexity points on these datasets.In addition, our method achieves 1.169 bits-per-character on the Penn Treebank Character dataset for character level language modeling.Each of these results constitute improvements over models without PDR in their respective settings.
How to Agree without Understanding Each Other: Public Announcement Logic with Boolean Definitions

In standard epistemic logic, knowing that p is the same as knowing that p is true, but it does not say anything about understanding p or knowing its meaning.In this paper, we present a conservative extension of Public Announcement Logic (PAL) in which agents have knowledge or belief about both the truth values and the meanings of propositions.We give a complete axiomatization of PAL with Boolean Definitions and discuss various examples.An agent may understand a proposition without knowing its truth value or the other way round.Moreover, multiple agents can agree on something without agreeing on its meaning and vice versa.
Understanding Peripheral Audiences: From Subtle to Full Body Gestures

Full body interaction is used to allow a more immersive and cognitive implication of users in some contexts, for instance, in a museum. However, full body gestures can be stressful for some users in some environments. Specially, if these interactions are performed in the presence of an audience. We have analyzed a spectrum of full body gestures to understand the reaction of the Peripheral audience. Instead of the interactive users, the Peripheral audience is tested against selected gestures.
Average Performance Analysis of the Stochastic Gradient Method for Online PCA

This paper studies the complexity of the stochastic gradient algorithm for PCA when the data are observed in a streaming setting. We also propose an online approach for selecting the learning rate. Simulation experiments confirm the practical relevance of the plain stochastic gradient approach and that drastic improvements can be achieved by learning the learning rate.
A Correlational Study of Digital Literacy Comprehension Toward Students' Writing Originality

This research was aimed to know how far the students’ understanding of digital literacy, to know how high the students’ writing originality, and to know the correlation between students’ understanding of digital literacy and students’ writing originality. Digital literacy is someone’s capability to use the digital device, receive, communicate process, evaluate, and share the information cognitively and technically. Originality is emphasized in writing because it is used as the measurement of the writers’ idea and creativity in writing. This research used correlational study to check the correlation between variable. The population of this research was the 2015th generation of English Education Department Students of IAIN Salatiga. It used cluster random sampling to decide the sample of this research. Class A was used as the sample of the research that consisted of 32 students. The data that was collected was analyzed using SPSS 20. Based on the data analysis, it resulted that r(count) 0.83 &gt; r (table) 0.349. From the result, it can be concluded that there was positive perfect correlation between digital literacy understandings with student’s writing originality
Towards Lossless Encoding of Sentences

A lot of work has been done in the field of image compression via machine learning, but not much attention has been given to the compression of natural language.Compressing text into lossless representations while making features easily retrievable is not a trivial task, yet has huge benefits.Most methods designed to produce feature rich sentence embeddings focus solely on performing well on downstream tasks and are unable to properly reconstruct the original sequence from the learned embedding.In this work, we propose a near lossless method for encoding long sequences of texts as well as all of their sub-sequences into feature rich representations.We test our method on sentiment analysis and show good performance across all sub-sentence and sentence embeddings.
Interjections, phonetics, and the body

Emotive interjections are normally associated with the spoken language, but in this article, I will investigate the use of two interjections, åh ‘oh’ and puha ‘whew’ in written communication on Facebook – on the two Danish Facebook groups built around the illness and death of two young children in 2015: “Fighting for Magnus (Miv)” and “Commemorative site for Lærke Rønde Timm”. Interjections are understood as affective expressions because they reflect some of the bodily reactions and participatory investment of the followers of the two groups. The main argument in the article is that the participants write interjections as a way to deal with affective extraordinary experiences. Through phonetic analysis the interjections are seen as bodily felt and triggered reaction.&#x0D;
gk: An R Package for the g-and-k and Generalised g-and-h Distributions

The g-and-k and (generalised) g-and-h distributions are flexible univariate distributions which can model highly skewed or heavy tailed data through only four parameters: location and scale, and two shape parameters influencing the skewness and kurtosis.These distributions have the unusual property that they are defined through their quantile function (inverse cumulative distribution function) and their density is unavailable in closed form, which makes parameter inference complicated.This paper presents the gk R package to work with these distributions.It provides the usual distribution functions and several algorithms for inference of independent identically distributed data, including the finite difference stochastic approximation method, which has not been used before for this problem.