Reviewerly/exHarmony huggingface dataset:
History-Driven Fix for Code Quality Issues

To ensure the internal code quality of contributions in open source software (OSS) communities, static analysis tools (e.g. Code Climate and SonarQube) have been integrated into the modern pull-based workflow for detecting code quality issues (CQIs). Automated CQI fixing is conducive to improve the efficiency of converging massive contributions. In this paper, we propose a history-driven approach to automatically fix CQIs utilizing the fixing knowledge mined from the change history in the code repositories. We collected 5,047,678 CQI isstances, 31,013 fixes of the CQIs that were detected by SonarQube from 206 GitHub projects and mined 68 common Fix Patterns for 56 CQI types. Evaluated by fixing the unfix CQI Instances, we find that the average correctness of our approach is 80% in top 1 fix patch and 69.17% of Top 5. We further conducted a live study by sending CQI's fix patches to GitHub projects. The results show that developers approved or merged 11 of 17 patches.
Robotic Search and Rescue through In-Pipe Movement

So far, we have been engaged in the research and development of various kinds of robots that could be applied to in-pipe inspections that existing methods (screwdrive type, parallel multi-modular type, and articulated wheeled type) cannot perform.In this chapter, we categorized each in-pipe inspection robot depending on its configuration and structure, which includes the design of the propulsive mechanism, steering mechanism, stretching mechanism, and the locations of the wheel and joint axes.On the basis of this classification and from a developer's point of view, we also discussed the various kinds of robots that we have developed, along with their advantages and disadvantages.
User Interface for Turkish Word Network KeNet

Word networks are graphs that represent relations between literals by constructing synonym sets and connecting these sets via various semantic relations. WordNet was constructed in 1990 for English, which is the most popular word network in natural language processing, whereas, KeNet was proposed in 2018, which is the most comprehensive network for Turkish. To the best of our knowledge, there is no user interface for KeNet, which contains 80000 synonym sets and 25 different semantic relations. In this work, we present a user interface, which enables traversing synonym sets in KeNet via semantic relations. Thanks to this interface, KeNet can be searched for a literal and can be traversed using the relations such as hypernym/hyponym and holonym/meronym. Moreover, for any synonym set, ID of the corresponding WordNet synonym set can be seen, if exists and this synonym set can be viewed in the WordNet's web page.
Assisting viewpoint to understand own posture as an avatar in-situation

Understanding the correct self-posture is known to improve the performance of motor skills in sports, dance, ballet, walking, and running. However, it is difficult to understand the accurate self-posture and move one's body as intended for imitation or learning, especially in-situation. Based on previous physiological research, we herein propose the addition of a new assisting viewpoint to the human body interface for understanding the correct self-posture without interrupting the training process. This new viewpoint enables the users to see their current posture as a three-dimensional skeletal image that is an avatar which synchronizes with the owner's movements. The Optical See-Through Head Mounted Display (OST-HMD) and Full-Body Motion Capture (MoCap) are used for creating the proposed viewpoint. The position and the angle of view of an avatar is determined by the rotation of the user's head interactively. Moreover, visualizing the avatar's trail helps users to understand how to moved their own body correctly. We conducted several experiments to confirm the avatar system's validity and the availability for an expert athlete. Our results showed that the proposed viewpoint was successful in enabling the user to understand the accurate self-posture in-situation without occurring interruption.
Deep Learning:Neural Network, Optimizing Method and Libraries Review

Deep learning is received special attention in the last decade following the increasing popularity of artificial intelligence. Famous universities and technical companies are launching their own deep learning libraries. In this paper, we review basic knowledge of deep learning. Then we introduce some of the most famous libraries and make some comparisons among these libraries.
Unsupervised Routine Discovery in Egocentric Photo-Streams

The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people.
When the Attacker Knows a Lot: The GAGA Graph Anonymizer

When releasing graph data (e.g., social network) to public or third parties, data privacy becomes a major concern. It has been shown that state-of-the-art graph anonymization techniques suffer from a lack of strong defense against De-Anonymization (DA) attacks mostly because of the bias towards utility preservation. In this paper, we propose GAGA, an Efficient Genetic Algorithm for Graph Anonymization, that simultaneously delivers high anonymization and utility preservation. To address the vulnerability against DA attacks especially when the adversary can re-identify the victim not only based on some information about the neighbors of a victim but also some knowledge on the structure of the neighbors of the victim’s neighbors, GAGA puts the concept of k(d)-neighborhood-anonymity into action by developing the first general algorithm for any d distance neighborhood. GAGA also addresses the challenge of applying minimum number of changes to the original graph to preserve data utilities via an effective and efficient genetic algorithm. Results of our evaluation show that GAGA anonymizes the graphs in a way that it is more resistant to modern DA attacks than existing techniques – GAGA (with d = 3) improves the defense against DA techniques by reducing the DA rate by at least a factor of 2.7 $$\times $$ in comparison to the baseline. At the same time it preserves the data utilities to a very high degree – it is the best technique for preserving 11 out of 16 utilities. Finally, GAGA provides application-oriented level of control to users via different tunable parameters.
Analysis of National Competency Standards(NCS)-based Curriculums of Fisheries and Maritime High School

The purpose of this study is to provide data that could help fisheries & maritime high schools effectively manage the NCS-based curriculum considering the problems that may arise when organizing and operating the curriculum. In order to complete the purpose, NCS-based curriculum of 11 fisheries & maritime high schools nationwide were analyzed. The Fisheries and Maritime High Schools restructured departments in the order of ship & navigation, agriculture & forestry & fisheries & Ocean, food processing , and machinery of the NCS-based curriculum. The total units of specialized courses were generally similar but, practical courses are organized lower than basic courses except for some schools. The conclusions and implications based on the results of this study are as follows: First, it is necessary to organize and operate the curriculum in consideration of the fusion of two or more jobs. Second, subsequent research, such as a complete survey or qualitative research, is required to find out the exact facts, problems and improvements in the experiences of students and teachers. Third, it is necessary to reflect the process-oriented evaluation on performance so that the education and on-the-job skills can be unified.
On the Expressivity and Applicability of Model Representation Formalisms

A number of first-order calculi employ an explicit model representation formalism in support of non-redundant inferences and for detecting satisfiability. Many of these formalisms can represent infinite Herbrand models. The first-order fragment of monadic, shallow, linear, Horn (MSLH) clauses, is such a formalism used in the approximation refinement calculus (AR). Our first result is a finite model property for MSLH clause sets. Therefore, MSLH clause sets cannot represent models of clause sets with inherently infinite models. Through a translation to tree automata, we further show that this limitation also applies to the linear fragments of implicit generalizations, which is the formalism used in the model-evolution calculus (ME), to atoms with disequality constraints, the formalisms used in the non-redundant clause learning calculus (NRCL), and to atoms with membership constraints, a formalism used for example in decision procedures for algebraic data types. Although these formalisms cannot represent models of clause sets with inherently infinite models, through an additional approximation step they can. This is our second main result. For clause sets including the definition of an equivalence relation with the help of an additional, novel approximation, called reflexive relation splitting, the approximation refinement calculus can automatically show satisfiability through the MSLH clause set formalism.
Unsupervised Domain-Adaptive Person Re-Identification Based on Attributes

Pedestrian attributes, e. g., hair length, clothes type and color, locally describe the semantic appearance of a person. Training person re-identification (ReID) algorithms under the supervision of such attributes have proven to be effective in extracting local features which are important for ReID. Unlike person identity, attributes are consistent across different domains (or datasets). However, most of ReID datasets lack attribute annotations. On the other hand, there are several datasets labeled with sufficient attributes for the case of pedestrian attribute recognition. Exploiting such data for ReID purpose can be a way to alleviate the shortage of attribute annotations in ReID case. In this work, an unsupervised domain adaptive ReID feature learning framework is proposed to make full use of attribute annotations. We propose to transfer attribute-related features from their original domain to the ReID one: to this end, we introduce an adversarial discriminative domain adaptation method in order to learn domain invariant features for encoding semantic attributes. Experiments on three large-scale datasets validate the effectiveness of the proposed ReID framework.
MoRTy: Unsupervised Learning of Task-specialized Word Embeddings by Autoencoding

Word embeddings have undoubtedly revolutionized NLP.However, pre-trained embeddings do not always work for a specific task (or set of tasks), particularly in limited resource setups.We introduce a simple yet effective, self-supervised post-processing method that constructs task-specialized word representations by picking from a menu of reconstructing transformations to yield improved end-task performance (MORTY).The method is complementary to recent state-ofthe-art approaches to inductive transfer via fine-tuning, and forgoes costly model architectures and annotation.We evaluate MORTY on a broad range of setups, including different word embedding methods, corpus sizes and end-task semantics.Finally, we provide a surprisingly simple recipe to obtain specialized embeddings that better fit end-tasks.
Named entity recognition for Hindi language: A survey

Named Entity Recognition (NER) is an important task that is used as a pre-processing step in various natural language processing (NLP) applications. It is useful as it mostly increases the performance of NLP applications. A large number of researchers are focusing this problem using various techniques such as rule based, machine learning based and hybrid approaches. In the recent times, deep learning algorithms are also emerging for developing NER models. It is very challenging to build NER system for Hindi language in particular because it is an ambiguous, morphologically rich and resource scarce language. In this paper, we present a state-of-the-art survey covering various approaches of NER for mainly Hindi Language.
On the Possibility of Quantum Simulation of Electromagnetic Structures

The prospects of quantum simulation and design of complex electromagnetic structures is discussed. Quantum superposition and entanglement has the potential of high parallelism and efficiency in CAD. The transmission line matrix (TLM) scheme for EM field simulation provides a basis for a quantum algorithm for EM field modeling. The implementation and problems to be solved are discussed.
A Josephson Traveling Wave Parametric Amplifier for Quantum Coherent Signal Processing

Josephson traveling wave parametric amplifiers have enabled ultra-sensitive detection of microwave photons in radio astronomy and real-time readout of superconducting quantum bits. To achieve a large gain, the interaction time with the non-linearity of the amplifier needs to be maximized. Traveling wave parametric amplifiers exhibit long interaction times by providing long propagation paths, enabling a greater gain and higher bandwidths than other microwave parametric amplifiers. In the case of the degenerate Josephson parametric amplifier (DJPA) below threshold of parametric oscillation, a squeezed vacuum state can be generated, and above threshold a second bifurcation point exists, where the device generates amplitude squeezed radiation.
A dynamic logic for learning theory

Building on previous work [4], [5] that bridged Formal Learning Theory and Dynamic Epistemic Logic in a topological setting, we introduce a Dynamic Logic for Learning Theory (DLLT), extending Subset Space Logics [18], [10] with dynamic observation modalities [o]φ, as well as with a learning operator L(o→), which encodes the learner's conjecture after observing a finite sequence of data o→. We completely axiomatise DLLT, study its expressivity and use it to characterise various notions of knowledge, belief, and learning.
A Conceptual Approach to Enhance the Well-Being of Elderly People

The number of elderly people living alone is increasing. Consequently, a lot of research works have been addressing this issue in order to propose solutions that can enhance the quality of life of elderly people. Most of them have been concerned in dealing with objective issues such as forgetfulness or detecting falls. In this paper, we propose a conceptual approach of a system that intends to enhance the daily sense of user’s well-being. For that, our proposal consists in a system that works as a social network and a smartwatch application that works unobtrusively and collects the user’s physiological data. In addition, we debate how important features such as to detect user’s affective states and to potentiate user’s memory could be implemented. Our study shows that there are still some important limitations which affect the success of applications built in the context of elderly care and which are mostly related with accuracy and usability of this kind of system. However, we believe that with our approach we will be able to address some of those limitations and define a system that can enhance the well-being of elderly people and improve their cognitive capabilities.
Variational Deep Embedding with Regularized Student-t Mixture Model

This paper proposes a new motion classifier using variational deep embedding with regularized student-t mixture model as prior, named VaDE-RT, to improve robustness to outliers while maintaining continuity in latent space. Normal VaDE uses Gaussian mixture model, which is sensitive to outliers, and furthermore, all the components of mixture model can freely move in the latent space, which would lose the continuity in the latent space. In contrast, VaDE-RT aims to exploit a heavy-tailed feature of student-t distribution for robustness, and regularize the mixture model to standard normal distribution, which is employed in a standard variational autoencoder as prior. To do so, three reasonable approximations for (i) reparameterization trick, (ii) Kullback-Leibler (KL) divergence between student-t distributions, and (iii) KL divergence of the mixture model, are introduced to make backpropagation in VaDE-RT possible. As a result, VaDE-RT outperforms the original VaDE and a simple deep-learning-based classifier in terms of classification accuracy. In addition, VaDE-RT yields both continuity and natural topology of clusters in the latent space, which make robot control adaptive smoothly.
Composite Quantile Regression Long Short-Term Memory Network

Based on quantile long short-term memory (Q-LSTM), we consider the comprehensive utilization of multiple quantiles, proposing a simultaneous estimation version of Q-LSTM, composite quantile regression LSTM (CQR-LSTM). The method simultaneously estimates multiple quantile functions instead of estimating them separately. It makes sense that simultaneous estimation allows multiple quantiles to share strength among them to get better predictions. Furthermore, we also propose a novel approach, noncrossing composite quantile regression LSTM (NCQR-LSTM), to solve the quantile crossing problem. This method uses an indirect way as follows. Instead of estimating multiple quantiles directly, we estimate the intervals between adjacent quantiles. Since the intervals are guaranteed to be positive by using exponential functions, this completely avoids the problem of quantile crossing. Compared with the commonly used constraint methods for solving the quantile crossing problem, this indirect method makes model optimization easier and more suitable for deep learning. Experiments on a real wind speed dataset show that our methods improve the probabilistic prediction performance and reduce the training cost. In addition, our methods are simple to implement and highly scalable.
Siamese Dilated Inception Hashing With Intra-Group Correlation Enhancement for Image Retrieval

For large-scale image retrieval, hashing has been extensively explored in approximate nearest neighbor search methods due to its low storage and high computational efficiency. With the development of deep learning, deep hashing methods have made great progress in image retrieval. Most existing deep hashing methods cannot fully consider the intra-group correlation of hash codes, which leads to the correlation decrease problem of similar hash codes and ultimately affects the retrieval results. In this article, we propose an end-to-end siamese dilated inception hashing (SDIH) method that takes full advantage of multi-scale contextual information and category-level semantics to enhance the intra-group correlation of hash codes for hash codes learning. First, a novel siamese inception dilated network architecture is presented to generate hash codes with the intra-group correlation enhancement by exploiting multi-scale contextual information and category-level semantics simultaneously. Second, we propose a new regularized term, which can force the continuous values to approximate discrete values in hash codes learning and eventually reduces the discrepancy between the Hamming distance and the Euclidean distance. Finally, experimental results in five public data sets demonstrate that SDIH can outperform other state-of-the-art hashing algorithms.
Robustness of Deep LSTM Networks in Freehand Gesture Recognition

We present an analysis of the robustness of deep LSTM networks for freehand gesture recognition against temporal shifts of the performed gesture w.r.t. the “temporal receptive field”. Such shifts inevitably occur when not only the gesture type but also its onset needs to be determined from sensor data, and it is imperative that recognizers be as invariant as possible to this effect which we term gesture onset variability. Based on a real-world hand gesture classification task we find that LSTM networks are very sensitive to this type of variability, which we confirm by creating a synthetic sequence classification task of similar dimensionality. Lastly, we show that including gesture onset variability in the training data by a simple data augmentation strategy leads to a high robustness against all tested effects, so we conclude that LSTM networks can be considered good candidates for real-time and real-world gesture recognition.
Multi-Sensor Depth Fusion Framework for Real-Time 3D Reconstruction

For autonomous robots, 3D perception of environment is an essential tool, which can be used to achieve better navigation in an obstacle rich environment. This understanding requires a huge amount of computational resources; therefore, the real-time 3D reconstruction of surrounding environment has become a topic of interest for countless researchers in the recent past. Generally, for the outdoor 3D models, stereo cameras and laser depth measuring sensors are employed. The data collected through the laser ranging sensors is relatively accurate but sparse in nature. In this paper, we propose a novel mechanism for the incremental fusion of this sparse data to the dense but limited ranged data provided by the stereo cameras, to produce accurate dense depth maps in real-time over a resource limited mobile computing device. Evaluation of the proposed method shows that it outperforms the state-of-the-art reconstruction frameworks which only utilizes depth information from a single source.
Optimal Feature Selection for Activity Recognition based on Ant Colony Algorithm

Feature selection is an important task which can significantly affect the performance of a pattern recognition system. In this paper, we present a feature selection algorithm based on Ant Colony Optimization (ACO) for human activity recognition. The algorithm incorporates the classification performance into the state transition rule, and uses the pheromone of the ant colony to find the optimal feature set, then selects feature set with small size and high classification accuracy. We perform experiments on human activity dataset using four classifiers, Naive Bayes, Random Forest, K-Nearest Neighbor and Support Vector Machine(NB, RF, KNN and SVM). The results show that the proposed method can improve the recognition accuracy, and it is comparable to other feature selection methods.
Augmentation of Images through DCGANs

Now-a-days, extending images has become a challenging task to implement. Many of algorithms like convolution neural networks (CNN), Generative Adversarial Network (GAN) are used to fill out the image spaces. But the challenge arrives to guess or make appropriate assumption of image extended borders. We used Deep Convolution Generative Adversarial Network(DCGAN) over GAN and CNN to implement it.[1], [2] GAN's are implemented to guess the space in image by generating fake examples using generator and decides using discriminator which determines how much the image is real. DCGAN is improved version of GAN which generates spatial correlations [5].