Reviewerly/exHarmony huggingface dataset:
A New Approach to Color Edge Detection by Means of Transforming RGB Images into an 8-Dimension Color Space

In this paper, different ways of aggregating color information in edge extraction task are explored. One of these approaches is based on a previous transformation of the RGB images into a new color space of 8 dimensions. This increases the number of dimensions, making differences between colors easier to detect, and in this way improving the performance of edge detection task. Sobel and Canny algorithms are employed over the RGB images and its transformed version of 8 dimensions -that we have named Super8 image-. The set of employed images is the one from Berkeley's dataset. In order to evaluate the performance, precision, recall and F measure are computed. The way of aggregating the color information is showed to be relevant. For some well-known algorithms, the new 8 dimension color space overtakes RGB's for edge detection problem.
Democratization of Smart Contracts: A Prototype for Automated Contract Generation

The blockchain technology gets attention because of its special characteristics: safety, immutability, transparency, and no need for middlemen. However, not only cryptocurrencies are discussed. Smart contracts are considered as promising application implemented with blockchain technology. Nevertheless, smart contracts are very difficult to implement and are even challenging for experienced developers. In order to allow anyone to create smart contracts, users should be able to read and interpret them. Therefore, we created a prototype that allows to generate smart contracts, and therefore, increase the ease of use.
An efficient algorithm to compute the X-ray transform

We propose a new algorithm to compute the X-ray transform of an image represented by unit (pixel/voxel) basis functions. The fundamental issue is equivalently calculating the intersection lengths of the ray with associated units. For any given ray, we first derive the sufficient and necessary condition for non-vanishing intersectability. By this condition, we then distinguish the units that produce valid intersections with the ray. Only for those units rather than all the individuals, we calculate the intersection lengths by the obtained analytic formula. The proposed algorithm is adapted to 2D/3D parallel beam and 2D fan beam. Particularly, we derive the transformation formulas and generalize the algorithm to 3D circular and helical cone beams. Moreover, we discuss the intrinsic ambiguities of the problem itself, and present a solution. The algorithm not only possesses the adaptability with regard to the center position, scale and size of the image, but also is suited to parallelize with optimality. The comparison study demonstrates the proposed algorithm is fast, more complete, and is more flexible with respect to different scanning geometries and different basis functions. Finally, we validate the correctness of the algorithm by the aforementioned scanning geometries.
FastIsomapVis: A Novel Approach for Nonlinear Manifold Learning

The classical Isomap is the most common unsupervised nonlinear manifold method and widely being used in visualizations and dimension reductions. However, when it applied to real-world datasets, it shows shortcomings for the shortest path between all pairs of data points, which are based on the nearest neighborhood G graph via the Dijkstra algorithm, which makes it a very time-consuming step. The other critical problem is the classical Isomap has a lack of topological stability on the nearest neighborhood G graph. In this paper, we propose a novel technique called the FastIsomapVis for the above problems of the classical Isomap. The FastIsomapVis uses hierarchal divide, conquer, and combine approach through two algorithms, which are randomized division tree (KD-tree) and Dijkstra Buckets Double (DKD). The primary aim of the FastIsomapVis is to increase the efficiency and accuracy of the graph. This research paper focuses on transforming the high dimensional datasets into a low dimensional Isomap visualization. The FastIsomapVis makes it easy to construct an accurate K nearest neighborhood G graph and scale high dimensional data points into low dimensional space. Our proposed method is compared to the classical Isomap to verify its effectiveness and provide highly authentic results of the high dimensional datasets. The finding of the current study shows that our proposed method is much fastened than classical Isomap.
New Digital Realities – Blending our Reality with Virtuality

Abstract New digital reality as a spectrum of technologies and experiences that digitally simulate and extend reality in one way or another across different human senses has received considerable attention in recent years. In particular, we have witnessed great advances in mixed reality (MR) technologies, such as Virtual Reality (VR) and Augmented Reality (AR) technology, which provide enormous potential for application domains like training, simulation, education, entertainment, health, and sports. However, also other forms of digitally enhanced reality (XR) supports novel forms of immersion and experiences while generating, visualizing and interacting with digital content either displayed in fully-immersive virtual environments or superimposed into our view of the real world, and will significantly change the way we work, travel, play, and communicate. Consequently, we face dramatic changes in interactive media creation, access, and perception. In this special issue, we solicit work that addresses novel interaction design, interfaces, and implementation of new digital reality in which our reality is blended with the virtuality with a focus on users’ needs, joy, and visions.
A Hybrid Data Preprocessing Technique based on Maximum Likelihood Logistic Regression with Filtering for Enhancing Software Defect Prediction

Software Defect Prediction (SDP) is critical to ensure product reliability and customer satisfaction. Many studies conducted to predict defective modules in the software development process applied historical software defect data mined from online depositories. However, data obtained from online depositories have problems associated with high dimensionality and noisy. High dimensionality comes when the data contains a huge number of independent attributes for developing models of classification. Noisy data refer to those located deep inside the region of different class other than their own. The adverse effect of these data problems for the prediction performance of most statistical learning methodologies is self-evident. To this end, we suggest a hybrid preprocessing approach in which iterative partitioning filtering is conducted prior to feature selection (FS) and the technique is validated using selected FS methods including Chisquare (CS), information gain (IG), gain ratio (GR), relief (RF) and symmetric uncertainty (SU) and maximum likelihood logistic regression (MLLR). It is important to note that the idea of MLLR for FS in the context of software defect prediction have not been fully investigated despite the potential usefulness. For the combined use of variable selection and noise filtering (NF), two cases are considered: (1) variable selection technique applied on original dataset and relevant variables selected from original data (2) variable selection approach applied on a cleaned data and variables selected from cleaned data. We apply this method on six software defect datasets in their clean and noisy form to select useful features for classification modeling of Software Defect Prediction (SDP). The Random Forest (RaF), K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) classifiers are tested on the selected feature. The performance of the models captured using three indicators are compared. The results confirm that the MLLR can be useful in selecting the optimal feature subset for more accurate prediction of defective modules in the software development process.
Heuristic and Neural Network Based Prediction of Project-Specific API Member Access

Code completion is to predict the rest of a statement a developer is typing. Although advanced code completion approaches have greatly improved the accuracy of code completion in modern IDEs, it remains challenging to predict project-specific API method invocations or field accesses because little knowledge about such elements could be learned in advance. To this end, in this paper we propose an accurate approach called HeeNAMA to suggesting the next project-specific API member access. HeeNAMA focuses on a specific but common case of code completion: suggesting the following member access whenever a project-specific API instance is followed by a dot on the right hand side of an assignment. By focusing on such a specific case, HeeNAMA can take full advantages of the context of the code completion, including the type of the left hand side expression of the assignment, the identifier on the left hand side, the type of the base instance, and similar assignments typed in before. All such information together enables highly accurate code completion. Given an incomplete assignment, HeeNAMA generates the initial candidate set according to the type of the base instance, and excludes those candidates that are not type compatible with the left hand side of the assignment. If the enclosing project contains assignments highly similar to the incomplete assignment, it makes suggestions based on such assignments. Otherwise, it selects the one from the initial candidate set that has the greatest lexical similarity with the left hand side of the assignment. Finally, it employs a neural network to filter out risky predictions, which guarantees high precision. Evaluation results on open-source applications suggest that compared to the state-of-the-art approaches and the state-of-the-practice tools HeeNAMA improves precision and recall by 70.68 and 25.23 percent, relatively.
Community Mining and Cross-Community Discovery in Online Social Networks

This paper presents a new approach for cross community mining and discovery using topic modeling. Our approach identifies automatically the communities in a dataset in an unsupervised way and extracts relationships between these communities. These relationships represent the interaction between communities which helps to identify the cross communities and the shared information between them. Our approach consists of a two layer model based on a statistical framework serving as knowledge discovery tools at different levels of analysis. In the first level communities are discovered using a topic model based method, then cross communities are identified using a statistical measure based on the KL divergence. We empirically demonstrate through extensive experiments using a real social network dataset, that our proposed approach discovers communities and relationships between them.
Fine-Tuned Transformer Model for Sentiment Analysis

Sentiment analysis is one of important natural language processing (NLP) tasks. The transformer-based language models have become new baseline in sentiment analysis tasks in recent years. These models trained by efficient unsupervised learning methods have pushed accuracy of sentiment analysis tasks to a higher level. In this paper, we propose a set of novel transformation methods to tackle a sentiment analysis task. Specifically, we design four novel transformations in our transformer-models. For comparison purpose, the new data manipulation method is implemented on two language models (BERT and RoBERTa) separately. The nonlinear transformation method is evaluated on the large-scale real world dataset (Weibo sentiment analysis dataset ( https://www.datafountain.cn/competitions/423 )). The experiment results demonstrate the superiority of the proposed method.
Improved Performance of GANs via Integrating Gradient Penalty with Spectral Normalization

Despite the growing prominence of generative adversarial networks (GANs), improving the performance of GANs is still a challenging problem. To this end, a combination method for training GANs is proposed by coupling spectral normalization with a zero-centered gradient penalty technique (the penalty is done on the inner function of Sigmoid function of discriminator). Particularly, the proposed method not only overcomes the limitations of networks convergence and training instability but also alleviates the mode collapse behavior in GANs. Experimentally, the improved method becomes more competitive compared with some of recent methods on several datasets.
Image Processing: What, How and Future

There is a well-known saying: an image is worth more than a thousand words. The wonders of this proverb are very well visible in our day-to-day life. In this paper, we have presented the current and trending applications of imaging in our day-to-day life having a wide scope of research. The digital image processing has revolutionized the field of technical endeavor and there is a lot more yet to be researched in this field. A huge amount of work can be carried out in the area of image processing. This paper summarizes the fundamental steps involved in digital image processing and focuses on the applicative areas of image processing in which research can be carried out for the betterment and quality improvement of human life.
EcoBoost: Efficient Bootstrapping for Confidential Transactions

One of the major challenges of cryptocurrencies is the lack of confidentiality, Without confidentiality, the amount of each transaction on a blockchain is not private. While confidential transactions can hide transaction amounts, its storage overhead is extremely high (e.g., 10~20X). This substantial overhead significantly raises users' overheads in bootstrapping and impedes new nodes, especially for resource-limited devices.We devise a new method, referred to as EcoBoost, to advance the efficiency of bootstrapping for blockchains supporting confidential transactions. Specifically, we harness random sampling to verify the correctness of confidential transactions with high probability. As a result, the verification overhead is sublinear with regard to the number of transactions (compared to linear in current solutions). Our experiment results show that, if Bitcoin implements confidential transactions, EcoBoost can save over 86% storage and verification time in bootstrapping but still detect bogus transactions with a probability of 99%.
Fulfillment and Responsiveness on Online Travel Agencies Using Multiclass Classification

In recent years, Online Travel Agencies (OTA) is widely used by people due to its simplicity and efficiency. Tight competition between industries makes companies must pay attention to the quality of their services since it is capable to enhance customer satisfaction. To evaluate its service, the company needs to comprehend their position as OTA providers. Users' opinion in social media is essential for recognizing the company performances. In this case, sentiment analysis and multiclass classification methods help the company to understand their service quality specifically. As a case study, we use the most popular Online Travel Agencies (OTA) providers in Indonesia: Traveloka, Tiket.com, and Pegipegi. Based on our criterion, we examine the fulfillment and responsiveness dimensions of these three OTA providers. We apply Naïve Bayes Classifier (NB) model to classify users' opinions. This model has accuracy around 75-85% for the three OTA providers. The result reveals that Pegipegi obtains better service quality with 57% positive and 43% negative sentiment than Traveloka and Tiket.com with 56% positive and 44% negative. The overall result shows general topics of fulfillment and responsiveness dimensions are related to the ticket availability and customer service performances.
Generative Adversarial Networks for Video Prediction with Action Control

The ability of predicting future frames in video sequences, known as video prediction, is an appealing yet challenging task in computer vision. This task requires an in-depth representation of video sequences and a deep understanding of real-word causal rules. Existing approaches for tackling the video prediction problem can be classified into two categories: deterministic and stochastic methods. Deterministic methods lack the ability of generating possible future frames and often yield blurry predictions. On the other hand, although current stochastic approaches can predict possible future frames, their models lack the ability of action control in the sense that they cannot generate the desired future frames conditioned on a specific action. In this paper, we propose new generative adversarial networks (GANs) for stochastic video prediction. Our framework, called VPGAN, employs an adversarial inference model and a cycle-consistency loss function to empower the framework to obtain more accurate predictions. In addition, we incorporate a conformal mapping network structure into VPGAN to enable action control for generating desirable future frames. In this way, VPGAN is able to produce fake videos of an object moving along a specific direction. Experimental results show that a combination of VPGAN and pre-trained image segmentation models outperforms existing stochastic video prediction methods.
BSRNG: A High Throughput Parallel BitSliced Approach for Random Number Generators

In this work, a high throughput method for generating high-quality Pseudo-Random Numbers using the bitslicing technique is proposed. In such a technique, instead of the conventional row-major data representation, column-major data representation is employed, which allows the bitslicing implementation to take full advantage of all the available datapath of the hardware platform. By employing this data representation as building blocks of algorithms, we showcase the capability and scalability of our proposed method in various PRNG methods in the category of block and stream ciphers. The LFSR-based (Linear Feedback Shift Register) nature of the PRNG in our implementation perfectly suits the GPU’s many-core structure due to its register oriented architecture. In the proposed SIMD vectorized GPU implementation, each GPU thread can generate several 32 pseudo-random bits in each LFSR clock cycle. We then compare our implementation with some of the most significant PRNGs that display a satisfactory performance throughput and randomness criteria. The proposed implementation successfully passes the NIST test for statistical randomness and bit-wise correlation criteria. For computer-based PRNG and the optical solutions in terms of performance and performance per cost, this technique is efficient while maintaining an acceptable randomness measure. Our highest performance among all of the implemented CPRNGs with the proposed method is achieved by the MICKEY 2.0 algorithm, which shows 40% improvement over state of the art NVIDIA’s proprietary high-performance PRNG, cuRAND library, achieving 2.72 Tb/s of throughput on the affordable NVIDIA GTX 2080 Ti.
Approximating morphological operators with part-based representations learned by asymmetric auto-encoders

Abstract This paper addresses the issue of building a part-based representation of a dataset of images. More precisely, we look for a non-negative, sparse decomposition of the images on a reduced set of atoms, in order to unveil a morphological and explainable structure of the data. Additionally, we want this decomposition to be computed online for any new sample that is not part of the initial dataset. Therefore, our solution relies on a sparse, non-negative auto-encoder, where the encoder is deep (for accuracy) and the decoder shallow (for explainability). This method compares favorably to the state-of-the-art online methods on two benchmark datasets (MNIST and Fashion MNIST) and on a hyperspectral image, according to classical evaluation measures and to a new one we introduce, based on the equivariance of the representation to morphological operators.
A Dynamic Answering Path Based Fusion Model for KGQA

The Knowledge Graph Question Answering (KGQA) task is useful for information retrieval systems, intelligent customer service systems, etc., which has attracted the attention of a large number of researchers. Although the performance of KGQA has been further improved by introducing the Deep Learning models, there are still some difficulties to be solved, such as the representation of questions and answers, the efficient construction way of candidate path set, etc. In this paper, we propose a complete approach for KGQA task. Firstly, we devise a novel candidate path generation process, which effectively improves computation performance by reducing the number of candidate paths corresponding to a question and at the same time guarantees the accuracy of results. Secondly, considering the textual expression diversity of questions and stochastic of candidate paths, we present four models to learn semantic features of Chinese sequence with different focuses. Finally, in order to combine the advantages of each presented model, we propose a dedicated fusion policy which can get the most suitable path from the path set predicted by our presented models. We conduct experiments on Chinese Knowledge Base Question Answering (CKBQA) dataset. Experiment results show that our approach achieves better performance than the best one published in CCKS2019 competition.
Measurement and refactoring for package structure based on complex network

Abstract Software structure is the backbone for software systems. During the long time of software evolution, it is gradually weakened by continuous code modification and expansion driven by new requirements. Therefore, measuring software and refactoring codes are necessary to keep software structure stable and clean. In this paper, we propose two metrics of cohesion and coupling to characterize package structure. We consider not only the dependencies of intra-package and inter-package, but also the backward dependencies of inter-package. The two metrics are proved theoretically that they are satisfied with Briand’s four properties. Based on these metrics, a refactoring algorithm is presented to improve the quality of package structure. Through tests on ten open source software systems, the experiment result shows our metrics can measure software structure correctly and improve codes to fit for the rule of high cohesion and low coupling.
Extending Sliding-Step Importance Weighting from Supervised Learning to Reinforcement Learning

Stochastic gradient descent (SGD) has been in the center of many advances in modern machine learning. SGD processes examples sequentially, updating a weight vector in the direction that would most reduce the loss for that example. In many applications, some examples are more important than others and, to capture this, each example is given a non-negative weight that modulates its impact. Unfortunately, if the importance weights are highly variable they can greatly exacerbate the difficulty of setting the step-size parameter of SGD. To ease this difficulty, Karampatziakis and Langford [6] developed a class of elegant algorithms that are much more robust in the face of highly variable importance weights in supervised learning. In this paper we extend their idea, which we call “sliding step”, to reinforcement learning, where importance weighting can be particularly variable due to the importance sampling involved in off-policy learning algorithms. We compare two alternative ways of doing the extension in the linear function approximation setting, then introduce specific sliding-step versions of the TD(0) and Emphatic TD(0) learning algorithms. We prove the convergence of our algorithms and demonstrate their effectiveness on both on-policy and off-policy problems. Overall, our new algorithms appear to be effective in bringing the robustness of the sliding-step technique from supervised learning to reinforcement learning.