Reviewerly/exHarmony huggingface dataset:
Make Full Use of Priors

Multi-view video plus depth (MVD) is the promising and widely adopted data representation for future 3D visual applications and interactive media. However, compression distortions on depth videos impede the development of such applications, and filters are crucially needed for the quality enhancement at the terminal side. Cross-view priors can intuitively be involved in filter design, but these priors are also distorted in compression and thus the contribution of them can hardly be considered in previous research. In this article, we propose a cross-view optimized filter for depth map quality enhancement by making full use of inner- and cross-view priors. We dedicate to evaluate the contributions of distorted cross-view priors in filtering the current view of depth, and then both inner- and cross-view priors can be involved in the filter design. Thus, distortions of cross-view priors are not barriers again as before. For the purpose of that, mutual information guided cross-view consistency is designed to evaluate the contributions of cross-view priors from compression distortions of MVD. After that, under the framework of global optimization, both inner- and cross-view priors are modeled and taken to minimize the designed energy function where both data accuracy and spatial smoothness are modeled. The experimental results show that the proposed model outperforms state-of-the-art methods, where 3.289 dB and 0.0407 average gains on peak signal-to-noise ratio and structural similarity metrics can be obtained, respectively. For the subjective evaluations, object details and structure information are recovered in the compressed depth video. We also verify our method via several practical applications, including virtual view synthesis for smooth interaction and point cloud for 3D modeling for accuracy evaluation. In these verifications, the ringing and malposition artifacts on object contours are properly handled for interactive video, and discontinuous object surfaces are restored for 3D modeling. All of these results suggest that compression distortions in MVD can be properly filtered by the proposed model, which provides a promising solution for future bandwidth constrained 3D and interactive visual applications.
An Arabic Grammar Auditor Based on Dependency Grammar

The Arabic language has many complex grammar rules that may seem complicated to the average user or learner. Automatic grammar checking systems can improve the quality of the text, reduce the costs of the proofreading process, and play a role in grammar teaching. This paper presents an initiative toward developing a novel and comprehensive Arabic auditor that can address vowelized texts. We called the “Arabic Grammar Detector” (AGD-أَجِــدْ). AGD was successfully implemented based on a dependency grammar and decision tree classifier model. Its purpose is to extract patterns of grammatical rules from a projective dependency graph in order to designate the appropriate syntax dependencies of a sentence. The current implementation covers almost all regular Arabic grammar rules for nonvowelized texts as well as partially or fully vowelized texts. AGD was evaluated using the Tashkeela corpus. It can detect more than 94% of grammatical errors and hint at their causes and possible corrections.
HFD-SRGAN: Super-Resolution Generative Adversarial Network with High-frequency discriminator

The high-frequencies of images is very important both in keeping the edges and suppressing artifacts. To improve the performance of single image super-resolution (SISR) based on the SRGAN framework, we propose Super-Resolution Generative Adversarial Networks with high-frequency discriminator (HFD- SRGAN) by designing an additional discriminator for image's high-frequencies extracted by wavelets. Based on SRGAN, the image's high frequencies extracted by discrete wavelet transformations (DWT) were then introduced into GAN. Moreover, an additional discriminator for these high frequencies was built. Since the proposed model provides a direct and efficient way to locates and estimates the high frequencies of the reconstruction image, the visual effects of reconstructed the images can be improved with fewer computation costs. Experiments show that HFD-SRGAN has improved the visual effects of SRGAN when using the same generator network as SRGAN. The evaluation results show the performance of our method is equal to the state-of-the-art methods.
A systemic analysis of green computing adoption using genetically evolved fuzzy cognitive map: a Philippine scenario

Purpose This paper aims to propose a new genetically evolved fuzzy cognitive mapping approach as a decision-making framework for analyzing the relationships between the drivers and strategies for green computing adoption. Design/methodology/approach A focus group discussion among stakeholders in the Philippines is used to establish the relationships between the drivers and strategies of green computing adoption. Findings The proposed approach significantly reduces the time complexity for developing the fuzzy cognitive maps and provides a basis for comprehensively clustering drivers and strategies that share similar characteristics. Research limitations/implications This paper’s results provide insights into how the drivers and strategies of green computing adoption facilitate the intention of adopting stakeholders. Moreover, it provides a framework for analyzing structural relationships that exist between factors in a compliant manner. Originality/value To the best of the author’s knowledge, the paper is the first to analyze the drivers and strategies of green computing under a complex systems’ perspective. Moreover, this is the first study to offer lenses in a Philippine scenario.
Optimizing stochastic computing for low latency inference of convolutional neural networks

The appealing property of low area, low power, flexible precision, and high bit error tolerance has made Stochastic Computing (SC) a promising alternative to conventional binary arithmetic for many computation intensive tasks, e.g., convolutional neural networks (CNNs). However, to relieve the intrinsic fluctuation noise in SC, long bit stream is normally required in SC-based CNN accelerators to achieve satisfactory accuracy, which leads to extortionate latency. Although the bit parallel structure of a SC multiplier has been proposed to reduce latency, the resulting extra overhead still considerably degrade the overall efficiency of SC. In this paper, we optimize both the micro-architecture of SC multiply-and-accumulate (MAC) unit and the overall acceleration scheme of CNN accelerator to favor SC. An optimized and scalable SC-MAC unit, which fully utilizes the property of low-discrepancy bit stream, is proposed with adjustable parameters to reduce the latency with minor area increase. For the overall accelerator, the parallel dimensions of SC-based MAC array are extended to reuse hardware resources and improve throughput, since the judiciously chosen loop unrolling strategy can better benefit SC operations. The proposed CNN accelerator with extended SC-MAC array is synthesized and demonstrated using TSMC 28nm CMOS on several representative CNNs, which gains 2× performance speedup, 2.8× energy savings and 15% area reduction compared to state-of-the-art SC based CNN accelerator.
Fair Work Distribution on Permissioned Blockchains: a Mobile Window Based Approach

In permissioned blockchains, a set of identifiable miners are in charge of consolidating the new blocks. The agreement protocol is usually based on a consensus mechanism inspired by a voting Byzantine Fault Tolerance (BFT) mechanism rather than the well-known proof-of-work (PoW). However, PoW provides some benefits with respect to BFT, i.e., it allows the final user to quantify the effort required to change a consolidated transaction without the need of trusting the consortium of miners. In this paper, we address some of the problems that arise from the application of PoW in permissioned blockchains. In fact, in standard PoW, it may be easy for one miner to temporary reach 50% of the total hash power and since mining rewards are usually not expected, the problem of balancing the efforts of the miner becomes crucial. We propose a simple solution based on a sliding window algorithm to address these problems and analyse its effectiveness in terms of security. Additionally, we propose a quantitative model whose solution allows us to assess its capacity of balancing the hash power provided by heterogeneous miners.
Mobile Sensors Path Planning for Cooperative Monitoring of Different Mission Importance Areas

This paper considers an offline path planning problem about cooperative monitoring in different mission importance areas of interest (AOIs) by mobile sensor nodes. Each node is equipped with a radar sensor and limited by maneuver constraints. To evaluate the cooperative surveillance performance, we propose an effective monitoring metric and a revisit metric, which are based on the detection performance of the mobile surveillance system in AOIs during the mission time. Since the formulated optimization problem is high dimensionality and has complex constraints, we propose a solution that contains two parts: 1) an optimization algorithm based on particle swarm optimization (PSO) to reduce the computation load with the constrains satisfied and 2) a path smoothing method based on third-degree B-spline to smooth the optimized paths. Finally, simulation results verify the feasibility and efficiency of the algorithm.
I/O Strength-Aware Credit Scheduler for Virtualized Environments

With the evolution of cloud technology, the number of user applications is increasing, and computational workloads are becoming increasingly diverse and unpredictable. However, cloud data centers still exhibit a low I/O performance because of the scheduling policies employed, which are based on the degree of physical CPU (pCPU) occupancy. Notably, existing scheduling policies cannot guarantee good I/O performance because of the uncertainty of the extent of I/O occurrence and the lack of fine-grained workload classification. To overcome these limitations, we propose ISACS, an I/O strength-aware credit scheduler for virtualized environments. Based on the Credit2 scheduler, ISACS provides a fine-grained workload-aware scheduling technique to mitigate I/O performance degradation in virtualized environments. Further, ISACS uses the event channel mechanism in the virtualization architecture to expand the scope of the scheduling information area and measures the I/O strength of each virtual CPU (vCPU) in the run-queue. Then, ISACS allocates two types of virtual credits for all vCPUs in the run-queue to increase I/O performance and concurrently prevent CPU performance degradation. Finally, through I/O load balancing, ISACS prevents I/O-intensive vCPUs from becoming concentrated on specific cores. Our experiments show that compared with existing virtualization environments, ISACS provides a higher I/O performance with a negligible impact on CPU performance.
Application of the Genetic Algorithm to the Rule Extraction Problem

Artificial Neural Networks are considered as a black box. They are unable to explain its classification decision. Several rule extraction algorithms from trained neural networks have been developed to overcome this problem. The aim is to have a set of rules to explain how ANNs solves a given problem. A global rule extraction algorithm from trained neural networks, based on evolutionary algorithms is presented. The extracted rules are evaluated from three criteria: fidelity, accuracy and comprehensibility. The fidelity indicates how the extracted rules mimic the decision of the trained neural networks. The accuracy is calculated from dataset, it indicates the ability of the rules to satisfy the test data. The comprehensibility designates the number of the extracted rules. The proposed method is evaluated on 03 UCI datasets. A tradeoff between the accuracy, the fidelity and the comprehensibility has been showed. The results of these experiments are presented and compared with existing rule extraction methods. Our proposal achieves a best accuracy and comprehensibility over breast cancer dataset.
Blind atmospheric turbulence deconvolution

A new blind image deconvolution technique is developed for atmospheric turbulence deblurring to overcome limitations of ‘generic’ blind deconvolution algorithms that do not take into account the complicated physics of the turbulence. The originality of the proposed approach relies on an actual physical model, known as the Fried kernel, that quantifies the impact of the atmospheric turbulence on the optical resolution of images. While the original expression of the Fried kernel can seem cumbersome at first sight, the authors show that it can be reparameterised in a much simpler form. This simple expression allows to efficiently embed this kernel in the proposed blind atmospheric turbulence deconvolution (BATUD) algorithm. BATUD is an iterative algorithm that alternately performs deconvolution and estimates the Fried kernel by jointly relying on a Gaussian mixture model prior to natural image patches and controlling for the square Euclidean norm of the Fried kernel. Numerical experiments show that the proposed blind deconvolution algorithm behaves well in different simulated turbulence scenarios, as well as on real images. Not only BATUD outperforms state-of-the-art approaches used in atmospheric turbulence deconvolution in terms of image quality metrics but is also faster.
SPAM: Simplifying Python for Approaching Machine Learning

This WIP paper presents an approach to teaching Python in a first course in machine learning for non-majors. The flexibility of Python enables a skilled programmer to be very expressive, but it can be troublesome to the new student. Students who come to Python from an imperative language often approach the language "non-Pythonically", leading to reliance on traditional programming constructs. We present an alternative approach whereby the concept of functors and monads are introduced early to aid in teaching and learning machine learning.
Lifelong Machine Learning for Regional-Based Image Classification in Open Datasets

Deep Learning algorithms are becoming common in solving different supervised and unsupervised learning problems. Different deep learning algorithms were developed in last decade to solve different learning problems in different domains such as computer vision, speech recognition, machine translation, etc. In the research field of computer vision, it is observed that deep learning has become overwhelmingly popular. In solving computer vision related problems, we first take a CNN (Convolutional Neural Network) which is trained from scratch or some times a pre-trained model is taken and further fine-tuned based on the dataset that is available. The problem of training the model from scratch on new datasets suffers from catastrophic forgetting. Which means that when a new dataset is used to train the model, it forgets the knowledge it has obtained from an existing dataset. In other words different datasets does not help the model to increase its knowledge. The problem with the pre-trained models is that mostly CNN models are trained on open datasets, where the data set contains instances from specific regions. This results into predicting disturbing labels when the same model is used for instances of datasets collected in a different region. Therefore, there is a need to find a solution on how to reduce the gap of Geo-diversity in different computer vision problems in developing world. In this paper, we explore the problems of models that were trained from scratch along with models which are pre-trained on a large dataset, using a dataset specifically developed to understand the geo-diversity issues in open datasets. The dataset contains images of different wedding scenarios in South Asian countries. We developed a Lifelong CNN that can incrementally increase knowledge i.e., the CNN learns labels from the new dataset but includes the existing knowledge of open data sets. The proposed model demonstrates highest accuracy compared to models trained from scratch or pre-trained model.
Real Estate Investment Estimation Based on BP Neural Network

Investment estimation is an important link in the feasibility study stage of residential projects. In this paper, a genetic algorithm is used to improve the BP neural network to estimate the construction and installation cost of residential construction project investment. Taking the project data of a construction company as an example, a genetic BP neural network model is constructed. Through the detection of the actual data, the error is within 10%, which meets the actual investment estimation needs.
Adaptive filtering of projective quantum measurements using discrete stochastic methods

The authors discuss the application of classical filtering techniques to the problem of discrete projective measurements in quantum systems, and demonstrate that the theoretical basis of classical nonlinear filtering applies in this context. The results may pave the way for broader adoption of control-theoretic techniques in quantum technology.
Scale-aware Insertion of Virtual Objects in Monocular Videos

In this paper, we propose a scale-aware method for inserting virtual objects with proper sizes into monocular videos. To tackle the scale ambiguity problem of geometry recovery from monocular videos, we estimate the global scale objects in a video with a Bayesian approach incorporating the size priors of objects, where the scene objects sizes should strictly conform to the same global scale and the possibilities of global scales are maximized according to the size distribution of object categories. To do so, we propose a dataset of sizes of object categories: Metric-Tree, a hierarchical representation of sizes of more than 900 object categories with the corresponding images. To handle the incompleteness of objects recovered from videos, we propose a novel scale estimation method that extracts plausible dimensions of objects for scale optimization. Experiments have shown that our method for scale estimation performs better than the state-of-the-art methods, and has considerable validity and robustness for different video scenes. Metric-Tree has been made available at: https://metric-tree.github.io
Engineering Management: Problems and Humanitarian Risks

Informatization of the public environment is associated with the creation of a wide network of machine-information environments that fundamentally transformed production, management, educational flows and their interaction in the first quarter of the 21st century. Engineering Management as an interdisciplinary field of human educational and industrial activities is an effective tool for the transformation and management of the new social reality. A new member of social production forms by an old educational system which methods do not reflect a new social changes. It's necessary to move away from a narrowly disciplinary approach, from a strict distinction between the humanities and natural sciences. A synergetic methodology as a universal theory of self-organisation of open dynamically interacting natural, technical and social systems should be a foundation of modern education. It will allow to form specialist's ability of a systematic approach to solving practical problems, dynamic way of thinking and breadth of views. That will meet the labour market's demand of not in field experts but in a multi-skilled specialists. It is important to add more humanitarian subjects to IT-science degree programs and aware of social consequences of applying information technologies. It allows to form soft-skills which are most demanded nowadays. The euphoria over advances in high technology highlights the growing need for digital sanitation to avoid humanitarian risks that threaten humanity and one-sided technocratic thinking. It is important to popularize knowledges about social and ethical problems of information technologies.
Learning Categories with Spiking Nets and Spike Timing Dependent Plasticity

An exploratory study of learning a neural network for categorisation shows that commonly used leaky integrate and fire neurons and Hebbian learning can be effective. The system learns with a standard spike timing dependent plasticity Hebbian learning rule. A two layer feed forward topology is used with a presentation mechanism of inputs followed by outputs a simulated ms. later to learn Iris flower and Breast Cancer Tumour Malignancy categorisers. An exploration of parameters indicates how this may be applied to other tasks.
Research on ShadowsocksR Traffic Identification Based on Xgboost Algorithm

It is very difficult to identify Shadowsocks (SS) traffic, most of which stay in the laboratory environment, and there are very few published research results in this field at home and abroad. ShadowsocksR (SSR) is an enhanced version of SS. It can disguise the traffic of SS as that of conventional protocol, such as HTTP traffic, TLS traffic, etc., which makes it more difficult to identify SSR traffic. Based on Xgboost algorithm, this paper proposes a method to identify SSR traffic for the first time. The experimental results show that this method has a good recognition effect on SSR traffic, and the precision, the recall, the accuracy is all above 95.3%.
Autonomous Control of Fixed-wing Unmanned Aerial System by Reinforcement Learning

This paper presents a near real-time aerial maneuver trajectory planning framework for fixed-wing UAS, which combines the Multi-Objective Monte Carlo Tree Search (MOMCTS) algorithm with parallel computing and receding horizon control techniques. Experimental validation over typical maneuvers including loop and break turn on a fixed- wing aircraft model are carried out with varying initial flight states. A calculation time of 2 seconds is taken to achieve a 2- seconds-long action plan for the maneuver mission in the successive time step, which enables the uninterrupted real-time flight maneuver control.
RETRACTED: Research on 3D to 2D atlas of tujiayang opera mask characters based on fuzzy image reconstruction

Character library routine is executed for the frame, and the mask is a space for establishing the connection—a graphical summary of each cover everything thing as a clear letter. By showing the arch together all the masks, the viewer is connected with the universe of letters. Also intend to build from the inside and outside to see another way to display the complex links between our internal and external world. By showing photographs or record player in the most offensive 2D recording zero return, these significant bit tests are rooted in the conflict before the flatness sensor material network. Ability Tsujiayang Opera Mask Character Processing (TOMCP) of a three-dimensional human face cover Wi-Fi singular 2D surface in this article, the method will examine with a positive response. Besides, through Kinect acquired, regardless of the tone and depth of information, the counter estimates the counter to investigate the local binary mode. For this reason, 3D, effortlessly capturing the depth camera, put forward a Masked Attack Database (3DMAD), a three-dimensional sculpture of information infrastructure. A wide range of effective and feasible to represent 2D face cover face verification frame real danger, 3DMAD display check, for example in the manufacture, processing, quality awareness and lack of control of parts of the great weapon to kill the industry cycle propulsion method of computing vision, image processing seems to be the basis of a fluffy, has become part of research in the field of to system. Item, especially at the edges of the captured image, is detected by a mechanical process using a computer framework. Tsujiayang Opera Mask Character Processing (TOMCP) extends the possible outcomes, solves problems, and offers many answers beyond traditional policy limitations.