Reviewerly/exHarmony huggingface dataset:
How separable median filters can get better results than full 2D versions

The well-known method of median filtering is used both in a wide range of application frameworks and as a standalone filter. Small-window median filters can highly reduce the power of salt and pepper or additive Gaussian noise and minimize edge blurring. Large-window filters are also used, for example to estimate the background of images. Currently, the window size should not be an issue as a constant time algorithm and several implementations, including GPU-based codes, have been proposed in recent years. Unfortunately, none of these constant time implementations manage to fully exploit the capabilities of modern GPUs, and thus the throughputs of large-window median filters remain far below the peak throughputs allowed by recent GPU models. This paper aims at showing that a separable approximation of a 2D median filtering is often stronger than its full 2D implementation. Statistical and theoretical analysis are conducted to show and explain this fact which had so far remained unobserved. It is confirmed by experimentations on a dataset composed of 10,000 images, corrupted by different levels of salt and pepper noise. Separable and full 2D median filter algorithms are compared with several metrics, notably PSNR and MSSIM. In addition, a GPU implementation of 2D separable median filters is also proposed. This implementation is able to output up to 125 billion pixels per second on a recent Volta V100, which significantly outperforms existing implementations like Nvidia’s NPP library or Green’s code, resulting in the fastest median filtering solution to date.
Safe Distributional Reinforcement Learning

Safety in reinforcement learning (RL) is a key property in both training and execution in many domains such as autonomous driving or finance. In this paper, we formalize it with a constrained RL formulation in the distributional RL setting. Our general model accepts various definitions of safety (e.g., bounds on expected performance, CVaR). To ensure safety during learning, we extend a safe policy optimization method to solve our problem. The distributional RL perspective leads to a more efficient algorithm while additionally catering for natural safe constraints. We empirically validate our propositions against appropriate state-of-the-art safe RL algorithms.
Research on Graph Network Recommendation Algorithm Based on Random Walk and Convolutional Neural Network

As a general form of describing and modeling complex systems, networks widely exist in different scenes and tasks in various fields of the real world. Therefore, how to effectively calculate (graph Computing) and analyze (graph mining) data based on network structure has always been the core basic research direction in the field of computing science and data mining, and has been continuously studied by scholars from computer, sociology, biology and other disciplines. Network representation learning can better analyze the information hidden in complex networks, and with the help of graph neural network, it provides a universal method to solve various practical problems under the background of network structure data, which has attracted the common attention of academia and industry. At the same time, traditional recommendation algorithms generally analyze the user's rating data on items and then make preference recommendations. These methods have some problems, such as difficulty in extracting deep features, single data processing method, etc. These problems will lead to low prediction accuracy and unreasonable results. Therefore, in this paper. The recommendation algorithm for heterogeneous networks based on feature embedding is improved (RW-CNN), and the random walk algorithm of convolution and graph networks in deep learning is used to process the text feature of item names. Firstly, the network topology similarity calculation module is used to measure the consistency and complementarity of each view network, and then the word vector learning technology is used to generate the representation vector of multi view network: the word vector module is used to distinguish the complementarity between views, and the hierarchical hidden state based on multi views is used to extract these information, To retain the unique complementary information of each view; The word vector module is used to recommend multiple views with stronger consistency. Through the middle hidden state shared by multiple views, the graph convolution technology is used to aggregate information and realize the fusion of consistent information. Finally, the learned representation vector is used for link prediction and node classification tasks. The experimental results on a variety of real data sets show that the effect of this model is significantly improved.
Using deep learning algorithms for texture segmentation of ultra-high resolution satellite images

This paper presents the results of textural segmentation of satellite images with spatial resolution &lt;1 m using U-Net convolutional neural networks. To conduct numerical experiments, a panchromatic image of the WorldView-2 test site on the territory of the Bronnitsky Forestry (Moscow region) used. The possibilities of automating the selection of neural network parameters based on genetic algorithms investigated. The proposed method makes it possible to effectively segment the main types of natural and man-made objects, as well as to distinguish structural classes of woodlands.
Layered Learning for Acute Hypotensive Episode Prediction in the ICU: An Alternative Approach

Precise machine learning models for the early identification of anomalies based on biosignal data retrieved from bedside monitors could improve intensive care, by helping clinicians make decisions in advance and produce on-time responses. However, traditional models show limitations when dealing with the high complexity of this task. Layered Learning (LL) emerges as a solution, as it consists of the hierarchical decomposition of the problem into simpler tasks. This paper explores the uncovered potential of LL in the early detection of Acute Hypotensive Episodes (AHEs). We leverage information from the MIMIC-III Database to test different subdivisions of the main task and study how to combine the outcomes from distinct layers. In addition to this, we also test a novel approach to reduce false positives in AHE predictions.
Performance Optimization of Short Video Using Convolutional Neural Network for IOT Applications

The concept behind Internet of things is about connecting just everything to the Internet in real time, which directly improves our quality of life and experiences. Processing of real-time streaming video data rich makes memory and computations intensive. For the efficient outcome of the applications in the context of Internet of things edge devices computing, deep learning model techniques are required to process and produce the precise results. To attain real-time frame rate in video streaming, frame interpolation is applied to increase the frame rate of video for producing smoother results. Convolutional neural network is used to analyze and enhance the quality of the intermediate interpolated frames. The rectified linear unit activation function is used to convert an input signal of a layer in the neural network to an output signal, and function allows neural network model to account for nonlinear effects and produces results with better convergence. Weight decay hyper-parameter tuning is the optimization technique used to minimize loss function that yield the best model performance with low latency. To solve the under-fitting problem when a model is small with high bias, regularization technique is used to achieve the rate of processing work. Overall compressing the model and embedding them on edge devices is performed, in order to get fast real-time inference on the Internet of thing networks.
Development of Domain Knowledge Graph: A Case Study on Flotation Process

A growing number of researches about knowledge graph have been studied and improved recently, but they are rarely applied in industries. At present, due to the lack of data and the scattered knowledge distribution in industry, constructing industrial domain knowledge graph is expensive and of low quality. This paper proposes a domain knowledge graph construction framework based on multi-source knowledge extraction, entity disambiguation, relation completion, and assisted decision-making, taking the flotation process as a case to study. Firstly, design the ontology layer of the flotation field, obtain corpus by crawling technology according to key words, and complete knowledge extraction. Secondly, use similarity calculation to entity disambiguation. Finally, apply the domain knowledge graph to achieve industrial applications, such as intelligent recommendation and assisted-decision in the flotation process.
Virtual reality application with nano-display improves training of pipeline installation in oil and gas industry

Nanotechnology has the potential to have a huge effect on all industries, including the oil and gas industry. Virtual Reality (VR) also has large and comprehensive perspective in the interest of oil and gas industry. VR approach becomes essential at different phases in oil and gas industry. This study aims to identify the causes of Thermoelectric Generator (TEG) defect in oil and gas industry. This work also develops VR training platform with nano-display devices. The training platform is to be used in the oil and gas industry for heat pipe removal and pipeline construction. Implementation of VR training platform offers huge advantage to the industry as it does not interrupt the operation and does not require investment in the training facilities. Case study was used to determine the causes of TEG defects. Continuous exposure to heat, over production and prolonged run are the three factors that were found to be the defect of Thermoelectric Generator (TEG) in oil and gas industry. Unity 3D, HTC™ Vive device, Autodesk Maya software and C# Programming were used to develop the VR platform. Finally, the 3D models of TEGs were designed and modelled for heat pipe removal and pipeline installation in oil and gas industry. As we understand that nanotechnology plays an integral role in hardware and communications for virtual reality technology, one might propose nano-display which provides the best resolution and pixels smaller than a micrometer.
A GRU-based Pipeline Approach for Word-Sentence Segmentation and Punctuation Restoration in English

In this study, we propose a Gated Recurrent Unit (GRU) model to restore the following features: word and sentence boundaries, periods, commas, and capitalisation for unformatted English text. We approach feature restoration as a binary classification task where the model learns to predict whether a feature should be restored or not. A pipeline approach is proposed, in which only one feature (word boundary, sentence boundary, punctuation, capitalisation) is restored in each component of the pipeline model. To optimise the model, we conducted a grid search on the parameters. The effect of changing the order of the pipeline is also investigated experimentally; PERIODS > COMMAS > SPACES > CASING yielded the best result. Our findings highlight several specific action points with optimisation potential to be targeted in follow-up research.
A Federated Learning Based Chinese Text Classification Model with Parameter Factorization Weighting

Federated learning (FL), as an emerging field of machine learning, has received wide attention since this concept was proposed. In this, paper, we conduct research on text classification based on Federated Learning, and propose a Federated Learning via Local Batch Normalization and Parameter Factorization Weighting based Chinese Text Classification Model (FedBN-PW-CTC). We evaluate our approach on both homogenous and non-homogenous datasets and confirm its effect of 2.95% improvement of accuracy and 4.7% improvement of F1 score on non-homogeneous dataset.
Introduction to Face Recognition

This chapter introduces the general concepts concerning our face recognition task of automatic recognition of human faces in dynamic environments. The development of intelligent environments has been highlighted recently by the ‘Smart Rooms’ projects at the MIT Media Lab, which enable novel forms of interactive control for computer systems. There is great commercial interest in logging and interpretation of activity within domestic or commercial environments. Applications include access control and personalization of domestic appliances such as computers, telephones and televisions. Psychophysical studies with monkeys suggest that they use interpolation between 2-D, viewer-centred representations for object recognition. The environment and manner in which a database of face images is collected is vital to the success of any face recognition system in which it is used. Face detection in image sequences is very much easier, due to motion cues, than for single images and can be integrated into tracking techniques.
Hybrid Filter-Wrapper Approach for Feature Selection in Deceptive Consumer Review Classification

Nowadays, due to the prevailing situation of the world, people are heavily focusing on online transactions. There has been a rapid increase in online transactions and several types of data generated through such transactions during the last few years. As there is no other involvement in purchasing decisions, customers make purchasing judgments through the reviews. Therefore, not only for making purchasing decisions but also customer reviews provide valuable information regarding the products for decision-makers. By considering this as an advantage, fraudulent reviewers tend to write reviews to promote or downgrade products. Deceptive reviews can be identified via reviewer behavioural features, content-related features, or review features. But all the extracted features may not be critical for identifying deceptive. This research introduces a novel filter-wrapper hybrid approach to select optimal features to identify deceptive online customer reviews. A combination of univariate and multivariate filter methods as well as a wrapper method with the bidirectional search were used to select the features. The model was evaluated using the K-Nearest Neighbor (KNN) classifier. The proposed hybrid approach shows the highest model accuracy against the sole traditional approaches. The selected optimal features used for model building are effective as they reveal the most statistically significant features when predicting the deceptive reviews..
Remote Probing Solution with Augmented Reality

Remote Probing System (RPS) is an innovative touchless semi-automatic probing system which make use of IoT, Robotics and Augmented Reality (AR) that aim to provide virtually control movement of oscilloscope probes to obtain signal integrity and waveform measurement. This solution allows remote access to perform validation and integrity test on platform that lack on availability. One of main features is 3-axis probing, which is X-axis (left and right movement), Y-axis (front and back movement), and Z-axis (up and down movement). This system supports up to 15kg of board weight with double-sided probing features without tangling the wires. In addition, an AR layout viewer will be used to overlay every aspect of a design directly onto the platform. It supports electronic design automation which also known as electronics computer-aided design and integrates with unified components search engines.
Image Stitching Method Based on Adaptive Weighted Fusion

Aiming at the problem of stitching seams and uneven brightness caused by illumination and noise during the image stitching process, a stitching method based on adaptive weighted fusion is proposed. The SURF algorithm is used to extract the feature points of the image to be stitched, the Euclidean distance is used to calculate the similarity of the feature point pairs, and complete the image registration. After that, the adaptive weighted method is applied to fuse the brightness difference of the overlapping area to achieve the smooth transition of the spliced image and eliminate stitching seams. Compared with the traditional gradual algorithm and the nonlinear fusion algorithm, the experiment results show that the adaptive fusion algorithm can obtain better image fusion effect and effectively improve the quality of image stitching.
Pseudospectral method-based safe motion planning for quadrotors in a cluttered environment

This paper presents an optimal-control-based motion planning framework for quadrotors in a dense obstacle field. On the backbone of a pseudospectral (PS) transcription of the optimal motion control problem (OCP), we compute and embed the safe flight corridor (SFC) to ensure the feasibility of a safe path and to improve computational efficiency by employing linear inequality constraints from SFC. However, the solution path is imperfect in terms of keeping constraints since only state and control input at collocation points are enforced to stay within the constraints. To tackle these challenges, the model predictive controller (MPC) tracks the computed path while keeping the constraints by locally solving the OCP in a receding time horizon. A numerical example of minimum-time motion planning of a quadrotor in a three-dimensional cluttered environment validates the proposed framework.
Depth from axial differential perspective

We introduce an imaging-based passive on-axis technique for measuring the distance of individual objects in complex scenes. Two axially separated pupil positions acquire images (can be realized simultaneously or sequentially). Based on the difference in magnification for objects within the images, the distance to the objects can be inferred. The method avoids some of the disadvantages of passive triangulation sensors (e.g., correspondence, shadowing), is easy to implement and offers high lateral resolution. Due to the principle of operation it is especially suited for applications requiring only low to medium axial resolution. Theoretical findings, as well as follow-up experimental measurements, show obtainable resolutions in the range of few centimeters for distances of up to several meters.
A Confrontational Bare Bones Particle Swarm Optimization Algorithm

Optimization problems are common and important in artificial intelligence. Population-based methods are usually used in solving these problems. However, in recent years, optimization problems become complicated and high dimensional. Traditional methods are difficult to present effective results when searching in the high dimensional hyper-cube. To cross these shortcomings, a novel confrontational bare bones particle swarm optimization (CBBPSO) algorithm is proposed in this paper. Different from traditional population-based algorithms, the global worst particle is also recorded by the CBBPSO. A new confrontation operator is proposed. The confrontation operator offers every particle a second choice. In each iteration, each particle can move to the global best particle or the global worst particle. To verify the optimization ability of the proposed method, five famous benchmark functions from CEC2005 are used in the experiment. Experimental results show that the CBBPSO can solve high-dimensional optimization problems.
A Deep Learning Model Fused with Word Sense Knowledge for Textual Entailment Recognition

Textual entailment recognition is an essential research task in the field of natural language processing. The mainstream textual entailment recognition method based on deep learning does not integrate word sense knowledge in training data, so the inference knowledge of model learning is limited. In addition, polysemy has become a significant challenge for the fusion of semantic knowledge. Therefore, we propose a textual entailment recognition model fused with word sense knowledge. It enhances the explicit knowledge of the data by fusing the synonyms in the CiLin. We use word sense disambiguation to fuse the meaning of polysemy. We use the RoBERTa word vector and the word vector representation based on the sememe to initialize sentence encoding. Our model achieves an accuracy of 80.77% on the CNLI dataset and 80.74% on the XNLI dataset.
Yahoo Yahoo: Cyber-enabled Crime and Criminality in Nigeria

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Video Coding Method in a Condition of Providing Security and Promptness of Delivery

In the course of the research, the research of discriminatory methods of handling video information resource based on the JPEG platform was carried out. This research showed a high interest of the scientific world in identifying important data at different phases of handling. However, the discriminatory handling of the video information resource after the quantization phase is not well understood. Based on the research data, the goal is to find possible ways to operation a video information resource based on a JPEG platform in order to identify important data in a telecommunications system. At the same time, the proposed strategies must provide the required pace of dynamic picture grade and hiding in the context of limited bandwidth. The fulfillment of the condition with limited bandwidth is achieved through the use of a lossless compression algorism based on arithmetic coding. The purpose of the study is considered to be achieved if the following requirements are met:1.Reduction of the volume of dynamic pictures by 30% compared to the initial amount;2.The quality pace is confirmed by an estimate of the peak signal-to-noise ratio for an authorized user, which is Ψ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">author</inf> ≥ 20 dB;3.The pace of hiding is confirmed by an estimate of the peak signal-to-noise ratio for unauthorized access, which is Ψ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unauthor</inf> ≤ 9 dBThe first strategy is to use encryption tables. The advantage of this strategy is its high hiding strength.The second strategy is the important matrix method. The advantage of this strategy is higher performance.Thus, the goal of the study on the development of possible ways of handling a video information resource based on a JPEG platform in order to identify important data in a telecommunication system with the given requirements is achieved.
Side Channel Attack-Aware Slice Mapping in 5G RAN

A Side Channel Attack(SCA)-aware slicing algorithm is proposed to achieve secure slicing in 5G RAN. Results show that our algorithm can effectively balance the resource utilization and security performance of slices.