Reviewerly/exHarmony huggingface dataset:
Reliable fusion of ToF and stereo data based on joint depth filter

To obtain reliable depth images with high resolution, a novel method is proposed in this study that fuses data acquired from time-of-flight (ToF) and stereo cameras, through which the advantages of both active and passive sensing are utilised. Based on the classic error model of the ToF, gradient information is introduced to establish the likelihood distribution for all disparity candidates. The stereo likelihood is estimated in parallel based on a 3D adaptive support-weight approach. The two independent likelihoods are unified using a maximum likelihood estimation, a process also referred to as a joint depth filter herein. Conventional post-processing methods such as a mutual consistency check are also used after applying a joint depth filter. We also propose a novel hole-filling method based on the seed-growing algorithm to retrieve missing disparities. Experiment results show that the proposed fusion method can produce reliable high-resolution depth maps and outperforms other compared methods.
Teaching Cybersecurity to Computer Science Students Utilizing Terminal Sessions Recording Software as a Pedagogical Tool

This Innovate Practice Full Paper presents our experience with teaching a cybersecurity course to undergraduate students utilizing terminal sessions recording software as a pedagogical tool.Developing a practical cybersecurity course demands numerous amount of preparation and the deployment of various security tools. One of the significant benefits of providing hands-on assignments is the opportunity for students to practice and experiment with tools similar to the ones used by both the professionals and malicious actors.This paper presents an innovative approach to conducting interactive demonstrations in the classroom and as a supplement to the weekly practical assignments. By combining two simple open-source software, we managed to create several short tutorials on how to use various command-line security tools. The educational tool was deployed in an undergraduate cybersecurity class, and the results from the anonymous online survey were overwhelmingly positive. Furthermore, the participants responded that the demos improved their understanding of the subject content and enabled them to learn the material effectively.
Structure-aware Roadview Synthesis for Testing Autonomous Perception

To understanding the generalization of visual perception for autonomous vehicles, sufficient and diverse enough test samples need to be extracted from traffic scenarios. However, the “long-tailed” effect makes it exhausted and expensive to acquire data from tail categories e.g. scenarios backboned with S-shape winding roads. Instead of rendering semi-realistic images in graphics engines like Unity 3D, we propose to synthesis road views with given projection of any road structure by exploiting the statistical relationship between virtual-real image pairs. To tackle non-uniform structural misalignments in the learning data, we introduce a latent structure similarity constraint in the genre of cycle consistency. In this case, synthesized views can better retain the required road's geometry while being rendered with realism. Experimental results demonstrate the effectiveness of the proposed method even when large and massive misalignments exist in training pairs. Further study reveals the direct or indirect impact on autonomous perception caused by road's varying geometry.
Recognition and Control of the Athlete's Movements Using a Wearable Electronics System

The paper presents a wearable electronics system and an algorithm for recognizing and controlling the athlete's movements accuracy. The proposed system is composed of a sensor network, which comprises a microcontroller, accelerometers, myosensors and a heart rate module. The presented motion recognition algorithm consists of signal acquisition, signal preprocessing, feature extraction and normalization, dynamic human motion (exercise) detection and recognition, and the exercise accuracy control. The proposed algorithm is based on the use of modern machine learning methods, in particular, on the basis of recurrent neural networks. As an experiment, basic exercises were used: push-ups, squats and the deadlift. Experimental results showed that our approach could reach a 97% recognition level as well as make it possible to detect errors during the exercising.
Robots Understanding Contextual Information in Human-Centered Environments Using Weakly Supervised Mask Data Distillation

Contextual information contained within human environments, such as text on signs, symbols and objects provide important information for robots to use for exploration and navigation. To identify and segment contextual information from images obtained in these environments data-driven methods such as Convolutional Neural Networks (CNNs) can be used. However, these methods require significant amounts of human labeled data which is time-consuming to obtain. In this paper, we present the novel Weakly Supervised Mask Data Distillation (WeSuperMaDD) architecture for autonomously generating pseudo segmentation labels (PSLs) using CNNs not specifically trained for the task of text segmentation, e.g., CNNs alternatively trained for: object classification or image captioning. WeSuperMaDD is uniquely able to generate PSLs using learned image features from datasets that are sparse and with limited diversity, which are common in robot navigation tasks in human-centred environments (i.e., malls, stores). Our proposed architecture uses a new mask refinement system which automatically searches for the PSL with the fewest foreground pixels that satisfies cost constraints. This removes the need for handcrafted heuristic rules. Extensive experiments were conducted to validate the performance of WeSuperMaDD in generating PSLs for datasets containing text of various scales, fonts, orientations, curvatures, and perspectives in several indoor/outdoor environments. A detailed comparison study conducted with existing approaches found a significant improvement in PSL quality. Furthermore, an instance segmentation CNN trained using the WeSuperMaDD architecture achieved measurable improvements in accuracy when compared to an instance segmentation CNN trained with Naïve PSLs. We also found our method to have comparable performance to existing text detection methods.
Boosting Face Recognition under Drastic Views Using a Pose AutoAugment Manner

Face recognition under drastic pose drops rapidly due to the limited samples during the model training. In this paper, we propose a pose-autoaugment face recognition framework (PAFR) based on the training of a Convolutional Neural Network (CNN) with multi-view face augmentation. The proposed framework consists of three parts: face augmentation, CNN training, and face matching. The face augmentation part is composed of pose autoaugment and background appending for increasing the pose variations of each subject. In the second part, we train a CNN model with the generated facial images to enhance the pose-invariant feature extraction. In the third part, we concatenate the feature vectors of each face and its horizontally flipped face from the trained CNN model to obtain a robust feature. The correlation score between the two faces is computed by the cosine similarity of their robust features. Comparable experiments are demonstrated on Bosphorus and CASIA-3D databases.
Employing SABRE relay network for Country-Wide Blockchain network

Internet is taking a turn; services are moving more from centralised to decentralised approach bringing in the revolutions and speeds in the large-industrial applications and energy sector. These sectors are large scale and need a reliable and robust big network for its activities to work. In this paper, we try to support the Public Blockchains as the base for large country-wide networks to carry on its activities. We discuss the SABRE(Secure and Scalable Bitcoin Relay Network), which is a novel relay network designed as a countermeasure of the BGP highjacking attacks on the Bitcoin Network. We see how this method can be generalised for all the Blockchains and how it helps to reach a better network trust. We also discuss that it creates a market place out of block propagation while serving the needs of targetted country-wide Blockchain networks.
Towards Accurate Low Bit DNNs with Filter-wise Quantization

Execution of deep neural networks (DNNs) on a resource constraint device has become a rising issue of recent neural network research. Quantization using low bit-widths for networks is one of the most effective compression techniques. Although there have been many studies that use different bit-widths per layer to compress further the model instead of using a single bit-width, they achieved a limited reduction on the parameter size due to the layer-wise bit-width assignment. In this paper, we propose a more fine-grained and multi-precision quantization technique, called filter-wise quantization. Regularization is used while training networks to partition filters into various precision. In experiments, we show that our technique can provide better accuracy at a smaller parameter size at various DNN models for CIFAR-10 and CIFAR-100 data sets.
Findings on detection of fraud smartphone apps by ranking using mining leading sessions

Abstract The world as we know now is full of mobile users everywhere and the majority leads with smartphone users which are filled with android and iOS applications. This has produced huge development in smartphone technology at a lightning rate because the apps have become popular and a common concept. For this we are dwelling into data mining in which the concept of mining data from a specific application is tough and pivotal job. These are the methodologies we are using in this paper. We are combining these two methods of mining data and ranking fraud in app store which will be a formidable task. According to report at google play store and apple store there are 2.4 million apps as of May 2019 so that shows quick surge in the past few years. Furthermore, above 500k independent app developers competing to for all the end users. Primarily finding fraud apps is done by scanning the top apps ranked apps which will be around 30-40 of them but they are on top for short period of time i.e. few days for verifying their authenticity. However, this can’t be applied when dealing with thousands of apps for each day. Hence, we have to go for an extensive approach in which we can apply a methodology to validate each app. As the importance of mining is established so we need to mine the critical data of app which includes reviews, ratings and rankings of an app that get integrated into an algorithm that can determine the fraud. So, in this paper of discovering the ranking fraud of smartphone app, we need to develop a precise and faultless methodology that gives result of trusted apps.
Contrastive Study of Distributed Multitask Fuzzy C-means Clustering and Traditional Clustering Algorithms

Clustering has been widely used in every field,but traditional classical clustering unable to handle multiple tasks at the same time under the scenario of data sets.Comparing with the classical clustering algorithm,the clustering results of Distributed Multitask Fuzzy C-means Clustering(MT-FCM) and traditional clustering algorithms can be verified from different multitask scenarios according to the features of MT-FCM algorithm.MT-FCM algorithm is suitable for multitask environment with a moderate number of tasks,in which case the clustering effect is better than that of other traditional clustering algorithms,but there are also non-applicable scenarios by the analysis of experimental results.This experiment not only summarizes and improves the characteristics of the MT-FCM algorithm,but also finds out the shortcomings of the algorithm,which provides a valuable reference for the follow-up research of Distributed Multitask Fuzzy C-means Clustering.
Research on Method of Image Quality Evaluation Based on Structural Similarity

Image quality evaluation method (IQEM) plays an important role in image processing and recognition. In this paper, the structural similarity algorithm of objective digital image quality evaluation method (SSIM) is studied in depth, and evaluate the performance of single and multi-scale SSIM algorithm were compared. Compared to the single-scale method SSIM, the multi-scale SSIM image quality prediction score is more accurate, and is more consistent with the subjective score. The method based on structural similarity for the distorted image is more in line with the characteristics of HVS. The SSIM algorithm combines with the features of HVS, and the edge-based improved algorithm (ESSIM) is analyzed and simulated. The experimental results show that the ESSIM algorithm is more in line with the objective quality evaluation model of HVS characteristics, which provides ideas and directions for the improvement of structural similarity algorithm in future.
Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention Network

In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in Single Image Super-Resolution (SISR) tasks. However, these methods are usually computationally expensive, which constrains their application in mobile scenarios. In addition, most of the existing methods rarely take full advantage of the intermediate features which are helpful for restoration. To address these issues, we propose a moderate-size SISR network named matrix channel attention network (MCAN) by constructing a matrix ensemble of multi-connected channel attention blocks (MCAB). Several models of different sizes are released to meet various practical requirements. Extensive benchmark experiments show that the proposed models achieve better performance with much fewer multiply-adds and parameters (Source code is at https://github.com/macn3388/MCAN).
Fine-Grained Classification via Categorical Memory Networks

Motivated by the desire to exploit patterns shared across classes, we present a simple yet effective class-specific memory module for fine-grained feature learning. The memory module stores the prototypical feature representation for each category as a moving average. We hypothesize that the combination of similarities with respect to each category is itself a useful discriminative cue. To detect these similarities, we use attention as a querying mechanism. The attention scores with respect to each class prototype are used as weights to combine prototypes via weighted sum, producing a uniquely tailored response feature representation for a given input. The original and response features are combined to produce an augmented feature for classification. We integrate our class-specific memory module into a standard convolutional neural network, yielding a Categorical Memory Network. Our memory module significantly improves accuracy over baseline CNNs, achieving competitive accuracy with state-of-the-art methods on four benchmarks, including CUB-200-2011, Stanford Cars, FGVC Aircraft, and NABirds.
Obstacle Avoidance for AUV by Q-Learning based Guidance Vector Field

Autonomous obstacle avoidance technology is the key to determine whether the autonomous underwater vehicle (AUV) can reach its destination safely. The Q-Learning based guidance vector field is proposed to solve the 3-dimensional obstacle avoidance problem for AUV. Firstly the initial guidance vector field in free space is constructed to guide AUV reach the destination along the shortest path. Then the modulation matrix is proposed to quantify the influence generated by the obstacles so that the modified guidance vector field in obstacle environment is obtained. For the case of AUV entering the trap area, the Q-learning algorithm is used to find the shortest path from the current position to the destination. A virtual target is chosen to guide AUV escape from the trap area. Finally the simulation results show that this method is useful for AUV to complete the obstacle avoidance task in complex marine environment.
Feature Selection and Classification Methods for Predicting Search Engine Ranking

In the two-past decade, by using the methods of machine learning, the accuracy of performing computer-aided tasks successfully improved. Search engines (Google, Baidu, Bing...) use classification methods to rank the billion pages available on the world wide web. Rankings are made according to the algorithms with various features, which classify each page for a search engine request. The purpose of this paper is to analyze the performance of various machine learning models applied on features selected through different techniques. A dataset, composed of 31 features with 28,000 observations, has been evaluated considering only the characteristics with the highest correlation. To achieve that goal three filter methods were used (Chi-square, Gini index and Fisher) and three wrapper methods (Forward Selection, Backward Elimination and Bidirectional Elimination). To continue the research various classification algorithms were tested to create combination models with previous filtered and wrapper methods. Then, a comparison was done to determine the optimal features' combinations, to improve the correct prediction for an URL to be on Google Top10 SERP. From the research, it can be concluded that for this dataset, the Random Forest model combined with the Fisher filter method or Backward Elimination wrapper method could produce the best results among others.
Use of distance technologies in education of pupils with disabilities

Abstract The article is devoted to the consideration of the problem of using distance educational technologies in the educational process of a comprehensive school for pupils with disabilities in the context of modern educational practice. Education is the basis and resource for creating ideas about the values of the world and a human among schoolchildren, therefore, the development and use of pedagogical technologies, the formation of value knowledge and attitudes towards the world, and the involvement of schoolchildren in value-semantic activities are necessary in the conditions of modern inclusive education. The methodological approaches to the organization and implementation of the educational process using distance technologies in a general education school for children with disabilities are described. Significant results are shown and deficiencies in the use of distance learning technologies in the educational process of a comprehensive school for children with disabilities are indicated.
Do financial development and political institutions act as substitutes or complements?

This paper examines the interactive impact of financial development and political institutions on a specific development outcome: gross domestic investment. We explore whether financial development and political institutions act as substitutes or complements in the context of domestic investment. Using data from the period 1975–2017 for 131 countries to construct annual and five-year interval panels, we employ Fixed Effect (FE) and Dynamic Panel estimators (System GMM) to test our hypothesis. We find a significant interactive impact of political institutions and financial development on domestic investment. More specifically, we find a substitution effect among these factors. In the presence of inefficient institutions, financial development mitigates the negative impact of political institutions on domestic investment, and vice-versa.
A Novel Snake-like Data Placement Policy for Improving Video Processing in Hadoop Clusters

Video data include useful information that can be exploited in various video surveillance systems. However, these systems undergo challenges such as needing considerable storage to store a massive volume of video data and significant computing power to process them. Apache Hadoop is a well-known technology that provides distributed storage and parallel processing for large amounts of data, including videos. This paper proposes a novel data placement policy to improve processing video data on Hadoop clusters by minimizing load imbalances among the computing nodes. Change detection is the method used in this paper to process video data by considering the amount of change in different video segments that generate various workloads. The workload diversity leads to load imbalances even if the video segments, having the same size, are equally distributed among the processing nodes of a homogeneous cluster. The conducted empirical study shows the performance improvement achieved by the proposed data placement policy.
Asymptotic Privacy Loss Due to Time Series Matching of Dependent Users

The Internet of Things (IoT) promises to improve user utility by tuning applications to a user’s current behavior, but the user’s behavior can be matched to characteristics learned from prior observations to compromise the user’s identity and hence privacy. Our previous work has established the rate at which anonymization must be performed to prevent such matching in a Bayesian setting when faced with a powerful adversary who has extensive knowledge of each user’s past behavior. However, even sophisticated adversaries do not often have such extensive knowledge; hence, in this letter, we turn our attention to an adversary who must learn user behavior from past data traces of limited length under the assumptions that: (i) there exists dependency between data traces of different users; and (ii) the data points of each user are drawn from a normal distribution. Results on the lengths of training sequences and rates of anonymization for the data sequences that result in a loss of user privacy are presented.
Image Retrieval using Modified Multi Texton and Rotation Invariant Local Binary Pattern

Image retrieval is a standard method used to search a digital image in large image databases. One of the most critical things in image retrieval is the feature extraction process. Several techniques can be used in the image feature extraction process, one of which is Multi Texton Histogram (MTH). MTH is usually carried out with two main processes, namely color quantization, and texture orientation. Improvement of MTH method performance can be made by adding texton. In our experiment, we test the method by using rotated images. In this paper, we develop the use of an invariant method for rotation, namely the Local Binary Pattern Rotation Invariant (LBPROT) method to improve image retrieval precision. The results of combining these two methods produce features of each image. The features produced by each image are compared using a distance matrix. A query image that has the smallest distance matrix value is an image that has the same class as the compared database image. Based on our experiments on typical images, the use of Modified MTH and LBPROT can improve image retrieval performance by increasing the percentage of precision and recall by 3.63% and 0.62%. However, The Modified MTH is better when compared to the merging of the Modified MTH and LBPROT on 45 °, 135 °, 225 °, and 315 ° angle rotations with precision values by 24% -27%. Meanwhile, our testing result using rotated images, the combination of Modified MTH and LBPROT produces an increase in precision at angles rotation of 90 °, 180 °, and 270 ° by 2% -3%.
Maximum Number of Transition Points in 3D Linear Symmetric Tensor Fields

Transition points are well defined topological features in 3D tensor fields, which are important for the study of other prominent topological singularities such as wedges and trisectors. In this paper, we study the maximum number of transition points in a linear tensor field, which is important to process wedge and trisector classification along degenerate curves.