Reviewerly/exHarmony huggingface dataset:
Scalable Block Execution via Parallel Validation

A dominant part in blockchain networks is reaching an agreement on block transactions and their impact on the network state. We follow a common scenario where a node is selected to propose a block and its implied state updates. The proposal is then validated by other nodes that examine the block impact on the state. Typically, all validators execute the complete block and provide an indication based on comparing the results of their execution to the updated state in the proposal. With the increase in the number of participants in blockchain networks, we suggest a time-efficient block validation through splitting it into multiple disjoint tasks. This can be challenging due to possible dependencies between the block transactions. We describe the additional information the leader has to provide to enable that. Moreover, we describe a unique proof for the block partition computed by the leader such that when validated in part by the different committees guarantees the correctness of the execution by the leader. We compare the approach to traditional solutions based on real data of the Ethereum blockchain.
Uniform Generic Representation for Single Sample Face Recognition

In this article, we propose a uniform generic representation (UGR) method to solve the single sample per person (SSPP) problem in face recognition, which aims to find consistency between the global and local generic representations. For the local generic representation, we require the probe patches of the same image to be constructed respectively by the corresponding patches of the same gallery image and the intra-class variation dictionaries. Therefore, the probe patches' coefficients, corresponding to patch gallery dictionaries, should be similar to each other. For the global generic representation, the probe image's coefficient, corresponding to the gallery dictionary, should be similar to those of its probe patches. In order to meet the two requirements, we combine local generic representation with global generic representation in soft form. We obtain the representation coefficients by solving a simple quadratic optimization problem. UGR has been evaluated on Extended Yale B, AR, CMU-PIE, and LFW databases. Experimental results show the robustness and effectiveness of our method to illumination, expression, occlusion, time variation, and pose.
A Novel Algorithm for the Automatic Detection and Classification of Microcalcification Clusters Using Wavelets

In the cancerous women society breast cancer can be found to be Second harmful cancer which causes death. According to statistical data the death rates are increasing in every year. Early detection and removal of the cancerous part is the most effective way to heal a cancer which can improve survival rates to a large level. Breast abnormality is find out with the distinguish features which can be simply misconstrue or missed by the radiologist. Again the mammographic screening sensitivity will vary with respect to the quality of image and ability of the radiologist. This means that there are no effective ways for the screening process. By early detecting the presence of micro calcification in the mammograms we can diagnose the breast cancer. This paper is to develop a novel algorithm for distinguishing benign clusters and malignant clusters. The proposed classification system reduces the classification errors and is further proficient in correct diagnosis which will be confirmed by experimental results.
Numerical Simulation of Tsunami Evacuation Route Planning in Padang Using Linear Programming

This paper proposes an implementation of linear programming in tsunami horizontal evacuation routes decision making. A tsunami is a huge wave that travels with high speed towards a coastal area. Padang is the capital city of West Sumatera Province that is located in the coastal area as well as have a history of the tsunami disaster. The main problem is only a few minutes of the interval for the tsunami waves arrive at the coast after tsunami hazard warning. In the evacuation concept, the evacuees from the high-risk zone are moving to the shelter through a safe evacuation route. This research aims to determine the optimal evacuation routes with the constraints of a few minutes of the interval of evacuation time, the capacity of each shelter, and the number of evacuees. The evacuation time is an objective function that depends on the number of population, people density, road width, distance from the high-risk zone to the shelter, and velocity. The numerical simulation is done using linear programming with Matlab. The results show that all evacuees in each high-risk region are proportionally evacuated to available shelter areas within allocated evacuation time. The results of this research can be used for supporting tsunami evacuation plans and disaster resilience management by the authorities in Padang.
Investigating aesthetics to afford more ‘felt’ knowledge and ‘meaningful’ navigation interface designs

Aesthetically manipulating the visual variables of a navigation interface design has the potential for substantial improvements in the interpretation of, and subsequent navigational choices made resulting from that design. This paper reports on a study that explores how an `optimal' path is understood across fifteen different types of route map designs for ten cities (approximately 150 route map designs in total). We are interested in how participants make sense of the route map, and subsequently choose an optimal pathway. The findings show that participants who experience certain aesthetically designed route maps are more inclined to meaningfully link information and create connections. By more deeply understanding people's perceptions of the aesthetics of a navigation problem space - particularly the ways in which people value and connect with aesthetic elements and how these impact the decisions made - a novel insight into individuals' understanding of data visualisation and how aesthetics affect is achieved.
Research on Employment Prediction and Fine Guidance based on Decision Tree Algorithm under the Background of Big Data

Abstract Refinement of college employment guidance is the demand of the current era. Big data thinking and technology have created the conditions for it. In this paper, the factors influencing college students’ employment are discussed and the method of applying decision tree algorithm to predict college students’ employment is studied. On this basis, the paper analyses the effective ways to improve the refinement of college employment guidance based on employment prediction, from the four aspects of improving the scientificity of decision-making, the pertinence of guidance, the accuracy of service, and the timelines of assistance.
A Single Image Mosquito Streaks Removal Method of High-Speed Railway Traction Substation Using DCNN

Abstract With the proposal of intelligent high-speed railway, the research on remotely monitoring the intelligent traction substation becomes a key subject of high-speed railway safe operation. However, mosquitos adhered to the glass window or camera lens can severely hamper the visibility of a background scene, and degrade images considerably. Therefore, we propose a single image mosquito streaks removal method of high-speed railway traction substation based on the deep convolutional neural network. First, we employ guided filter to split input image into smoothing image and edge-preserving image. Then, the edge-preserving image is fed into our designed convolutional neural network to obtain learning map, which solves problems of background interference and focuses the model on the structure of rain streak in images, and the clean image is finally generated through adding the input image and learning map. The experiment results on Heishan traction substation real datasets show the effectiveness of our proposed method.
Usability Study for an Augmented Reality Content Management System

With the propagation of digitalization in engineering education, tools like Augmented Reality are increasingly focused by educators. However, creating content to use in education is a time-consuming task that requires a basic understanding of 3D modeling and programming. To solve those issues the authors developed an Augmented Reality Content Management System for use in laboratory experiments. In this paper the authors describe and discuss a conducted usability study for the developed system. Students from different fields used the system to conduct a previously unknown experiment. Afterwards, a questionnaire based on the NASA Task Load Index was filled out to judge not only the perceived stress level but also the usability and usefulness of the system so that future development efforts can be based on the results.
Salient Object Detection Using Recurrent Guidance Network With Hierarchical Attention Features

Fully convolutional networks (FCNs) play an significant role in salient object detection tasks, due to the capability of extracting abundant multi-level and multi-scale features. However, most of FCN-based models utilize multi-level features in a single indiscriminative manner, which is difficult to accurately predict saliency maps. To address this problem, in this article, we propose a recurrent network which uses hierarchical attention features as a guidance for salient object detection. First of all, we divide multi-level features into low-level features and high-level features. Multi-scale features are extracted from high-level features using atrous convolutions with different receptive fields to obtain contextual information. Meanwhile, low-level features are refined as supplement to add detailed information in convolutional features. It is observed that the attention focus of hierarchical features is considerably different because of their distinct information representations. For this reason, a two-stage attention module is introduced for hierarchical features to guide the generation of saliency maps. Effective hierarchial attention features are obtained by aggregating the low-level and high-level features, but the attention of integrated features may be biased, leading to deviations in the detected salient regions. Therefore, we design a recurrent guidance network to correct the biased salient regions, which can effectively suppress the distractions in background and progressively refine salient objects boundaries. Experimental results show that our method exhibits superior performance in both quantitative and qualitative assessments on several widely used benchmark datasets.
Three-Point Marking Method to Overcome External Parameter Disturbance in Stereo-DIC Based on Camera Pose Self-Estimation

Stereo digital image correlation (stereo-DIC) requires the stereo-camera system to remain fixed during the measurement process. This requirement brings a lot of inconvenience to the practical application of stereo-DIC. In many application scenarios such as dynamic measurement, the external parameters of the camera are almost inevitably disturbed, which will bring great errors to the DIC measurement results. To overcome this problem, this paper proposes a three-point marking method to determine the world coordinate system and a camera pose self-estimation method based on speckle patterns. The three-point marking method can use three undeformed feature points in the field of view (FOV) to determine the world coordinate system. The proposed camera pose self-estimation method can calculate the pose parameters of the binocular camera at the shooting time of each frame of image. This method does not require plane assumptions and has good applicability. Simulations and experiments were carried out to verify the effectiveness of the proposed scheme in overcoming external parameter disturbances.
Characterizing the Cost-Accuracy Performance of Cloud Applications

Emergence of applications that produce results with different accuracy allows cloud consumers to leverage the advantages of elastic cloud resources and pay-per-use pricing model. However, the trade-off between cost, accuracy and execution time of cloud applications has not been well studied due to multiple challenges. A key challenge faced by a cloud consumer is tuning the application and determining cloud resource configuration that achieves the desired application accuracy among the configuration space.
Performance Boost of Attribute-aware Semantic Segmentation via Data Augmentation for Driver Assistance

This paper is an extension of our work in developing an attribute-aware semantic segmentation method which focuses on pedestrian understanding in a traffic scene. Recently, the trending topic of semantic segmentation has been expanded to be able to collaborate with the object’s attributes recognition task; Here, it refers to recognizing a pedestrian’s body orientation. The attribute-aware semantic segmentation can be more beneficial for driver assistance compared to the conventional semantic segmentation because it can provide a more informative output to the system. In this paper, we conduct a study of the data augmentation usage as an effort to enhance the performance of the attribute-aware semantic segmentation task. The experiments show that the proposed method in augmenting the training data is able to improve the model’s performance. We also demonstrate some of qualitative results and discuss the benefits to a driver assistance system.
A Dual Stream Model for Activity Recognition: Exploiting Residual- CNN with Transfer Learning

Visual content has a protagonist role in this age of data revolution. These days, computer vision research community is fascinated towards application of convolution neural networks and transfer learning for various image and video analysis tasks. Residual connection in CNN can facilitate the training process in the deep networks. This paper investigates and uses deep residual networks with fusion based dual stream pre-trained models for activity recognition from video streams. The architecture is further trained and evaluated using standard video actions benchmarks of UCF-101, HMDB-51 and NTU RGB. Performance of depth-based variants of residual networks is also analysed. The proposed approach not only provides competitive results but also better at exploiting pre-trained model and annotated image data.
A Radical Approach to Reducing Effects of Quartz Crystal Oscillator Instability on GNSS Receiver Performance

The paper is devoted to a radical way to increase vibration resistance of global navigation satellite systems (GNSS) signal receivers (GLONASS, GPS, etc.). The method is based on the use of a common (quartz) loop in addition to individual loops- traditional for GNSS receivers phase locked loops (PLL), each of which monitors a separate radio signal of one of the GNSSs. Such a system with one common loop and many individual loops will be called quartz locked loop (QLL) system. In the absence of common loop, to increase the vibration resistance, the PLL band is expanded, but this leads to a decrease in the noise immunity of the receiver. The method discussed in the paper makes it possible to increase the vibration resistance much more than it can be done by expanding the band of independent PLLs, and at the same time practically avoids reducing noise immunity of GNSS receivers. Different analytical methods (based on z-transformations) are used in the paper, as well as a simulation method (only some results are given without detail description), semi-natural simulation, and full-scale tests carried out with a vibrational bench. In the paper the specified methods of semi-natural simulation and full-scale tests are used for studying the QLL system, as well as systems of independent PLLs.
Work-in-Progress: Using Augmented Reality Mobile App to Improve Student’s Skills in Using Breadboard in an Introduction to Electrical Engineering Course

The enhancement of first-year electrical engineering students’ skills in building circuits using the breadboard is essential. To overcome difficulties in dealing with internal breadboard connections, Augmented Reality (AR) technology used to build a 3D application. AR could improve students’ ability with respect to efficient design and build the electrical circuits in the breadboard due to the support for viewing the internal breadboard connections and learning step by step how to build circuits.This paper will explore the integration of AR into the Introduction to Electrical Engineering course in order to provide an interactive learning experience, in which abstract thinking is supported, and concept visualization, exploration, and evaluation are facilitated during the various stages of the circuits building steps. Such an AR app will permit the students to visualize virtual representations of the circuit on the breadboard in real-world settings by mixing the 3D circuit element models with a camera view of the students’ smartphone and 3D animations for circuit-building procedures.
Evolutionary-Based Weighted Broad Learning System for Imbalanced Learning

As a novel family of connectionist model, broad learning system (BLS) with the form of flat structure is established for broadly-fused feature representation. As a result of two assumptions, namely, the same misclassification cost and balanced class distribution, BLS usually fails to achieve favorable classification performance in the presence of severely skewed category distribution. To deal with this problem, we develop BLS as a cost-sensitive classification learning that assigns different misclassification costs for different categories, and minimizes the cumulative classification cost. To be more actionable, we formulate the setting of penalty factors as a multi-objective optimization problem, which can be solved by an evaluation algorithms. Thus, without rules of thumb, the proposed BLS can address various imbalanced tasks in an adaptive manner. Moreover, due to the sparse form of the cost matrix we design, the optimization problem has less objectives to be solved. Comprehensive experiments have been conducted on several benchmark and real-world data sets, the experimental results show that the proposed method achieves competitive results compared with the existing state-of-the-art imbalanced learning.
Enhanced Secure Comparison Schemes Using Homomorphic Encryption

Comparing two integers under the encrypted form is useful for privacy-preserving data mining, secure auction, and so on. Based on the ring-learning with errors (ring-LWE) assumption, Saha and Koshiba proposed a practical approach—SK17—in [NBiS2017], established under the 3-party computation model where two clients (one of them has a decryption key) compare their data via an outsource cloud server without revealing their data. In this study, we propose three enhanced schemes from SK17 to improve efficiency, security, and flexibility. We implement the first protocol to show its efficiency using the ring-LWE-based homomorphic encryption scheme proposed by Lauter et al., while providing security analysis and practicality evaluation in the theory for the other two protocols.
CTR Prediction with User Behavior: An Augmented Method of Deep Factorization Machines

In Click-Through-Rate(CTR) prediction problems, video and comment thumbs-up prediction has important commercial values. The click rate prediction system can find the target population more accurately, which not only improves the user experience but also brings profits to the companys. Click behavior has a great correlation with personal habits. The Deep Factorization Machine(DeepFM) model is at the cutting edge of CRT by learning both high-level and low-level features. But it cannot hightlight the impact of user's personal behavior with has a great correlation with click behavior. In this paper, we propose behavior-empahsized algorithms, DeepFM & Habit (Pro) and DeepFM & Habit (LR), for different situations. The experimental results on TikTok dataset (short video data) demonstrate that our proposed methods achieve superior performance with less time consumption on this CTR prediction problems.
Handwheelchair.q: New Prototype of Manual Wheelchair for Everyday Life

In this paper the second prototype of a manual wheelchair, named Handwheelchair.q, is presented. Handwheelchair.q is a manual wheelchair with an innovative system of propulsion inspired by a rowing gesture which has been made possible employing a cable for the motion transmission to a special freewheel connected to the wheel. As a result this gesture generates a traction force on the shoulder instead of the compression force generated by the gesture with the hand-rim system. The main goal of the innovative prototype is to increase the mobility of the disabled people reducing the stress on shoulder employing a conventional manual wheelchair. The functional design and the prototype are presented. Moreover, the methods and the results of preliminary tests are described and discussed. The tests aim to evaluate the traction force and the efficiency of the prototype.
Autonomous Coverage Path Planning using Artificial Neural Tissue for Aerospace Applications

Although many algorithms exist for robotic complete, coverage path planning (CPP), most algorithms are not practical for real-world use because they rely on perfect, prior knowledge of a static target environment, hardwired path planning or substantial human interaction, among other things. Moreover, many algorithms do not consider the real-world constraints of limited on-board power, computing, memory or communications, especially for low cost, multi-agent swarms. For aerospace applications, power-constrained CPP algorithms are critical because they can impact the effectiveness of future applications, such as the development of autonomous multi-robot teams for lunar site-preparation, mining and construction, or the development of terrestrial multi-robot teams to conduct visual or x-ray inspections of aircraft bodies. In this paper, we apply the Artificial Neural Tissue (ANT) control algorithm [1] [2] to solve simulated CPP tasks, where multiple agents cooperate and completely or almost completely, cover 2-dimensional, basic geometric, open grid areas in linear or quasilinear time, where time complexity is measured by the number of robot time steps and the open grid cells to cover. In these ANT simulations, there is no central controller and the agents are constrained by limited time steps, a priori knowledge of the target environment, on-board memory and sensors. Also, the agents do not communicate among themselves. However, the ANT agents do rely on pheromones/markers to track whether a grid cell has been visited, and receive information from a central station concerning total area coverage, time and global reference directions. In these CPP tasks, the performance of ANT is comparable to the best-known, grid-based, heuristic coverage algorithm with a quasilinear upper bound cover time [3] [4].
Applying Convolutional Neural Network for Object Detection on FT-Matrix 7002 DSP

Object detection, as one of the most important components in image recognition, has always been the focus and difficulty in the field of computer vision research. It has a very important use value and is now widely used in various fields. In the military field, object detection plays an important role in unmanned combat, battlefield detection, and defensive alerting. The traditional object detection mainly adopts the method of manually extracting features, and its algorithm has strong limitations and limited space for improvement. With the rapid development of artificial intelligence theory and deep learning technology in recent years, new algorithms and ideas have been introduced into the research of object detection, which has made breakthrough progress. Based on the latest deep learning theory, this paper develops a mobile military object detection system based on convolutional neural network, and deploys and optimizes it on the high-performance, low-power FT -Matrix7002 DSP embedded platform.