Reviewerly/exHarmony huggingface dataset:
Bonded Mining: Difficulty Adjustment by Miner Commitment

Proof-of-work blockchains must implement a difficulty adjustment algorithm (DAA) in order to maintain a consistent inter-arrival time between blocks. Conventional DAAs are essentially feedback controllers, and as such, they are inherently reactive. This approach leaves them susceptible to manipulation and often causes them to either under- or over-correct. We present Bonded Mining, a proactive DAA that works by collecting hash rate commitments secured by bond from miners. The difficulty is set directly from the commitments and the bond is used to penalize miners who deviate from their commitment. We devise a statistical test that is capable of detecting hash rate deviations by utilizing only on-blockchain data. The test is sensitive enough to detect a variety of deviations from commitments, while almost never misclassifying honest miners. We demonstrate in simulation that, under reasonable assumptions, Bonded Mining is more effective at maintaining a target block time than the Bitcoin Cash DAA, one of the newest and most dynamic DAAs currently deployed. In this preliminary work, the lowest hash rate miner our approach supports is 1% of the total and we directly consider only two types of fundamental attacks. Future work will address these limitations.
Pedestrian detection based on improved Faster RCNN algorithm

Pedestrian detection is receiving more attention with the development of deep learning and smart driving technology. However, the performance of existed pedestrian detection schemes are influenced by complicated natural scenes. To address the problem, we improve the Faster RCNN original framework by combining feature concatenation and hard negative mining strategies to boost the performance on challenging pedestrian detection datasets. In this paper, the proposed model is trained on Daimler pedestrian dataset and then tested on public Caltech and INRIA pedestrian datasets, which achieves the miss rate of 24.27% and 10.31%. The results indicate that the improved Faster RCNN network outperforms the state-of-the-art methods.
Sprague-Grundy function of matroids and related hypergraphs

We consider a generalization of the classical game of Nim called hypergraph Nim. Given a hypergraph H on the ground set V={1,…,n} of n piles of stones, two players alternate in choosing a hyperedge H∈H and strictly decreasing all piles i∈H. The player who makes the last move is the winner. In this paper we give an explicit formula that describes the Sprague-Grundy function of hypergraph Nim for several classes of hypergraphs. In particular we characterize all 2-uniform hypergraphs (that is graphs) and all matroids for which the formula works. We show that all self-dual matroids are included in this class.
Machine and Deep Learning Algorithms for Twitter Spam Detection

Twitter allows users to send short text-based messages with up to 280 characters which is called “tweets”. The reputation of Twitter attracts the spammers to spread malevolent programming through URLs attached in tweets. Twitter spam has become a critical problem. Spam refers to a variety of prohibited behaviours that violate the Twitter rules. In this paper, different machine and deep learning algorithms are used to detect if the tweet is spammer or not. The performance of six machine learning algorithms, namely Random Forest (RF), Naive Bayes (NB), Bayesian Network (BN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Multi-Layer Perceptron (MLP) and one deep learning algorithm which is Recurrent Neural Network (RNN) are evaluated. Different test options are used, namely cross validation and percentage split tests. Results show that RF predicts the best result with lowest error rate and highest classification accuracy rate with different test options comparing to all algorithms.
A Robust Fundamental Matrix Estimation Method Based on Epipolar Geometric Error Criterion

In this paper, a robust fundamental matrix estimation method based on epipolar geometric error criterion is proposed.First, the method removes outliers into the computation of the fundamental matrix instead of taking it as an independent processing step.The potential error corresponding points are eliminated by iteration to achieve the stable estimation of the fundamental matrix.Then, the epipolar geometry error criterion is used to identify outliers and the estimation results of the fundamental matrix are obtained during each iteration.The iterative process can converge quickly.Even if a large number of matched outliers are present, the calculated values will soon become stable.Experiments have been carried out for synthetic and real image pairs, which show that the proposed method performs very well in terms of robustness to noises and outliers.Additionally it has a low computational cost and is convenient for use in practical applications.
Linear codes over signed graphs

We give formulas, in terms of graph theoretical invariants, for the minimum distance and the generalized Hamming weights of the linear code generated by the rows of the incidence matrix of a signed graph over a finite field, and for those of its dual code. Then we determine the regularity of the ideals of circuits and cocircuits of a signed graph, and prove an algebraic formula in terms of the multiplicity for the frustration index of an unbalanced signed graph.
Salient object detection based on novel graph model

In this paper, we present a salient object detection method based on novel graph structure. Given image is segmented into small image regions as basic units, we firstly construct an effective background-based map, each image region’s saliency value is determined by its feature contrast with the image boundary. Then, saliency propagation mechanism is used to update all regions’ saliency values by introducing a novel graph structure to better exploit the relationship between adjacent image regions. Finally, we propose an optimization method to further highlight salient objects and suppress background noises. Experimental results demonstrate adequately the superiority of proposed approach.
Temporal and Spatial Routing for Large Scale Safe and Connected UAS Traffic Management in Urban Areas

Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic scheduling and management functions are needed to coordinate the launching of sUAS from different launch sites and plan their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airbone sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm for sUAS trajectory management in a high density urban area. It plans sUAS movements in a spatial and temporal maze with the consideration of obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms reference route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, the conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used as a solution to improve the efficiency of airspace and communication resource utilization for next generation smart city and smart transportation.
Generalized-mean Cramér-Rao bounds for multiparameter quantum metrology

In multiparameter quantum metrology, the weighted-arithmetic-mean error of estimation is often used as a scalar cost function to be minimized during design optimization. However, other types of mean error can reveal different facets of permissible error combination. By introducing the weighted $f$-mean of estimation error and quantum Fisher information, we derive various quantum Cram\'er-Rao bounds on mean error in a very general form and also give their refined versions with complex quantum Fisher information matrices. We show that the geometric- and harmonic-mean quantum Cram\'er-Rao bounds can help to reveal more forbidden region of estimation error for a complex signal in coherent light accompanied by thermal background than just using the ordinary arithmetic-mean version. Moreover, we show that the $f$-mean quantum Fisher information can be considered as information-theoretic quantities to quantify asymmetry and coherence as quantum resources.
Luminance-based video backdoor attack against anti-spoofing rebroadcast detection

We introduce a new backdoor attack against a deep-learning video rebroadcast detection network. In addition to the difficulties of working with video signals rather than still images, injecting a backdoor into a deep learning model for rebroadcast detection presents the additional problem that the backdoor must survive the digital-to-analog and analog-to-digital conversion associated to video rebroadcast. To cope with this problem, we have built a backdoor attack that works by varying the average luminance of video frames according to a predesigned sinusoidal function. In this way, robustness against geometric transformation is automatically achieved, together with a good robustness against luminance transformations associated to display and recapture, like Gamma correction and white balance. Our experiments demonstrate the effectiveness of the proposed backdoor attack, especially when the attack is carried out by also corrupting the labels of the attacked training samples.
Study on the Motivational and Physical Effects of Two VR Exergames

Head-mounted displays for accessing virtual reality have in the past years begun to achieve their commercial potential. Gameplay using the provided game controls and space, often results in increased mobility due to this the games may have unintentional health benefits. In this study we compare two distinct exergames. We conducted a study with 17 participants and collected mixed data in the form of heart rate, interviews and accelerometer data, to assess the exercise benefit of these games. Virtual reality games can be played for exercise purposes. Participants were more eager to recommend others to exercise with exergames than exercise themselves using them. With a more hectic game, it was shown that their heart rate increased, and they moved a lot during the game sessions.
Discrete Double-bit Hashing

Hashing has been widely used for nearest neighbors search over big data. Hashing encodes high dimensional data points into binary codes. Most hashing methods use the single-bit quantization (SBQ) strategy for coding the data. However, this strategy often encodes neighboring points into totally different bits. Recently, a double-bit quantization (DBQ) strategy was proposed, which can better preserve the similarity of the data. The hashing problems are generally NP-hard, due to the discrete constraints. For tractability, some relaxation methods were proposed by discarding the discrete constraints. However, such a manner makes the hash codes less effective, due to the large quantization error. To obtain high-quality hash codes, some discrete hashing methods were proposed, which directly solve the hashing problem without any relaxations. However, the existing discrete hashing methods can only deal with single-bit hashing. In this paper, we propose a discrete hashing method to solve double-bit hashing problems. To address the difficulty brought by the discrete constraints, we propose a method to transform the discrete hashing problem into an equivalent continuous optimization problem. Then, we devise algorithms based on DC (difference of convex functions) programming to solve the problem. Numerical experiments are provided to show the superiority of the proposed methods.
Video stabilization algorithm based on Pearson correlation coefficient

In the video image acquisition, the monitoring equipment is very easy to cause abnormal lens jitter due to external factors. This kind of jitter has a great influence on the background modeling effect. In view of the above situation, this paper proposes a video jitter correction algorithm based on Pearson correlation coefficient to remove abnormal lens shake. The initial frame of the video is removed from the top, bottom, left and right by 50 pixels, and the middle portion is taken as the reference frame. The reference frame and other frames of the video are projected horizontally and vertically. The Pearson correlation coefficient is used to describe the inter-frame correlation, and the other frames are found and the reference frame is found. The most relevant area, repairing video anomalies and getting a stable video.
Image based fixed time visual servoing control for the quadrotor UAV

In this study, the visual servoing control problem is investigated, and a fixed time based visual servoing control scheme is proposed for quadrotor unmanned aerial vehicles. First, based on the perspective projection principle and target points, effective image features are defined on an image plane called the virtual image plane. Then, the image feature dynamics and QUAV dynamics are combined to derive a coupled non-linear system, and the non-linear system is decoupled into two subsystems under inner–outer loop control framework. Furthermore, the novel sliding mode surfaces based fixed-time controllers are designed, and the resulting closed-loop systems are proved to be stable. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed control scheme.
Combined Edge- and Stixel-based Object Detection in 3D Point Cloud

Environment perception is critical for feasible path planning and safe driving for autonomous vehicles. Perception devices, such as camera, LiDAR (Light Detection and Ranging), IMU(Inertial Measurement Unit), etc., only provide raw sensing data with no identification of vital objects, which is insufficient for autonomous vehicles to perform safe and efficient self-driving operations. This study proposes an improved edge-oriented segmentation-based method to detect the objects from the sensed three-dimensional (3D) point cloud. The improved edge-oriented segmentation-based method consists of three main steps: First, the bounding areas of objects are identified by edge detection and stixel estimation in corresponding two-dimensional (2D) images taken by a stereo camera. Second, 3D sparse point clouds of objects are reconstructed in bounding areas. Finally, the dense point clouds of objects are segmented by matching the 3D sparse point clouds of objects with the whole scene point cloud. After comparison with the existing methods of segmentation, the experimental results demonstrate that the proposed edge-oriented segmentation method improves the precision of 3D point cloud segmentation, and that the objects can be segmented accurately. Meanwhile, the visualization of output data in advanced driving assistance systems (ADAS) can be greatly facilitated due to the decrease in computational time and the decrease in the number of points in the object's point cloud.
Research on CBM of the Intelligent Substation SCADA System

An equipment status management and maintenance platform of an intelligent substation monitoring and control system is built in the Tai-Tam substation of the Taipower company. The real-time operating status of the equipment, such as the server, supervisory control and data acquisition (SCADA) human–machine interface (HMI) software, switches, intelligent electronic devices (IEDs), merging units (MUs), as well as the entire SCADA system, are evaluated comprehensively. First, the status information of all equipment is collected, and the theory of relative deterioration degree (RDD) and fuzzy theory (FT) are applied to calculate the fuzzy evaluation matrix of the equipment influencing factors. Then, the subjective analytic hierarchy process (AHP) and the objective entropy method for weighting are combined to calculate the comprehensive weights of the equipment influencing factors. Finally, the result of the equipment status evaluation is obtained using the fuzzy comprehensive evaluation (FCE) method and is presented at the equipment status management and maintenance platform. Such equipment status evaluation results can be used by the inspection and maintenance personnel to determine the priority for equipment maintenance and repair. The result of this study may serve as a valuable reference to utility companies when making maintenance plans.
Signature Recognition and Verification Using Multiple Classifiers Combination of Hu’s and HOG Features

In this paper, an offline HSR and verification work has been carried out using Hu's invariant moment and HOG feature descriptor. In the HOG feature descriptor, the distribution (histograms) of directions of gradients (oriented gradients) are used as features. Gradients (x and y derivatives) of an image are useful because the magnitude of gradients is large around edges and corners (regions of abrupt intensity changes).The Hu moments was used to characterize and quantify the shape of the HSR. This moment was used to calucate the centriod and orientation of the HSR. By using this local histogram features, the signature was divided into zones both the Cartesian and polar coordinate systems and two different histogram features are calculated for each zone. The fusion of all MC (global and user-dependent classifiers trained with each feature type), achieves a 15% equal error rate in skilled forgery test. As a result, a 98.33% percent test accuracy is obtained by using the proposed method.
Explaining What a Neural Network has Learned: Toward Transparent Classification

Deep Neural Networks (DNNs) have limited ability to explain their acquired knowledge or decision rationale. As a result, end-users perceive DNNs as black-boxes and are hesitant to fully adopt them in safety-critical applications. Therefore, developing explainable DNNs has become a prime interest in neural network research. This paper presents a methodology for linguistically explaining the knowledge a DNN classifier has acquired in training. The main objective is to help users understand what the DNN has learned about each class. The presented methodology is fuzzy logic based and involves end-users of the system in the explanation process, enabling users to customize the explanations to match their requirements. This paper presents the explanation methodology, metrics of explanation quality, validation steps, and a discussion of advantages and limitations. The explanation methodology was implemented on a benchmark classification problem. Experimental results demonstrated the method's capability to explain the DNN-knowledge and validated the explanations.
Improved image segmentation algorithms for detecting types of acute lymphatic leukaemia

A modified digital image processing technique is presented to accurately investigate the types of the acute lymphatic leukaemia (ALL). In this technique, three complementary steps are performed. In the first one, a colour segmentation procedure is used to obtain images including only the white blood cell. In the second step, the histogram equalisation and linear contrast stretching procedures are utilised to obtain images for the nucleus. In the third step, images for the cytoplasm only may be reconstructed from which the vacuoles may be detected. For accurate detection for ALL types, significant and discriminative parameters are introduced such as geometric shape of nucleus membrane, equivalent sizes for the nucleus and cytoplasm and their ratio when the shapes of nucleuses are regular or irregular. This method is applied to a blood smear images for real cases of ALL. To validate the present technique, a comparison is made between present results with their counterparts obtained by expert (manual) technique. Another assessment is performed by comparing the average accuracy of the present technique and the average accuracy of different image processing techniques in the literature. The assessment confirms the high efficiency of the present technique in detecting all types of ALL.
Every build you break: developer-oriented assistance for build failure resolution

Continuous integration is an agile software development practice. Instead of integrating features right before a release, they are constantly being integrated into an automated build process. This shortens the release cycle, improves software quality, and reduces time to market. However, the whole process will come to a halt when a commit breaks the build, which can happen for several reasons, e.g., compilation errors or test failures, and fixing the build suddenly becomes a top priority. Developers not only have to find the cause of the build break and fix it, but they have to be quick in all of it to avoid a delay for others. Unfortunately, these steps require deep knowledge and are often time-consuming. To support developers in fixing a build break, we propose Bart, a tool that summarizes the reasons for Maven build failures and suggests possible solutions found on the internet. We will show in a case study with 17 participants that developers find Bart useful to understand build breaks and that using Bart substantially reduces the time to fix a build break, on average by 37%. We have also conducted a qualitative study to better understand the workflows and information needs when fixing builds. We found that typical workflows differ substantially between various error categories, and that several uncommon build errors are both very hard to investigate and to fix. These findings will be useful to inform future research in this area.
Towards an Adaptive Control Strategy Based on Type-2 Fuzzy Logic for Autonomous Mobile Robots

The main purpose considered in this paper is to maintain a specific location and behavior for a robot that uses type-2 fuzzy logic for controlling its behavior. In this work, we propose a combination of behaviors by following a trajectory without leaving or losing it and avoiding obstacles in an omnidirectional mobile platform. The results of the simulation show the advantages of the proposed approach. We describe the previous knowledge about type-2 fuzzy logic, the virtualization of the mobile robot and its modeling according to real situations. The proposed control system is developed in Matlab/Simulink, the system can model and guide a mobile robot in a successful way in simulated and real environments.