Reviewerly/exHarmony huggingface dataset:
Evaluation and Comparison of Different Motion Models for Flight Navigation

Navigation performance of a flight has to be maintained with a certain level of accuracy in civilian or military operations. Global navigation satellite system (GNSS) based devices in flights act as primary source of navigation. In order to improve the accuracy and robustness of the navigation, information from other systems (e.g. IMU) are fused together. The accuracy is further enhanced when adequate motion models are used during the estimation process. In this paper, we present results of multiple motion models in association with aircraft navigation and evaluate their performances. GNSS and inertial measurement unit (IMU) data are recorded in an aerial flight for about three hours. In order to highlight the impact of different motion models, data captured is processed post flight and position estimates are computed with a linearized Kalman filter (LKF). The computed positions are then compared with a reference trajectory and errors are evaluated for all the motion models. For different flight segments, the estimated position root mean square error (RMSE) varies up to a maximum of about 4 decimeters with different motion models. Also, the magnitude of the maximum deviations in highly dynamic maneuvers is reduced by a large extent when compared with different motion models and performance improvement is about 72%.
CenterRepp: Predict Central Representative Point Set's Distribution For Detection

Object detection has long been an important issue in the discipline of scene understanding. Existing researches mainly focus on the object itself, ignoring its surrounding environment. In fact, the surrounding environment provides abundant information to help detectors classify and locate objects. This paper proposes CRPDet, viz. CenterRepp Detector, a framework for object detection. The main function of CRPDet is accomplished by the CenterRepp module, which takes into account the surrounding environment by predicting the distribution of the central representative points. CenterRepp converts labeled object frames into the mean and standard variance of the sampling points' distribution. This helps increase the receptive field of objects, breaking the limitation of object frames. CenterRepp defines a position-fixed center point with significant weights, avoiding to sample all points in the surroundings. Experiments on the COCO test-dev detection benchmark demonstrates that our proposed CRPDet has comparable performance with state-of-the-art detectors, achieving 39.4 mAP with 51 FPS tested under single size input.
Auditchain: a mechanism for ensuring logs integrity based on proof of existence in a public blockchain

Digital data are critical to people and companies, acting as a crucial element in the decision-making process in different areas. Accordingly, logs can track how this data changes over time and are essential to enable auditing. The traditional approach to ensure log integrity is to store them on well-kept servers, both from a physical and a digital security standpoint. However, this approach assumes that it is difficult to exploit these servers' vulnerabilities and that these systems' administrator is honest. Distributing trust is an alternative that does not rely on these assumptions, and blockchain-based approaches are promising in that aspect. A method that stands out for providing post-storage integrity guarantees at low transaction costs is the proof of existence, which consists of sending the hash of an object to a public blockchain, proving that the object existed before its hash became included in the blockchain. However, although this method is viable, no other works generalize the concept nor make it accessible, making its wide adoption difficult. Therefore, this work aims to propose an architecture to assure log integrity through proof of existence and develop an application for consuming logs indexed using Elasticsearch: the Auditchain. We present here a case study with qualitative and quantitative data collection. As a result, we obtained a comparison of PoE fulfillment APIs, an architecture to simplify PoE of logs, and an implementation of the proposed architecture.
Multi-Granularity Heterogeneous Graph for Document-Level Relation Extraction

Reading text to extract relational facts has been a long-standing goal in natural language processing. It becomes especially challenging when the extraction scope is extended to document level, where multiple entities in a document generally exhibit complex intra- and inter-sentence relations. In this paper, we propose a novel Multi-granularity Heterogeneous Graph (MHG) to tackle this challenge. Specifically, we define four types of nodes with different granularities and eight types of edges based on heuristic rules, entrusting the MHG two major advantages. On the one hand, it connects any two entities with a short path in the graph to better handle the complex inter-sentence interactions between entities. On the other hand, it enables rich interactions among nodes with different granularities to promote accurate multi-hop reasoning. Experimental results on the largest document-level relation extraction dataset suggest that the proposed model achieves new state-of-the-art performance.
Toward a collision avoidance system based on the integration of technologies

Integrating emerging technologies into current systems is critical to enhance the human quality of life. In the field of transportation, the automobile is the predominant locomotion method used by people. Even though new vehicular safety systems have been integrated into vehicles, road accidents are still one of the major reasons for death worldwide. In general, Vulnerable Road Users (VRUs) that share roads with vehicles have second priority in safety systems for the Intelligent Transportation System, since they are mostly focused on avoiding collision between vehicles. However, VRUs do represent a very significant percent of the victims of road accidents.
A Closer Look at Machine Learning Code

Software using Machine Learning algorithms is becoming ever more ubiquitous making it equally important to have good development processes and practices. Whether we can apply insights from software development research remains open though, since it is not yet clear, whether data-driven development has the same requirements as its traditional counterpart. We used eye tracking to investigate whether the code reading behaviour of developers differs between code that uses Machine Learning and code that does not. Our data shows that there are differences in what parts of the code people consider of interest and how they read it. This is a consequence of differences in both syntax and semantics of the code. This reading behaviour already shows that we cannot take existing solutions as universally applicable. In the future, methods that support Machine Learning must iterate on existing knowledge to meet the challenges of data-driven development.
Smaller RLZ-Compressed Suffix Arrays

Recently it was shown (Puglisi and Zhukova, Proc. SPIRE, 2020) that the suffix array (SA) data structure can be effectively compressed with relative Lempel-Ziv (RLZ) dictionary compression in such a way that arbitrary subar-rays can be rapidly decompressed, thus facilitating compressed indexing. In this paper we describe optimizations to RLZ-compressed SAs, including generation of more effective dictionaries and compact encodings of index components, both of which reduce index size without adversely affecting subarray access speeds relative to other compressed indexes. Our experimental analysis also elucidates the relationship between subarray size and per element access time.
Resolution Limits of 20 Questions Search Strategies for Moving Targets

We establish fundamental limits of tracking a moving target over the unit cube under the framework of 20 questions with measurement-dependent noise. In this problem, there is an oracle who knows the instantaneous location of a target. Our task is to query the oracle as few times as possible to accurately estimate the trajectory of the moving target, whose initial location and velocity is unknown. We study the case where the oracle’s answer to each query is corrupted by random noise with query-dependent discrete distribution. In our formulation, the performance criterion is the resolution, which is defined as the maximal absolute value between the true location and estimated location at each discrete time during the searching process. We are interested in the minimal resolution of any non-adaptive searching procedure with a finite number of queries and derive approximations to this optimal resolution via the second-order asymptotic analysis.
Reference gage calibration methods of probe nanometry systems

Abstract Reference gage calibration methods of probe nanometry systems are considered in this work. Existing methods for calibrating reference standards provide high measurement accuracy, but at the same time are distinguished by a high complexity of practical implementation, in particular, the interferometric method, or low accuracy, but with simple practical implementation. Therefore, the urgent task is to develop a calibration methodology that provides a sufficiently high measurement accuracy with a relatively simple practical implementation. The paper proposes a methodology based on the combined use of two methods: a comparative assessment of the step heights of the calibrated gages using one of the probe nanometry systems and the precision measurement of the step height of one of the calibrated gages by computer three-dimensional reconstruction of images in a scanning electron microscope. The components of the measurement uncertainty arising when determining the height of the steps of the calibration standards are investigated. The calculated value of the expanded uncertainty showed that the achievable measurement accuracy is comparable to the accuracy of the interferometric method with a simpler practical implementation. Practical testing of the proposed methodology has shown that the use of calibration techniques developed on its basis provides high accuracy and reproducibility of the results obtained.
Quantisation Scale-Spaces

Recently, sparsification scale-spaces have been obtained as a sequence of inpainted images by gradually removing known image data. Thus, these scale-spaces rely on spatial sparsity. In the present paper, we show that sparsification of the co-domain, the set of admissible grey values, also constitutes scale-spaces with induced hierarchical quantisation techniques. These quantisation scale-spaces are closely tied to information theoretical measures for coding cost, and therefore particularly interesting for inpainting-based compression. Based on this observation, we propose a sparsification algorithm for the grey-value domain that outperforms uniform quantisation as well as classical clustering approaches.
Norm Loss: An efficient yet effective regularization method for deep neural networks

Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to address these issues, researchers develop weight regularization methods and activation normalization methods. In this work we propose a weight soft-regularization method based on the Oblique manifold. The proposed method uses a loss function which pushes each weight vector to have a norm close to one, i.e. the weight matrix is smoothly steered toward the so-called Oblique manifold. We evaluate our method on the very popular CIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art architectures, namely the ResNet and wide-ResNet. Our method introduces negligible computational overhead and the results show that it is competitive to the state-of-the-art and in some cases superior to it. Additionally, the results are less sensitive to hyperparameter settings such as batch size and regularization factor.
Deep Semi-Supervised Metric Learning Via Identification of Manifold Memberships

Three of the key challenges in semi-supervised metric learning are the difficulty in sampling loss-producing triplets, the difficulty in locating similar data which are faraway from the anchor points, and the difficulty in making the model robust to noisy predicted pseudolabels. We propose a method which allows the use of class-representative anchors (proxies), and avoids the computational costs associated with triplet sampling. Our new semi-supervised metric learning method propagates labels along mutual nearest neighbor pairs, so that faraway similar data can be drawn to the anchors, while data which are not along these paths (and hence not on the same manifold as the anchors) can be pushed away from these anchors. By assessing the number of different labels which were propagated to the same point, we obtain an estimate of the probability that our prediction of the pseudolabel is accurate, and hence able to attenuate the effect of uncertain pseudolabels on our model by factoring in the confidence of these predictions. We show the superiority of our method over various state-of-the-art methods on four diverse public datasets.
Research on Face Emotional Recognition Algorithm

Since the traditional identification technology couldn't satisfy the requirements of real-time performance and accuracy, a face emotional recognition method based on the feature extraction algorithm of LBP and LPQ and support vector machine (SVM) was proposed. The face emotional image was selected for normalizing and image enhancement. The feature extraction algorithm of LBP and LPQ was used to extract the feature of the image. The principal component analysis (PCA) was used to reduce the dimensions of the massive feature information, and the SVM was used to classify and recognize the face emotion. The experiments were carried out with Matlab platform. The experiment results show that the average recognition rate of this method was 88.41%.
Highly efficient architecture of elliptic curve scalar multiplication with fault tolerance over GF(2&lt;sup&gt;m&lt;/sup&gt;)

In this paper, an efficient hardware architecture of scalar multiplication is proposed for elliptic curve cryptography. To reduce circuit area, we propose an elliptic curve operation unit architecture for Montgomery Ladder Algorithm in projective coordinates. The basic modular arithmetic circuit in elliptic curve group operation module is reused to realize coordinates transformation and y-coordinate recovery operation. Considering concurrent error-detecting and fault-tolerant, we improve the existing error detection scheme by reusing intermediate results and predicting fault. The simulation and DC synthesis results show that the scalar multiplication circuit designed according to the proposed architecture reduces the time cost of fault detection in single iteration to 1 clock cycle at 100% fault detection rate, and the efficiency is improved 96% than the existing literature.
Analysis of a generalised expectation–maximisation algorithm for Gaussian mixture models: a control systems perspective

The expectation–maximisation (EM) algorithm is one of the most popular methods used to solve the problem of parametric distribution-based clustering in unsupervised learning. In this paper, we propose to analyse a generalised EM (GEM) algorithm in the context of Gaussian mixture models, where the maximisation step in the EM is replaced by an increasing step. We show that this GEM algorithm can be understood as a linear time-invariant (LTI) system with a feedback nonlinearity. Therefore, we explore some of its convergence properties by leveraging tools from robust control theory. Lastly, we explain how the proposed GEM can be designed and present a pedagogical example to understand the advantages of the proposed approach.
Binary background model with geometric mean for author-independent authorship verification

Authorship verification (AV) is one of the main problems of authorship analysis and digital text forensics. The classical AV problem is to decide whether or not a particular author wrote the document in question. However, if there is one and relatively short document as the author’s known document, the verification problem becomes more difficult than the classical AV and needs a generalised solution. Regarding to decide AV of the given two unlabeled documents (2D-AV), we proposed a system that provides an author-independent solution with the help of a Binary Background Model (BBM). The BBM is a supervised model that provides an informative background to distinguish document pairs written by the same or different authors. To evaluate the document pairs in one representation, we also proposed a new, simple and efficient document combination method based on the geometric mean of the stylometric features. We tested the performance of the proposed system for both author-dependent and author-independent AV cases. In addition, we introduced a new, well-defined, manually labelled Turkish blog corpus to be used in subsequent studies about authorship analysis. Using a publicly available English blog corpus for generating the BBM, the proposed system demonstrated an accuracy of over 90% from both trained and unseen authors’ test sets. Furthermore, the proposed combination method and the system using the BBM with the English blog corpus were also evaluated with other genres, which were used in the international PAN AV competitions, and achieved promising results.
An Efficient Umpire Key Frame Segmentation in Cricket Video using HOG and SVM

The identification of most important frames in games videos is a universal issue for several purposes, such as games categorization, recognition of acts, Human identification, object classification and video summarization. These tasks can be carried out greater efficaciously as an alternative to maintaining the entire recording, only a handful of main frames are used. In an immense corpus of preparing information to show the models, existing key casing discovery strategies is commonly anticipated managed examination and include manual stamping of keyframes. Naming requires human marginalize from various foundations to clarify key casings in recordings which aren't just costly and tedious yet additionally inclined to emotional blunders and irregularities between the labelers. To beat these problems, we propose a programmed self-managed strategy for distinguishing key casings in a video. To beat these issues, we suggest programmed and self-directed division and examination of Umpire keyframe of side interest in cricket video photographs by means of filtering Blur, Sky oriented, Replay, and Crowded frames which are now not useful frames for the Umpire Action Recognition in Highlight Extraction. Our method comprises an algorithm which segments umpire Key Frames by filtering Blur, Crowded, Replay and Sky view oriented frames for the manually selected ODI Cricket Video of Length 2:12:38 seconds. The projected scheme works in two parts. In 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> part HOG features are calculated for filtering images to train the SVM model. In second part trained SVM test the remaining test video frames based on knowledge base to filter the unnecessary images of Cricket Video. Our classifier displays radiant outcomes with right non-informative Video Frames identification and arrangement have demonstrated diminished Frames of cricket video with decreased handling time.
Edge Feature based Moving Object Detection Using Aerial Images: A Comparative Study

Edge features or characteristics are the significant local intensity variation of pictorial structure in an image or frame or video sequence. Edge detection process finds the existence and position of pixels causes by significant differences in intensity of the images or frames or video sequences. However, selection of appropriate edge features specially to detect moving objects using aerial types of video sequences is still an elusive research concern because of various constraints for aerial frames, i.e. various altitudes, lack of features available for detection, motion variation etc. This research proposes comparative evaluation of different edge features based moving object detection methods to make it easy to decide which edge features based detection method is appropriate for extraction of moving object from aerial frames. Proposed research selected three edge features for detection, i.e. Sobel, Prewitt and Canny for moving object extraction to process high and low pictorial brightness variation for aerial video sequences. Proposed research illustrates the comparative performance of edge features based moving object detection. Each of the method is evaluated with extensive experimental analysis with standard performance metrics. Experimental results demonstrate effective comparison among each method with various performance metrics to ensure optimum detection performance for moving objects.
Group ID Authorized -Based Certificateless Encryption with Equality Test

Ciphertext equivalence test can compare data encrypted by different public keys. In electronic medical applications, only users within the patient group are expected to be able to test the equivalence of ciphertext. Based on certificateless public key cryptosystem and group user authorization mechanism, this paper designs a ciphertext equivalence test encryption scheme with group user authorization. Firstly, the framework of group id authorized-based certificateless encryption with equality test (GACLEET) is proposed. Secondly, this paper proposes a specific GACLEET scheme and analyzes the correctness of the scheme. Finally, based on the random oracle model, it is proved that the scheme has OW-CCA and IND-CCA security. The scheme can not only solve the key escrow problem of group id-based encryption with equality test scheme, but also realize group granularity authorization and equivalent testing functions.
Towards Automatic and Portable Data Loading Template Attacks on Microcontrollers

Although the original idea of profiling attacks is to recover the secret key of one cryptographic device with a leakage model generated from an “identical copy”, this concept (usually called portability) cannot always be upheld. Many previous works create the model and perform the attack on the same device, assuming that the generated model would work for another copy as well. However, intrinsic differences among different devices, trace sets, or experimental setups cause behavioral changes that deemed portability challenging and eventually leading to the attack failure. Besides, another critical issue for the success of template attacks (which is also sometimes overlooked) is the search for points in power traces where leakage depends on data. To address both issues at the same time, in this paper we show an automatic way to tune the selected points of interest in order to improve the performance of portable data loading template attacks. Our approach is able to find common points of leakage across devices in a completely automated manner, which we support with experimental results.