Reviewerly/exHarmony huggingface dataset:
Dual Inverse Sensor Model for Radar Occupancy Grids

Occupancy grids (OG) are widely used for low-level fusion of radar data in various automotive applications. At the core of OG generation, usually, there is an inverse sensor model (ISM), which is a conditional cell occupancy probability model. Traditional ISM's lack mechanisms decreasing occupancy likelihoods along directions that produce no detections; thus, false detections tend to perpetuate on the OG. In this paper, we propose a novel Inverse Sensor Model including a “positive” component describing occupancy probabilities induced by radar detections and a “negative” component handling lack of detections in a given direction. This dual model proves especially useful in multi-sensor/multi-frame context since false detections by different radars and/or at different moments are uncorrelated and thus can be efficiently mitigated.
EDCNN: A Novel Network for Image Denoising

In recent years, deep convolutional neural network (DCNN) has achieved impressive performance in image denoising. However, the existing CNN-based methods cannot work very well on those images with high-level noise. In order to solve this problem, we propose a novel method, named enhanced deep convolution neural network (EDCNN), for image de-noising in this work. Compared with existing models, ED-CNN adopts the residual learning in both global and local manners. In particular, we further apply a residual excitation strategy that enables a short path to be built directly from the input image to output layer. The final model, composed of 52 weight layers, is much deeper than existing ones. Experimental results on standard test images have demonstrated that the proposed method outperforms several state-of-the-art de-noising algorithms in terms of both quantitative measure and visual perception quality.
Ordering groups constructively

The existence of a linear order on a group that is compatible with the group structure generally requires transfinite methods. However, this can be circumvented by concentrating on the consistency of a suitable propositional theory. To this end, we work with Scott’s multiple-conclusion entailment relations to describe the positive cones of a group. The commutative case then leads to a constructive version of Levi’s theorem that an Abelian group be orderable if and only if it is torsion-free. Subsequently, Cederquist and Coquand’s fundamental theorem of entailment relations prompts a finitary version of Sikora’s theorem.
Short Text Understanding Combining Text Conceptualization and Transformer Embedding

Short text understanding is a key task and popular issue in current natural language processing.Because the content of short texts is characterized by sparsity and semantic limitation, the traditional search methods that analyze only the semantics of literal text for short text understanding and similarity matching have certain restrictions.In this paper, we propose a combined method based on knowledge-based conceptualization and a transformer encoder.Specifically, for each term in a short text, we obtain its concepts and enrich the short text information from a knowledge base based on cooccurrence terms and concepts, construct a convolutional neural network (CNN) to capture local context information, and introduce the subnetwork structure based on a transformer embedding encoder.Then, we embed these concepts into a low-dimensional vector space to obtain more attention from these concepts based on a transformer.Finally, the concept space and transformer encoder space construct the understanding models.An experiment shows that the method in this paper can effectively capture more semantics of short texts and can be applied to a variety of applications, such as short text information retrieval and short text classification.
A Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Ants

A color quantization technique that combines the operations of two existing methods is proposed. The first method considered is the Greedy orthogonal bi-partitioning method. This is a very popular technique in the color quantization field that can obtain a solution quickly. The second method, called Ant-tree for color quantization, was recently proposed and can obtain better images than some other color quantization techniques. The solution described in this article combines both methods to obtain images with good quality at a low computational cost. The resulting images are always better than those generated by each method applied separately. In addition, the results also improve those obtained by other well-known color quantization methods, such as Octree, Median-cut, Neuquant, Binary splitting or Variance-based methods. The features of the proposed method make it suitable for real-time image processing applications, which are related to many practical problems in diverse disciplines, such as medicine and engineering.
How to Improve Object Detection in a Driver Assistance System Applying Explainable Deep Learning

Reliable perception and detection of objects are one of the fundamental aspects of vehicle autonomy. Although model-based approaches perform well in the area of planning and control, they often fail when applied to perception due to the open-world nature of problems for autonomous vehicles. Therefore, data-driven approaches to object detection and location are likely to be used in both self-driving cars and advanced driver assistance systems. In particular, the deep neural networks proved to be excellent in detection and classification of objects from images, often achieving super-human performance. However, neural networks applied in intelligent vehicles need to be explainable, providing rationales for their decisions. In this paper, we demonstrate how such an interpretation can be provided for a deep learning system that detects specific objects (charging posts) for driver assistance in an electric bus. The interpretation, achieved by visualization of attention heat maps, has twofold use: it allows us to augment the dataset used for training, improving the results, but it also may be used as a tool when fielding the system with the given bus operator. Explaining which parts of the images triggered the decision helps to eliminate misdetections.
Proximal Splitting Networks for Image Restoration

Image restoration problems are typically ill-posed requiring the design of suitable priors. These priors are typically hand-designed and are fully instantiated throughout the process. In this paper, we introduce a novel framework for handling inverse problems related to image restoration based on elements from the half quadratic splitting method and proximal operators. Modeling the proximal operator as a convolutional network, we defined an implicit prior on the image space as a function class during training. This is in contrast to the common practice in literature of having the prior to be fixed and fully instantiated even during training stages. Further, we allow this proximal operator to be tuned differently for each iteration which greatly increases modeling capacity and allows us to reduce the number of iterations by an order of magnitude as compared to other approaches. Our final network is an end-to-end one whose run time matches the previous fastest algorithms while outperforming them in recovery fidelity on two image restoration tasks. Indeed, we find our approach achieves state-of-the-art results on benchmarks in image denoising and image super resolution while recovering more complex and finer details.
Discriminative Video Representation Learning Using Support Vector Classifiers

Most popular deep models for action recognition in videos generate independent predictions for short clips, which are then pooled heuristically to assign an action label to the full video segment. As not all frames may characterize the underlying action-indeed, many are common across multiple actions-pooling schemes that impose equal importance on all frames might be unfavorable. In an attempt to tackle this problem, we propose discriminative pooling, based on the notion that among the deep features generated on all short clips, there is at least one that characterizes the action. To identify these useful features, we resort to a negative bag consisting of features that are known to be irrelevant, for example, they are sampled either from datasets that are unrelated to our actions of interest or are CNN features produced via random noise as input. With the features from the video as a positive bag and the irrelevant features as the negative bag, we cast an objective to learn a (nonlinear) hyperplane that separates the unknown useful features from the rest in a multiple instance learning formulation within a support vector machine setup. We use the parameters of this separating hyperplane as a descriptor for the full video segment. Since these parameters are directly related to the support vectors in a max-margin framework, they can be treated as a weighted average pooling of the features from the bags, with zero weights given to non-support vectors. Our pooling scheme is end-to-end trainable within a deep learning framework. We report results from experiments on eight computer vision benchmark datasets spanning a variety of video-related tasks and demonstrate state-of-the-art performance across these tasks.
The ellipselet transform

A fair amount of important objects in natural images have circular and elliptical shapes. For example, the nucleus of most of the biological cells is circular, and a number of parasites such as Oxyuris have elliptical shapes in microscopic images. Hence, atomic representations by two-dimensional (2D) basis functions based on circle and ellipse can be useful for processing these images. The first researches have been done in this domain by introducing circlet transform.The main goal of this article is expanding the circlet to a new one with elliptical basis functions.In this article, we first introduce a new transform called ellipselet and then compare it with other X-let transforms including 2D-discrete wavelet transform, dual-tree complex wavelet, curvelet, contourlet, steerable pyramid, and circlet transform in the application of image denoising.Experimental results show that for noises under 30, the ellipselet is better than other geometrical X-lets in terms of Peak Signal to Noise Ratio, especially for Lena which contains more circular structures. However, for Barbara which has fine structures in its texture, it has worse results than dual-tree complex wavelet and steerable pyramid.
Checking Temporal Service Level Agreements for Web Service Compositions with Temporal Parameters

Temporal Service Level Agreements are an important quality criterion for the offering of composed web services. The formulation of temporal requirements of a service might involve parameters passed to the service. We propose a technique enabling service providers to check whether they can guarantee a temporal service level agreement (SLA) requested by a client. This techniques is based on analyzing the controllability of service compositions with temporal parameters taking into account range constraints on these parameters as specified in a temporal SLA.
Credit Card Fraud Prediction And Detection using Artificial Neural Network And Self-Organizing Maps

The credit card business has increased speedily over the last two decades. Corporations and establishments are moving towards various online services, which aims to permit their customers with high potency and accessibility. The evolution is a huge step towards potency, accessibility and profitableness of view. Nevertheless, it additionally has some downsides. These smart services are recently prone to significant security related vulnerabilities. Developing business through card depends on the fact that neither the card nor the user needs to be present at the point of transaction. Thus, it is impossible for merchandiser to check weather the cardholder is real or not. Companies' loss in recent times are majorly due to the credit card fraud and the fraudsters who ceaselessly obtain new ways to commit the unlawful activities. As we know that Artificial Neural Network has the ability to work as a human brain when trained properly. We have also implemented SOM for accuracy purpose. In this paper, we discuss about the performance of the network and their accuracy.
A Sequential Labelling Approach for the Named Entity Recognition in Arabic Language Using Deep Learning Algorithms

Named Entity Recognition (NER) involves finding and categorizing minute text components into pre- defined categories such as name of person, location etc. NER is a type of information extraction task which has a crucial role in improving the performance of various NLP applications. For a morphologically abundant Semitic language like Arabic, the NER task is highly challenging due to its unique morphological characteristics and peculiarities. This paper introduces a deep learning based approach for Arabic NER which make use of well-known deep neural network (DNN) architectures like Recurrent neural network (RNN), Long short term memory (LSTM), Gated recurrent unit (GRU), stacked and bidirectional versions of these three architectures. ANERcorp dataset is used for the evaluation of the Arabic NER model and Accuracy is chosen as the performance metric. On model evaluation, it is observed that bidirectional variants of DNNs provide better accuracy measures compared to their unidirectional variants.
Towards Developing a Mobile Application for Illiterate People to Reduce Digital Divide

Illiteracy is one of the main complications in developing countries like Bangladesh. The adoption and uses of the ICT based applications by the illiterate people of a developing country (like Bangladesh) is very essential to reduce the digital inequality and the digital divide for the social and economic development. The objective of this article is to develop a usable and adaptable mobile application focusing job search for illiterate people in Bangladesh. To achieve this objective, firstly, a conceptual framework was proposed based on the findings of the literature survey and a need finding study, while the need findings study was replicated with 40 illiterate people. Secondly, an android based mobile application with user-intuitive user interface was developed. To make the interface intuitive for illiterate people, it encompasses voice, icon, pictography, minimal number of text in native language, and the like. Finally, the application was evaluated with 40 illiterate people in a laboratory environment. The evaluation study showed that intuitive user interfaces is important to develop a usable mobile application for illiterate people that in turn help them to adopt and use the ICT based applications to reduce the digital divide in Bangladesh.
Enhanced Feature Subset Selection Using Niche Based Bat Algorithm

Redundant and irrelevant features disturb the accuracy of the classifier. In order to avoid redundancy and irrelevancy problems, feature selection techniques are used. Finding the most relevant feature subset that can enhance the accuracy rate of the classifier is one of the most challenging parts. This paper presents a new solution to finding relevant feature subsets by the niche based bat algorithm (NBBA). It is compared with existing state of the art approaches, including evolutionary based approaches. The multi-objective bat algorithm (MOBA) selected 8, 16, and 248 features with 93.33%, 93.54%, and 78.33% accuracy on ionosphere, sonar, and Madelon datasets, respectively. The multi-objective genetic algorithm (MOGA) selected 10, 17, and 256 features with 91.28%, 88.70%, and 75.16% accuracy on same datasets, respectively. Finally, the multi-objective particle swarm optimization (MOPSO) selected 9, 21, and 312 with 89.52%, 91.93%, and 76% accuracy on the above datasets, respectively. In comparison, NBBA selected 6, 19, and 178 features with 93.33%, 95.16%, and 80.16% accuracy on the above datasets, respectively. The niche multi-objective genetic algorithm selected 8, 15, and 196 features with 93.33%, 91.93%, and 79.16 % accuracy on the above datasets, respectively. Finally, the niche multi-objective particle swarm optimization selected 9, 19, and 213 features with 91.42%, 91.93%, and 76.5% accuracy on the above datasets, respectively. Hence, results show that MOBA outperformed MOGA and MOPSO, and NBBA outperformed the niche multi-objective genetic algorithm and the niche multi-objective particle swarm optimization.
Socially Aware Path Planning for a Flying Robot in Close Proximity of Humans

In this article, we present a preliminary motion planning framework for a cyber-physical system consisting of a human and a flying robot in vicinity. The motion planning of the flying robot takes into account the human’s safety perception. We aim to determine a parametric model for the human’s safety perception based on test data. We use virtual reality as a safe testing environment to collect safety perception data reflected on galvanic skin response (GSR) from the test subjects experiencing a flying robot in their vicinity. The GSR signal contains both meaningful information driven by the interaction with the robot and also disturbances from unknown factors. To address the issue, we use two parametric models to approximate the GSR data: (1) a function of the robot’s position and velocity and (2) a random distribution. Intuitively, we need to choose the more likely model given the data. When GSR is statistically independent of the flying robot, then the random distribution should be selected instead of the function of the robot’s position and velocity. We implement the intuitive idea under the framework of hidden Markov model (HMM) estimation. As a result, the proposed HMM-based model improves the likelihood compared to the Gaussian noise model, which does not make a distinction between relevant and irrelevant samples due to unknown factors. We also present a numerical optimal path planning method that considers the safety perception model while ensuring spatial separation from the obstacle despite the time discretization. Optimal paths generated using the proposed model result in a reasonably safe distance from the human. In contrast, the trajectories generated by the standard regression model with the Gaussian noise assumption, without consideration of unknown factors, have undesirable shapes.
A new method for locating Kikuchi bands in electron backscatter diffraction patterns

Electron backscatter diffraction (EBSD) device can provide crystal structure, orientation, and phase content data through analysis of EBSD patterns. The reliability and precision of these data depend on the quality of the band position and zone axes data. This study introduces a new image processing method that can accurately provide the location of Kikuchi bands and poles. In this method, pattern rotation and gray gradient calculation are employed after for the initial detection of Kikuchi lines. Hough transform and Gaussian function are used for the final definition of bands position. Based on the position of Kikuchi bands, the indices of lattice planes and zone axes can be obtained precisely and easily. Angles between zone axes are calculated using locating results. The maximum error for a single-crystal silicon sample is only 8.07%, illustrating the accuracy of this new method.
Dynamic security rules for legacy systems

Industry 4.0 tries to digitalize the production process further. The digitalization is achieved by connecting different entities (machines, worker) to data-exchange, which needs to be dynamic and to adapt to different changing situations and members in the process. However, just exchanging data might lead to confidentiality issues. The data-exchange needs to be protected to secure the confidentiality and trust in the system. Therefore, security rules need to adapt to these dynamic situations. One part of a possible solution might be dynamic access control rules. However in many cases, existing "legacy" systems are reused, which can in not handle dynamic access control rules. Due to this gap between the required and provided functionality, we propose an approach, which integrates dynamic access control based on the system-context into legacy systems. Our approach uses a security adaption controller, which dynamically adapts the access control rules to a new situation and integrates them into an existing legacy system. We discussed our approach with industrial practitioners and related our approach to their existing legacy system. In addition, we performed a scalability analysis to demonstrate the applicability of our approach in a realistic environment.
Ranking warnings from multiple source code static analyzers via ensemble learning

While there is a wide variety of both open source and proprietary source code static analyzers available in the market, each of them usually performs better in a small set of problems, making it hard to choose one single tool to rely on when examining a program looking for bugs in the source code. Combining the analysis of different tools may reduce the number of false negatives, but yields a corresponding increase in the absolute number of false positives (which is already high for many tools). A possible solution, then, is to filter these results to identify the issues least likely to be false positives. In this study, we post-analyze the reports generated by three tools on synthetic test cases provided by the US National Institute of Standards and Technology. In order to make our technique as general as possible, we limit our data to the reports themselves, excluding other information such as change histories or code metrics. The features extracted from these reports are used to train a set of decision trees using AdaBoost to create a stronger classifier, achieving 0.8 classification accuracy (the combined false positive rate from the used tools was 0.61). Finally, we use this classifier to rank static analyzer alarms based on the probability of a given alarm being an actual bug in the source code.
Using dates as contextual information for personalised cultural heritage experiences

We present semantics-based mechanisms that aim to promote reflection on cultural heritage by means of dates (historical events or annual commemorations), owing to their connections to a collection of items and to the visitors’ interests. We argue that links to specific dates can trigger curiosity, increase retention and guide visitors around the venue following new appealing narratives in subsequent visits. The proposal has been evaluated in a pilot study on the collection of the Archaeological Museum of Tripoli (Greece), for which a team of humanities experts wrote a set of diverse narratives about the exhibits. A year-round calendar was crafted so that certain narratives would be more or less relevant on any given day. Expanding on this calendar, personalised recommendations can be made by sorting out those relevant narratives according to personal events and interests recorded in the profiles of the target users. Evaluation of the associations by experts and potential museum visitors shows that the proposed approach can discover meaningful connections, while many others that are more incidental can still contribute to the intended cognitive phenomena.