Reviewerly/exHarmony huggingface dataset:
Escaping Local Minima in Search-Based Planning using Soft Duplicate Detection

Search-based planning for relatively low-dimensional motion-planning problems such as for autonomous navigation and autonomous flight has been shown to be very successful. Such framework relies on laying a grid over a state-space and constructing a set of actions (motion primitives) that connect the centers of cells. However, in some cases such as kinodynamic motion planning, planning for bipedal robots with high balance requirements, computing these actions can be highly non-trivial and often impossible depending on the dynamic constraints. In this paper, we explore a soft version of discretization, wherein the state-space remains to be continuous but the search tries to avoid exploring states that are likely to be duplicates of states that have already been explored. We refer to this property of the search as soft duplicate detection and view it as a relaxation of the standard notion of duplicate detection. Empirically, we show that the search can efficiently compute paths in highly-constrained settings and outperforms alternatives on several domains.
WiSe — Slide Segmentation in the Wild

We address the task of segmenting presentation slides, where the examined page was captured as a live photo during lectures. Slides are important document types used as visual components accompanying presentations in a variety of fields ranging from education to business. However, automatic analysis of presentation slides has not been researched sufficiently, and, so far, only preprocessed images of already digitalized slide documents were considered. We aim to introduce the task of analyzing unconstrained photos of slides taken during lectures and present a novel dataset for Page Segmentation with slides captured in the Wild (WiSe). Our dataset covers pixel-wise annotations of 25 classes on 1300 pages, allowing overlapping regions (i.e., multi-class assignments). To evaluate the performance, we define multiple benchmark metrics and baseline methods for our dataset. We further implement two different deep neural network approaches previously used for segmenting natural images and adopt them for the task. Our evaluation results demonstrate the effectiveness of the deep learning-based methods, surpassing the baseline methods by over 30%. To foster further research of slide analysis in unconstrained photos, we make the WiSe dataset publicly available to the community.
Security-Minded Verification of Space Systems

Modern space systems are increasing in complexity. The advent of the Internet of Space Things, coupled with the commercialisation of space has resulted in an ecosystem that is difficult to control and brings about new security challenges. In such critical systems, it is common to conduct verification strategies to ensure that the underpinning software is correct. Formal verification is achieved by modelling the system and verifying that the model obeys particular functional and safety properties. Many connected systems are now the target of a variety of threat actors attempting to realise different goals. Threat modelling is the approach employed to analyse and manage the threats from adversaries. Common practice is that these two approaches are conducted independently of one another. In this paper, we argue that the two should be mutually informed, and describe a methodology for security-minded formal verification that combines these analysis techniques. This approach will streamline the development process and give a more formal grounding to the security properties identified during threat analysis.
Fast Trajectory Planning for Multiple Quadrotors using Relative Safe Flight Corridor

This paper presents a new trajectory planning method for multiple quadrotors in obstacle-dense environments. We suggest a relative safe flight corridor (RSFC) to model safe region between a pair of agents, and it is used to generate linear constraints for inter-collision avoidance by utilizing the convex hull property of relative Bernstein polynomial. Our approach employs a graph-based multi-agent pathfinding algorithm to generate an initial trajectory, which is used to construct a safe flight corridor (SFC) and RSFC. We express the trajectory as a piecewise Bernstein polynomial and formulate the trajectory planning problem into one quadratic programming problem using linear constraints from SFC and RSFC. The proposed method can compute collision-free trajectory for 16 agents within a second and for 64 agents less than a minute, and it is validated both through simulation and indoor flight test.
Analysis and Categorization of Traffic Streams by Artificial Intelligence

This report presents an evaluation of artificial neural networks in terms of computational efficiency, by analyzing transmitted information flows for determination the type of defined traffic categories using artificial intelligence. The subject of study are Markov M/M/c circuits with unlimited number of waiting calls in the queue and fixed number of server stations in accordance with the desired test categories, as follows c=5, c=10 and c=15. Three layer architectures are applied to different types of neural output activators with Levenberg-Marquardt training, respectively linear, tangent-sigmoidal and logarithmic-sigmoidal. The lowest values of the Mean Squared Error (MSE) of 0.0080, 0.0041, and 0.1923 are experimentally established at 7, 3, and 25 hidden neurons for the indicated activation functions. An accuracy levels of 94.4%, 100.0%, and 70.6% were obtained against indicator levels for identical numbers of neurons.
PPM: A Provenance-Provided Data Sharing Model for Open Banking via Blockchain

Open banking becomes more and more prevailing in Australia in recent years. It aims to make the users’ personal financial data mutually transfer and exchange across different banks in a secure way. The sensitive data in a financial area requires higher authentication and provenance for participants. In this paper, we propose a provenance-provided data sharing model (PPM) via blockchain to meet the requirements of open banking. The model employs the programmable smart contracts as the middle witness between users and third-party services, and provides the modifications on data layer (data content, transaction structure), smart contract layer (ACL, logic), and application layer (customized APIs). Based on that, our PPM model possesses the properties of transparent authentication, privacy-provided control, and auditable provenance. The analyses and discussion show that our model is a secure and achievable system in the face of open banking.
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction

In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model.
Trajectory Estimation for Geo-Fencing Applications on Small-Size Fixed-Wing UAVs

The steadily increasing popularity of Unmanned Aerial Vehicles (UAVs) is creating new opportunities in diverse fields of technology and business. However, this increase of popularity also raises safety concerns. To tackle the primary concern of keeping the UAV inside a designated region, a novel trajectory estimation algorithm for geo-fencing applications is proposed. We derive the Beta-Trajectory that takes into account constraints in curvature as well as constraints in the change of curvature which is bounded by the maximum roll-rate of the aircraft. We incorporate the Beta-Trajectory into a geo-fencing algorithm. By using our open-source uavAP autopilot, the applicability and necessity of accurate trajectory estimation algorithms for geo-fencing applications are shown on small fixed-wing aircraft. The model and algorithm are validated in high-fidelity simulations as well as in real flight testing.
Dynamic Texture Recognition using a Hybrid Generative-Discriminative Approach with Hidden Markov Models and Support Vector Machines

Dynamic textures (DT) constitute of objects characterized by stationary properties in time such as how leaves move in a windy day. Classification of DTs has made tremendous impact in various domains such as video synthesis and segmentation. In this paper, we propose the use of Fisher kernels with Dirichlet based and Beta-Liouville (BL) based hidden Markov models (HMM) for DT recognition. Experiments demonstrate promising results on the DynTeX Alpha dataset using the proposed generative-discriminative approach. To the best of our knowledge, this is the first application of Dirichlet and BL HMMs to DT recognition.
Ontology based approach to development of augmented reality applications

In recent years, ontologies and semantic representations are becoming more and more used for improvement of software development process. In this paper, ontology based approach is adopted to development of augmented reality (AR) applications. As outcome, we propose a framework for automated AR application code generation leveraging ontologies and semantic annotations of builidng information model (BIM). It is tested in two case studies: electronic guide to university students and smart home application.
Study of Convolutional Neural Network in Recognizing Static American Sign Language

Sign language is a form of communication language to connect a deaf-mute person to the world. It involves the uses of hand gestures and body movement in order to express an idea. Nevertheless, general publics are mostly not educated to comprehend the sign language. For this reason, there is a need to have a translator to facilitate the communication. This paper would like to present a Convolutional Neural Network (CNN) model for predicting American Sign Language. There are 4800 images were captured to train and validate the proposed model. 95% recognition accuracy was attained in experiment, which shows robust performance in recognition 24 static American Sign Language pattern. The successful development of this model can be served as the basis to develop a more complicated sign language translator.
Integrating Machine Learning with Off-the-Shelf Traffic Flow Features for HTTP/HTTPS Traffic Classification

Accurate traffic classification is a key requirement for different network and security monitoring/planning tools. The evolution of Internet protocols and applications has caused traditional traffic classification approaches to be ineffective in certain cases. Key causes of the inaccuracy include: (i) the increase in the encrypted traffic; (ii) the rise in the usage of dynamic port numbers for different applications; and (iii) multiple applications running over HTTP/HTTPS protocols. Traditional solutions for traffic analysis, classification, and measurement fall short in providing visibility in users' activities - a key requirement for network and security monitoring tools. In this paper, we evaluate an automatic classifier for encrypted Social media, Video and Audio traffic without relying on particular application layer header fields that can be easily modified. We leverage machine learning algorithms together with the features provided by the well-known off-the-shelf traffic flow exporters. We evaluate the performance of such a system also for generalization (robustness) purposes on different networks. Experimental results show promising performances in terms of generating robust traffic classification on large traffic data when the trained model is moved to different networks.
A Mobile Application with Augmented Reality for the Learning of the Quechua Language in Pre-School Children

In Peru, Quechua-speaking people decrease every year due to disinterest, discrimination, lack of motivation for language learning even more in educational centers, lack of teachers willing to teach the language and lack of teaching materials with which children can interact during His learning. Augmented Reality (AR) is a great ally of education providing efficiency, productivity and increasing the interest of students in their academic activities. This paper is structured and implemented an application with AR for the learning of Quechua in preschool children, which will give children the possibility to connect with their environment, their own history and their ancestors in a fun way, and grow with a different awareness of the others, in addition to providing linguistic stimuli achieving greater ease for learning other languages, since after mastering this language develops skills for new learning.
A Character Attention Generative Adversarial Network for Degraded Historical Document Restoration

Despite of recent breakthroughs in the accuracy of single character recognition using the deeper convolution neural networks, one of the remaining problems is that OCRs almost fail to recognize character patterns when they are severely degraded, especially those of the historical documents. Another problem to recognize characters in historical documents is the lack of sufficient training patterns because of the heavy cost for annotation. This paper proposes a character attention generative adversarial network named CAGAN for restoring heavily degraded character patterns in historical documents so that OCRs improve their accuracy and even help archeologists to decode them. The network is based on the U-Net like architecture [1] with skip connections, and it is trained by the proposed loss function including the common adversarial loss (global loss) and the hierarchical character attentive loss (local loss). We made an experiment on 118 categories of most common Japanese Kanji characters, collected from severely damaged historical documents called Heijokyo mokkan written during the Nara period in Japan. The experiment shows that our method restores the shapes of characters and improves the recognition rate significantly, which is helpful for archeologists to decode damaged character patterns.
Image Compression using Run Length Encoding and its Optimisation

Images are among the most common and popular representations of data. Digital images are used for professional and personal use ranging from official documents to social media. Thus, any Organization or individual needs to store and share a large number of images. One of the most common issues associated with using images is the potentially large file-size of the image. Advancements in image acquisition technology and an increase in the popularity of digital content means that images now have very high resolutions and high quality, inevitably leading to an increase in size. Image compression has become one of the most important parts of image processing these days due to this. The goal is to achieve the least size possible for an image while not compromising on the quality of the image, that gives us the perfect balance. Therefore, to achieve this perfect balance many compression techniques have been devised and it is not possible to pinpoint the best one because it is really dependent on the type of image to be compressed. So here we are going to elaborate on converting images into binary images and the Run length Encoding (RLE) algorithm used for compressing binary images. Now, RLE is itself a very effective and simple approach for compression of images but, sometimes, the size of an image actually increases after RLE algorithm is applied to the image and this is one of the major drawbacks of RLE. In this research paper we are going to propose an extension or maybe an upgradation to RLE method which will ensure that the size of an image never exceeds beyond its original size, even in the worst possible scenario.
Collaborative model tracking with robust occlusion handling

Currently, the discriminative correlation filter-based trackers have achieved higher tracking accuracy. However, visual tracking still faces challenges in terms of heavy occlusion, scale variation and so on. In this study, the authors intend to solve heavy occlusion by introducing collaborative model into classifier-box. Firstly, they introduce complex colour features into correlation filter tracker to improve the effect of the tracker. Secondly, they introduce a multi-scale method into their tracker to ease the scale problem. Thirdly, in order to solve the heavy occlusion in the tracking process, they adopt the locally weighted distance and classifier-box. Their algorithm achieves distance precision rates of 81.7 and 77.4% on OTB2013 dataset and OTB2015 dataset, respectively. Their contribution focuses on solving heavy occlusion by using colour features, locally weighted distance and classifier-box. The experimental results on OTB2013 and OTB2015 datasets demonstrate their algorithm to perform better than state-of-the-art methods.
Deep Learning for the Detection of Tabular Information from Electronic Component Datasheets

The global electronic components supply chain consists of tens of thousands of e-component manufacturers who fabricate over a billion distinct components. These are described in datasheets that differ in style, layout and content, and frequently publish the salient product information in tables. Keeping up-to-date on this information consumes a great deal of human effort and corporate resources. Based on the motivation that AI-based techniques are strong candidates to minimize human intervention in many applications, in this paper, we aim at the first stage of this problem and conduct a comparison of deep learning methods in detecting tabular elements in these documents. Deep learning-based object detectors are shown to be state of the art in detection tasks in different domains therefore we chose two cutting-edge models to adapt to this field, namely Faster-RCNN and RetinaNet. We use backbone networks which are pre-trained on visually salient datasets then employ transfer learning techniques to adapt to our domain. We compare the two networks under two different datasets, namely a dataset that is widely used in academic studies and a private dataset that is used by the suppliers in real supply chains. Our numerical results show that the two networks adapt well to the domain with Faster-RCNN exhibiting marginally better precision with more than 1% difference. However, RetinaNet stands out with promising recall values indicating Feature Pyramid Network architecture can potentially detect technical documents better.
Fuzzy Information Retrieval Based on Continuous Bag-of-Words Model

In this paper, we study the feasibility of performing fuzzy information retrieval by word embedding. We propose a fuzzy information retrieval approach to capture the relationships between words and query language, which combines some techniques of deep learning and fuzzy set theory. We try to leverage large scale data and the continuous-bag-of words model to find the relevant feature of words and obtain word embedding. To enhance retrieval effectiveness, we measure the relativity among words by word embedding, with the property of symmetry. Experimental results show that the recall ratio, precision ratio, and harmonic average of two ratios of the proposed method outperforms the ones of the traditional methods.
EARLY FUSION for Goal Directed Robotic Vision

Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline. Modern vision architectures require the agent to build a summary representation of the entire scene, even if most of the input is irrelevant to the agent's current goal. In this work, we flip this paradigm, by introducing EARLYFUSION vision models that condition on a goal to build custom representations for downstream tasks. We show that these goal specific representations can be learned more quickly, are substantially more parameter efficient, and more robust than existing attention mechanisms in our domain. We demonstrate the effectiveness of these methods on a simulated item retrieval problem that is trained in a fully end-to-end manner via imitation learning.
Attentional Matrix Factorization with Document-context awareness and Implicit API Relationship for Service Recommendation

The rapid development of the mashup approach significantly plays a pivot role in building Web-based and mobile applications. Easy accessibility of data and functions are the main advantages for programmers to develop mashups from abundant sources of APIs. However, it simultaneously brings more difficulties to choose suitable APIs for a mashup, especially when the historical relations between APIs and mashups are very sparse. Existing probabilistic matrix factorization (PMF) recommender systems can effectively exploit the latent features of the invocations with the same weight. However, not all features are equally significant and predictive, and the useless features may bring noises to the model. Also, many current works explored the influence of mashups' relationships, but few of them sheds lights on the relationship between APIs and their contextual interaction, which can be mined from their content description. This paper improves the PMF model by distinguishing the importance of latent feature interactions. We present an Attentional PMF model, which leverages a neural attention network to learn the significance of feature interactions and uses Doc2Vec technique for mining the contextual information. We also exploit the relationship between APIs from both their contextual similarities and invocation history and add them to the prediction model as a regularization part. Our experiments are performed with datasets from ProgrammableWeb. The results show that our model significantly outperforms some state-of-art recommender systems in mashup service applications.
Standardization of forming and expressing preliminary evaluative opinions on digital evidence

The growing number of cases involving overlooked or misinterpreted digital evidence is raising concerns among factfinders and decision-makers about the reliability of digital forensic conclusions. To reduce the risk of mistakes and misinterpretations of forensic observations, including but not limited to digital evidence, there is a pressing need to standardize how evaluative opinions are formed and expressed. Responding to this need, the international community is drafting ISO-21043 and the UK Forensic Science Regulator is drafting an evaluative interpretation standard that promote a likelihood ratio approach. This approach is suitable for fully evaluative opinions in many forensic disciplines, but until more refined methods for evaluating digital evidence are developed, digital forensic practitioners require an interim solution to address immediate needs. More broadly, digital evidence is used in many non-judicial contexts that do not require fully evaluative opinions expressed as a likelihood ratio. This work proposes a standardized approach to formulating and expressing preliminary evaluative opinions in terms of strength of evidence in a manner that employs scientific reasoning within a logical Bayesian framework and can be understood by non-specialist factfinders. Illustrative case examples are presented that involve digital evidence tampering. In addition, this work presents a proof-of-concept database of cases involving tampering of digital evidence that could support assignment of strength of evidence in similar cases.