Reviewerly/exHarmony huggingface dataset:
Learning Background Subtraction by Video Synthesis and Multi-scale Recurrent Networks

This paper addresses the moving objects segmentation in videos, i.e. Background Subtraction (BGS) using a deep network. The proposed structure learns temporal associativity without losing spatial information by using the convolutional Long Short-Term Memory (LSTM). It learns the spatial relation by forming various-size spatial receptive fields through the various scale recurrent networks. The most serious problem in training the proposed network is that it is very difficult to find or make a sufficient number of pixel-level labeled video datasets. In order to overcome this limitation, we generate many training frames by combining the annotated foreground objects from some available datasets with the background of the target video. The contribution of this paper is to provide the first multi-scale recurrent networks for the BGS, which works well for many kinds of surveillance videos and provides the best performance in CDnet 2014 which is widely used for the BGS testing.
SGS-SRAD Filter for Denoising and Edge Preservation of Ultrasound Images

The speckle noise being multiplicative in nature degrades the quality of ultrasound images. This noise limits the contrast resolution, complicates quantitative and visual measurements and diagnosis based on image. In this work, a hybrid denoising technique is suggested which comprehends the properties of two speckle noise suppression filters. The proposed work combines noise reduction attribute of Savitzky-Golay smoothing (SGS) filter and edge preserving characteristic of speckle reducing anisotropic diffusion (SRAD) filter to denoise ultrasound images. The proposed hybrid filter is applied and tested on synthetic images, B-scan simulated images, and real ultrasound images. The conventional filters i.e. median, Lee, Wiener, Kuan, Frost, Non-local means, squeeze box filter, SGS and SRAD are also designed for comparative analysis. The quantitative and qualitative results of denoised synthetic and real ultrasound images show that the proposed SGS-SRAD filter outperforms the conventional denoising methods.
A 1 Gbps Chaos-Based Stream Cipher Implemented in 0.18 μm CMOS Technology

In this work, a novel chaos-based stream cipher based on a skew tent map is proposed and implemented in a 0.18 μm CMOS (Complementary Metal-Oxide-Semiconductor) technology. The proposed ciphering algorithm uses a linear feedback shift register that perturbs the orbits generated by the skew tent map after each iteration. This way, the randomness of the generated sequences is considerably improved. The implemented stream cipher was capable of achieving encryption speeds of 1 Gbps by using an approximate area of ~ 20 , 000 2-NAND equivalent gates, with a power consumption of 24.1 mW. To test the security of the proposed cipher, the generated keystreams were subjected to National Institute of Standards and Technology (NIST) randomness tests, proving that they were undistinguishable from truly random sequences. Finally, other security aspects such as the key sensitivity, key space size, and security against reconstruction attacks were studied, proving that the stream cipher is secure.
Child-display interaction

During the last decade, touchless gestural interfaces have been widely studied as one of the most promising interaction paradigms in the context of pervasive displays. In particular, avatars and silhouettes have been proved to be effective in communicating the touchless gestural interactivity supported by displays. In the paper, we take a child-display interaction perspective by exploring avatar-based touchless gestural interfaces. We believe that large displays offer an opportunity to stimulate child experience and engagement, for instance when learning about art, as well as bringing a number of challenges. The purpose of this study is twofold: 1) identifying the relevant aspects of children's interactions with a large display based on a touchless avatar-based interface, and 2) understanding the impact on recalling the content that arises from the interaction. We engaged 107 children over a period of five days during a public event at the university premises. Collected data were analyzed, and the outcomes transformed into three lessons learnt for informing the future design.
Dynamic obstacle avoidance based on multi-sensor fusion and Q-learning algorithm

Owing to the shortness from the single-function obstacle avoidance sensors itself, and the low efficiency brought by obstacle uncertainty under the dynamic environment, a solution is proposed for the problem that mobile robot would automatically avoid in the static and dynamic environment. In this paper, the feature level fusion of laser sensor and sonar sensor is used to make up for the shortcomings of single laser or single sonar. Then it is more flexible and convenient to avoid obstacles by adding the action angle of state transition in Q learning algorithm. Then, by adding the action angle of the state transition to the Q learning algorithm, the obstacles are more flexible and convenient. The validity of the proposed method is verified by simulation, and an example of a robot is given to illustrate the effectiveness of the proposed method.
Adaptive Learning Path Recommendation based on Graph Theory and an Improved Immune Algorithm

Adaptive learning in e-learning has garnered researchers' interest.In it, learning resources could be recommended automatically to achieve a personalized learning experience.There are various ways to realize it.One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements.This paper summarizes existing works and proposes an innovative approach.Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts.Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm.Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences.In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it.In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity.The results show that the proposed algorithm increases the computational efficiency and effectiveness.Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view.Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.
Advanced Quantum Based Neural Network Classifier and Its Application for Objectionable Web Content Filtering

In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content.
Data Replication on the Cloud/Edge

This work presents a database replication system capitalising on hybrid cloud/edge infra-structures, which may be used in novel software architectures like microservices' applications. The system aims to reduce the latency perceived by clients performing read and update operations on a database, by locating replicas on edge nodes nearer end users. The replicas' convergence algorithm is based on eventual consistency and presents a novel solution that combines Operations Transformation and CRDTs techniques. The system follows the MongoDB data model and is validated on geographically disperse Amazon AWS data centers simulating edge nodes.
TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks

Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media.The systems developed by TECHSSN team uses multi-level classification techniques.We have developed two systems.In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google's pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary.The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification.Input data with a probability of less than 0.70 in the first level are passed on to the second level.The misclassified examples are classified correctly in the second level.
Spammers Detection Based on Reviewers’ Behaviors Under Belief Function Theory

Nowadays, we note the dominance of the online reviews which become an essential factor in customers’ decision to purchase a product or service. Driven by the immense financial profits from reviews, some corrupt individuals or organizations deliberately post fake reviews to promote their products or to demote their competitors’ products, trying to mislead or influence customers. Therefore, it is crucial to spot these spammers in order to detect the deceptive reviews, to protect companies from this harmful action and to ensure the readers confidence. In this way, we propose a novel approach able to detect spammers and to accord a spamicity degree to each reviewer relying on some spammers indicators while handling the uncertainty in the different inputs through the strength of the belief function theory. Tests are conducted on a real database from Tripadvisor to evaluate our method performance.
Multi-atlas based neonatal brain extraction using a two-level patch-based label fusion strategy

Compared to adults, brain extraction from magnetic resonance (MR) images of newborn infants is particularly challenging due to their smaller brain size, which causes lower spatial resolution, lower tissue contrast and ambiguous tissue intensity distribution. In this work, a multi-atlas patch-based label fusion method is presented for automatic brain extraction from neonatal head MR images. In this method, a number of atlases are first selected uniformly among a set of training images. After nonlinear alignment of the selected atlas images to the target image, a probabilistic gray level-coded brain mask is created and used to assign brain/non-brain labels to voxels with different degree of uncertainty using a modified non-local patch-based label fusion method based on the integration of low-level and in-depth search patch selection strategies. Experiments with 40 neonates aged between 37 and 44 weeks showed an average Dice, Jaccard and Conformity coefficients of 0.993, 0.986 and 0.986, respectively. We compared the performance of the proposed method with two multi atlas-based methods, i.e. NLPB and MASS, and two popular non-learning-based methods, i.e. BSE and BET. Compared to these methods, our method achieved higher accuracy with brain masks very close to manually extracted ones and produced lower false negative and false positive rates. Our proposed method allows for accurate and efficient brain extraction, a crucial step in brain MRI applications such as brain tissue segmentation and volume estimation in neonates.
Burrows-Wheeler Transform of Words Defined by Morphisms

The Burrows-Wheeler transform (BWT) is a popular method used for text compression. It was proved that BWT has optimal performance on standard words, i.e. the building blocks of Sturmian words. In this paper, we study the application of BWT on more general morphic words: the Thue-Morse word and to generalizations of the Fibonacci word to alphabets with more than two letters; then, we study morphisms obtained as composition of the Thue-Morse morphism with a Sturmian one. In all these cases, the BWT efficiently clusters the iterates of the morphisms generating prefixes of these infinite words, for which we determine the compression clustering ratio.
Experimental verification of five-qubit quantum error correction with superconducting qubits

Quantum error correction is an essential ingredient for universal quantum computing. Despite tremendous experimental efforts in the study of quantum error correction, to date, there has been no demonstration in the realisation of universal quantum error correction code (QECC), with the subsequent verification of all key features including the identification of an arbitrary physical error, the capability for transversal manipulation of the logical state, and state decoding. To address this notoriously difficult challenge, we push the limits of the depth of superconducting quantum circuits and experimentally realise the universal five-qubit QECC, the so-called smallest perfect code that permits corrections of generic single-qubit errors. In the experiment, having optimised the encoding circuit, we employ an array of superconducting qubits to realise the five-qubit QECC for several typical logical states including the magic state, an indispensable resource for realising non-Clifford gates. The encoded states are prepared with an average fidelity of $57.1(3)\%$ while with a high fidelity of $98.6(1)\%$ in the code space. Then, the arbitrary single-qubit errors introduced manually are identified by measuring the stabilizers. We further implement logical Pauli operations with a fidelity of $97.2(2)\%$ within the code space. Finally, we realise the decoding circuit and recover the input state with a process fidelity of $57.4(7)\%$. After decoding, by identifying errors via the measurement of the four ancillae and then mathematically recovering the qubit state, we verify the power of error correction of this code. Thus, by demonstrating each key aspect of error correction with the five-qubit code, our work establishes the viability of experimental quantum error correction with superconducting qubits and paves the route to fault-tolerant quantum computing.
The complexity of prioritising patching

As American journalist and essayist HL Mencken once wrote: “For every complex problem there is a solution that is concise, clear, simple, and wrong.” Anyone working in or around vulnerability remediation knows the apparently ‘simple’ task of applying a patch is anything but. The vulnerability lifecycle is filled with pitfalls and deceptively complex tasks. Anyone working in or around vulnerability remediation knows that the apparently ‘simple’ task of applying a patch is anything but. The vulnerability lifecycle is filled with pitfalls. The time and effort needed to remediate any single vulnerability across an entire enterprise are often underestimated. This creates an obvious and urgent demand for prioritisation, which requires we understand more about the world of vulnerabilities. Michael Roytman of Kenna Security and Jay Jacobs at the Cyentia Institute explore what the open vulnerability landscape looks like and investigate multiple factors contributing to the remediation efforts.
Online Abstraction with MDP Homomorphisms for Deep Learning

Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy. In this paper, we propose a new algorithm for finding abstract MDPs in environments with continuous state spaces. It is based on MDP homomorphisms, a structure-preserving mapping between MDPs. We demonstrate our algorithm's ability to learn abstractions from collected experience and show how to reuse the abstractions to guide exploration in new tasks the agent encounters. Our novel task transfer method outperforms baselines based on a deep Q-network in the majority of our experiments. The source code is at~\urlhttps://github.com/ondrejba/aamas_19.
Train, Sort, Explain: Learning to Diagnose Translation Models

Robert Schwarzenberg, David Harbecke, Vivien Macketanz, Eleftherios Avramidis, Sebastian Möller. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 2019.
Building anatomically realistic jaw kinematics model from data

Recent work on anatomical face modeling focuses mainly on facial muscles and their activation. This paper considers a different aspect of anatomical face modeling: kinematic modeling of the jaw, i.e., the temporomandibular joint (TMJ). Previous work often relies on simple models of jaw kinematics, even though the actual physiological behavior of the TMJ is quite complex, allowing not only for mouth opening, but also for some amount of sideways (lateral) and front-to-back (protrusion) motions. Fortuitously, the TMJ is the only joint whose kinematics can be accurately measured with optical methods, because the bones of the lower and upper jaw are rigidly connected to the lower and upper teeth. We construct a person-specific jaw kinematic model by asking an actor to exercise the entire range of motion of the jaw while keeping the lips open so that the teeth are at least partially visible. This performance is recorded with three calibrated cameras. We obtain highly accurate 3D models of the teeth with a standard dental scanner and use these models to reconstruct the rigid body trajectories of the teeth from the videos (markerless tracking). The relative rigid transformations samples between the lower and upper teeth are mapped to the Lie algebra of rigid body motions in order to linearize the rotational motion. Our main contribution is to fit these samples with a three-dimensional nonlinear model parameterizing the entire range of motion of the TMJ. We show that standard principal component analysis (PCA) fails to capture the nonlinear trajectories of the moving mandible. However, we found these nonlinearities can be captured with a special modification of autoencoder neural networks known as nonlinear PCA. By mapping back to the Lie group of rigid transformations, we obtain a parametrization of the jaw kinematics which provides an intuitive interface allowing the animators to explore realistic jaw motions in a user-friendly way.
How Much I Can Rely on You: Measuring Trustworthiness of a Twitter User

Trustworthiness in an online environment is essential because individuals and organizations can easily be misled by false and malicious information receiving from untrustworthy users.Though existing methods assess users' trustworthiness by exploiting Twitter account properties, their efficacy is inadequate because of Twitter's restriction on profile and tweet size, the existence of missing or insufficient profiles, and ease to create fake accounts or relationships to pretend as trustworthy.In this paper, we present a holistic approach by exploiting ideas perceived from real-world organizations for trust estimation along with available Twitter information.Users' trustworthiness is determined by considering their credentials, recommendation from referees and the quality of the information in their Twitter accounts and tweets.We establish the feasibility of our approach analytically and further devise a multi-objective cost function for the A* search to find a quasi-optimal path between the trust evaluator and the user whose trustworthiness is being evaluated.We also propose an incentive mechanism to increase user participation in the trust evaluation process, and a threat model and trustworthiness measure of referees to thwart the possibility of providing an untruthful recommendation to inflate one's trustworthiness.The efficacy of our proposed approach is validated through experiments using Twitter data and extensive simulation in various scenarios.
Laser Ablation-Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS) elemental mapping and its applications in ore geology

With the development of two-dimensional trace element mapping by laser-ablation inductively-coupled-plasma mass spectrometry (LA-ICP-MS), it is now possible to reveal in much greater detail any compositional interior textures of pyrite, thus permitting the investigation of the history of pyrite crystals and providing new insight into the genesis and evolution of mineralisation.Exploiting the advantages of high spatial resolution, low detection limits, and capability of full quantification, LA-ICP-MS elemental mapping is being widely applied to imaging sulphides from various types of deposits.In this study, we applied the LA-ICP-MS elemental mapping technique on pyrite from different types of deposits from China and Canada, for the purpose of monitoring the changes of physio-chemical condition, defining ore genesis, and revealing the distribution patterns of precious metals.The interior compositional textures of pyrite show that the mineralization system of the Bayinwula roll-front U deposit in Inner Mongolia was open and continuously affected by the re-charge of oxidized groundwater, along with a periodic change of pH and Eh that affected the activities of sulphate bacteria and the associated U mineralization.The trace element maps of pyrite from the Zhenzigou Pb-Zn deposit in Liaoning Province indicate that several episodes of fluids were involved and responsible for the mineralisation, namely, an early stage of syn-sedimentary origin and a late hydrothermal mineralisation related to the Yanshanian magmatic activity.The trace element distribution patterns of pyrite from the Palaeoproterozoic placer Au deposit in Canada showed that the subeuhedral-euhedral pyrite, which is a dominant type of pyrite in the conglomerates, is detrital origin and later modified by hydrothermal (or metamorphic) fluids, and Au is mostly associated with the detrital pyrite grains/cores, and disseminated in the porosity on the surface.