Reviewerly/exHarmony huggingface dataset:
WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.
A Robust Trajectory Planning Method Based on Historical Information for Autonomous Vehicles

Trajectory planning is key component for autonomous vehicles. However, most existing trajectory planning methods which utilized a set of fixed weights to evaluate an optimal trajectory from a set of trajectory candidates may change abruptly in complex dynamic scenarios. In this study, a robust trajectory planning was proposed based on historical information. The developed trajectory planning is mainly consisting of candidate trajectories generation module, the collision detection module, the stability detection module and the speed planning module. Firstly, the candidate trajectories are generated according to the vehicle kinematics model. And then the collision detection module is used to remove the dangerous trajectories. Then, the optimal trajectory is selected by the multi-attribute indicators evaluation, and the selected trajectory is checked by the stability detection module based on the lateral deviation of the optimal trajectory from the historical trajectory. Based on its result, the current optimal trajectory or the historical trajectory is chosen as the output trajectory. Finally, based on the output trajectory, the speed planning module is executed to generate a smooth trajectory. Simulation and field experiments were conducted to evaluate the effectiveness of the proposed method.
Detection and Localisation of Pointing, Pairing and Grouping Gestures for Brainstorming Meeting Applications

The detection of gestures and their interpretation is crucial for blind and visually impaired people (BVIP). In a card-based brainstorming meeting, sighted users use non-verbal communication when referring to cards on a common workspace using pointing, grouping, or pairing gestures. While sighted users could easily interpret such gestures, they remain inaccessible to BVIP. Thus, there is a need for capturing, interpreting and translating gestures for BVIP. To address this problem, we developed a pointing gesture detection system using Unity with the SteamVR Plugin and HTC Vive. HTC’s trackers are attached to a user’s hands to measure the hand position in 3D space. With pointing gestures, a user controls a virtual ray that will intersect with a virtual whiteboard. This virtual whiteboard is invisible to the sighted users, but its position and size corresponds to a physical whiteboard. The intersection of the ray with the virtual whiteboard is calculated, resulting in a pointing trajectory on it. The shape of the trajectory is analyzed to determine, which artifacts are selected by the pointing gesture. A pointing gesture is detected when a user is pointing at a card on the screen and then ending the gesture by pointing outside of the screen. A pairing gesture is detected when pointing at one artifact and then on another one before leaving the screen. The grouping gesture is detected when performing an encircling gesture around multiple artifacts before leaving the screen.
Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer

Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks.Finding the most effective strategy to fine-tune these models on high-resource languages so that it transfers well to the zero-shot languages is a nontrivial task.In this paper, we propose a novel meta-optimizer to soft-select which layers of the pre-trained model to freeze during fine-tuning.We train the meta-optimizer by simulating the zero-shot transfer scenario.Results on cross-lingual natural language inference show that our approach improves over the simple fine-tuning baseline and X-MAML (Nooralahzadeh et al., 2020).
A Heterogeneous Graph Attention Network-Based Web Service Link Prediction

With the rise of service computing, the increasing number and diversity of web services make it an intractable task to search for suitable services. Service composition, service selection and recommendation have become the focus of service computing. As the fundamental research of service network, service link prediction is used to explore the composition mode between services, which can facilitate the development of service composition, service selection and recommendation. However, the existing link prediction methods are mainly based on manual modeling and derivation, which cannot make full use of the global structure information and perform poorly in complex networks. The challenging problem in service link prediction is the heterogeneity and sparseness of the service network. Therefore, we propose a novel web service link prediction method based on a heterogeneous graph attention network. By analyzing the interaction between services, five types of neighbors that are associated with service links are chosen, and two levels of attention are applied to learn the importance of neighbors and calculate the embedding of services. In addition, in order to improve accuracy, we design a Service-TextRank algorithm to extract the key information of the service description. Extensive experimental results on real-world data-ProgrammableWeb validate the effectiveness of our approach.
A reinterpretation of the Archaean stratigraphy south of Nkandla, southern Kaapvaal Craton, South Africa: Geophysical and stratigraphic constraints on a sheared granitoid-greenstone remnant

Abstract A new lithostratigraphic framework based upon a review of historic data, field mapping and remote sensing, including aerial photography, high-resolution airborne aeromagnetic and radiometric data, is proposed for the Archaean geology along the southeastern margin of the Kaapvaal Craton, South Africa. A synthesis of new and existing data reveals that previously accepted lithostratigraphic schemes require complete revision, with reinterpretations identifying multiple major shear zones and previously unidentified granitoid successions along the margin of the craton. In this new lithostratigraphic framework, lithologies of the Southern Syncline previously correlated with the Nsuze Group of the Pongola Supergroup, are redefined as greenstone lithologies associated with the Ilangwe Greenstone Belt. The geology of the Nkandla region can be subdivided into five distinct geophysical domains including: (i) an extension of the Ilangwe Greenstone Belt, (Domain 1) which is subdivided into; a lower volcanic succession, the Thathe Formation, comprising pillow and amygdaloidal volcanics; the adjoining Sabiza Formation, comprising pillow volcanics exposed in the southeast of the study area; the volcano-sedimentary Mtshwili Formation, which overlies the Thathe and Sabiza formations, consisting of quartz (sericite) schist, phyllite, metavolcanics and iron formation; the Nomangci Formation, which occurs as a region of highly deformed quartz-kyanite-sericite schists, and the Simbagwezi Formation, which comprises maroon to green phyllites and schists in the north of the study area. (ii) granitoids of the Impisi Granitoid Suite (Domain 2) which border the greenstone succession to the north, intruding the Nomangci and Simbagwezi formations. (iii) a southern complex of sheared granitoids termed the Umgabhi Granitoid Suite (Domain 3), which intrudes the Thathe, Sabiza and Mtshwili formations. (iv) The two remaining domains, comprise the Mesoproterozoic Mfongosi and Ntingwe Groups (Domain 4) and Mesoarchaean volcano-sedimentary sequences of the Pongola Supergroup (Domain 5).
Talking Through the Eyes: User Experience Design for Eye Gaze Redirection in Live Video Conferencing

In the post-corona era, more institutions are using videoconferencing (VC). However, when we talked in VC, we found that people could not easily concentrate on the conversation. This is a problem with computer architecture. Because the web camera records a person looking at a computer monitor, it appears as if people are staring at other places and having a conversation. This was a problem caused by overlooking eye contact, a non-verbal element of face-to-face conversation. To solve this problem, many studies focused on technology in literature. However, this study presented ER guidelines in terms of user experience: the function of selecting the direction of the face, the function of selecting a 3D avatar face divided into four stages through morphing, and a guideline on the function of intentionally staring at the camera using the teleprompter function.
User centric explanations: a breakthrough for explainable models

Thanks to recent developments in explainable Deep Learning models, researchers have shown that these models can be incredibly successful and provide encouraging results. However, a lack of model interpretability can hinder the efficient implementation of Deep Learning models in real-world applications. This has encouraged researchers to develop and design a large number of algorithms to support transparency. Although studies have raised awareness of the importance of explainable artificial intelligence, the question of how to solve the needs of real users to understand artificial intelligence remains unanswered. In this paper, we provide an overview of the current state of the research field at Human-Centered Machine Learning and new methods for user-centric explanations for deep learning models. Furthermore, we outline future directions for interpretable machine learning and discuss the challenges facing this research field, as well as the importance and motivation behind developing user-centric explanations for Deep Learning models.
Globally Convergent Multilevel Training of Deep Residual Networks

We propose a globally convergent multilevel training method for deep residual networks (ResNets). The devised method can be seen as a novel variant of the recursive multilevel trustregion (RMTR) method, which operates in hybrid (stochastic-deterministic) settings by adaptively adjusting minibatch sizes during the training. The multilevel hierarchy and the transfer operators are constructed by exploiting a dynamical system's viewpoint, which interprets forward propagation through the ResNet as a forward Euler discretization of an initial value problem. In contrast to traditional training approaches, our novel RMTR method also incorporates curvature information on all levels of the multilevel hierarchy by means of the limited-memory SR1 method. The overall performance and the convergence properties of the our multilevel training method are numerically investigated using examples from the field of classification and regression.
Structured Optimized Architecting of Full-Stack Quantum Systems in the NISQ era

In the midst of the NISQ era of quantum computers, the challenges are gravitating to encompass both architecting and full-stack engineering aspects, which are inherently algorithm-driven, so that there starts to be a convergence of bottom-up and top down design approaches, what we coin as the Quantum Architecting (QuArch) era. In face of many-fold diverse design proposals, in this paper it is postulated and proposed to apply the so-called Design Space Exploration (DSE) to the full vertical stack of quantum systems as an instrumental methodology to address such design diversity challenge. This structured design means, based upon composing a multidimensional input design space together with compressing the set of output performance metrics into an optimization-oriented overall figure of merit, provides a framework and method for optimization, for performance comparison. It yields as well a way to discriminate among alternative techniques at all layers and across layers, eventually as a structured and comprehensive design-oriented formal framework to address the quantum system design and evaluation complexity. The paper concludes by illustrating instances of this methodology in optimizing and comparing mapping techniques to address the resource-constrained current NISQ quantum chips, and to carry out a quantitative gap analysis of scalability trends aiming manycore distributed quantum architectures.
Brief Announcement: Variants of Approximate Agreement on Graphs and Simplicial Complexes

Approximate agreement is a weaker version of consensus where two or more processes must agree on a real number within a distance ε of each other. Many variants of this task have been considered in the literature: continuous or discrete ones; multi-dimensional ones; as well as agreement on graphs and other spaces. We focus on two variants of approximate agreement on graphs, edge agreement and clique agreement. We show that both tasks arise as special cases of a more general, higher-dimensional, approximate agreement task, where the processes must agree on the vertices of a simplex in a given simplicial complex. This new point of view gives rise to a novel topological perspective on the solvability of clique agreement.
Improved Line Detection in Images using Neural Networks and DTE Subclassifiers

It is widely accepted that deep neural networks are very efficient for object detection in images.They reach their limit when multiple long line instances have to be detected in very high resolution images.In this paper, we present an original methodology for the recognition of vine lines in high resolution aerial images.The process consists in combining a neural network with a subclassifier.We first compare a traditional U-Net architecture with a U-Net architecture designed for precision agriculture.We then highlight the significant improvement in vine line detection when a DTE is added after the customized U-Net.This methodology addresses the complex task of dissociating vine lines from other agricultural objects.The trained model is not sensitive to the orientation of the lines.Therefore, our experiments have improved the precision by around 15 % compared to our improved neural network.
Template-based Theory Exploration: Discovering Properties of Functional Programs by Testing

We present RoughSpec, a template-based extension of the theory exploration tool QuickSpec. QuickSpec uses testing to automatically discover equational properties about functions in a Haskell program. These properties can help the user understand the program or be used as a source of possible lemmas in proofs of the program’s correctness. In RoughSpec, the user supplies templates, which describe families of laws such as associativity and distributivity, and we only consider properties that match the templates. This restriction limits the search space and ensures that only relevant properties are discovered. In this way, we sacrifice broad search for more direction towards desirable property patterns, which makes theory exploration tractable and scalable. We also combine RoughSpec with QuickSpec, using QuickSpec to perform a complete search for smaller term sizes, while using templates for larger, more complex properties, in order to leverage the strengths of both systems.
Unmanned Driving System Based on DeepLabV3+ Semantic Segmentation

Recently, semantic segmentation technology has developed rapidly and is gradually applied in the field of autonomous driving. In this paper, DeepLabV3+ semantic segmentation is applied to the driverless system to realize the classification of road scene images at pixel level accuracy, so as to improve the accuracy of collected road information. Then, an automatic control strategy based on the Ackerman unmanned vehicle model with cascade PID front wheel steering control and rear wheel differential control is established to ensure that the vehicle can make the correct decision after receiving the traffic information. The cooperation of the above two technologies jointly realizes the automatic driving safely.
Probabilistic Bounds for a Class of Filtering Algorithms in the Scalar Case

We focus on the estimation error of a type of filtering algorithm in the scalar case, which is applicable to both linear and nonlinear systems. Under some regularity conditions, we construct a surrogate process that has a moment dominance property with respect to the true filtering error process. Then, moment-based probability inequalities can be used to compute probabilistic bounds for the filtering error. The sharpness of the bounds is tested on a simulated epidemic model with both Gaussian and non-Gaussian noise.
Quantum Technologies for future Quantum Optical Networks

The recent evolutions in the convergence of QKD and classical communication systems are described, together with the relevance of SDN as a suitable approach for a real integration between QKD and WDM networks. Preliminary experimental results on a discrete-variable QKD prototype working in the O-band co-propagating with 40 50-GHz C-band information channels are reported, showing the capabilities of QKD in short/medium reach optical networks.
Label distribution learning by maintaining label ranking relation

Label distribution learning (LDL) is a novel machine learning paradigm that can be seen as an extension of multi-label learning (MLL). Compared with MLL, the advantages of LDL are reflected in the following perspectives: (1) the label distribution gives the relevance description of each label to unknown instances in quantitative terms; (2) the distribution implicitly gives the relevance intensities relation of different labels to a particular instance in qualitative terms, i.e., the label ranking relation. All existing LDL models aim to fit the ground-truth label distribution by quantitatively minimizing the distance between distributions or maximizing the similarity between distributions, which only uses the first advantage of the label distribution but ignores the label ranking relation, which may lose some useful semantic information implied in the label distribution, thus reducing the performance of LDL. Therefore, we propose a novel algorithm to solve this problem by introducing the ranking loss function to LDL. In addition, in order to evaluate the LDL algorithms more comprehensively and verify that the ranking loss is beneficial for keeping the label ranking relation, we also introduce two popular ranking evaluation metrics for LDL. The experimental results on 13 real-world datasets validate the effectiveness of our method.
Segment‐Adaptive Spread Spectrum Audio Watermarking Using NSGA‐II

In the field of robust audio watermarking, how to seek a good trade-off between robustness and imperceptibility is challenging. The existing studies use the same embedding parameter for each part of the audio signal, which ignores that different parts may have different requirements for embedding parameters. In this work, the constraints on imperceptibility are first analysed. Then, we present a segment multi-objective optimization model of the scaling parameter under the constrained Signal-to-noise ratio (SNR) in Spread spectrum (SS) audio watermarking. Additionally, we adopt the Non-dominated sorting genetic algorithm II (NSGA-II) to solve the proposed model. Finally, we compare our algorithm (called SS-SNR-NSGA-II) with the existing methods. The experimental results show that the proposed SS-SNR-NSGA-II not only provides flexible choices for different application demands but also achieves more and better trade-offs between imperceptibility and robustness.
LDA-LFM

Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modeling technique (i.e., Latent Factor Model) with a topic modeling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models, we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.
Smoothing-Averse Control: Covertness and Privacy from Smoothers

In this paper we investigate the problem of controlling a partially observed stochastic dynamical system such that its state is difficult to infer using a (fixed-interval) Bayesian smoother. This problem arises naturally in applications in which it is desirable to keep the entire state trajectory of a system concealed. We pose our smoothing-averse control problem as the problem of maximising the (joint) entropy of smoother state estimates (i.e., the joint conditional entropy of the state trajectory given the history of measurements and controls). We show that the entropy of Bayesian smoother estimates for general nonlinear state-space models can be expressed as the sum of entropies of marginal state estimates given by Bayesian filters. This novel additive form allows us to reformulate the smoothing-averse control problem as a fully observed stochastic optimal control problem in terms of the usual concept of the information (or belief) state, and solve the resulting problem via dynamic programming. We illustrate the applicability of smoothing-averse control to privacy in cloud-based control and covert robotic navigation.
A study of the cloud security attacks and threats

Abstract Cloud storage has become an essential computational model today, providing flexible, defective resistant resources and dramatically reducing costs. However, there are major obstacles to protection issues in the wider adoption market. The multiformity of the cloud and the multi-location storing of data exacerbate these problems. The major issues to be discussed in protection are secrecy, authenticity, honesty, availability and auditability. Enabling more customers to switch to the cloud environment would allow for safe data and transfers from the service provider. Intrusion Detection Mechanisms, Cryptographic Methodology and Electronic Forensic Software are some of the assurance that a trustful service provider will have for the recuperation and collection of digital proof of intrusion activities. This paper provides a survey into some of the prevalent challenges, risks and ways to resolve these challenges in cloud platforms. We also discussed major cloud storage service for data storage and protection model.