Reviewerly/exHarmony huggingface dataset:
Multi-Kernel Maximum Correntropy Kalman Filter

Maximum correntropy criterion (MCC) has been widely used in Kalman filter to cope with heavy-tailed measurement noises. However, its performance on mitigating non-Gaussian process noises and unknown disturbance is rarely explored. In this letter, we extend the definition of correntropy from a single kernel to multiple kernels. Then, we derive a multi-kernel maximum correntropy Kalman filter (MKMCKF) to cope with multivariate non-Gaussian noises and disturbance. Three examples are provided to show the effectiveness of the proposed methods.
Genre as Weak Supervision for Cross-lingual Dependency Parsing

Recent work has shown that monolingual masked language models learn to represent data-driven notions of language variation which can be used for domain-targeted training data selection.Dataset genre labels are already frequently available, yet remain largely unexplored in cross-lingual setups.We harness this genre metadata as a weak supervision signal for targeted data selection in zeroshot dependency parsing.Specifically, we project treebank-level genre information to the finer-grained sentence level, with the goal to amplify information implicitly stored in unsupervised contextualized representations.We demonstrate that genre is recoverable from multilingual contextual embeddings and that it provides an effective signal for training data selection in cross-lingual, zero-shot scenarios.For 12 low-resource language treebanks, six of which are test-only, our genre-specific methods significantly outperform competitive baselines as well as recent embedding-based methods for data selection.Moreover, genre-based data selection provides new state-of-the-art results for three of these target languages.
Efficient Multi-modal Hashing with Online Query Adaption for Multimedia Retrieval

Multi-modal hashing supports efficient multimedia retrieval well. However, existing methods still suffer from two problems: (1) Fixed multi-modal fusion. They collaborate the multi-modal features with fixed weights for hash learning, which cannot adaptively capture the variations of online streaming multimedia contents. (2) Binary optimization challenge. To generate binary hash codes, existing methods adopt either two-step relaxed optimization that causes significant quantization errors or direct discrete optimization that consumes considerable computation and storage cost. To address these problems, we first propose a Supervised Multi-modal Hashing with Online Query-adaption method. A self-weighted fusion strategy is designed to adaptively preserve the multi-modal features into hash codes by exploiting their complementarity. Besides, the hash codes are efficiently learned with the supervision of pair-wise semantic labels to enhance their discriminative capability while avoiding the challenging symmetric similarity matrix factorization. Further, we propose an efficient Unsupervised Multi-modal Hashing with Online Query-adaption method with an adaptive multi-modal quantization strategy. The hash codes are directly learned without the reliance on the specific objective formulations. Finally, in both methods, we design a parameter-free online hashing module to adaptively capture query variations at the online retrieval stage. Experiments validate the superiority of our proposed methods.
1124O Prediction of distant relapse in patients with invasive breast cancer from deep learning models applied to digital pathology slides

Breast cancer (BC) has a favorable long-term prognosis, with an estimated average 5-year survival rate of 87%. Nevertheless, 10% of patients relapse after initial treatment each year. To better identify these patients, Owkin and Gustave Roussy conceived a diagnostic tool that applies deep learning (DL) to whole slide images (WSI) and clinical data, that could work as an aid in therapeutic decision.
LoRCA: Lightweight round block and stream cipher algorithms for IoV systems

The Internet of Vehicles (IoV) is a disruptive technology that has a great impact on people's lifestyle and activities. Fully autonomous vehicles are the next generation of connected cars. However, IoV systems suffer from several security threats that could offset the intended advantages. To address the security and privacy issues, several cryptographic and non-cryptographic security solutions should be adopted. However, traditional security solutions might burden the IoV network with further complexity and computational overhead. This would translate into additional performance issues in a network already suffering from big data and large-scale challenges. In this context, lightweight security solutions are needed to reduce the required resources in terms of computations, power and memory, and to optimize the network performance in terms of latency and bandwidth. In this paper, we propose a new lightweight cipher scheme, LoRCA, with dynamic key-dependent structure to provide data confidentiality with minimum resources' requirements. To validate our solution's robustness and efficiency within the IoV context, several performance and security tests were performed. The results show that the proposed solution strikes a good balance between the security level and performance. The proposed LoRCA ciphers achieve a high throughput with an enhancement of at least 100% improvement as compared to the Advanced Encryption Standard (AES), 358% compared to Simon, 388% improvement for Speck and 24% improvement compared to our previous one round cipher scheme.
Bitcoin’s value proposition: shorting expansionary monetary policies

Purpose Professionals and academics alike hold polarized opinions about Bitcoin’s purpose and its fundamental value. This paper aims to describe Bitcoin’s unique features that make it such an intriguing asset and proposes a new way to consider Bitcoin and its underlying value. Design/methodology/approach In this paper the author discusses Bitcoin’s defining features that make it a unique asset. The author argues that Bitcoin should not be considered as a single purpose asset only, but rather as a new digital financial asset serving several functions, at least partially. The author discusses the role of Bitcoin in the traditional financial system, contrasts Bitcoin to gold, considers the implications of the continuance of expansionary policies on Bitcoin and discusses the impact of the emergence of cryptocurrencies as a new asset class on public policies. Findings In addition to functioning as a means of payment (at least partially) and a diversification tool, part of Bitcoin’s value proposition stems from its worth as a short position on modern expansionary monetary policies. Indeed, Bitcoin’s value should rise if expansionary monetary policies are maintained, amounting to a tool to short these policies, which should be considered in future attempts to value Bitcoin. Originality/value The author adds a new layer to the ongoing thought process by arguing of a function played by Bitcoin unaccounted for thus far by the literature. Additionally, the author describes the features and mechanisms, allowing Bitcoin to play that role.
Simple But Effective GRU Variants

Recurrent Neural Network (RNN) is a widely used deep learning architecture applied to sequence learning problems. However, it is recognized that RNNs suffer from exploding and vanishing gradient problems that prohibit the early layers of the network from learning the gradient information. GRU networks are particular kinds of recurrent networks that reduce the short-comings of these problems. In this study, we propose two variants of the standard GRU with simple but effective modifications. We applied an empirical approach and tried to determine the effectiveness of the current units and recurrent units of gates by giving different coefficients. Interestingly, we realize that applying such minor and simple changes to the standard GRU provides notable improvements. We comparatively evaluate the standard GRU with the proposed two variants on four different tasks: (1) sentiment classification on the IMDB movie review dataset, (2) language modeling task on Penn TreeBank (PTB) dataset, (3) sequence to sequence addition problem, and (4) question answering problem on Facebook’s bAbitasks dataset. The evaluation results indicate that the proposed two variants of GRU consistently outperform standard GRU.
Elephants Don’t Pack Groceries: Robot Task Planning for Low Entropy Belief States

Recent advances in computational perception have significantly improved the ability of autonomous robots to perform state estimation with low entropy.Such advances motivate a reconsideration of robot decision-making under uncertainty.Current approaches to solving sequential decision-making problems model states as inhabiting the extremes of the perceptual entropy spectrum.As such, these methods are either incapable of overcoming perceptual errors or asymptotically inefficient in solving problems with low perceptual entropy.With low entropy perception in mind, we aim to explore a happier medium that balances computational efficiency with the forms of uncertainty we now observe from modern robot perception.We propose an approach for efficient task planning for goal-directed robot reasoning.Our approach combines belief space representation with the fast, goal-directed features of classical planning to efficiently plan for low entropy goal-directed reasoning tasks.We compare our approach with current classical planning and belief space planning approaches by solving low entropy goal-directed grocery packing tasks in simulation.Our approach outperforms these approaches in planning time, execution time, and task success rate in our simulation experiments.We also demonstrate our approach on a real world grocery packing task with physical robot.
Gradient-Based Label Binning in Multi-label Classification

In multi-label classification, where a single example may be associated with several class labels at the same time, the ability to model dependencies between labels is considered crucial to effectively optimize non-decomposable evaluation measures, such as the Subset 0/1 loss. The gradient boosting framework provides a well-studied foundation for learning models that are specifically tailored to such a loss function and recent research attests the ability to achieve high predictive accuracy in the multi-label setting. The utilization of second-order derivatives, as used by many recent boosting approaches, helps to guide the minimization of non-decomposable losses, due to the information about pairs of labels it incorporates into the optimization process. On the downside, this comes with high computational costs, even if the number of labels is small. In this work, we address the computational bottleneck of such approach—the need to solve a system of linear equations—by integrating a novel approximation technique into the boosting procedure. Based on the derivatives computed during training, we dynamically group the labels into a predefined number of bins to impose an upper bound on the dimensionality of the linear system. Our experiments, using an existing rule-based algorithm, suggest that this may boost the speed of training, without any significant loss in predictive performance.
Automatic Parking Path Planning Based on Ant Colony Optimization and the Grid Method

This paper analyzes the path planning problem in the automatic parking process, and studies a path planning method for automatic parking. The grid method and the ant colony optimization are combined to find the shortest path from the parking start point to the end point. The grid method is used to model the parking environment to simulate the actual parking space of automatic parking; then this paper makes some improvements to the basic ant colony optimization, finds the destination by setting the ants’ movement rules in the grid, and finds the shortest path after N iterations; since the optimal path found is a polyline, it will increase the difficulty of controlling vehicle path tracking and affect the accuracy of vehicle path tracking. The bezier curve is used to generate a smooth path suitable for vehicle walking. Finally, through matlab simulation, the obstacles in the environment are simulated, and the parking trajectory is obtained. The results show that the path planning method proposed in this paper is feasible.
Hierarchical Task Assignment of Multiple UAVs with Improved Firefly Algorithm Based on Simulated Annealing Mechanism

In the task assignment of multiple UAVs problem, task combination scale may expand significantly with UAVs and targets increasing. This paper proposes a new hierarchical task assignment method by means of multiple UAVs forming several groups to perform multi-task on a set of clusters. In the hierarchical decomposition phase, using the balance cluster method simplifies the large-scale UAV system and reduces computational complexity. In the task assignment phase, an improved firefly algorithm is proposed, which discretizes the original problem through double chains coding and adopts multi-neighbor search mechanism to maintain the diversity of the population. Finally, the Metropolis criterion in simulated annealing is introduced to avoid the algorithm falling into the local optimum. The simulation results show that, the fine effect of the proposed algorithm in terms of search ability and convergence speed, is demonstrated by comparison with other algorithms. And the hierarchical decomposition structure can significantly reduce assignment time in large-scale multi-UAV task assignment.
Multi-Level Curriculum for Training A Distortion-Aware Barrel Distortion Rectification Model

Barrel distortion rectification aims at removing the radial distortion in a distorted image captured by a wide-angle lens. Previous deep learning methods mainly solve this problem by learning the implicit distortion parameters or the nonlinear rectified mapping function in a direct manner. However, this type of manner results in an indistinct learning process of rectification and thus limits the deep perception of distortion. In this paper, inspired by the curriculum learning, we analyze the barrel distortion rectification task in a progressive and meaningful manner. By considering the relationship among different construction levels in an image, we design a multi-level curriculum that disassembles the rectification task into three levels, structure recovery, semantics embedding, and texture rendering. With the guidance of the curriculum that corresponds to the construction of images, the proposed hierarchical architecture enables a progressive rectification and achieves more accurate results. Moreover, we present a novel distortion-aware pre-training strategy to facilitate the initial learning of neural networks, promoting the model to converge faster and better. Experimental results on the synthesized and real-world distorted image datasets show that the proposed approach significantly outperforms other learning methods, both qualitatively and quantitatively.
Modelling of Unmanned Aerial Vehicles with Vertical Take Off and Landing Capabilities

UAVs offer an affordable and sustainable way to improve monitoring of large areas contributing, as example, to gather large amount of data or to improve the surveillance of sites in which criminal activities such as clandestine immigration should harm safety of peoples. Patrolling of large areas involves the use of Unmanned Aerial Vehicles with large autonomies, a feature that is much easier to be obtained with fixed wing planes. Also, VTOL (Vertical Take Off and Landing) features are highly desirable to assure an easy and fast deployment. For these reasons, a hybrid propulsion layout is adopted: wings assure lift for prolonged missions in an efficient way, while multiple rotors with fixed axis are used to assure hovering and VTOL capabilities. So, the superposition of these two propulsion systems assures a relatively simple and robust construction and control of the UAV. However, some further cautions must be taken during the transition from cruise speed to hovering condition and vice versa. For these reasons authors propose in this work a simplified model aiming to simulate the behavior of the UAV to support a better comprehension of involved dynamics and a better design optimization of the system.
Cloud Computing: Serverless

A serverless architecture is a new approach to offering services over the Internet. It combines BaaS (Backend-as-a-service) and FaaS (Function-as-a-service). With the serverless architecture no own or rented infrastructures are needed anymore. In addition, the company does not have to worry about scaling any longer, as this happens automatically and immediately. Furthermore, there is no need any longer for maintenance work on the servers, as this is completely taken over by the provider. Administrators are also no longer needed for the same reason. Finally, many ready-made functions are offered, with which the development effort can be reduced. As a result, the serverless architecture is very well suited to many application scenarios, and it can save considerable costs (server costs, maintenance costs, personnel costs, electricity costs, etc.). The company only must subdivide the source code of the application and upload it to the provider’s server. The rest is done by the provider.
An Isotropy Analysis in the Multilingual BERT Embedding Space

Several studies have explored various advantages of multilingual pre-trained models (such as multilingual BERT) in capturing shared linguistic knowledge.However, less attention has been paid to their limitations.In this paper, we investigate the multilingual BERT for two known issues of the monolingual models: anisotropic embedding space and outlier dimensions.We show that, unlike its monolingual counterpart, the multilingual BERT model exhibits no outlier dimension in its representations while it has a highly anisotropic space.There are a few dimensions in the monolingual BERT with high contributions to the anisotropic distribution.However, we observe no such dimensions in the multilingual BERT.Furthermore, our experimental results demonstrate that increasing the isotropy of multilingual space can significantly improve its representation power and performance, similarly to what had been observed for monolingual CWRs on semantic similarity tasks.Our analysis indicates that, despite having different degenerated directions, the embedding spaces in various languages tend to be partially similar with respect to their structures. 1.
Air-Text

Text entry takes an important role of effectively delivering the intention of users to computers, where physical and soft keyboards have been widely used. However, with the recent trends of developing technologies like augmented reality and increasing contactless services due to COVID-19, a more advanced type of text entry is required. To tackle this issue, we propose Air-Text which is an intuitive system to write in the air using fingertips as a pen. Unlike previously suggested air-writing systems, Air-Text provides various functionalities by the seamless integration of air-writing and text-recognition modules. Specifically, the air-writing module takes a sequence of RGB images as input and tracks both the location of fingertips (5.33 pixel error in 640x480 image) and current hand gesture class (98.29% classification accuracy) frame by frame. Users can easily perform writing operations such as writing or deleting a text by changing hand gestures, and tracked fingertip locations can be stored as a binary image. Then the text-recognition module, which is compatible with any pre-trained recognition models, predicts a written text in the binary image. In this paper, examples of single digit recognition with MNIST classifier (96.0% accuracy) and word-level recognition with text recognition model (79.36% character recognition rate) are provided.
Emotion Classification in Spanish: Exploring the Hard Classes

The study of affective language has had numerous developments in the Natural Language Processing area in recent years, but the focus has been predominantly on Sentiment Analysis, an expression usually used to refer to the classification of texts according to their polarity or valence (positive vs. negative). The study of emotions, such as joy, sadness, anger, surprise, among others, has been much less developed and has fewer resources, both for English and for other languages, such as Spanish. In this paper, we present the most relevant existing resources for the study of emotions, mainly for Spanish; we describe some heuristics for the union of two existing corpora of Spanish tweets; and based on some experiments for classification of tweets according to seven categories (anger, disgust, fear, joy, sadness, surprise, and others) we analyze the most problematic classes.
Predictive Runtime Monitoring for Mobile Robots using Logic-Based Bayesian Intent Inference

We propose a predictive runtime monitoring framework that forecasts the distribution of future positions of mobile robots in order to detect and avoid impending property violations such as collisions with obstacles or other agents. Our approach uses a restricted class of temporal logic formulas to represent the likely intentions of the agents along with a combination of temporal logic-based optimal cost path planning and Bayesian inference to compute the probability of these intents given the current trajectory of the robot. First, we construct a large but finite hypothesis space of possible intents represented as temporal logic formulas whose atomic propositions are derived from a detailed map of the robot&#x2019;s workspace. Next, our approach uses real-time observations of the robot&#x2019;s position to update a distribution over temporal logic formulae that represent its likely intent. This is performed by using a combination of optimal cost path planning and a Boltzmann noisy rationality model. In this manner, we construct a Bayesian approach to evaluating the posterior probability of various hypotheses given the observed states and actions of the robot. Finally, we predict the future position of the robot by drawing posterior predictive samples using a Monte-Carlo method. We evaluate our framework using two different trajectory datasets that contain multiple scenarios implementing various tasks. The results show that our method can predict future positions precisely and efficiently, so that the computation time for generating a prediction is a tiny fraction of the overall time horizon.
Bistable Latch Ising Machines

Ising machines have been attracting attention due to their ability to use mixed discrete/continuous mechanisms to solve difficult combinatorial optimization problems. We present BLIM, a novel Ising machine scheme that uses latches (bistable elements) with controllable gains as Ising spins. We show that networks of coupled latches have a Lyapunov or “energy” function that matches the Ising Hamiltonian in discrete operation, enabling them to function as Ising machines. This result is established in a general coupled-element Ising machine framework that is not limited to BLIM. Operating the latches periodically in analog/continuous mode, during which bistability is removed, helps the system traverse to better minima. CMOS realizations of BLIM have desirable practical features; implementation in other physical domains is an intriguing possibility.