Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots

Robotics has proved to be an indispensable tool in many industrial as well as social applications, such as warehouse automation, manufacturing, disaster robotics, etc. In most of these scenarios, damage to the agent while accomplishing mission-critical tasks can result in failure. To enable robotic adaptation in such situations, the agent needs to adopt policies which are robust to a diverse set of damages and must do so with minimum computational complexity. We thus propose a damage aware control architecture which diagnoses the damage prior to gait selection while also incorporating domain randomization in the damage space for learning a robust policy. To implement damage awareness, we have used a Long Short Term Memory based supervised learning network which diagnoses the damage and predicts the type of damage. The main novelty of this approach is that only a single policy is trained to adapt against a wide variety of damages and the diagnosis is done in a single trial at the time of damage.
Gradient attenuation as an emergent property of reset-based Retinex models

The Retinex image filtering algorithms have been inspired by experimental findings on the behavior of the Human Vision System. They are known to locally adjust image color and contrast by preserving edges and attenuating gradients. In a reference formulation of the algorithm by Land and McCann, edge preservation and gradient attenuation are granted by two ad-hoc mechanisms: called respectively reset (the distinctive feature of all the Retinex algorithms) and thresholding. A somehow unanticipated finding is that gradient attenuation is also observed with algorithm variants that do not include the latter mechanism, which was explicitly devised to implement gradient attenuation. In this work, we provide an analytic demonstration of the capability of Retinex models to attenuate gradients using only the "reset" mechanism, combined with the local character of the mutual pixel influences. We show that this capability is an emergent property of all the reset-based Retinex models.
Kotless: A Serverless Framework for Kotlin

Recent trends in Web development demonstrate an increased interest in serverless applications, i.e. applications that utilize computational resources provided by cloud services on demand instead of requiring traditional server management. This approach enables better resource management while being scalable, reliable, and cost-effective. However, it comes with a number of organizational and technical difficulties which stem from the interaction between the application and the cloud infrastructure, for example, having to set up a recurring task of reuploading updated files. In this paper, we present Kotless - a Kotlin Serverless Framework. Kotless is a cloud-agnostic toolkit that solves these problems by interweaving the deployed application into the cloud infrastructure and automatically generating the necessary deployment code. This relieves developers from having to spend their time integrating and managing their applications instead of developing them. Kotless has proven its capabilities and has been used to develop several serverless applications already in production. Its source code is available at https://github.com/JetBrains/kotless, a tool demo can be found at https://www.youtube.com/watch?v=IMSakPNl3TY.
The complete list of genera of quotients of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>-maximal Hermitian curve for q ≡ 1 (mod 4)

Let Fq2 be the finite field with q2 elements. Most of the known Fq2-maximal curves arise as quotient curves of the Fq2-maximal Hermitian curve Hq. After a seminal paper by Garcia, Stichtenoth and Xing, many papers have provided genera of quotients of Hq, but their complete determination is a challenging open problem. In this paper we determine completely the spectrum of genera of quotients of Hq for any q≡1(mod4).
Complexity Theory and Dynamic Characteristics of Cognitive Processes

The features of modeling of the cognitive component of social and humanitarian systems have been considered. An example of using entropy multiscale, multifractal, recurrence and network complexity measures has shown that these and other synergetic models and methods allow us to correctly describe the quantitative differences of cognitive systems. The cognitive process is proposed to be regarded as a separate implementation of an individual cognitive trajectory, which can be represented as a time series and to investigate its static and dynamic features by the methods of complexity theory. Prognostic possibilities of the complex systems theory will allow to correct the corresponding pedagogical technologies. It has been proposed to track and quantitatively describe the cognitive trajectory using specially transformed computer games which can be used to test the processual characteristics of thinking.
Lexical-Functional Grammar: An Overview

Lexical-Functional Grammar (LFG) is a model for the analysis of language in which different types of linguistic information are represented in separate dimensions, each with its own formalism. These dimensions are linked by mapping principles. In this article, I describe the architecture of the model and illustrate some dimensions of information and the mapping between them in more detail. I also provide an outline of the analysis of long-distance dependencies and control to illustrate the advantages of this type of model. I briefly mention some further areas where LFG has proven to be a useful tool for analysis and provide references for the reader to follow up.
Forgery Detection and Localization of Modifications at the Pixel Level

In this paper, we present a new technique of image forgery detection. The proposed technique uses digital signatures embedded in the least significant bits of the selected pixels of each row and column. The process maintains a symmetry in the use of pixels for computing and hiding the digital signatures. Each row and column of the image symmetrically contributes to both processes, with the number of pixels per row or column used for computing the signature, and the pixels used for embedding are not equal and are asymmetric. The pixels in each row and column of an image are divided into two groups. One group contains pixels of a row or column used in the calculation of digital signatures, and the second group of pixels is used for embedding the digital signatures of the respective row or column. The digital signatures are computed using the hash algorithm, e.g., message digest five (MD5). The least significant bits substitution technique is used for embedding the computed digital signature in the least significant bits of the selected pixels of the corresponding row or column. The proposed technique can successfully detect the modification made in an image. The technique detects pixel level modification in a single or multiple pixels.
High-throughput fuzzy clustering on heterogeneous architectures

The Internet of Things (IoT) is pushing the next economic revolution in which the main players are data and immediacy. IoT is increasingly producing large amounts of data that are now classified as "dark data" because most are created but never analyzed. The efficient analysis of this data deluge is becoming mandatory in order to transform it into meaningful information. Among the techniques available for this purpose, clustering techniques, which classify different patterns into groups, have proven to be very useful for obtaining knowledge from the data. However, clustering algorithms are computationally hard, especially when it comes to large data sets and, therefore, they require the most powerful computing platforms on the market. In this paper, we investigate coarse and fine grain parallelization strategies in Intel and Nvidia architectures of fuzzy minimals (FM) algorithm; a fuzzy clustering technique that has shown very good results in the literature. We provide an in-depth performance analysis of the FM's main bottlenecks, reporting a speed-up factor of up to 40× compared to the sequential counterpart version.
Social-media-based risk communication for data co-security on the cloud

Purpose Cloud computing has disrupted the information technology (IT) industry. Associated benefits such as flexibility, payment on an on-demand basis and the lack of no need for IT staff are among the reasons for its adoption. However, these services represent not only benefits to users but also threats, with cybersecurity issues being the biggest roadblock to cloud computing success. Although ensuring data security on the cloud has been the responsibility of providers, these threats seem to be unavoidable. In such circumstances, both providers and users have to coordinate efforts to minimize negative consequences that might occur from these events. The purpose of this paper is to assess how providers and users can rely on social media to communicate risky events. Design/methodology/approach Based on the Situational Theory of Publics and trust, the authors developed three research questions to analyze stakeholders’ communication patterns after a security breach. By gathering Twitter data, the authors analyzed the data security breach faced by the Premera Blue Cross’ Web application. Findings The results indicate that Premera acted as the main source of information for Twitter users, while trustworthy actors such as IT security firms, specialists and local news media acted as intermediaries, creating small communities around them. Theoretical and practical implications are also discussed. Originality/value Social media could be used for diffusing information of potential threats; no research has assessed its usage in a cloud-based security breach context. The study aims to fill this gap and propose a framework to engage cloud users in co-securing their data along with cloud providers when they face similar situations.
Secure and efficient public key multi-channel broadcast encryption schemes

In the modern era of digital technology, sending different messages to different group of users are essential in many real-life applications, like TV broadcast, radio broadcast etc. Multi-channel broadcast encryption (MCBE) is a mechanism which sends different messages to different group of users efficiently. We have proposed two multi-channel broadcast encryption schemes in the public key setting. Our first construction is selectively secure against chosen-plaintext attack, whereas the second one achieves adaptive security which is the strongest security model in the broadcast setting. Besides, our second construction is dynamic, whereas none of the existing MCBE constructions supports the dynamic property. Moreover, our second MCBE construction is the first inclusive-exclusive MCBE construction, where the broadcaster can reduce the encryption and decryption cost by choosing the smallest set between the set of subscribes and revoked users in the encryption and decryption phase respectively.
ResFPA-GAN: Text-to-Image Synthesis with Generative Adversarial Network Based on Residual Block Feature Pyramid Attention

Text-to-image synthesis based on generative adversarial networks (GAN) is a challenging task. The developed methods have show prominent progress on visual quality of the synthesized images, but it still face challenge in the image synthesis of details. In this paper, we introduce an image synthesis algorithm based on semantic description and propose a residual block feature pyramid attention generative adversarial network, called ResFPA-GAN. This network introduces multiscale feature fusion by embedding feature pyramid structure to achieve the fine-grained image synthesis. The quality of the image synthesis can be improved via the iterative training of GAN, while the reference of attention can enhance the network's learning of the details of image texture. Through extensive experimental comparison on the CUB dataset, our method can achieve significant improvement on the variety and authenticity for the synthesised images.
Orbital-angular-momentum-based experimental test of Hardy's paradox for multisetting and multidimensional systems

Characterizing high-dimensional entangled states is of crucial importance in quantum information science and technology. Recent theoretical progress has been made to extend Hardy's paradox into a general scenario with multisetting and multidimensional systems, which enables the maximum probability of nonlocal events to surpass the bound that was limited in Hardy's original test. Hitherto, no experimental verification has been conducted to verify Hardy's paradox, as most of the previous experimental efforts were restricted to two-dimensional systems. Here, based on two-photon high-dimensional orbital angular momentum (OAM) entanglement, we report an experiment to demonstrate Hardy's paradox for multiple settings and multiple outcomes. We demonstrate the paradox for two-setting higher-dimensional OAM subspaces up to $d=7$, which reveals that the nonlocal events increase with dimension. Furthermore, we showcase the nonlocality with an experimentally recorded probability of 36.77% for a five-setting three-dimensional OAM subspace via entanglement concentration, thus showing a sharper contradiction between quantum mechanics and classical theory.
Predicting Rate Control Target Through A Learning Based Content Adaptive Model

Rate Control (RC) plays an important role in video encoding. Traditional solutions are using fixed rate or fixed quantization parameters as the unified rate-control targets for all videos in one given video application. However, unified rate-control targets tend to have some bad encoding cases because of applying wrong rate for the video content. In this paper, we propose one content-adaptive rate control solution. We employ one neural-network based model which can end-to-end learn the optimal rate-control target appropriate to the content characteristics. The experimental results show that the proposed model can predict the optimal rate-factor value with the accuracy up to 77.637%. With this model, the proposed video-encoding method can significantly decrease the encoding quality fluctuation.
Semantic Prior Guided Face Inpainting

Face inpainting is a sub-task of image inpainting designed to repair broken or occluded incomplete portraits. Due to the high complexity of face image details, inpainting on the face is more difficult. At present, face-related tasks often draw on excellent methods from face recognition and face detection, using multitasking to boost its effect. Therefore, this paper proposes to add the face prior knowledge to the existing advanced inpainting model, combined with perceptual loss and SSIM loss to improve the model repair efficiency. A new face inpainting process and algorithm is implemented, and the repair effect is improved.
Geographical Security Questions for Fallback Authentication

Fallback authentication is the backup authentication method used when the primary authentication method (e.g., passwords, biometrics, etc.) fails. Currently, widely-deployed fallback authentication methods (e.g., security questions, email resets, and SMS resets) suffer from documented security and usability flaws that threaten the security of accounts. These flaws motivate us to design and study Geographical Security Questions (GeoSQ), a system for fallback authentication. GeoSQ is an Android application that utilizes autobiographical location data for fallback authentication. We performed security and usability analyses of GeoSQ through an in-person two-session lab study (n=36, 18 pairs). Our results indicate that GeoSQ exceeds the security of its counterparts, while its usability (specifically login time and memorability) has room for improvement.
A DFA Attack on White-Box Implementations of AES with External Encodings

Attacks based on DFA are an important threat to the security of white-box AES implementations. DFA typically requires that the output of AES is known. The use of external encodings that obfuscate this output is therefore a straightforward and well-known measure against such attacks. This paper presents a new DFA attack on a class of white-box implementations of AES that use a specific type of external encoding on the output. The expected work factor of the new attack is dominated by $$2^{32}$$ executions of the white-box implementation.
Next cashtag prediction on social trading platforms with auxiliary tasks

Social trading platforms provide a forum for investors to share their analysis and opinions. Posts on these platforms are characterized by narrative styles which are much different from posts on general social platforms, for instance tweets. As a result, recommendation systems for social trading platforms should leverage tailor-made latent features. This paper presents a representation for these latent features in both textual data and market information. A real-world dataset is adopted to conduct experiments involving a novel task called next cashtag prediction. We propose a joint learning model with an attentive capsule network. Experimental results show positive results with the proposed methods and the corresponding auxiliary tasks.
Antlion optimization and Whale optimization Algorithm for multilevel thresholding segmentation

Multi-level image segmentation is a critical task in image processing that involves multiple threshold values. As the high computational cost of an exhaustive search is inefficient and cumbersome, the optimal thresholds algorithms make for a better path to venture; hence a comparison of optimization algorithms to set the optimal thresholds is highly essential and beneficial. In this paper, a practical comparison is made to deduce the best optimization technique amongst the whale optimization and antlion optimization algorithm, to solve the multilevel threshold problem, to find the optimal multilevel thresholds. Otsu's function is maximized to perform optimized thresholding-based image segmentation. The experimental results showed that the Antlion optimization algorithm gave better performance in solving the problem for higher level multi-thresholding.
Improving k-Nearest Neighbors Algorithm for Imbalanced Data Classification

Abstract The k-Nearest Neighbors (k-NN) algorithm is a classic non-parametric method that has wide applications in data classification and prediction. Like many other machine learning schemes, the performance of k-NN classifiers will be significantly impacted by the imbalanced class distributions of data. That is, the data instances in the majority class tend to dominate the prediction of the test instances. In this paper, we look into the data pre-processing techniques that can be used to rebalance the training data and enhance the performance of k-NN classifiers in imbalanced data sets. We conduct extensive experiments on 14 real-world data sets collected from different application domains. We also perform statistical tests to verify the significance of different data pre-processing techniques in terms of boosting k-NN classification precision.
Wi-Fi RSSI Based Optimal Path Planning and Collision Avoidance in A Multi Robot Environment

The most notable feature about Swarm robotics is the ability of multiple robots to communicate and coordinate among themselves to accomplish the given tasks. Optimization of time and space plays a key role in such a multi robot environment. The paper proposes a priority based decentralized approach for collision avoidance optimal path planning system by monitoring the Received Signal Strength Indicator(RSSI) data of Wi-Fi signals and the readings of proximity sensors. Firebird V robots sharing the same physical workspace with static obstacles are used to implement the proposed algorithm.
Visual analytics for exploring air quality data in an AI-enhanced IoT environment

Visual analytics have an important role in the exploration and analysis of large amounts of data in IoT applications. Data visualizations can provide overviews of different aspects of data and user interaction can assist exploration. Recent advances in machine learning and Artificial Intelligence have provided methods that can be used in conjunction with visual analytics to enhance user perception. However, AI methods are often used as "black boxes", making them difficult for end-users to trust. In this paper, a novel visual analytics platform is presented, targeting two goals: a) an architecture for the creation of custom interactive visual analytics dashboards using well-defined components linked to each other, and b) the inclusion of components specifically for making AI methods more explainable. The proposed architecture and components are being used in the context of the AI4IoT pilot within the AI4EU project, which targets air quality monitoring through AI and visualization.
Deep Distillation Metric Learning

Due to the emergence of large-scale and high-dimensional data, measuring the similarity between data points becomes challenging. In order to obtain effective representations, metric learning has become one of the most active researches in the field of computer vision and pattern recognition. However, models using trained networks for predictions are often cumbersome and difficult to be deployed. Therefore, in this paper, we propose a novel deep distillation metric learning (DDML) for online teaching in the procedure of learning the distance metric. Specifically, we employ model distillation to transfer the knowledge acquired by the larger model to the smaller model. Unlike the 2-step offline and mutual online manners, we propose to train a powerful teacher model, who transfer the knowledge to a lightweight and generalizable student model and iteratively improved by the feedback from the student model. We show that our method has achieved state-of-the-art results on CUB200-2011 and CARS196 while having advantages in computational efficiency.
Scalable Hyperparameter Optimization with Lazy Gaussian Processes

Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities. Hence, several automatic selection algorithms have been introduced to overcome tedious manual (try and error) tuning of these parameters. Due to its very high sample efficiency, Bayesian Optimization over a Gaussian Processes modeling of the parameter space has become the method of choice. Unfortunately, this approach suffers from a cubic compute complexity due to underlying Cholesky factorization, which makes it very hard to be scaled beyond a small number of sampling steps. In this paper, we present a novel, highly accurate approximation of the underlying Gaussian Process. Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy. First experiments show speedups of a factor of 162 in single node and further speed up by a factor of 5 in a parallel environment.