Reviewerly/exHarmony huggingface dataset:
Dynamical Obstacle Avoidance of Task- Constrained Mobile Manipulation Using Model Predictive Control

Task-constrained motion planning (TCMP) is involved in many practical applications, such as opening the door, opening the drawer, twisting the screw, and so on. Because of the trend of man-machine collaboration and the existence of dynamic in environments, planning the collision-free motions for task-constrained manipulation is a significative problem. This paper explores the TCMP issue for mobile manipulation, which uses a mobile base to enhance the working range and flexibility but simultaneously makes the problem harder than that of single manipulation because of the additional degrees of freedom (dofs). We propose an optimization-based method to plan the obstacle avoidance motion in real-time. First, the global robotic Jacobian matrix that combines the omnidirectional base and the robotic arm is derived. Second, model predictive control is used to plan the control rule in order to maximize the closest distance between the obstacles and the mobile manipulator and minimize the velocities of null space at the same time. We have deduced the model with four differential equations that represent the law of the distance over time. Third, the distances are calculated and sent to the model to calculate the velocity of each joint of the arm and the base using ACADO that is an open-source toolkit. Using the velocities, the mobile manipulator can move away from the approaching people while still fixing the end of the arm to manipulate tool at the same time. Our method is verified on the mobile manipulator that consists of four Mecanum wheels, a base, a UR10 arm, and four kinect cameras in Gazebo simulation with using the ROS operation.
Bayesian Uncertainty Matching for Unsupervised Domain Adaptation

Domain adaptation is an important technique to alleviate performance degradation caused by domain shift, e.g., when training and test data come from different domains. Most existing deep adaptation methods focus on reducing domain shift by matching marginal feature distributions through deep transformations on the input features, due to the unavailability of target domain labels. We show that domain shift may still exist via label distribution shift at the classifier, thus deteriorating model performances. To alleviate this issue, we propose an approximate joint distribution matching scheme by exploiting prediction uncertainty. Specifically, we use a Bayesian neural network to quantify prediction uncertainty of a classifier. By imposing distribution matching on both features and labels (via uncertainty), label distribution mismatching in source and target data is effectively alleviated, encouraging the classifier to produce consistent predictions across domains. We also propose a few techniques to improve our method by adaptively reweighting domain adaptation loss to achieve nontrivial distribution matching and stable training. Comparisons with state of the art unsupervised domain adaptation methods on three popular benchmark datasets demonstrate the superiority of our approach, especially on the effectiveness of alleviating negative transfer.
Card-Based Physical Zero-Knowledge Proof for Kakuro

Kakuro is a popular logic puzzle, in which a player fills in all empty squares with digits from 1 to 9 so that the sum of digits in each (horizontal or vertical) line is equal to a given number, called a clue, and digits in each line are all different. In 2016, Bultel, Dreier, Dumas, and Lafourcade proposed a physical zero-knowledge proof protocol for Kakuro using a deck of cards; their proposed protocol enables a prover to convince a verifier that the prover knows the solution of a Kakuro puzzle without revealing any information about the solution. One possible drawback of their protocol would be that the protocol is not perfectly extractable, implying that a prover who does not know the solution can convince a verifier with a small probability; therefore, one has to repeat the protocol to make such an error become negligible. In this paper, to overcome this, we design zero-knowledge proof protocols for Kakuro having perfect extractability property. Our improvement relies on the ideas behind the copy protocols in the field of card-based cryptography. By executing our protocols with a real deck of physical playing cards, humans can practically perform an efficient zero-knowledge proof of knowledge for Kakuro.
Saliency Detection via Depth-Induced Cellular Automata on Light Field

Incorrect saliency detection such as false alarms and missed alarms may lead to potentially severe consequences in various application areas. Effective separation of salient objects in complex scenes is a major challenge in saliency detection. In this paper, we propose a new method for saliency detection on light field to improve the saliency detection in challenging scenes. We construct an object-guided depth map, which acts as an inducer to efficiently incorporate the relations among light field cues, by using abundant light field cues. Furthermore, we enforce spatial consistency by constructing an optimization model, named Depth-induced Cellular Automata (DCA), in which the saliency value of each superpixel is updated by exploiting the intrinsic relevance of its similar regions. Additionally, the proposed DCA model enables inaccurate saliency maps to achieve a high level of accuracy. We analyze our approach on one publicly available dataset. Experiments show the proposed method is robust to a wide range of challenging scenes and outperforms the state-of-the-art 2D/3D/4D (light-field) saliency detection approaches.
Higra: Hierarchical Graph Analysis

Higra — Hierarchical Graph Analysis is a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods capable of handling large amount of data. The main aspects of hierarchical graph analysis addressed in Higra are the construction of hierarchical representations (agglomerative clustering, mathematical morphology hierarchies, etc.), the analysis and processing of such representations (filtering, clustering, characterization, etc.), and their assessment. Higra targets a large audience, from students and practitioners wanting an accessible library for quickly experimenting, to researchers developing new methods for hierarchical analysis of graph data. Higra is a generic toolbox for graph analysis and can be utilized in a large variety of application fields like machine learning, data science, pattern analysis and computer vision. Moreover, it contains an image analysis module easing the handling of pixel grid graphs by providing efficient algorithms dedicated to this field.
Flux-Driven Josephson Traveling-Wave Parametric Amplifier

We have developed a concept for a traveling-wave parametric amplifier driven by a magnetic flux wave. The circuit consists of a serial array of symmetric dc SQUIDs coupled inductively to a separate superconducting LC transmission line carrying the pump microwave; thereby the signal and pump are applied to different ports. The proposed three-wave-mixing amplifier includes, firstly, an adjustable phase velocity of the signal (idler) wave and hence good phase matching yielding large gain in a wide frequency range and, secondly, overcoming of the pump depletion problem. The experimental parameters and characteristics of this amplifier have been evaluated and show promise for applications in quantum-information single-photon circuits.
Securing color image transmission using compression-encryption model with dynamic key generator and efficient symmetric key distribution

There are a few issues related to the existing symmetric encryption models for color image data, such as the key generation and distribution problems. In this paper, we propose a compression-encryption model to solve these problems. This model consists of three processes. The first process is the dynamic symmetric key generation. The second one is the compression process, which is followed by encryption using keystreams and S-Boxes that are generated using a chaotic logistic map. The last process is the symmetric key distribution. The symmetric key is encrypted twice using Rivest-Shamir-Adleman (RSA) to provide both authentication and confidentiality. Then, it is inserted into the cipher image using the End of File (EoF) method. The evaluation shows that the symmetric key generator model can produce a random and dynamic symmetric key. Hence, the image data is safe from ciphertext-only attacks. This model is fast and able to withstand entropy attacks, statistical attacks, differential attacks, and brute-force attacks.
Quasiflat band enabling subradiant two-photon bound states

We study theoretically the radiative lifetime of bound two-particle excitations in a waveguide with an array of two-level atoms, realising a 1D Dicke-like model. Recently, Zhang et al. [arXiv:1908.01818] have numerically found an unexpected sharp maximum of the bound pair lifetime when the array period $d$ is equal to $1/12$th of the light wavelength $\lambda_0$]. We uncover a rigorous transformation from the non-Hermitian Hamiltonian with the long-ranged radiative coupling to the nearest-neigbor coupling model with the radiative losses only at the edges. This naturally explains the puzzle of long lifetime: the effective mass of the bound photon pair also diverges for $d=\lambda_0/12$, hampering an escape of photons through the edges. We also link the oscillations of the lifetime with the number of atoms to the nonmonotous quasi-flat-band dispersion of the bound pair.
Assessing the accuracy of automatic speech recognition for psychotherapy

Abstract Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring. Here we show that automatic speech recognition is feasible in psychotherapy, but further improvements in accuracy are needed before widespread use. Our HIPAA-compliant automatic speech recognition system demonstrated a transcription word error rate of 25%. For depression-related utterances, sensitivity was 80% and positive predictive value was 83%. For clinician-identified harm-related sentences, the word error rate was 34%. These results suggest that automatic speech recognition may support understanding of language patterns and subgroup variation in existing treatments but may not be ready for individual-level safety surveillance.
Artificial Intelligence: A Primer for Breast Imaging Radiologists

Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.
Higher key rate of measurement-device-independent quantum key distribution through joint data processing

We propose a method named as double-scanning method, to improve the key rate of measurement-device-independent quantum key distribution (MDI-QKD) drastically. In the method, two parameters are scanned simultaneously to tightly estimate the counts of single-photon pairs and the phase-flip error rate jointly. Numerical results show that the method in this work can improve the key rate by $35\%-280\%$ in a typical experimental set-up. Besides, we study the optimization of MDI-QKD protocol with all parameters including the source parameters and failure probability parameters, over symmetric channel or asymmetric channel. Compared with the optimized results with only the source parameters, the all-parameter-optimization method could improve the key rate by about $10\%$.
Generative Adversarial Network Technologies and Applications in Computer Vision

Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and image generation. GANs are widely used not only in image generation and style transfer but also in the text, voice, video processing, and other fields. However, there are still some problems with GANs, such as model collapse and uncontrollable training. This paper deeply reviews the theoretical basis of GANs and surveys some recently developed GAN models, in comparison with traditional GAN models. The applications of GANs in computer vision include data enhancement, domain transfer, high-quality sample generation, and image restoration. The latest research progress of GANs in artificial intelligence (AI) based security attack and defense is introduced. The future development of GANs in computer vision is also discussed at the end of the paper with possible applications of AI in computer vision.
Explaining models: an empirical study of how explanations impact fairness judgment

Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvements. To facilitate the process we need effective, unbiased, and user-friendly explanations that people can confidently rely on. Towards that end, we conducted an empirical study with four types of programmatically generated explanations to understand how they impact people's fairness judgments of ML systems. With an experiment involving more than 160 Mechanical Turk workers, we show that: 1) Certain explanations are considered inherently less fair, while others can enhance people's confidence in the fairness of the algorithm; 2) Different fairness problems-such as model-wide fairness issues versus case-specific fairness discrepancies-may be more effectively exposed through different styles of explanation; 3) Individual differences, including prior positions and judgment criteria of algorithmic fairness, impact how people react to different styles of explanation. We conclude with a discussion on providing personalized and adaptive explanations to support fairness judgments of ML systems.
Comparing Neural Network Based Decoders for the Surface Code

Matching algorithms can be used for identifying errors in quantum systems, being the most famous the Blossom algorithm. Recent works have shown that small distance quantum error correction codes can be efficiently decoded by employing machine learning techniques based on neural networks (NN). Various NN-based decoders have been proposed to enhance the decoding performance and the decoding time. Their implementation differs in how the decoding is performed, at logical or physical level, as well as in several neural network related parameters. In this work, we implement and compare two NN-based decoders, a low level decoder and a high level decoder, and study how different NN parameters affect their decoding performance and execution time. Crucial parameters such as the size of the training dataset, the structure and the type of the neural network, and the learning rate used during training are discussed. After performing this comparison, we conclude that the high level decoder based on a Recurrent NN shows a better balance between decoding performance and execution time and it is much easier to train. We then test its decoding performance for different code distances, probability datasets and under the depolarizing and circuit error models.
Personalization of learning through adaptive technologies in the context of sustainable development of teachers’ education

The article highlights the issues of personalized learning as the global trend of the modern ICTbased educational systems development. The notion, the main stages of evolution, the main features and principles of adaptive learning systems application for teachers training are outlined. It is emphasized that the use and elaboration of the adaptive cloud-based learning systems are essential to provide sustainable development of teachers education. The current trends and peculiarities of the cloud-based adaptive learning systems development and approach of their implementation for teachers training are considered. The general model of the adaptive cloud-based learning system structure is proposed. The main components of the model are described; the issues of tools and services selection are outlined. The methods of the cloudbased learning components introduction within the adaptive systems of teacher training are considered. The current research developments of modeling and implementation of the adaptive cloud-based systems are outlined.
CNN-Based Fast Source Device Identification

Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this paper we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs). Specifically, we propose a 2-channel-based CNN that learns a way of comparing camera fingerprint and image noise at patch level. The proposed solution turns out to be much faster than the conventional approach and to ensure an increased accuracy. This makes the approach particularly suitable in scenarios where large databases of images are analyzed, like over social networks. In this vein, since images uploaded on social media usually undergo at least two compression stages, we include investigations on double JPEG compressed images, always reporting higher accuracy than standard approaches.
MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages.Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework.The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data.All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu 1 To reduce the threshold to participation, two of the target frameworks represented in MRP 2019 are not in focus this year, viz. the purely bi-lexical DELPH-IN MRS Bi-Lexical Dependencies and Prague Semantic Dependencies (PSD).These graphs largely overlap with the corresponding (but richer) frameworks in 2020, EDS and PTG, respectively, and the original bi-lexical semantic dependency graphs remain independently available (Oepen et al., 2015).
A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering the necessity of specialized knowledge has been the focus for companies who create and sell these tools. Furthermore, image forgeries are presently so realistic that it becomes difficult for the naked eye to differentiate between fake and real media. This can bring different problems, from misleading public opinion to the usage of doctored proof in court. For these reasons, it is important to have tools that can help us discern the truth. This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods. In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification of computer graphics images and detection of emerging Deepfake images. With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them. A review of different image databases and an overview of anti-forensic methods are also presented. Finally, we suggest some future working directions that the research community could consider to tackle in a more effective way the spread of doctored images.
Automatic question generation and answer assessment: a survey

Abstract Learning through the internet becomes popular that facilitates learners to learn anything, anytime, anywhere from the web resources. Assessment is most important in any learning system. An assessment system can find the self-learning gaps of learners and improve the progress of learning. The manual question generation takes much time and labor. Therefore, automatic question generation from learning resources is the primary task of an automated assessment system. This paper presents a survey of automatic question generation and assessment strategies from textual and pictorial learning resources. The purpose of this survey is to summarize the state-of-the-art techniques for generating questions and evaluating their answers automatically.
Deep Source-Channel Coding for Sentence Semantic Transmission With HARQ

Recently, semantic communication has been brought to the forefront because deep learning (DL)-based methods, such as Transformer, have achieved great success in semantic extraction. Although semantic communication has been successfully applied in sentence transmission to reduce semantic errors, the existing architecture is usually fixed in terms of codeword length and inefficient and inflexible for varying sentence lengths. In this study, we exploit hybrid automatic repeat request (HARQ) to reduce the semantic transmission error further. We combine semantic coding (SC) with Reed-Solomon (RS) channel coding and HARQ (called SC-RS-HARQ). SC-RS-HARQ exploits the superiority of SC and the reliability of conventional methods successfully. Although SC-RS-HARQ can be easily applied in existing HARQ systems, we also develop an end-to-end architecture called SCHARQ to pursue enhanced performance. Numerical results demonstrate that SCHARQ significantly reduces the required number of bits for semantic sentence transmission and the sentence error rate. We also attempt to replace error detection from cyclic redundancy check to a similarity detection network called Sim32 to allow the receiver to reserve wrong sentences with similar semantic information and conserve transmission resources.