Reviewerly/exHarmony huggingface dataset:
Efficient Qubit Measurement with a Nonreciprocal Microwave Amplifier

The act of observing a quantum object fundamentally perturbs its state, resulting in a random walk toward an eigenstate of the measurement operator. Ideally, the measurement is responsible for all dephasing of the quantum state. In practice, imperfections in the measurement apparatus limit or corrupt the flow of information required for quantum feedback protocols, an effect quantified by the measurement efficiency. Here, we demonstrate the efficient measurement of a superconducting qubit using a nonreciprocal parametric amplifier to directly monitor the microwave field of a readout cavity. By mitigating the losses between the cavity and the amplifier, we achieve a measurement efficiency of $(72\ifmmode\pm\else\textpm\fi{}4)%$. The directionality of the amplifier protects the readout cavity and qubit from excess backaction caused by amplified vacuum fluctuations. In addition to providing tools for further improving the fidelity of strong projective measurement, this work creates a test bed for the experimental study of ideal weak measurements, and it opens the way toward quantum feedback protocols based on weak measurement such as state stabilization or error correction.
Large-Scale Nonlinear AUC Maximization via Triply Stochastic Gradients

Learning to improve AUC performance for imbalanced data is an important machine learning research problem. Most methods of AUC maximization assume that the model function is linear in the original feature space. However, this assumption is not suitable for nonlinear separable problems. Although there have been some nonlinear methods of AUC maximization, scaling up nonlinear AUC maximization is still an open question. To address this challenging problem, in this paper, we propose a novel large-scale nonlinear AUC maximization method (named as TSAM) based on the triply stochastic gradient descents. Specifically, we first use the random Fourier feature to approximate the kernel function. After that, we use the triply stochastic gradients w.r.t. the pairwise loss and random feature to iteratively update the solution. Finally, we prove that TSAM converges to the optimal solution with the rate of O(1/t) after t iterations. Experimental results on a variety of benchmark datasets not only confirm the scalability of TSAM, but also show a significant reduction of computational time compared with existing batch learning algorithms, while retaining the similar generalization performance.
Agricultural Supply Chain Management Using Blockchain Technology

In this day and age, agricultural producers are faced with multiple obstacles, from seasonal changes to the broken supply chain; their occupation is very laborious and demanding. In such a situation, a distinct information database consisting of credible information would be very helpful. Transfer of knowledge is important in all aspects of this occupation, may it be about market trends or about profitable practices. Third-party interference in this aspect may cause the spread of misinformation. This can be curbed by using blockchain, a data ledger which is reliable and incorruptible. Here an attempt to analyse the different ways in which blockchain technology can be incorporated in the agricultural supply chain, as a transparent and dependable transaction mechanism is explored.
A System for Explainable Answer Set Programming

We present xclingo, a tool for generating explanations from ASP programs annotated with text and labels.These annotations allow tracing the application of rules or the atoms derived by them.The input of xclingo is a markup language written as ASP comment lines, so the programs annotated in this way can still be accepted by a standard ASP solver.xclingo translates the annotations into additional predicates and rules and uses the ASP solver clingo to obtain the extension of those auxiliary predicates.This information is used afterwards to construct derivation trees containing textual explanations.The language allows selecting which atoms to explain and, in its turn, which atoms or rules to include in those explanations.We illustrate the basic features through a diagnosis problem from the literature.
Deep Reinforcement Learning for Navigation in Cluttered Environments

Collision-free motion is essential for mobile robots.Most approaches to collision-free and efficient navigation with wheeled robots require parameter tuning by experts to obtain good navigation behavior.In this paper, we aim at learning an optimal navigation policy by deep reinforcement learning to overcome this manual parameter tuning.Our approach uses proximal policy optimization to train the policy and achieve collision-free and goal-directed behavior.The output of the learned network are the robot's translational and angular velocities for the next time step.Our method combines path planning on a 2D grid with reinforcement learning and does not need any supervision.Our network is first trained in a simple environment and then transferred to scenarios of increasing complexity.We implemented our approach in C++ and Python for the Robot Operating System (ROS) and thoroughly tested it in several simulated as well as real-world experiments.The experiments illustrate that our trained policy can be applied to solve complex navigation tasks.Furthermore, we compare the performance of our learned controller to the popular dynamic window approach (DWA) of ROS.As the experimental results show, a robot controlled by our learned policy reaches the goal significantly faster compared to using the DWA by closely bypassing obstacles and thus saving time.
Using a multimedia semantic graph for web document visualization and summarization

Abstract The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information.
An optimized BP neural network model for teaching management evaluation

To improve the effectiveness and intelligence of university teaching management evaluation, the particle swarm optimization BP neural network algorithm is applied to the analysis of university teaching management evaluation data. BP neural network is used to model the evaluation index of teaching management, and then particle swarm optimization is used to optimize the weight and threshold of the neural network transfer function to ensure that the output of the BP neural network can obtain the global optimal solution. The experimental results show that the proposed algorithm has a good fit between the predicted value and the actual value of the evaluation object of teaching management in Colleges and universities, and has a strong promotion value.
Distorted underwater image reconstruction for an autonomous underwater vehicle based on a self-attention generative adversarial network

Imaging through the wavy air–water surface suffers from severe geometric distortions, which are caused by the light refraction effect that affects the normal operations of underwater exploration equipment such as the autonomous underwater vehicle (AUV). In this paper, we propose a deep learning-based framework, namely the self-attention generative adversarial network (SAGAN), to remove the geometric distortions and restore the distorted image captured through the water–air surface. First, a K-means-based image pre-selection method is employed to acquire a less distorted image that preserves much useful information from an image sequence. Second, an improved generative adversarial network (GAN) is trained to translate the distorted image into the non-distorted image. During this process, the attention mechanism and the weighted training objective are adopted in our GAN framework to get the high-quality restored results of distorted underwater images. The network is able to restore the colors and fine details in the distorted images by combining the three objective losses, i.e., the content loss, the adversarial loss, and the perceptual loss. Experimental results show that our proposed method outperforms other state-of-the-art methods on the validation set and our sea trial set.
CSE-HRNet: A Context and Semantic Enhanced High-Resolution Network for Semantic Segmentation of Aerial Imagery

Semantic segmentation of high-resolution aerial images is a concerning issue of remote sensing applications. To address the issues of intra-class heterogeneity and inter-class homogeneity, a novel end-to-end semantic segmentation network, namely Context and Semantic Enhanced High-Resolution Network (CSE-HRNet), is proposed in this paper. Two procedures are considered comprehensively, which are multi-scale contextual feature extractor and multi-level semantic feature producer. Nested Dilated Residual Block (NDRB) is designed firstly, which could enhance the representational power of multi-scale contexts and tackle the issue of intra-class heterogeneity. The pyramidal feature hierarchy is introduced secondly, by which multi-level feature fusions could be utilized to enlarge inter-class semantic differences. Experimental results verify that, based on the Potsdam and Vaihingen benchmarks, the proposed CSE-HRNet can achieve competitive performance compared with other state-of-the-art methods.
Fuzzy-in-the-Loop-Driven Low-Cost and Secure Biometric User Access to Server

Fuzzy systems can aid in diminishing uncertainty and noise from biometric security applications by providing an intelligent layer to the existing physical systems to make them reliable. In the absence of such fuzzy systems, a little random perturbation in captured human biometrics could disrupt the whole security system, which may even decline the authentication requests of legitimate entities during the protocol execution. In the literature, few fuzzy logic-based biometric authentication schemes have been presented; however, they lack significant security features including perfect forward secrecy (PFS), untraceability, and resistance to known attacks. This article, therefore, proposes a novel two-factor biometric authentication protocol enabling efficient and secure combination of physically unclonable functions, a physical object analogous to human fingerprint, with user biometrics by employing fuzzy extractor-based procedures in the loop. This combination enables the participants in the protocol to achieve PFS. The security of the proposed scheme is tested using the well-known real-or-random model. The performance analysis signifies the fact that the proposed scheme not only offers PFS, untraceability, and anonymity to the participants, but is also resilient to known attacks using light-weight symmetric operations, which makes it an imperative advancement in the category of intelligent and reliable security solutions.
What’s in my Room? Object Recognition on Indoor Panoramic Images

In the last few years, there has been a growing interest in taking advantage of the 360 panoramic images potential, while managing the new challenges they imply. While several tasks have been improved thanks to the contextual information these images offer, object recognition in indoor scenes still remains a challenging problem that has not been deeply investigated. This paper provides an object recognition system that performs object detection and semantic segmentation tasks by using a deep learning model adapted to match the nature of equirectangular images. From these results, instance segmentation masks are recovered, refined and transformed into 3D bounding boxes that are placed into the 3D model of the room. Quantitative and qualitative results support that our method outperforms the state of the art by a large margin and show a complete understanding of the main objects in indoor scenes.
Distributed resource allocation with multi-agent deep reinforcement learning for 5G-V2V communication

We consider the distributed resource selection problem in Vehicle-to-vehicle (V2V) communication in the absence of a base station. Each vehicle autonomously selects transmission resources from a pool of shared resources to disseminate Cooperative Awareness Messages (CAMs). This is a consensus problem where each vehicle has to select a unique resource. The problem becomes more challenging when---due to mobility---the number of vehicles in vicinity of each other is changing dynamically. In a congested scenario, allocation of unique resources for each vehicle becomes infeasible and a congested resource allocation strategy has to be developed. The standardized approach in 5G, namely semi-persistent scheduling (SPS) suffers from effects caused by spatial distribution of the vehicles. In our approach, we turn this into an advantage. We propose a novel Distributed Resource Allocation mechanism using multi-agent reinforcement Learning (DIRAL) which builds on a unique state representation. One challenging issue is to cope with the non-stationarity introduced by concurrently learning agents which causes convergence problems in multi-agent learning systems. We aimed to tackle non-stationarity with unique state representation. Specifically, we deploy view-based positional distribution as a state representation to tackle non-stationarity and perform complex joint behavior in a distributed fashion. Our results showed that DIRAL improves PRR by 20% compared to SPS in challenging congested scenarios.
Interactive technology of pedagogical assistance as a means of adaptation of foreign first-year students

The relevance of this study and its social significance are due the need to improve the readiness of the foreign first-year students for the learning process in educational environment of a Russian technical university. The purpose of the article is to develop an interactive technology of pedagogical assistance, identifying the features of adaptation of foreign first-year students. To assess the adaptation process we estimated: students' motivation toward science learning, their abilities of self-regulation in the learning process and classroom climate. The effectiveness of the proposed model is confirmed by the results of a pedagogical experiment, using proven, valid questionnaires on the subject of the study. On the basis of the obtained results, it is proved that to improve the quality of training of foreign students, it is necessary to take into account their motives, cognitive style of mastering the educational material, the used educational strategies and features of the organization of communication in the educational group. Most effectively, these factors can be taken into account in the organization of pedagogical assistance of the adaptation process through the electronic educational environment of university.
Syntax grounded graph convolutional network for joint entity and event extraction

Event extraction aims to determine entity mentions, event triggers and argument roles in text, such as news articles. For this task, dependency syntax has been recognized as a valuable source of information. Previous work integrates dependency trees by using one-best results from a parser, where potential incorrect dependencies may impact the event extraction performance. We propose to use syntax features in an implicit approach, by adopting Soft Head Vectors (SHV) drawn from a well-trained parser as an adjacency matrix, densely connecting all words with the probabilistic head scores. SHV can be also regarded as dependency forests where multiple possible structures can be taken into account. A two-phase graph neural network is adopted to represent the forests, automatically differentiating the salient syntactic information from noisy parsers. Experiments on the two public datasets show that our model outperforms various baselines including graph-based one-best tree as well as recent transition-based decoding, giving the state-of-the-art results in the literature.
New Practices оf Engineering Education in Conditions of Distance Learning

The paper focuses on the analysis of the experience of the university’s rapid transition to the distance learning format, with the emphasis on the introduction of tools for the development of educational interaction between tutors and students in order to prepare engineering students with competencies in information and cross-cutting technologies. The experience of introducing digital services to support the educational process at Orenburg State University is analyzed; an analysis is made of the students’ comprehensive perception of the new educational practice specifics associated with the creation of the “digital footprint”. The presented data show an increase in students’ demand for flexible individual educational paths, for a variety of forms of learning and for the formation of new competencies in the field of digital skills, creativity, the ability to self-learn and manage attention, create communities, and reflectivity. The authors argue that the digital competence of engineering graduates must exceed the existing range of competencies in order to work ahead of the situation. Attention is paid to the development in the distance learning format of basic personal skills that contribute to the formation of professional capital of a future engineer.
Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson’s disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson’s disease patients acquired here are made available to further contribute to research related to this topic.
OFFER: A Motif Dimensional Framework for Network Representation Learning

Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency.
GO FIGURE: A Meta Evaluation of Factuality in Summarization

While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question. This major discrepancy between the surface-level fluency and the content-level correctness of neural generation has motivated a new line of research that seeks automatic metrics for evaluating the factuality of machine text. In this paper, we introduce GO FIGURE, a meta-evaluation framework for evaluating factuality evaluation metrics. We propose five necessary and intuitive conditions to evaluate factuality metrics on diagnostic factuality data across three different summarization tasks. Our benchmark analysis on ten factuality metrics reveals that our meta-evaluation framework provides a robust and efficient evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated.