Reviewerly/exHarmony huggingface dataset:
Deep learning based channel estimation in fog radio access networks

As a promising paradigm of the fifth generation networks, fog radio access network (F-RAN) has attracted lots of attention nowadays. To fully utilize the promising gain of F-RANs, the acquisition of accurate channel state information is significant. However, conventional channel estimation approaches are not suitable in F-RANs due to the large training and feedback overhead. In this paper, we consider the channel estimation in F-RANs with fog access point (F-AP) equipped with massive antennas. Thanks to the computing ability of F-AP and the sparsity of channel matrices in angular domain, Gated Recurrent Unit (GRU), a data-driven based channel estimation is proposed at F-AP to reduce the training and feedback overhead. The GRU-based method can capture the hidden sparsity structure automatically through the network training. Moreover, to further improve the channel estimation, a bidirectional GRU based method is proposed, whose target channel structure is decided by previous and subsequent structures. We compare the performance of our proposed channel estimation with traditional methods (Orthogonal Matching Pursuit (OMP) and Simultaneous OMP (SOMP)). Simulation results show that the proposed approaches have better performance compared with the traditional OMP and SOMP methods.
Dialog State Tracking with Reinforced Data Augmentation

Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data. In this paper, we address this difficulty by proposing a reinforcement learning (RL) based framework for data augmentation that can generate high-quality data to improve the neural state tracker. Specifically, we introduce a novel contextual bandit generator to learn fine-grained augmentation policies that can generate new effective instances by choosing suitable replacements for specific context. Moreover, by alternately learning between the generator and the state tracker, we can keep refining the generative policies to generate more high-quality training data for neural state tracker. Experimental results on the WoZ and MultiWoZ (restaurant) datasets demonstrate that the proposed framework significantly improves the performance over the state-of-the-art models, especially with limited training data.
Increasing the parallelism of graph coloring via shortcutting

Graph coloring is an assignment of colors to the vertices of a graph such that no two adjacent vertices get the same color. It is a key building block in many applications. Finding a coloring with a minimal number of colors is often only part of the problem. In addition, the solution also needs to be computed quickly. Several parallel implementations exist, but they may suffer from low parallelism depending on the input graph. We present an approach that increases the parallelism without affecting the coloring quality. On 18 test graphs, our technique yields an average of 3.4 times more parallelism. Our CUDA implementation running on a Titan V is 2.9 times faster on average and uses as few or fewer colors as the best GPU codes from the literature.
The echo index and multistability in input-driven recurrent neural networks

A recurrent neural network (RNN) possesses the echo state property (ESP) if, for a given input sequence, it ``forgets'' any internal states of the driven (nonautonomous) system and asymptotically follows a unique, possibly complex trajectory. The lack of ESP is conventionally understood as a lack of reliable behaviour in RNNs. Here, we show that RNNs can reliably perform computations under a more general principle that accounts only for their local behaviour in phase space. To this end, we formulate a generalisation of the ESP and introduce an echo index to characterise the number of simultaneously stable responses of a driven RNN. We show that it is possible for the echo index to change with inputs, highlighting a potential source of computational errors in RNNs due to characteristics of the inputs driving the dynamics.
An improved method of Tiny YOLOV3

Abstract Target detection is the basic technology of self-driving system. In this paper, the problem of high detection rate of pedestrians and other small targets is studied in real-time detection of Tiny YOLOV3 target detection algorithm, and the network structure of Tiny YOLOV3 algorithm is improved. 2-step convolutional layers are added to the network, and deep separable convolution constructs are used to replace the traditional convolutions. On the basis of the original two-scales prediction target of the network, a scale is added to form a three-scales prediction, which can makes the detection of small targets such as pedestrians more accurate. The experimental results show that the average accuracy of the improved target detection algorithm is 8.6% higher than that of Tiny YOLOV3, and it meets the real-time requirements and has certain robustness.
Image Segmentation of Breast Cancer Histopathology Images Using PSO-Based Clustering Technique

Image segmentation has key influence in numerous medical imaging uses. An image segmentation model that is based on the particle swarm optimizer (PSO) is developed in this paper for breast cancer histopathology images of different magnification levels (40X, 100X, 200X and 400X), thus simplifying image representation and making it meaningful and easier for future analysis. As lower the magnification level, the bigger is the field of view and as it can provide greater detail, thus more time and care must be taken to use such images. Thus, finding a segmentation method that works equally well for all zoom levels of images is a big challenge. To explicate the better performance of the proposed method and its applicability on breast cancer images, results of applications and tests are augmented which shows PSO image clustering approach using intra-cluster distance as an optimization function, performs superior to cutting edge strategies, namely K-means and genetic algorithm (GA). The algorithms, when given specified number of clusters, find the centroids, thus grouping similar image primitives. The influence of different estimations of PSO control parameters on execution is additionally outlined.
COS-LDL: Label Distribution Learning by Cosine-Based Distance-Mapping Correlation

Label distribution learning (LDL) is a popular research trend in multi-label learning. Competing methods have been designed to improve the predictive performance. In this paper, we propose a method called cosine-based correlation for LDL (COS-LDL). The key issue is how to exploit correlations among different labels of the same instance. We propose a distance-mapping function for this purpose. With this mapping function, we design an objective function and its corresponding learning algorithm. Experiments undertaken on thirteen real-world datasets compare with eight LDL state-of-the-art methods. Results demonstrate that COS-LDL outperforms them in eight out of ten popular measures.
Influence of aberrations and roughness on the chromatic confocal signal based on experiments and wave-optical modeling

Abstract This paper addresses the effect and influence of wave optical aberrations and surface roughness on the chromatic confocal signal and resulting measurement errors. Two possible approaches exist for implementing chromatic confocal imaging based on either refraction or diffraction. Both concepts are compared and an expression for the expected chromatic longitudinal aberrations when using a diffractive optical element is derived. Since most chromatic confocal sensors are point sensors, the discussion on wave-optical aberrations is focused on spherical aberrations. Against common belief, the effect of spherical aberrations cannot be eliminated in the calibration process using for instance a piezo mounted mirror. It will be shown in the following that even a diffraction limited system with peak to valley spherical aberration smaller than 0.25 wavelength suffers from measurement errors. Experimental results will be shown to highlight this important issue. In order to develop a deeper understanding of the underlying physics, a wave-optical simulation environment has been realized. This wave-optical model furthermore enables the investigation of the influence of roughness. Herethereto the correct choice of numerical aperture when investigating a rough surface is based on a heuristic approach. Using the wave-optical simulations an explanation for the increased noise when employing a low numerical aperture to examine rough surfaces will be derived. Furthermore, a formula is presented to support the selection of the correct numerical aperture with regards to the roughness parameters of the surface under investigation.
Developing a new model for predicting global solar radiation on a horizontal surface located in Southwest Region of Algeria

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Blazing Fast OT for Three-Round UC OT Extension

Oblivious Transfer (OT) is an important building block for multi-party computation (MPC). Since OT requires expensive public-key operations, efficiency-conscious MPC protocols use an OT extension (OTE) mechanism [Beaver 96, Ishai et al. 03] to provide the functionality of many independent OT instances with the same sender and receiver, using only symmetric-key operations plus few instances of some base OT protocol. Consequently there is significant interest in constructing OTE friendly protocols, namely protocols that, when used as base-OT for OTE, result in extended OT that are both round-efficient and cost-efficient. We present the most efficient OTE-friendly protocol to date. Specifically: For comparison, the state of the art for base OTs that result in 3-round OTE are proven only in the programmable ROM, and require 4 exponentiations under Interactive DDH or 6 exponentiations under DDH [Masney-Rindal 19]. We also implement our protocol and benchmark it against the Simplest OT protocol [Chou and Orlandi, Latincrypt 2015], which is the most efficient and widely used OT protocol but not known to suffice for OTE. The computation cost is roughly the same in both cases. Interestingly, our base OT is also 3 rounds. However, we slightly modify the extension mechanism (which normally adds a round) so as to preserve the number of rounds in our case.
Cryptanalysis of LEDAcrypt

We report on the concrete cryptanalysis of LEDAcrypt, a 2nd Round candidate in NIST’s Post-Quantum Cryptography standardization process and one of 17 encryption schemes that remain as candidates for near-term standardization. LEDAcrypt consists of a public-key encryption scheme built from the McEliece paradigm and a key-encapsulation mechanism (KEM) built from the Niederreiter paradigm, both using a quasi-cyclic low-density parity-check (QC-LDPC) code. In this work, we identify a large class of extremely weak keys and provide an algorithm to recover them. For example, we demonstrate how to recover 1 in $$2^{47.72}$$ of LEDAcrypt’s keys using only $$2^{18.72}$$ guesses at the 256-bit security level. This is a major, practical break of LEDAcrypt. Further, we demonstrate a continuum of progressively less weak keys (from extremely weak keys up to all keys) that can be recovered in substantially less work than previously known. This demonstrates that the imperfection of LEDAcrypt is fundamental to the system’s design.
Robot teams stay safe with blockchains

To deploy robot swarms in our daily lives, they need to be resilient to malfunctioning errors and protected against malicious attacks. Blockchain technology could provide an essential level of protection.
Minimizing minor embedding energy: an application in quantum annealing

Abstract A significant challenge in quantum annealing is to map a real-world problem onto a hardware graph of limited connectivity. When the problem graph is not a subgraph of the hardware graph, one might employ minor embedding in which each logical qubit is mapped to a tree of physical qubits. Pairwise interactions between physical qubits in the tree are set to be ferromagnetic with some coupling strength $$F&lt;0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>F</mml:mi><mml:mo>&lt;</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:math> . Here we address the theoretical question of what the best value F should be in order to achieve unbroken trees in the pre-quantum-processing. The sum of | F | for each logical qubit is defined as minor embedding energy, and the best value F is obtained when the minor embedding energy is minimized. We also show that our new analytical lower bound on | F | is a tighter bound than that previously derived by Choi (Quantum Inf Process 7:193–209, 2008). In contrast to Choi’s work, our new method depends more delicately on minor embedding parameters, which leads to a higher computational cost.
Informative path planning for anomaly detection in environment exploration and monitoring

An unmanned autonomous vehicle (UAV) is sent on a mission to explore and reconstruct an unknown environment from a series of measurements collected by Bayesian optimization. The success of the mission is judged by the UAV’s ability to faithfully reconstruct any anomalous features present in the environment, with emphasis on the extremes (e.g., extreme topographic depressions or abnormal chemical concentrations). We show that the criteria commonly used for determining which locations the UAV should visit are ill-suited for this task. We introduce a number of novel criteria that guide the UAV toward regions of strong anomalies by leveraging previously collected information in a mathematically elegant and computationally tractable manner. We demonstrate superiority of the proposed approach in several applications, including reconstruction of seafloor topography from real-world bathymetry data, as well as tracking of dynamic anomalies. A particularly attractive property of our approach is its ability to overcome adversarial conditions, that is, situations in which prior beliefs about the locations of the extremes are imprecise or erroneous.
Reducing Annotation Burden Through Multimodal Learning

Choosing an optimal data fusion technique is essential when performing machine learning with multimodal data. In this study, we examined deep learning-based multimodal fusion techniques for the combined classification of radiological images and associated text reports. In our analysis, we (1) compared the classification performance of three prototypical multimodal fusion techniques: Early, Late, and Model fusion, (2) assessed the performance of multimodal compared to unimodal learning; and finally (3) investigated the amount of labeled data needed by multimodal versus unimodal models to yield comparable classification performance. Our experiments demonstrate the potential of multimodal fusion methods to yield competitive results using less training data (labeled data) than their unimodal counterparts. This was more pronounced using the Early and less so using the Model and Late fusion approaches. With increasing amount of training data, unimodal models achieved comparable results to multimodal models. Overall, our results suggest the potential of multimodal learning to decrease the need for labeled training data resulting in a lower annotation burden for domain experts.
Pediatric Speech Audiometry Web Application for Hearing Detection in the Home Environment

This paper describes the development of the speech audiometry application for pediatric patients in Slovak language and experiences obtained during testing with healthy children, hearing-impaired children, and elderly persons. The first motivation behind the presented work was to reduce the stress and fear of the children, who must undergo postoperative audiometry, but over time, we changed our direction to the simple game-like mobile application for the detection of possible hearing problems of children in the home environment. Conditioned play audiometry principles were adopted to create a speech audiometry application, where children help the virtual robot Thomas assign words to pictures; this can be described as a speech recognition test. Several game scenarios together with the setting condition issues were created, tested, and discussed. First experiences show a positive influence on the children’s mood and motivation.
New Valid Inequalities in Branch-and-Cut-and-Price for Multi-Agent Path Finding

BCP, a state-of-the-art algorithm for optimal Multi-agent Path Finding, uses the branch-and-cut-and-price framework to decompose the problem into (1) a master problem that selects a set of collision-free low-cost paths, (2) a pricing problem that adds lower-cost paths to the master problem, (3) separation problems that resolve various kinds of conflicts in the master problem, and (4) branching rules that split the nodes in the high-level branch-and-bound search tree. This paper focuses on the separation aspects of the decomposition by introducing five new classes of fractional conflicts and valid inequalities that remove these conflicts to tighten the linear programming relaxation in the master problem. Experimental results on 12820 instances across 16 maps indicate that including the five families of inequalities allows BCP to solve an additional 585 instances, optimize the same instances 41% faster, and solve 2068 more instances than CBSH-RM and 157 more than Lazy CBS.
Rapid deployment of smartphone‐based augmented reality tools for field and online education in structural biology

Abstract Structural biology education commonly employs molecular visualization software, such as PyMol, RasMol, and VMD, to allow students to appreciate structure–function relationships in biomolecules. In on‐ground, classroom‐based education, these programs are commonly used on University‐owned devices with software preinstalled. Remote education typically involves the use of student‐owned devices, which complicates the use of such software, owing to the fact that (a) student devices have differing configurations (e.g., Windows vs MacOS) and processing power, and (b) not all student devices are suitable for use with such software. Smartphones are near‐ubiquitous devices, with smartphone ownership exceeding personal computer ownership, according to a recent survey. Here, we show the use of a smartphone‐based augmented reality app, Augment, in a structural biology classroom exercise, which students installed independently without IT support. Post‐lab attitudinal survey results indicate positive student experiences with this app. Based on our experiences, we suggest that smartphone‐based molecular visualization software, such as that used in this exercise, is a powerful educational tool that is particularly well‐suited for use in remote education.
Identifiability of nonparametric mixture models and Bayes optimal clustering

Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable by introducing a novel framework involving clustering overfitted parametric (i.e., misspecified) mixture models.These identifiability conditions generalize existing conditions in the literature and are flexible enough to include, for example, mixtures of infinite Gaussian mixtures.In contrast to the recent literature, we allow for general nonparametric mixture components and instead impose regularity assumptions on the underlying mixing measure.As our primary application we apply these results to partition-based clustering, generalizing the notion of a Bayes optimal partition from classical parametric model-based clustering to nonparametric settings.Furthermore, this framework is constructive, so that it yields a practical algorithm for learning identified mixtures, which is illustrated through several examples on real data.The key conceptual device in the analysis is the convex, metric geometry of probability measures on metric spaces and its connection to the Wasserstein convergence of mixing measures.The result is a flexible framework for nonparametric clustering with formal consistency guarantees.
Bat Algorithm Based on an Integration Strategy and Gaussian Distribution

The bat algorithm (BA) is a recent heuristic optimization algorithm based on the echolocation behavior of bats. However, the bat algorithm tends to fall into local optima and its optimization results are unstable because of its low global exploration ability. To solve these problems, a novel bat algorithm based on an integration strategy (IBA) is proposed in this paper. Through the integration strategy, an appropriate operator is adaptively selected to perform global search, so that the global search ability of the IBA is improved. Furthermore, the IBA disturbs the local optimum through a linear combination of Gaussian functions with different variances to avoid becoming trapped in local optima. The IBA also updates the velocity equation with an adaptive weight to further balance the exploration and exploitation. Moreover, the global convergence of the IBA is proved based on the convergence criterion of a stochastic algorithm. The performance of the IBA is evaluated on CEC2013 benchmark functions and compared with that of the standard BA as well as several of its variants. The results show that the IBA is superior to other algorithms.
Summary of MuSe 2020

The first Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 was a Challenge-based Workshop held in conjunction with ACM Multimedia'20. It addresses three distinct 'in-the-wild` Sub-challenges: sentiment/ emotion recognition (MuSe-Wild), emotion-target engagement (MuSe-Target) and trustworthiness detection (MuSe-Trust). A large multimedia dataset MuSe-CaR was used, which was specifically designed with the intention of improving machine understanding approaches of how sentiment (e.g. emotion) is linked to a topic in emotional, user-generated reviews. In this summary, we describe the motivation, first of its kind 'in-the-wild` database, challenge conditions, participation, as well as giving an overview of utilised state-of-the-art techniques.
FeatherWave: An Efficient High-Fidelity Neural Vocoder with Multi-Band Linear Prediction

In this paper, we propose the FeatherWave, yet another variant of WaveRNN vocoder combining the multi-band signal processing and the linear predictive coding. The LPCNet, a recently proposed neural vocoder which utilized the linear predictive characteristic of speech signal in the WaveRNN architecture, can generate high quality speech with a speed faster than real-time on a single CPU core. However, LPCNet is still not efficient enough for online speech generation tasks. To address this issue, we adopt the multi-band linear predictive coding for WaveRNN vocoder. The multi-band method enables the model to generate several speech samples in parallel at one step. Therefore, it can significantly improve the efficiency of speech synthesis. The proposed model with 4 sub-bands needs less than 1.6 GFLOPS for speech generation. In our experiments, it can generate 24 kHz high-fidelity audio 9x faster than real-time on a single CPU, which is much faster than the LPCNet vocoder. Furthermore, our subjective listening test shows that the FeatherWave can generate speech with better quality than LPCNet.