Reviewerly/exHarmony huggingface dataset:
Assisting the Visually Impaired in Multi-object Scene Description Using OWA-Based Fusion of CNN Models

Abstract Advances in technology can provide a lot of support for visually impaired (VI) persons. In particular, computer vision and machine learning can provide solutions for object detection and recognition. In this work, we propose a multi-label image classification solution for assisting a VI person in recognizing the presence of multiple objects in a scene. The solution is based on the fusion of two deep CNN models using the induced ordered weighted averaging (OWA) approach. Namely, in this work, we fuse the outputs of two pre-trained CNN models, VGG16 and SqueezeNet. To use the induced OWA approach, we need to estimate a confidence measure in the outputs of the two CNN base models. To this end, we propose the residual error between the predicted output and the true output as a measure of confidence. We estimate this residual error using another dedicated CNN model that is trained on the residual errors computed from the main CNN models. Then, the OAW technique uses these estimated residual errors as confidence measures and fuses the decisions of the two main CNN models. When tested on four image datasets of indoor environments from two separate locations, the proposed novel method improves the detection accuracy compared to both base CNN models. The results are also significantly better than state-of-the-art methods reported in the literature.
Collaborative and efficient privacy-preserving critical incident management system

When a critical incident occurs, timely location-based status messages (known as alerts) conveyed by a witness present at the incident site to the competent authority constitute a key part of an effective approach to handle the situation. Details provided by witnesses are of extreme significance, but their collaboration with the authority may make them susceptible to various threats such as loss of anonymity, false implication, unwarranted surveillance, etc. As a solution, an anonymous reporting mechanism can be provided to encourage the witnesses to report the incident to the concerned authority. However, there is a possibility of system collapse in case the anonymous witnesses inundate the authority with multiple fake reports, resulting in delayed response to the critical incident. In this paper, we present a critical incident reporting system that provides privacy to honest witnesses but can disclose the identity and punish the malicious ones. The proposed system facilitates the indistinguishability of the witness among a group of users such that reports are linked to groups of k (or more) users instead of a single user. We use a game-theoretic approach based on the co-utility principle to encourage the users of the system to engage in mutually beneficial collaboration, which leads to a “rewards and punishment” mechanism to encourage legitimate information and discourage false information. The incident management authority rewards honest witnesses with a blockchain-based cryptocurrency, which can be redeemed anonymously by the awardees from the city council. We have analyzed the security and privacy properties of the system, and carried out real-device testing to evaluate the system performance and its feasibility.
A novel completely mapped crossover operator for genetic algorithm to facilitate the traveling salesman problem

As an intelligent search technique, genetic algorithm (GA) is a key member of optimization research methods. Motivated by the ability of GA in resolving complex and nondeterministic polynomial problems, this research proposes a new operator, namely, completely mapped crossover operator to facilitates the search of optimal solutions. Under a very general setting of the state-of-the-art parameters, the performance of newly proposed method is explored with respect to five vibrant and most commonly used techniques existent in the literature. Furthermore, the generality of the applicability of contemporary techniques is maintained by considering eighteen benchmarks from the library of traveling salesman problem, possessing different levels of complexity. Based on diverse performance assessment criteria, we observe that propose method outperforms the contemporary alternatives.
Three-branch architecture for stereoscopic 3D salient object detection

Existing stereoscopic 3D (S3D) salient object detection (SOD) networks typically employ a two-branch architecture, in which the RGB and depth channels are learned independently. Conventional methods based on conventional neural networks generally fuse the two branches by combining their deep representations at a later stage with only one path, which can be inefficient and insufficient for retaining a large amount of cross-modal data. In this study, we combine the RGB branch and depth branch to generate a third branch. The first branch is the embedded attention branch containing the attention mechanism, and we introduce the embedded attention module in this branch to give the allocation of available processing resources to the most informative components of an input signal. The second branch is the boundary refinement branch combined with the low-level information of RGB and depth images. Additionally, we propose a new module, called the detail correlation module, to ensure clear object boundaries and salient object refinement. The third branch is the global deep-view branch containing the global view module, which fuses high-level information and expands the sensor field. We also use three different loss functions to match our special SOD network. Extensive experiments demonstrate the effectiveness and robustness of the proposed architecture and show that it represents a significant improvement over other state-of-the-art SOD approaches.
A Novel Steganography Algorithm Based on Quantization Table Modification and Image Scrambling in DCT Domain

In this paper, a steganography algorithm has been proposed which is based on quantization table modification and image scrambling in the DCT domain. First, the algorithm homogenizes the energy by scrambling the cover image to improve the number of DCT coefficients suitable for information embedding. Second, the embedding capacity of the DCT block is determined by the value of the quantization table in the algorithm, different quantization tables get different embedding effects. In addition, this paper proposes an optimized modified quantization table. Extensive experiments show that the proposed algorithm achieves a great potential for confidential data and indiscernible image quality.
Robust Compare Network for Few-Shot Learning

Making machines learn like humans is the ultimate goal of artificial intelligence.Few-shot learning attempts to simulate the learning mechanism of humans, which is a task that can learn novel concepts from very few labeled samples.Due to the lack of sufficient labeled training data, existing methods often increase the risk of over-fitting or cause a considerable gap between clean and augmented data.To solve these problems, we present a novel compare network to perform robust few-shot learning in a meta-learned end-to-end manner.Specifically, we argue that it is desirable to learn a robust encoder that can draw inferences about other cases from one example.To this end, we improve the accuracy of few-shot learning by mining the internal mechanism of deep networks, which can leverage label information more effectively.By introducing shift-invariant blocks and a self-attention block in our architecture, all these components are seamlessly integrated into our framework, which can give feedback to each other without data augmentation.Furthermore, we provide ablative analyses of different blocks to help understand how each term contributes to performance.Extensive experiments demonstrate that our method can provide more robust results and outperform state-of-the-art few-shot learning methods.
Domain relation extraction from noisy Chinese texts

Relation extraction is a typical method to extend the knowledge graph (KG); nevertheless, when it is applied in a particular domain, the issue of text sparsity becomes noteworthy. As a remedy, distant supervision is introduced to mitigate the issue, which, however, brings about noise simultaneously. The two issues have been notorious in Chinese domain, since Chinese KGs are less developed in comparison with popular languages like English. To tackle the challenge, we propose a complementary convolution neural network (com-CNN) with attentional multiple instance learning (MIL) to obtain highly comprehensive features for extracting relations, and alleviate the negative effect caused by sentence-level noise. Our model com-CNN fully captures information from two different representations of a relation instance, raw word sequence (RWS) and multiple dependency path (MDP), and enables them to complement each other. To achieve better combination of RWS and MDP, we design a flexible feature fusion method. To mitigate the over-fitting problem of attention mechanism, which is caused by sparse texts, entity information is employed to guide the computation of attention scores for multiple instances in a bag to alleviate the impact of the wrongly labelled data. Experiments on Chinese relation extraction show that our proposal outperforms state-of-the-art approaches, and that the combination of RWS and MDP can generate more representative features for relation extraction. Besides, the empirical results validate our intention that entity-integrated attentional MIL offers the best performance in denoising for sparse domain texts compared with alternatives.
Detection of traffic patterns in the radio spectrum for cognitive wireless network management

Dynamic Spectrum Access allows using the spectrum opportunistically by identifying wireless technologies sharing the same medium. However, detecting a given technology is, most of the time, not enough to increase spectrum efficiency and mitigate coexistence problems due to radio interference. As a solution, recognizing traffic patterns may lead to select the best time to access the shared spectrum optimally. To this extent, we present a traffic recognition approach that, to the best of our knowledge, is the first non-intrusive method to detect traffic patterns directly from the radio spectrum, contrary to traditional packet-based analysis methods. In particular, we designed a Deep Learning (DL) architecture that differentiates between Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) traffic, burst traffic with different duty cycles, and traffic with varying rates of transmission. As input to these models, we explore the use of images representing the spectrum in time and time-frequency. Furthermore, we present a novel data randomization approach to generate realistic synthetic data that combines two state-of-the-art simulators. Finally, we show that after training and testing our models in the generated dataset, we achieve an accuracy of ≥ 96 % and outperform state-of-the-art methods based on IP-packets with DL.
Joint Event Extraction Model based on Multi-feature Fusion

Event extraction is a challenging problem in information extraction, designed to extract structured information from unstructured text. The existing event extraction methods are mostly based on the pipeline model and use the traditional language representation model for word embedding. It is impossible to model the polysemy and the features is very simple, besides, the pipeline model is also easy to generate cascading errors. Aiming at the above problems, we propose a joint event extraction model, which combines part-of-speech features, entity features and word representation of BERT model. Because of the BERT can’t capture local features, we use multi-CNN to capture the local features of different scales. and further use BiLSTM to capture context features to assist joint event extraction. Experimental results show that the precision, recall and F1 of the method on the CEC dataset are 88.83%, 90.91% and 89.86%, respectively, which are 1.61%, 3.6% and 2.6% higher than the PLMEE model.
Testing-and-Evaluation Platform for Haptics-based Aerial Manipulation with Drones

This paper presents a haptics-based testing-and-evaluation platform for drone-based aerial manipulation. This platform serves towards the designs of a human-in-the-loop approach to dexterous manipulation tasks. The notion stems from using drones to perform tasks like sensor insertion, object positioning, and tool handling for operations like bridge deck maintenance, agricultural crop harvesting, and field services. Presented are the platform design, force sensing and displacement results, and indoor flight tests.
On the Benefits of Compact Blocks in Bitcoin

Compact blocks and compact block protocol are a recent addition to the Bitcoin (BTC) data propagation protocol that aims to reduce bandwidth requirements and, possibly, reduce latency as well. In this work we have evaluated improvement of operation of BTC network under a mix of regular and compact block traffic in low-bandwidth mode. We have performed queuing analysis of the BTC network and obtained performance descriptors of block and transaction delivery times as well as forking probability. Although compact block size is more than an order of magnitude smaller than regular block size, improvement of delivery times is within bounds of 0% to 20%. Forking probability shows highest improvement of 25%. However, further analysis shows that compact block protocol requires high transaction traffic in order to prevent transaction pool deficit which causes further interaction among the peers.
Carrying an arbitrarily large amount of information using a single quantum particle

Theoretically speaking, a photon can travel arbitrarily long before it enters into a detector, resulting a click. How much information can a photon carry? We study a bipartite asymmetric "two-way signaling" protocol as an extension of that proposed by Del Santo and Daki\'{c}. Suppose that Alice and Bob are distant from each other and each of them has an $n$-bit string. They are tasked to exchange the information of their local n-bit strings with each other, using only a single photon during the communication. It has been shown that the superposition of different spatial locations in a Mach-Zehnder (MZ) interferometer enables bipartite local encodings. We show that, after the travel of a photon through a cascade of $n$-level MZ interferometers in our protocol, the one of Alice or Bob whose detector clicks can access the other's full information of $n$-bit string, while the other can gain one-bit of information. That is, the wave-particle duality makes two-way signaling possible, and a single photon can carry arbitrarily large (but finite) information.
Interfacing an industrial robot and MATLAB for predictive visual servoing

Purpose Real-time implementation of sophisticated algorithms on robotic systems demands a rewarding interface between hardware and software components. Individual robot manufacturers have dedicated controllers and languages. However, robot operation would require either the knowledge of additional software or expensive add-on installations for effective communication between the robot controller and the computation software. This paper aims to present a novel method of interfacing the commercial robot controllers with most widely used simulation platform, e.g. MATLAB in real-time with a demonstration of visual predictive controller. Design/methodology/approach A remote personal computer (PC), running MATLAB, is connected with the IRC5 controller of an ABB robotic arm through the File Transfer Protocol (FTP). FTP server on the IRC5 responds to a request from an FTP client (MATLAB) on a remote computer. MATLAB provides the basic platform for programming and control algorithm development. The controlled output is transferred to the robot controller through Ethernet port as files and, thereby, the proposed scheme ensures connection and control of the robot using the control algorithms developed by the researchers without the additional cost of buying add-on packages or mastering vendor-specific programming languages. Findings New control strategies and contrivances can be developed with numerous conditions and constraints in simulation platforms. When the results are to be implemented in real-time systems, the proposed method helps to establish a simple, fast and cost-effective communication with commercial robot controllers for validating the real-time performance of the developed control algorithm. Practical implications The proposed method is used for real-time implementation of visual servo control with predictive controller, for accurate pick-and-place application with different initial conditions. The same strategy has been proven effective in supervisory control using two cameras and artificial neural network-based visual control of robotic manipulators. Originality/value This paper elaborates a real-time example using visual servoing for researchers working with industrial robots, enabling them to understand and explore the possibilities of robot communication.
A bivariate autoregressive Poisson model and its application to asthma‐related emergency room visits

There are no gold standard methods that perform well in every situation when it comes to the analysis of multiple time series of counts. In this paper, we consider a positively correlated bivariate time series of counts and propose a parameter‐driven Poisson regression model for its analysis. In our proposed model, we employ a latent autoregressive process, AR ( p ) to accommodate the temporal correlations in the two series. We compute the familiar maximum likelihood estimators of the model parameters and their standard errors via a Bayesian data cloning approach. We apply the model to the analysis of a bivariate time series arising from asthma‐related visits to emergency rooms across the Canadian province of Ontario.
Color-Constrained Dehazing Model

In this paper, we address the insufficiency of the popular atmospheric scattering model (ASM) used in the image dehazing problem. Unlike ASM assumes the global uniform atmospheric light and attenuation coefficients and thus often introduces unrealistic color after dehazing, we propose a novel dehazing model by relaxing the global uniform atmospheric assumption to local with additional color constraints to ensure more appealing and realistic dehazed results. More precisely, we make the modeling process as an optimization problem, whose cost function is composed of color constraint, local smooth of transmission map and atmospheric light. Consequently, we are able to generate more realistic dehazed images comparing to ASM, implying that deep neural networks trained with these samples could effectively learn how to dehaze images of complicated cases, especially when the global atmospheric assumption fails. Our extensive experimental studies also confirm that the proposed dehazing model outperforms the state-of-the-art methods by a noticeable margin on all three public benchmarks including HazeRD, RESIDE, and O-HAZE in terms of SSIM and PSNR.
Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training

In modulation classification domain, handcrafted feature based method can fit well from a few labeled samples, while deep learning based method require a large amount of samples to achieve the superior classification performance. In order to improve the modulation classification accuracy under the constraint of limited labeled samples, this paper proposes a few-shot modulation classification method based on feature dimension reduction and pseudo-label training (FDRPLT), which combines handcrafted feature based method with deep learning based method. First, an optimal low-dimensional feature subset is created by the combination of the handcrafted features and autoencoder-extracted features post-processed by a feature selection algorithm. Then, a fully connected network (FCN), trained on a small number of labeled signals, is designed for the automated annotating, where unlabeled samples can be annotated and used for the later convolution neural network (CNN) training. The simulation results show that the classification accuracy of eight kinds of modulation types can reach to 98.3% when the SNR is 20dB.
Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches

Learning rates in stochastic neural network training are currently determined a priori to training, using expensive manual or automated iterative tuning. Attempts to resolve learning rates adaptively, using line searches, have proven computationally demanding. Reducing the computational cost by considering mini-batch sub-sampling (MBSS) introduces challenges due to significant variance in information between batches that may present as discontinuities in the loss function, depending on the MBSS approach. This study proposes a robust approach to adaptively resolve learning rates in dynamic MBSS loss functions. This is achieved by finding sign changes from negative to positive along directional derivatives, which ultimately converge to a stochastic non-negative associated gradient projection point. Through a number of investigative studies, we demonstrate that gradient-only line searches (GOLS) resolve learning rates adaptively, improving convergence performance over minimization line searches, ignoring certain local minima and eliminating an otherwise expensive hyperparameter. We also show that poor search directions may benefit computationally from overstepping optima along a descent direction, which can be resolved by considering improved search directions. Having shown that GOLS is a reliable line search allows for comparative investigations between static and dynamic MBSS.
Manufacturing Hate 4.0: Can Media Studies Rise to the Challenge?

This article reflects on the growing scourge of hate speech and its propagation via digital social media networks. It discusses how media studies has drawn attention to salient aspects of online hate speech including technological affordances, communication tactics, representational tropes, and audience response. It argues that insights from media studies are vital for unpacking the societal impact of the media and indeed for tackling a destructive force such as online hate speech. It further encourages media studies scholars to engage vigorously with colleagues in and across other disciplines to forge interdisciplinary research collaborations to address pressing societal issues. It urges media studies scholars to connect with the realms of industry, policy making, and civic society to ensure that the public discourse on the challenges of digitalization and mediatization is academically informed, evidence-based, and finely balanced.