Reviewerly/exHarmony huggingface dataset:
RNIC-A retrospect network for image captioning

As cross-domain research combining computer vision and natural language processing, the current image captioning research mainly considers how to improve the visual features; less attention has been paid to utilizing the inherent properties of language to boost captioning performance. Facing this challenge, we proposed a textual attention mechanism, which can obtain semantic relevance between words by scanning all generated words. The retrospect network for image captioning (RNIC) proposed in this paper aims to improve input and prediction process by using textual attention. Concretely, the textual attention mechanism is applied to the model simultaneously with the visual attention mechanism to provide the input of the model with the maximum information required for generating captions. In this way, our model can learn to collaboratively attend on both visual and textual features. Moreover, the semantic relevance between words obtained by retrospect is used as the basis for prediction, so that the decoder can simulate the human language system and better make predictions based on the already generated contents. We evaluate the effectiveness of our model on the COCO image captioning datasets and achieve superior performance over the previous methods.
Adaptive Emotion-Centered Fuzzy Tracking Controller With Robust Outer Feedback

We develop a novel robust self-organizing construction based on two powerful patterns—the elegance of cognitive (brain emotional learning) and the influence of decision making (fuzzy logic). Early in the design of the proposed controller, the friction, coriolis, centrifugal, and gravity parts of the robot model are considered as they are uncertain, and then they are compensated by the proposed structure. Unlike the existing approaches, the proposed self-organizing fuzzy emotional (SFE) approach achieves not only optimum parameters but also optimum constructions. In structure learning, a new index based on the measured error, the number of rules, and the max sensory input is considered to generate new rules. The parameter learning condition depends on the stability. Furthermore, we formulate a controller based on SFE for a two-link robot and 3-PSP spatial parallel manipulator. To deal with the increasing and decreasing weight of Amygdala, the robust outer feedback is augmented to the whole controller. Simulation results show the priority of the proposed approach in comparison to alternative approaches especially in improving the ability to deal with uncertainty.
An MPC-based Task Priority Management Approach for Connected and Automated Vehicles Reference Tracking with Obstacle Avoidance

We present a reference tracking control problem with obstacle avoidance for connected and automated vehicles. The proposed approach allows to deal with obstacles in the control loop in the first instance, coping with real-time operations while safely waiting for an eventually new planned trajectory from the guidance loop. An obstacle avoidance algorithm is designed to produce suitable constraints for an optimization control problem to be solved via Nonlinear Model Predictive Control. Such an algorithm is based on task priority management, so that the reference tracking task is handled as a lower priority task with respect to the obstacle avoidance task. Automated vehicles are managed in a decentralized fashion, so that they can process independently any sensed potential obstacles, including conventional vehicles. In the presence of vehicle connectivity, vehicles may exchange information about their states to make decisions based on more accurate predictions. The proposed method is evaluated via simulation experiments, for a set of scenarios in the context of urban traffic.
Logics of Ignorance and Being Wrong

Abstract This article investigates the connections between the logics of being wrong, introduced in Steinsvold (2011, Notre Dame J. Form. Log., 52, 245–253), and factive ignorance, presented in Kubyshkina and Petrolo (2021, Synthese, 198, 5917–5928). The first part of the paper provides a sound and complete axiomatization of the logic of factive ignorance that corrects errors in Kubyshkina and Petrolo (2021, Synthese, 198, 5917–5928) and resolves questions about the expressivity of the language. In the second half, it is shown that the relationship between the two logics suggests an alternative axiomatization for the logic of factive ignorance, the adequacy of which is easily proved via straightforward translations.
Personal Factors of Adapting to Academic Environment in First-Year Medical Students

The article features personality determinants of adaptation in first-year medical students. The study involved R. Cattell’s questionnaire of personality qualities and S. R. Panteleev’s test of self-attitude components. The factor analysis revealed six personality factors that covered 74.7 % of the primary characteristics included in the statistical procedure. The participants had different adaptation levels: high, medium, and low. Based on the zones of positive or negative factor values, the identified determinants were divided into two groups. The group of successful adaptation factors included emotional maturity, positive attitude to oneself and to life, readiness to change, orientation towards social approval, social activity and importance of communication, intelligence and mental abilities. The factors of adaptation disorders included conservative attitudes and caution with a prominent need for sympathy from others, idealistic worldview, and internal illusions. The emotional and volitional qualities proved to be the most important factor of professional self, while self-attitude components appeared to be underrepresented, and intellectual qualities had ambiguous impact on the process of adaptation. The obtained data reflected both general scientific ideas about the phenomenon of adaptation, i.e. the role of emotions at the initial stage of university education. The article also describes the personality development of students at this stage of ontogenesis, e.g. incomplete self-attitude formation, etc. The results can be used in medical universities as a part of psychological support in order to prevent personality adaptation disorders in first-year students.
Efficient Tree Multiplier Design by using Modulo 2<sup>n</sup> + 1 Adder

Residue Number System is a carry-free system used in various applications of high speed arithmetic component like digital signal processing, image processing and cryptography. The main work of RNS is to reduce the complexity and to perform operations on overflow detection, sign detection and magnitude comparison. In this paper, we propose the parallel architecture based on parallel prefix tree is helpful for computation at higher speed. This work is carried out to get a regular binary multiplication technique, basically consists of modulo reduction operation. Conversion has been done between binary and diminished-1 representation for a large value of inputs. Proposed work with parallel prefix tree adder improves the speed of multiplication. This modified parallel prefix adder consumes less area as compared to Kogge Stone adder. The proposed work is consisting of a carry-computation unit depends on the carry-generate and carry-propagate terms. In this paper, the area of carry computation unit of modulo adder has been reduced. Then, the proposed adder is used to design the modulo multiplier for less area and higher speed.
Analysis of Image Forgery Detection Using Canny Edge Detector

As now-a-days many image processing software and editing tools are available using which image can be easily faked. In various fields digital images are used as legal evidence, for forensics investigations,, so there is need of making such system that can detect image forgery. The passive approach of image forgery detection provides image authencity without having any information about the digital image. Further in this paper we have discussed various forgery techniques which were used for creating forgeries in the digital picture. We proposed an optimized method that can detect the forgery in the image using k-means and feature matching algorithm which uses canny edge detector. It gives recall between 85 to 95% and precision 100%.We run this system over two datasets.
Human pose estimation based on parallel atrous convolution and body structure constraints

Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets.
Throwaway Shadows Using Parallel Encoders Generative Adversarial Network

Face photographs taken on a bright sunny day or in floodlight contain unnecessary shadows of objects on the face. Most previous works deal with removing shadow from scene images and struggle with doing so for facial images. Faces have a complex semantic structure, due to which shadow removal is challenging. The aim of this research is to remove the shadow of an object in facial images. We propose a novel generative adversarial network (GAN) based image-to-image translation approach for shadow removal in face images. The first stage of our model automatically produces a binary segmentation mask for the shadow region. Then, the second stage, which is a GAN-based network, removes the object shadow and synthesizes the effected region. The generator network of our GAN has two parallel encoders—one is standard convolution path and the other is a partial convolution. We find that this combination in the generator results not only in learning an incorporated semantic structure but also in disentangling visual discrepancies problems under the shadow area. In addition to GAN loss, we exploit low level L1, structural level SSIM and perceptual loss from a pre-trained loss network for better texture and perceptual quality, respectively. Since there is no paired dataset for the shadow removal problem, we created a synthetic shadow dataset for training our network in a supervised manner. The proposed approach effectively removes shadows from real and synthetic test samples, while retaining complex facial semantics. Experimental evaluations consistently show the advantages of the proposed method over several representative state-of-the-art approaches.
Learn to Be Clear and Colorful: An End-to-End Network for Panchromatic Image Enhancement

Benefiting from the high coverage and re-visiting frequency, the satellite imagery is an ideal data for large-scale, real-time earth observation. However, due to the limited resolution and chromatic information, the satellite images, especially the panchromatic images, are not capable of being used for accurate earth observations, such as road extraction, vehicle detection, and building segmentation. In this research, we propose a cascaded fully convolutional network (CFCN) consists of a residual dense super-resolution network (RDSRN) for grayscale image super-resolution (SR) and a residual deconvolution colorization network (RDCN) for grayscale image colorization. The unique architecture can simultaneously learn texture detail and color information from aerial images and then transfer to enhance panchromatic images. Furthermore, we also introduce an indirect evaluation metric, learned extraction similarity (LES), to estimate the image quality of the generated image in the absence of the ground truth. Experiments on a multispectral image dataset demonstrate that panchromatic images enhanced by the proposed CFCN are with both texture and color fidelity as compared to aerial image. For pre-trained U-Net, compared to the performance on raw panchromatic images, the CFCN enhanced images increase 12.8% of LES of overall accuracy (96.4% versus 83.6%).
Combined Metapath Based Attention Network for Heterogenous Networks Node Classification

In recent years, Graph Neural Networks(GNNs) have been widely used as representation learning methods on graphs especially homogeneous graphs, and demonstrated remarkable performance in various tasks. However, GNNs on Heterogeneous Graphs(HGs) haven’t been fully explored, and existing methods on HGs use either metapaths to extract semantics on generated graphs or construct attention mechanics to deal with the original graph directly. The former methods strongly depend on metapaths which makes their performance unstable, and the latter ones can hardly capture deep patterns on HGs as metapaths do. In this paper, we classify information between HG nodes into two parts, prior node information and direct node information, and propose a Combined metapath based Attention Network(CAN) to combine them that making up each one’s disadvantages. Moreover, any number of metapaths can be used in CAN which makes the proposed method more flexible. Based on metapaths we extract the prior node information, and with a novel attention mechanism, we extract the direct node information. Through additional semantic-level attention, we combine them into unique representations. Node classification experiments on real-world datasets demonstrate the performance of the proposed method.
Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews

Recently, the interaction information from reviews has been modeled to acquire representations between users and items and improve the sparsity problem in recommendation systems. Reviews are more responsive to information about users’ preferences for the different aspects and attributes of items. However, how to better construct the representation of users (items) still needs further research. Inspired by the interaction information from reviews, auxiliary ID embedding information is used to further enrich the word-level representation in the proposed model named MPCAR. In this paper, first, a multipointer learning scheme is adopted to extract the most informative reviews from user and item reviews and represent users (items) in a word-by-word manner. Then, users and items are embedded to extract the ID embedding that can reveal the identity of users (items). Finally, the review features and ID embedding are input to the gated neural network for effective fusion to obtain richer representations of users and items. We randomly select ten subcategory datasets from the Amazon dataset to evaluate our algorithm. The experimental results show that our algorithm can achieve the best results compared to other recommendation approaches.
Multiple Histograms Shifting-Based Video Data Hiding Using Compression Sensing

With the development of multimedia editing technologies, the copyright protection has attacked more attentions. Reversible data hiding (RDH), in which the cover can be recovered losslessly, is an effect method to eliminate embedding distortions. As a typical RDH method, histogram shifting (HS) is used widely. Most existing RDH schemes based on HS usually build sharp histograms by predicting and sorting techniques. To make use of spatial correlations of multimedia, several RDH schemes based on multiple HS (MHS) are proposed to protect copyright, in which some rigid rules are used to build multiple histograms. Against images, videos have more spatial and temple correlations and it is easier to acquire sharper histograms. In this paper, a video MHS scheme based on compression sensing (CS) is proposed. As a linear sensing algorithm, CS can measure macroblock residuals by reducing corrections among pixels to acquire distinguishable macroblock features, while keeping their statistical characteristics immutable. By employing CS, macroblocks with similar characteristics cluster together to formulate multiple histograms. For each of these histograms, data embedding is implemented to reduce shifting distortions by expanding the outermost bins while other bins are unchanged. Experimental results show that the quality of most test videos in our scheme are higher than that in the state-of-art schemes.
SLA Violation Detection in Multi-Cloud Environment using Hyperledger Fabric Blockchain

The increased usage of cloud computing technology and its industry-wide adoption has led to almost all modern consumer services being heavily dependent on cloud computing platforms. The user and the Cloud Service Provider (CSP) must agree on a Service Level Agreement (SLA) to keep performance above a given threshold and maintain a certain Quality of Service (QoS) (quality of service). This SLA is enforced by the CSP, who monitors the performance of the computers and compensates the user using the logs created by the underlying machinery. Similarly, a cautious customer can keep a monitoring system in place to examine the operation of the virtualized resources assigned to them regularly. This leads to distrust between the CSP and the customer, as neither party believes the other’s monitoring system is reliable. In this work, we propose a blockchain-based which guarantees the integrity of the client’s logs and verifies the SLA violations creating a trustworthy ecosystem. Furthermore, we carry out the scalability and performance analysis of proposed system using Hyperledger Fabric blockchain platform.
Intelligent Multi-Agent System for Improving the Quality of Software by Taking into Account the Information of the Subject Area at All Stages of its Development

For improving the quality of software, it is necessary to take into account the information of the subject area at all stages of its development. The paper first time proposes the structure of an intelligent multi-agent system for improving the quality of software by taking into account the information of the subject area at all stages of its development, which: performs semantic parsing of real software documents to find the necessary elements of information of the subject area; forms appropriate real ontologies for each document; compares ideal and real ontologies; estimates the magnitudes of the subject area information losses for each software document.
Pandemonium: a clustering tool to partition parameter space—application to the B anomalies

Abstract We introduce the interactive tool to cluster model predictions that depend on a set of parameters. The model predictions are used to define the coordinates in observable space which go into the clustering. The results of this partitioning are then visualized in both observable and parameter space to study correlations between them. The tool offers multiple choices for coordinates, distance functions and linkage methods within hierarchical clustering. It provides a set of diagnostic statistics and visualization methods to study the clustering results in order to interpret the outcome. The methods are most useful in an interactive environment that enables exploration, and we have implemented them with a graphical user interface in R. We demonstrate the concepts with an application to phenomenological studies in flavor physics in the context of the so-called B anomalies, exploring the tension between $$P_5^\prime $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>P</mml:mi> <mml:mn>5</mml:mn> <mml:mo>′</mml:mo> </mml:msubsup> </mml:math> and $$R_K$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>R</mml:mi> <mml:mi>K</mml:mi> </mml:msub> </mml:math> and quantifying the resolution in parameter space that can be provided by a given observable set.
Topological Voting Method for Image Segmentation

Image segmentation is one of the main problems in image processing. In order to improve the accuracy of segmentation, one often creates a number of masks (annotations) for a same image and then uses some voting methods on these masks to obtain a more accurate mask. In this paper, we propose a voting method whose voting rule is not pixel-wise but takes into account the natural geometric-topological properties of the masks. On three concrete examples, we show that our voting method outperforms the usual arithmetical voting method.
Coq’s vibrant ecosystem for verification engineering (invited talk)

Program verification in the large is not only a matter of mechanizing a program logic to handle the semantics of your programming language. You must reason in the mathematics of your application domain--and there are many application domains, each with their own community of domain experts. So you will need to import mechanized proof theories from many domains, and they must all interoperate. Such an ecosystem is not only a matter of mathematics, it is a matter of software process engineering and social engineering. Coq's ecosystem has been maturing nicely in these senses.
Minimal blowing pressure allowing periodic oscillations in a model of bass brass instruments

In this study, an acoustic resonator – a bass brass instrument – with multiple resonances coupled to an exciter – the player’s lips – with one resonance is modelled by a multidimensional dynamical system, and studied using a continuation and bifurcation software. Bifurcation diagrams are explored with respect to the blowing pressure, in particular with focus on the minimal blowing pressure allowing stable periodic oscillations and the associated frequency. The behaviour of the instrument is first studied close to a (non oscillating) equilibrium using linear stability analysis. This allows to determine the conditions at which an equilibrium destabilises and as such where oscillating regimes can emerge (corresponding to a sound production). This approach is useful to characterise the ease of playing of a brass instrument, which is assumed here to be related – as a first approximation – to the linear threshold pressure. In particular, the lower the threshold pressure, the lower the physical effort the player has to make to play a note [The Science of Brass Instruments. Springer-Verlag, 2021]. Cases are highlighted where periodic solutions in the bifurcation diagrams are reached for blowing pressures below the value given by the linear stability analysis. Thus, bifurcation diagrams allow a more in-depth analysis. Particular attention is devoted to the first playing regime of bass brass instruments (the pedal note and the ghost note of a tuba in particular), whose behaviour qualitatively differs from a trombone to a euphonium for instance.