Reviewerly/exHarmony huggingface dataset:
High Edge-Quality Light-Field Salient Object Detection Using Convolutional Neural Network

The detection result of current light-field salient object detection methods suffers from loss of edge details, which significantly limits the performance of subsequent computer vision tasks. To solve this problem, we propose a novel convolutional neural network to accurately detect salient objects, by digging effective edge information from light-field data. In particular, our method is divided into four steps. Firstly, the network extracts multi-level saliency features from light-field data. Secondly, edge features are extracted from low-level saliency features and optimized by ground-truth guidance. Then, to sufficiently leverage high-level saliency features and edge features, the network hierarchically fuses them in a complementary manner. Finally, spatial correlations between different levels of fused features are considered to detect salient objects. Our method can accurately locate salient objects with exquisite edge details, by extracting clear edge information and accurate saliency information and fully fusing them. We conduct extensive evaluations on three widely used benchmark datasets. The experimental results demonstrate the effectiveness of our method, and it is superior to eight state-of-the-art methods.
Face Dataset Augmentation with Generative Adversarial Network

Abstract Face recognition is a widely used scene of artificial intelligence technology. However, some face occlusions cause the face to be unable to be effectively detected in a specific environment. Although many algorithms have been proposed to solve this problem, in essence, a large number of face image data containing occlusion elements is needed to train to improve the detection ability of the algorithm. In recent years, this problem can be effectively solved by using the image generation ability of generative adversarial network. This paper proposes an improved Generative Adversarial Networks (GAN), which improves the effect of occluded face image generation by adding coding module. Through the expansion of data set, the detection accuracy of several classic face detection models for occluded faces is improved by more than 3%. At the moment when the epidemic has not been over, occlusion face data is of great significance to improve the performance of face detection systems in specific public places such as customs security inspection and medical centers.
Performance prediction of the Micro Solar Dome in different climatic regions of India from pilot-scale by Random Forest algorithm

As people living in remote areas still lack access to indoor lighting, the intervention of appropriate need-based innovations to improve basic indoor lighting facilities are essential. The present work discusses the development of a hybrid lighting device, Micro Solar Dome, that utilizes both the active and passive forms of solar energy to ensure sustainable lighting facilities for rural houses. The work elaborates the design, simulation, and pilot-scale installation of the device in 8 states in India. Based on the performance, a Principal Component Analysis was done, which indicated that more than 93% variation of the performance of the device was due to Global Horizontal Irradiance and Relative Humidity. A novel predictive model utilizing the Random Forest algorithm was developed for major Köppen climatic zones in India to determine the night-time performance of the device and validate the Principal Component Analysis results. As per the prediction, the average operation time of the device is about 3.5hrs at night, with the highest of 4.6 hrs in the Hot Desert Climate and the lowest of 2.4 hrs in the Humid Subtropical climate when 9 Köppen Zones are considered covering more than 95% of the geographic area of the country.
I-GANs for Synthetical Infrared Images Generation

Due to the insensitivity of infrared images to changes in light intensity and weather conditions, these images are used in many surveillance systems and different fields. However, despite all the applications and benefits of these images, not enough data is available in many applications due to the high cost, time-consuming, and complicated data preparation. Two deep neural networks based on Conditional Generative Adversarial Networks are introduced to solve this problem and produce synthetical infrared images. One of these models is only for problems where the pair to pair visible and infrared images are available, and as a result, the mapping between these two domains will be learned. Given that in many of the problems we face unpaired data, another network is proposed in which the goal is to obtain a mapping from visible to infrared images so that the distribution of synthetical infrared images is indistinguishable from the real ones. Two publicly available datasets have been used to train and test the proposed models. Results properly demonstrate that the evaluation of the proposed system in regard to peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) has improved by 4.6199% and 3.9196%, respectively, compared to previous models.
Intelligent color matching model for wood dyeing using Genetic Algorithm and Extreme learning machine

Wood dyeing technology is of great significance to improve the utilization rate of inferior wood resources. The challenge to imitating precious wood species by inferior wood is to quickly and accurately obtain the dyeing formula of precious wood species. This study uses Genetic Algorithm (GA) to optimize Extreme learning machine (ELM), and then a predictive model based on GA-ELM is proposed for predicting the dyeing formula of precious wood species. The sum of the relative deviations of the three dyes between the predicted formula and the actual formula, that is, the relative deviation of the formula, is calculated to evaluate the model’s prediction accuracy. The simulation results show that the average relative deviations of the formula predicted by Back Propagation (BP) neural network, Radial Basis Function (RBF) neural network, ELM, and GA-ELM are 0.808, 0.717, 0.708, and 0.262. The prediction deviation of the GA-ELM is much smaller than that of other traditional neural networks, which can achieve good results in wood production.
Enhanced Audio Encryption using 2-D Zaslavsky Chaotic Map

Audio communication using internet is increasing day by day at a dramatic level. So, in order to secure the communication and increase the privacy of the audio communication this paper proposes a new method using the chaos theory. The zaslavsky map, one of a chaotic map is used because of its hyper chaotic nature and highest positive Lyapunov exponent. The 2-dimensional zaslavsky map values are generated and passed as a key to the AES algorithm. These numbers allow the encryption method to use a larger number of random keys. This ensures that the key length is effective and that the audio files transferred over the internet are secure. An increase in the key space available for the encryption technique precludes brute force attacks on any cryptography schemes, as well as sniffer attacks on the audio files being sent.
Time-varying spectral matrix estimation via intrinsic wavelet regression for surfaces of Hermitian positive definite matrices

Intrinsic wavelet transforms and denoising methods are introduced for the purpose of time-varying Fourier spectral matrix estimation. A non-degenerate time-varying spectral matrix constitutes a surface of Hermitian positive definite matrices across time and frequency and any spectral matrix estimator ideally adheres to these geometric constraints. Spectral matrix estimation of a locally stationary time series by means of linear or nonlinear wavelet shrinkage naturally respects positive definiteness at each time-frequency point, without any postprocessing. Moreover, the spectral matrix estimator enjoys equivariance in the sense that it does not nontrivially depend on the chosen basis or coordinate system of the multivariate time series. The algorithmic construction is based on a second-generation average-interpolating wavelet transform in the space of Hermitian positive definite matrices equipped with an affine-invariant metric. The wavelet coefficient decay and linear wavelet thresholding convergence rates of intrinsically smooth surfaces of Hermitian positive definite matrices are derived. Furthermore, practical nonlinear thresholding based on the trace of the matrix-valued wavelet coefficients is investigated. Finally, the time-varying spectral matrix of a nonstationary multivariate electroencephalography (EEG) time series recorded during an epileptic brain seizure is estimated.
Questions, Presuppositions and Fallacies

Abstract In this paper I focus on the fallacy known as Complex Question or Many Questions. After a brief introduction, in Sect. 2 I highlight its pragmatic dimension, and in Sect. 3 its dialectical dimension. In Sect. 4 I present two accounts of this fallacy developed in argumentation theory, Douglas Walton’s and the Pragma-Dialectics’, which have resources to capture both its pragmatic and its dialectical nature. However, these accounts are unsatisfactory for various reasons. In Sect. 5 I focus on the pragmatic dimension of the fallacy and I suggest amendments to the accounts mentioned drawing on the study of the phenomenon of presupposition in theoretical pragmatics. I argue that the central notion in the definition of the fallacy is that of an informative presupposition. In Sect. 6 I focus on the dialectical dimension of the fallacy. This dimension needs to be explicitly acknowledged in the definition of the fallacy in order to distinguish it from a different, non-dialectical, fallacious argumentative move involving presuppositions.
An Intelligent College Enquiry Bot using NLP and Deep Learning based techniques

Every year freshers and their parents do visit our college website in order to get their queries clarified. Also, college students do visit the website in order to get their queries resolved. Thus we felt the need for an &#x2018;Intelligent Enquiry Bot&#x2019; to be associated with the official college website. The Bot should be intelligent enough to resolve the queries of freshers, parents, students, and faculty. The college enquiry chatbot is designed using certain algorithms which understands and analyzes the user queries. This System is basically a web application that provides valid responses to the various queries of the users, which will make use of Natural Language Processing (NLP) and Long Short Term Memory (LSTM) networks, which are a special kind of recurrent Deep Neural Networks (DNN). In this paper, we have completed building a quite intellegent chatbot based on NLP and DNN for basic college-related enquiries and admission related queries especially.
A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating

The monitoring infrastructure of an industrial fleet can rely on the so-called unit-level and subfleet-level models to observe the behavior of a target unit. However, such infrastructure has to confront several challenges. First, from an anomaly detection perspective of monitoring a target unit, unit-level and subfleet-level models can give different information about the nature of an anomaly, and which approach or level model is appropriate is not always clear. Second, in the absence of well-understood prior models of unit and subfleet behavior, the choice of a base model at their respective levels, especially in an online/streaming setting, may not be clear. Third, managing false alarms is a major problem. To deal with these challenges, we proposed to rely on the conformal anomaly detection framework. In addition, an ensemble approach was deployed to mitigate the knowledge gap in understanding the underlying data-generating process at the unit and subfleet levels. Therefore, to monitor the behavior of a target unit, a unit-level ensemble model (ULEM) and a subfleet-level ensemble model (SLEM) were constructed, where each member of the respective ensemble is based on a conformal anomaly detector (CAD). However, since the information obtained by these two ensemble models through their p-values may not always agree, a combined ensemble model (CEM) was proposed. The results are based on real-world operational data obtained from district heating (DH) substations. Here, it was observed that CEM reduces the overall false alarms compared to ULEM or SLEM, albeit at the cost of some detection delay. The analysis demonstrated the advantages and limitations of ULEM, SLEM, and CEM. Furthermore, discords obtained from the state-of-the-art matrix-profile (MP) method and the combined calibration scores obtained from ULEM and SLEM were compared in an offline setting. Here, it was observed that SLEM achieved a better overall precision and detection delay. Finally, the different components related to ULEM, SLEM, and CEM were put together into what we refer to as TRANTOR: a conformal anomaly detection based industrial fleet monitoring framework. The proposed framework is expected to enable fleet operators in various domains to improve their monitoring infrastructure by efficiently detecting anomalous behavior and controlling false alarms at the target units.
Fairness-aware scheduling of dynamic cross-job coflows in shared datacenters based on meta learning

In today’s shared datacenters, communications among tasks across jobs (applications) typically generate large amounts of coflows. System efficiency and job fairness oriented coflow scheduling is critical for improving both system performance and user satisfaction in the application level. Since the metrics of fairness and efficiency usually conflict to each other, how to schedule coflows at the desired tradeoff between the metrics is a challenging problem. Due to the great variety of jobs and tasks within each job, communications among them have different characteristics, resulting their coflows presented in different patterns. Existing coflow schedulers attempt to optimize either only one metric or a tradeoff of both for static patterns of coflows (within the same job) because of their incapability of capturing the inherent patterns of coflows that are dynamically changing specially for cross-job coflows. This paper proposes a novel fairness-aware coflow scheduling algorithm that combines a link-embedded multi-layer neural network with a meta-learning framework for unsupervised learning of dynamic coflows across different jobs and hence adaptive scheduling of the coflows to achieve the desired fairness–efficiency tradeoff. In our algorithm, while the neural network takes care of mining the relationships among coflow allocations, the meta-learning framework effectively captures the dynamic patterns of a large number of sample coflows and trains the neural network to achieve the desired scheduling performance. Extensive experimental results demonstrate that our algorithm outperforms the state-of-the-art coflow schedulers on scheduling performance for achieving the desired fairness–efficiency tradeoff.
Learning Streamed Attention Network from Descriptor Images for Cross-Resolution 3D Face Recognition

In this article, we propose a hybrid framework for cross-resolution 3D face recognition which utilizes a Streamed Attention Network (SAN) that combines handcrafted features with Convolutional Neural Networks (CNNs). It consists of two main stages: first, we process the depth images to extract low-level surface descriptors and derive the corresponding Descriptor Images (DIs), represented as four-channel images. To build the DIs, we propose a variation of the 3D Local Binary Pattern (3DLBP) operator that encodes depth differences using a sigmoid function. Then, we design a CNN that learns from these DIs. The peculiarity of our solution consists in processing each channel of the input image separately, and fusing the contribution of each channel by means of both self- and cross-attention mechanisms. This strategy showed two main advantages over the direct application of Deep-CNN to depth images of the face; on the one hand, the DIs can reduce the diversity between high- and low-resolution data by encoding surface properties that are robust to resolution differences. On the other, it allows a better exploitation of the richer information provided by low-level features, resulting in improved recognition. We evaluated the proposed architecture in a challenging cross-dataset, cross-resolution scenario. To this aim, we first train the network on scanner-resolution 3D data. Next, we utilize the pre-trained network as feature extractor on low-resolution data, where the output of the last fully connected layer is used as face descriptor. Other than standard benchmarks, we also perform experiments on a newly collected dataset of paired high- and low-resolution 3D faces. We use the high-resolution data as gallery, while low-resolution faces are used as probe, allowing us to assess the real gap existing between these two types of data. Extensive experiments on low-resolution 3D face benchmarks show promising results with respect to state-of-the-art methods.
Single Feistel lapse acting on reduced ASCII codes followed by a genetic crossover

Abstract This article describes a new cryptosystem that incorporates the most powerful genetic operators. With the aim of better integration in the field of color image encryption security, this technique starts with the application of an advanced Feistel scheme and ends with the implementation of deeply modified genetic operators. After vectorization of the original image, an application of the advanced Feistel scheme on blocks of random size will be launched. The output vector is transcribed in restricted ASCII code to achieve a genetic cross suitable for the encryption of color images. The output vector obtained is transcribed in restricted ASCII code to considerably improve the effects of the genetic crossing. This step increases the impact of the avalanche effect and protects the system from differential attacks. Simulations performed on a large volume of images of different sizes and formats ensure that our approach is not exposed to known attacks. Article Highlights The one Feistel rounds in our algorithm represent a large modification of a classical scheme. The main new features are: First Feistel lap and Cross-over genetic operator. Switching to ASCII code. Genetic crossover arrangement.
LitCall: learning implicit topology for CNN-based aortic landmark localization

Landmark detection is a critical component of the image processing pipeline for automated aortic size measurements. Given that the thoracic aorta has a relatively conserved topology across the population and that a human annotator with minimal training can estimate the location of unseen landmarks from limited examples, we proposed an auxiliary learning task to learn the implicit topology of aortic landmarks through a CNN-based network. Specifically, we created a network to predict the location of missing landmarks from the visible ones by minimizing the Implicit Topology loss in an end-to-end manner. The proposed learning task can be easily adapted and combined with Unet-style backbones. To validate our method, we utilized a dataset consisting of 207 CTAs, labeling four landmarks on each aorta. Our method outperforms the state-of-the-art Unet-style architectures (ResUnet, UnetR) in terms of localization accuracy, with only a light (#params=0.4M) overhead. We also demonstrate our approach in two clinically meaningful applications: aortic sub-region division and automatic centerline generation.
Learning infinite-word automata with loop-index queries

We propose a new framework for active learning of deterministic infinite-words automata. In our framework, the teacher answers standard membership and equivalence queries, and additionally it provides the loop index of the target automaton on wvω, which is the minimal number of letters of wvω past which the target automaton reaches the final cycle on wvω. We argue that in potential applications if one can answer Boolean part in membership (and equivalence) queries, one can compute the loop index as well. Our framework is an extension of Angluin's L⁎-algorithm with the crucial difference that the queries about the loop index depend on a particular automaton representing an ω-regular language. This allows us to bypass the NP-hardness coming from the minimisation problem for deterministic Büchi automata and provide a polynomial-time algorithm for learning deterministic weighted automata with value function. The algorithm can be easily adjusted for deterministic automata with value function and deterministic Büchi automata. Finally, deterministic parity automata, which recognize all ω-regular languages, can be considered as deterministic automata with value function and hence they can be learned in our framework.
Implicit computation complexity in higher-order programming languages

Abstract This paper is meant to be a survey about implicit characterizations of complexity classes by fragments of higher-order programming languages, with a special focus on type systems and subsystems of linear logic. Particular emphasis will be put on Martin Hofmann’s contributions to the subject, which very much helped in shaping the field.
Blockchain Powered Government Financial System

This is the century of technology and digitization. Existing problems require modern solutions. The problems that have existed through decades or even centuries can now be solved through current technologies. Blockchain is one of the technologies that is going to have an extensive and comprehensive change in people&#x0027;s lives. Blockchain is an emerging technology and its fields of application are being explored. The domain of field chosen in this paper for its application is the public sector. Pioneering research done in the field of public services working shows that bureaucracy, corruption can be reduced as well as on the other hand, efficiency and level of trust in public record keeping can be increased using blockchain technology. Blockchain can be used in government fund management. Whole government budgeting, funding, auditing and financial records can be brought on the blockchain network. Today, in general, the allocated funds are not traceable, there is no concrete knowledge as to how, where and what percentage of funds are being utilized and a large part of these expenditures are never shown in records due to corruption. To culminate this problem, a Blockchain system has been proposed to provide transparency and proficiency.