Reviewerly/exHarmony huggingface dataset:
Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-World Hazy Scenes

In this paper, a transmission-guided lightweight neural network called TGL-Net is proposed for efficient image dehazing. Unlike most current dehazing methods that produce simulated transmission maps from depth data and haze-free images, in the proposed work, guided transmission maps are computed automatically using a filter-refined dark-channel-prior (F-DCP) method from real-world hazy images as a regularizer, which facilitates network training not only on synthetic data, but also on natural images. A double-error loss function that combines the errors of a transmission map with the errors of a dehazed image is used to guide network training. The method provides a feasible solution for introducing priors obtained from traditional non-learning-based image processing techniques as a guide for training deep neural networks. Extensive experimental results demonstrate that, in terms of several reference and non-reference evaluation criteria for real-world images, the proposed method can achieve state-of-the-art performance with a much smaller network size and with significant improvements in efficiency resulting from the training guidance.
Investigation on mixed particle classification based on imaging processing with convolutional neural network

For the classification of mixed particle with imaging method, the traditional processing techniques tend to extract the particle features from binary images, where particles can be classified by combining feature design corresponding to the particle type and classifiers, such as BP neural network and Support Vector Machine (SVM). However, the accuracies of these methods would be seriously influenced by particle agglomeration and imprecise feature design. In the work, the convolutional neural network (CNN) was introduced to extract the particle features in the image, where the particle localizations were obtained by implementing the region proposal network (RPN). Meanwhile, the particle segmentation at pixel level was achieved by a developed classifier and a fully convolutional network. A series of experiments were performed to a flowing mixed particle system consisting of spherical, elongated and irregular particles, and it showed that both the average accuracy and recall rate of SVM method were up to 87% with artificial feature design, while they were increased to 97% and 93% with CNN, respectively. For the median diameter (Dn50) of irregular particles, the latter method can also reduce the analysis error by more than 11%. It revealed that some shortages like imprecise feature design in traditional methods can be covered, and an end-to-end classified system can be formed to provide a more effective way for online analysis of flowing mixed particles.
The Effect of Learning Facilities and Family Environment on Motivation to Learn of Prospective Elementary Teacher Education on Online Learning

This research purpose determines the effect of learning facilities and family environment on motivation to learn of prospective elementary teacher education, Faculty of Teacher Training and Education, Universitas Riau on online learning. The research method used is descriptive quantitative. The data used are primary data obtained directly from filling out a questionnaire by prospective elementary teacher education Class 2018, 2019, and 2020 students totaling 180 students. The results showed that there was a positive and significant effect of learning facilities and family environment on motivation to learn of prospective elementary teacher education in learning during, with the results of testing with the F test that obtained an F value of 21.272 with a significance value of F of 0.000 or a sig F value of &lt;0.05. The multiple regression test results show that the coefficient of determination (R2) is 0.440 or 44%.
Transductive transfer learning based Genetic Programming for balanced and unbalanced document classification using different types of features

Document classification is one of the predominant tasks in Natural Language Processing. However, some document classification tasks do not have ground truth while other similar datasets may have ground truth. Transfer learning can utilize similar datasets with ground truth to train effective classifiers on the dataset without ground truth. This paper introduces a transductive transfer learning method for document classification using two different text feature representations—the term frequency (TF) and the semantic feature doc2vec. It has three main contributions. First, it enables the sharing knowledge in a dataset using TF and a dataset using doc2vec in transductive transfer learning for performance improvement. Second, it demonstrates that the partially learned programs from TFs and from doc2vecs can be alternatively used to "label then learn" and they improve each other. Lastly, it addresses the unbalanced dataset problem by considering the unbalanced distributions on categories for evolving proper Genetic Programming (GP) programs on the target domains. Our experimental results on two popular document datasets show that the proposed technique effectively transfers knowledge from the GP programs evolved from the source domains to the new GP programs on the target domains using TF or doc2vec. There are obviously more than 10 percentages improvement achieved by the GP programs evolved by the proposed method over the GP programs directly evolved from the source domains. Also, the proposed technique effectively utilizes GP programs evolved from unbalanced datasets (on the source and target domains) to evolve new GP programs on the target domains, which balances predictions on different categories.
Sentiment Prediction using Enhanced XGBoost and Tailored Random Forest

A large quantity of data is being generated in the form of blogs, tweets and updates of opinions on the topic of interest.People give their feelings and opinions on different topics such as movies, products, education, politics, news and so on.Analysis of such data is very useful to understand the views/opinions/sentiments of the society.Such analysis would also be more useful in decision making .The major challenge in analysis is the usage of jorgon words, spelling mistakes, hash tags, hyperlinks and irrelevant words.This research aims to know the opinion of people on particular topics considering their tweets.These can be evaluated as classification problem to analyse the tweets expressed in texts for hidden sentiments.For this purpose, we proposed and evaluated a tailored random forest and enhanced XGBoost algorithms.We achieved significantly better accuracy by enhancing XGBoost compared to tailored random forest and naive bayes for tweets classification.
An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition

With the rapid development of artificial intelligence technology, multitasking textual translation has attracted more and more attention. Especially after the application of deep learning technology, the performance of multitask translation text detection and recognition has been greatly improved. However, because multitasking contains the interference problem faced by the translated text, there is a big gap between recognition performance and actual application requirements. Aiming at multitasking and translation text detection, this paper proposes a text localization method based on multichannel multiscale detection of the largest stable extreme value region and cascade filtering. This paper selects the appropriate color channel and scale to extract the maximum stable extreme value area as the character candidate area and designs a cascaded filter from coarse to fine to remove false detections. The coarse filter is based on some simple morphological features and stroke width features, and the fine filter is trained by a two-recognition convolutional neural network. The remaining character candidate regions are merged into horizontal or multidirectional character strings through the graph model. The experimental results on the text data set prove the effectiveness of the improved deep learning network character model and the feasibility of the textual implication translation analysis method based on this model. Among them, the text contains translation character recognition results prove that the model has good description ability. The characteristics of the model determine that this method is not sensitive to the scale of the sliding window, so it performs better than the existing typical methods in retrieval tasks.
Actively revealing card attack on card-based protocols

Abstract In 1989, den Boer presented the first card-based protocol, called the “five-card trick,” that securely computes the AND function using a deck of physical cards via a series of actions such as shuffling and turning over cards. This protocol enables a couple to confirm their mutual love without revealing their individual feelings. During such a secure computation protocol, it is important to keep any information about the inputs secret. Almost all existing card-based protocols are secure under the assumption that all players participating in a protocol are semi-honest or covert, i.e., they do not deviate from the protocol if there is a chance that they will be caught when cheating. In this paper, we consider a more malicious attack in which a player as an active adversary can reveal cards illegally without any hesitation. Against such an actively revealing card attack, we define the t -secureness, meaning that no information about the inputs leaks even if at most t cards are revealed illegally. We then actually design t -secure AND protocols. Thus, our contribution is the construction of the first formal framework to handle actively revealing card attacks as well as their countermeasures.
Persian Sentiment Analysis Without Training Data Using Cross-Lingual Word Embeddings

In this paper, a low-cost Persian Sentiment Analysis is performed without any Persian training data. A cross-lingual method is proposed to overcome the shortage of labeled Persian Sentiment Analysis datasets by using English as a high-resource language. A cross-lingual model between English and Persian is trained to generate aligned word embeddings that are used as the feature vectors in the sentiment model. Monolingual word embeddings used in cross-lingual approach are English FastText and Persian GloVe. VecMap method is used as the cross-lingual tool to make English and Persian word embeddings aligned in a supervised mode. Furthermore, a 5,000-word English-Persian bilingual dictionary is used as the supervision. Bilingual lexicon induction evaluation reveals that English and Persian are aligned properly in the joint space. The proposed Sentiment Analysis model is trained on an English dataset, and then is tested on Persian using aligned English-Persian word embeddings. The dataset used as the training data is Amazon Fine Food Reviews and Persian Snapp Food dataset is utilized as the test data. The model results show significant efficiency in the Sentiment Analysis task, though it does not use any Persian dataset in training procedure. The proposed cross-lingual Sentiment Analysis shows a good performance with F1-score of 78.16% on Persian test data.
A neurocomputational theory of how rule-guided behaviors become automatic.

This article introduces a biologically detailed computational model of how rule-guided behaviors become automatic. The model assumes that initially, rule-guided behaviors are controlled by a distributed neural network centered in the prefrontal cortex, and that in addition to initiating behavior, this network also trains a faster and more direct network that includes projections from sensory association cortex directly to rule-sensitive neurons in the premotor cortex. After much practice, the direct network is sufficient to control the behavior, without prefrontal involvement. The model is implemented as a biologically detailed neural network constructed from spiking neurons and displaying a biologically plausible form of Hebbian learning. The model successfully accounts for single-unit recordings and human behavioral data that are problematic for other models of automaticity. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Adversarial score matching and improved sampling for image generation

Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently found success in generative modeling. The approach works by first training a neural network to estimate the score of a distribution, and then using Langevin dynamics to sample from the data distribution assumed by the score network. Despite the convincing visual quality of samples, this method appears to perform worse than Generative Adversarial Networks (GANs) under the Frechet Inception Distance, a standard metric for generative models. We show that this apparent gap vanishes when denoising the final Langevin samples using the score network. In addition, we propose two improvements to DSM-ALS: 1) Consistent Annealed Sampling as a more stable alternative to Annealed Langevin Sampling, and 2) a hybrid training formulation, composed of both Denoising Score Matching and adversarial objectives. By combining these two techniques and exploring different network architectures, we elevate score matching methods and obtain results competitive with state-of-the-art image generation on CIFAR-10.
Semantic Task Planning for Service Robots in Open Worlds

This paper presents a planning system based on semantic reasoning for a general-purpose service robot, which is aimed at behaving more intelligently in domains that contain incomplete information, under-specified goals, and dynamic changes. First, Two kinds of data are generated by Natural Language Processing module from the speech: (i) action frames and their relationships; (ii) the modifier used to indicate some property or characteristic of a variable in the action frame. Next, the task’s goals are generated from these action frames and modifiers. These goals are represented as AI symbols, combining world state and domain knowledge, which are used to generate plans by an Answer Set Programming solver. Finally, the plan’s actions are executed one by one, and continuous sensing grounds useful information, which makes the robot use contingent knowledge to adapt to dynamic changes and faults. For each action in the plan, the planner gets its preconditions and effects from domain knowledge, so during the execution of the task, the environmental changes, especially those conflict with the actions, not only the action being performed but also the subsequent actions, can be detected and handled as early as possible. A series of case studies are used to evaluate the system and verify its ability to acquire knowledge through dialogue with users, solve problems with the acquired causal knowledge, and plan for complex tasks autonomously in the open world.
ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CherBass/ICAM</uri> .
Toward Verifiable Phrase Search Over Encrypted Cloud-Based IoT Data

Phrase search encryption, as an important technique in cloud-based IoT system, allows users to retrieve encrypted IoT data that contains a set of consecutive keywords. It plays an important role in cloud-based e-healthcare diagnosis system, machine learning applications for cloud-based IoT system, etc. However, to the best of our knowledge, the existing phrase search encryption schemes cannot achieve the complete verification for search results. They either cannot verify whether the returned files correctly containing the query phrase or cannot verify whether all files containing this query phrase are returned. Result verification is very important for some cloud-based IoT applications. If the search result is incorrect in the cloud-based e-healthcare diagnosis system, it will lead to misdiagnosis even endanger the patient's life. In order to deal with this problem, this article explores how to achieve verifiable phrase search over encrypted cloud-based IoT data. Specifically, we design novel look-up tables which can be utilized to determine and verify the position relationship among keywords. Meanwhile, we adopt a two-phase query strategy. In the first query phase, the data user can know the identifiers of files containing the keywords in the query phrase, and generate the search trapdoor based on these identifiers for the next phase. In the second query phase, the data user can obtain the verification information to check whether all files containing the query phrase are correctly returned. We present the security analysis of our scheme and conduct extensive experiments. The results prove the high security and efficiency of our proposed scheme.
Object Detection Based on Faster R-Cnn

In recent years there is rapid improvement in Object detection in areas of video analysis and image processing applications. Determing a desired object became an important aspect, so that there are many numerous of methods are evolved in Object detection. In this regard as there is rapid development in Deep Learning for its high-level processing, extracting deeper features, reliable and flexible compared to conventional techniques. In this article, the author proposes Object detection with deep neural networks and faster region convolutional neural networks methods for providing a simple algorithm which provides better accuracy and mean average precision.
Consistent Sparse Deep Learning: Theory and Computation

Deep learning has been the engine powering many successes of data science. However, the deep neural network (DNN), as the basic model of deep learning, is often excessively over-parameterized, causing many difficulties in training, prediction and interpretation. We propose a frequentist-like method for learning sparse DNNs and justify its consistency under the Bayesian framework: the proposed method could learn a sparse DNN with at most O(n/ log (n)) connections and nice theoretical guarantees such as posterior consistency, variable selection consistency and asymptotically optimal generalization bounds. In particular, we establish posterior consistency for the sparse DNN with a mixture Gaussian prior, show that the structure of the sparse DNN can be consistently determined using a Laplace approximation-based marginal posterior inclusion probability approach, and use Bayesian evidence to elicit sparse DNNs learned by an optimization method such as stochastic gradient descent in multiple runs with different initializations. The proposed method is computationally more efficient than standard Bayesian methods for large-scale sparse DNNs. The numerical results indicate that the proposed method can perform very well for large-scale network compression and high-dimensional nonlinear variable selection, both advancing interpretable machine learning.
Automated Via Detection for PCB Reverse Engineering

Abstract Reverse engineering (RE) is the only foolproof method of establishing trust and assurance in hardware. This is especially important in today's climate, where new threats are arising daily. A Printed Circuit Board (PCB) serves at the heart of virtually all electronic systems and, for that reason, a precious target amongst attackers. Therefore, it is increasingly necessary to validate and verify these hardware boards both accurately and efficiently. When discussing PCBs, the current state-of-the-art is non-destructive RE through X-ray Computed Tomography (CT); however, it remains a predominantly manual process. Our work in this paper aims at paving the way for future developments in the automation of PCB RE by presenting automatic detection of vias, a key component to every PCB design. We provide a via detection framework that utilizes the Hough circle transform for the initial detection, and is followed by an iterative false removal process developed specifically for detecting vias. We discuss the challenges of detecting vias, our proposed solution, and lastly, evaluate our methodology not only from an accuracy perspective but the insights gained through iteratively removing false-positive circles as well. We also compare our proposed methodology to an off-the-shelf implementation with minimal adjustments of Mask R-CNN; a fast object detection algorithm that, although is not optimized for our application, is a reasonable deep learning model to measure our work against. The Mask R-CNN we utilize is a network pretrained on MS COCO followed by fine tuning/training on prepared PCB via images. Finally, we evaluate our results on two datasets, one PCB designed in house and another commercial PCB, and achieve peak results of 0.886, 0.936, 0.973, for intersection over union (IoU), Dice Coefficient, and Structural Similarity Index. These results vastly outperform our tuned implementation of Mask R-CNN.
Pseudo Supervised Solar Panel Mapping based on Deep Convolutional Networks with Label Correction Strategy in Aerial Images

Solar panel mapping has gained a rising interest in renewable energy field with the aid of remote sensing imagery. Significant previous work is based on fully supervised learning with classical classifiers or convolutional neural networks (CNNs), which often require manual annotations of pixel-wise ground-truth to provide accurate supervision. Weakly supervised methods can accept image-wise annotations which can help reduce the cost for pixel-level labelling. Inevitable performance gap, however, exists between weakly and fully supervised methods in mapping accuracy. To address this problem, we propose a pseudo supervised deep convolutional network with label correction strategy (PS-CNNLC) for solar panels mapping. It combines the benefits of both weak and strong supervision to provide accurate solar panel extraction. First, a convolutional neural network is trained with positive and negative samples with image-level labels. It is then used to automatically identify more positive samples from randomly selected unlabeled images. The feature maps of the positive samples are further processed by gradient-weighted class activation mapping to generate initial mapping results, which are taken as initial pseudo labels as they are generally coarse and incomplete. A progressive label correction strategy is designed to refine the initial pseudo labels and train an end-to-end target mapping network iteratively, thereby improving the model reliability. Comprehensive evaluations and ablation study conducted validate the superiority of the proposed PS-CNNLC.