Reviewerly/exHarmony huggingface dataset:
LTT-GAN: Looking Through Turbulence by Inverting GANs

In many applications of long-range imaging, we are faced with a scenario where a person appearing in the captured imagery is often degraded by atmospheric turbulence. However, restoring such degraded images for face verification is difficult since the degradation causes images to be geometrically distorted and blurry. To mitigate the turbulence effect, in this paper, we propose the first turbulence mitigation method that makes use of visual priors encapsulated by a well-trained GAN. Based on the visual priors, we propose to learn to preserve the identity of restored images on a Spatial Periodic Contextual Distance (SPCX). Such a distance takes both the realism of restored images from the GAN and identity difference as the objective of the network learning. In addition, Hierarchical Pseudo Connections (HPC) are proposed for facilitating the identity-preserving learning by introducing more appearance variance without identity changing. Extensive experiments show that our method significantly outperforms prior art in both the visual quality and face verification accuracy of restored results.
Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory

Advances in information technology have given rise to online hotel reservation options. The user review feature is an important factor during the online booking of hotels. Generally, most online hotel booking service providers provide review and rating features for assessing hotels. However, not all service providers provide rating features or recap reviews for every aspect of the hotel services offered. Therefore, we propose a method to summarise reviews based on multiple aspects, including food, room, service, and location. This method uses long short-term memory (LSTM), together with hidden layers and automation of the optimal number of hidden neurons. The F1-measure value of 75.28% for the best model was based on the fact that (i) the size of the first hidden layer is 1,200 neurons with the tanh activation function, and (ii) the size of the second hidden layer is 600 neurons with the ReLU activation function. The proposed model outperforms the baseline model (also known as standard LSTM) by 10.16%. It is anticipated that the model developed through this study can be accessed by users of online hotel booking services to acquire a review recap on more specific aspects of services offered by hotels
Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks

Unsupervised image transfer enables intra- and inter-modality image translation in applications where a large amount of paired training data is not abundant. To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired image transfer are commonly based on cycle-consistency, causing additional computational resources and instability due to the learning of an inverse mapping. This paper presents a novel method for uni-directional domain mapping that does not rely on any paired training data. A proper transfer is achieved by using a GAN architecture and a novel generator loss based on patch invariance. To be more specific, the generator outputs are evaluated and compared at different scales, also leading to an increased focus on high-frequency details as well as an implicit data augmentation. This novel patch loss also offers the possibility to accurately predict aleatoric uncertainty by modeling an input-dependent scale map for the patch residuals. The proposed method is comprehensively evaluated on three well-established medical databases. As compared to four state-of-the-art methods, we observe significantly higher accuracy on these datasets, indicating great potential of the proposed method for unpaired image transfer with uncertainty taken into account. Implementation of the proposed framework is released here: https://github.com/anger-man/unsupervised-image-transfer-and-uq .
Integrated-Photonics-Based Architectures for Polarization-Gradient and EIT Cooling of Trapped Ions

We develop a framework for two advanced trapped-ion cooling schemes, polarization-gradient and electromagnetically-induced-transparency cooling, for 88 Sr + ions using a visible-wavelength integrated-photonics platform and present the design of the key integrated devices.
A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data

A novel feature selection approach is presented in this paper. Sammon’s Stress Function transforms the high dimension data to a lower dimension data set. A data set is divided into small partitions. The features are assigned randomly to these partitions. Using GA with Sammon Error as fitness value, a small, desired number of features are selected from every partition. The combination of the reduced subsets of the features from these partitions is again divided into small partitions. After a certain number of iterating the process, a desired small number of features is obtained. For experimental validation, the proposed method has been tested on 11 standard datasets with three classifiers namely, Decision Tree, MLP and KNN. The classification accuracies obtained by the proposed method is highest on most of the considered datasets against the results reported in literature. Moreover, the proposed method selects comparatively less number of features in comparison to considered methods. The optimistic results obtained from the proposed method justify its strength.
Denoise Network Structure for User Alignment Across Networks via Graph Structure Learning

User alignment aims to identify accounts of one natural person across networks. Nevertheless, different social purposes in multiple networks and randomness of following friends form the diverse local structures of the same person, leading to a high degree of non-isomorphism across networks. The edges resulting in non-isomorphism are harmful to learn consistent representations of one natural person across networks, i.e., the structural “noisy data” for user alignment. Furthermore, these edges increase the time complexity, compromising the model’s efficiency. To this end, we propose a network structure denoising framework to learn an alignment driven structure heuristically. Specifically, under the guidance of alignment driven loss, parameter sharing encoder and graph neural network for structure denoising are learned using an iterative learning schema. Experiments on real-world datasets demonstrate the outperformance of the proposed framework in terms of efficiency and transferability.
Analysis of English Communication Development using VIKOR Method

Good listeners use rationalization and reflection strategies to verify what the alternative individual has said and avoid confusion.It is the language of science, aviation, computers, diplomacy and tourism.Last however now not least, it's miles the language of worldwide conversation, media and the Internet.Whether for professional or private motives, know-how the significance of English will help you achieve your goals.These consist of: behaviour alternate conversation, mass verbal exchange and advocacy communication.communication in English involves 4 modes: reading, writing, speaking and listening.Give your complete interest to the person talking and really take note of what they're saying and what they are not announcing.Good listeners use rationalization and reflection strategies to verify what the alternative individual has said and avoid confusion.It is the language of science, aviation, computers, diplomacy and tourism.Last however now not least, it's miles the language of worldwide conversation, media and the Internet.Whether for professional or private motives, know-how the significance of English will help you achieve your goals.These consist of: behaviour alternate conversation, mass verbal exchange and advocacy communication.The objective of this paper is to find VIKOR best and worst English Communication Development at this value.The VIKOR (VIšekriterijumsko Kompromisno Rangiranje) Optimal replacement Select method is used Social-affective strategies, Accuracy-oriented strategies, Nonverbal strategies, Help-seeking strategies, Circumlocution strategies and evaluation parameters, Categories, Number of words produced, Periods of silence , average no. of words produced/average period of silence.Circumlocution strategies is got the first rank whereas is the Help-seeking strategies is having the Lowest rank.
MUST-VQA: MUltilingual Scene-Text VQA

In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks.
How Can One Share a User’s Activity during VR Synchronous Augmentative Cooperation?

Collaborative virtual environments allow people to work together while being distant. At the same time, empathic computing aims to create a deeper shared understanding between people. In this paper, we investigate how to improve the perception of distant collaborative activities in a virtual environment by sharing users’ activity. We first propose several visualization techniques for sharing the activity of multiple users. We selected one of these techniques for a pilot study and evaluated its benefits in a controlled experiment using a virtual reality adaptation of the NASA MATB-II (Multi-Attribute Task Battery). Results show (1) that instantaneous indicators of users’ activity are preferred to indicators that continuously display the progress of a task, and (2) that participants are more confident in their ability to detect users needing help when using activity indicators.
4D-StOP: Panoptic Segmentation of 4D LiDAR Using Spatio-Temporal Object Proposal Generation and Aggregation

In this work, we present a new paradigm, called 4D-StOP, to tackle the task of 4D Panoptic LiDAR Segmentation. 4D-StOP first generates spatio-temporal proposals using voting-based center predictions, where each point in the 4D volume votes for a corresponding center. These tracklet proposals are further aggregated using learned geometric features. The tracklet aggregation method effectively generates a video-level 4D scene representation over the entire space-time volume. This is in contrast to existing end-to-end trainable state-of-the-art approaches which use spatio-temporal embeddings that are represented by Gaussian probability distributions. Our voting-based tracklet generation method followed by geometric feature-based aggregation generates significantly improved panoptic LiDAR segmentation quality when compared to modeling the entire 4D volume using Gaussian probability distributions. 4D-StOP achieves a new state-of-the-art when applied to the SemanticKITTI test dataset with a score of 63.9 LSTQ, which is a large (+7%) improvement compared to current best-performing end-to-end trainable methods. The code and pre-trained models are available at: https://github.com/LarsKreuzberg/4D-StOP .
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-Set Unlabeled Data

Semi-supervised learning (SSL) has been a powerful strategy to incorporate few labels in learning better representations. In this paper, we focus on a practical scenario that one aims to apply SSL when unlabeled data may contain out-of-class samples - those that cannot have one-hot encoded labels from a closed-set of classes in label data, i.e., the unlabeled data is an open-set. Specifically, we introduce OpenCoS, a simple framework for handling this realistic semi-supervised learning scenario based upon a recent framework of self-supervised visual representation learning. We first observe that the out-of-class samples in the open-set unlabeled dataset can be identified effectively via self-supervised contrastive learning. Then, OpenCoS utilizes this information to overcome the failure modes in the existing state-of-the-art semi-supervised methods, by utilizing one-hot pseudo-labels and soft-labels for the identified in- and out-of-class unlabeled data, respectively. Our extensive experimental results show the effectiveness of OpenCoS under the presence of out-of-class samples, fixing up the state-of-the-art semi-supervised methods to be suitable for diverse scenarios involving open-set unlabeled data.
Intelligent Software Bug Prediction Framework with Parameter-Tuned LSTM with Attention Mechanism Using Adaptive Target-Based Pooling Deep Features

In recent years, various researchers have designed a software bug prediction model for classifying the nonfaulty and faulty modules in software that are correlated with software constraints. Software bug or defect prediction helps programmers or developers discover the possibilities of bugs and minimize maintenance costs. However, most approaches do not solve the class-imbalance issue regarding the software bug prediction model. To solve these issues, the latest software bug prediction model using enhanced deep-structured architecture is developed. Here, the software modules are obtained from online sources, which undergo pre-processing to remove unnecessary data. These pre-processed texts are considered for deep feature extraction, performed using a Convolutional Neural Network (CNN) with an adaptive target-based pooling method to get effective deep features. Here, the parameter tuning in CNN is performed using Hybrid Rat-Barnacle Mating Swarm Optimization (HR-BMSO) to enhance the prediction performance. These deep features are inserted into Adaptive Features-based Parameter-Tuned Attention Long Short Term Memory (AF-PTALSTM) for predicting the software bugs, in which the optimization of certain parameters takes place with the same HR-BMSO to get accurate predicted results. The accuracy and [Formula: see text]1-score of the designed AF-PTALSTM method attain 97% and 94% through analysis. Thus, the experimental analysis of the designed software bug prediction model depicts higher efficiency while estimating with traditional approaches.
Knowledge Extraction From National Standards for Natural Resources

National standards for natural resources (NSNR) plays an important role in promoting efficient use of China's natural resources, which sets standards for many domains such as marine and land resources. Its revision is difficult since standards in different domains may overlap or conflict. To facilitate the revision of NSNR, this paper extracts structural knowledge from the NSNR files to assist its revision. NSNR files are in multi-domain texts, where the traditional knowledge extraction methods could fall short in recalling multi-domain entities. To address this issue, this paper proposes a knowledge extraction method for multi-domain texts, including sub-domain relation discovery (SRD) and domain semantic features fusion (DSFF) module. SRD splits NSNR into sub-domains to facilitate the relation discovery. DSFF integrates relation features in the conditional random field (CRF) model to improve the capability of multi-domain entity recognition. Experimental results demonstrate that the proposed method could effectively extract structural knowledge from NSNR.
Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization

Low-illumination image enhancement can provide more information than the original image in low-light scenarios, e.g., nighttime driving. Traditional deep-learning-based image enhancement algorithms struggle to balance the performance between the overall illumination enhancement and local edge details, due to limitations of time and computational cost. This paper proposes a histogram equalization–multiscale Retinex combination approach (HE-MSR-COM) that aims at solving the blur edge problem of HE and the uncertainty in selecting parameters for image illumination enhancement in MSR. The enhanced illumination information is extracted from the low-frequency component in the HE-enhanced image, and the enhanced edge information is obtained from the high-frequency component in the MSR-enhanced image. By designing adaptive fusion weights of HE and MSR, the proposed method effectively combines enhanced illumination and edge information. The experimental results show that HE-MSR-COM improves the image quality by 23.95% and 10.6% in two datasets, respectively, compared with HE, contrast-limited adaptive histogram equalization (CLAHE), MSR, and gamma correction (GC).
Adaptive Manifold Graph representation for Two-Dimensional Discriminant Projection

Two-dimensional (2D) local discriminant analysis is one of the popular techniques for image representation and recognition. Conventional 2D methods extract features of images relying on the affinity relationships among samples. However, affinity graph learning and dimensionality reduction are two separate processes, and the predefined affinity graph is sensitive to noisy and irrelevant features. To this end, we propose a graph embedding model, named as Two-Dimensional Discriminant Projection with Adaptive Manifold Graph (2DDP-AMG). In our model, the local manifold structure of data is characterized by the k-nearest-neighbor (kNN) graph within each class. More importantly, the above graph is updated adaptively in the process of dimensionality reduction to eliminate the interference of irrelevant features and noise. Furthermore, F-norm is introduced to measure the distance between homogeneous samples and improve the robustness of the model to outliers. To solve this model, an efficient algorithm is developed to update each variable iteratively. Extensive experiments conducted on benchmark face image data sets show the superiority and robustness of our model compared to state-of-the-art alternatives.
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection

3D object detection is a critical task in autonomous driving. Recently multi-modal fusion-based 3D object detection methods, which combine the complementary advantages of LiDAR and camera, have shown great performance improvements over mono-modal methods. However, so far, no methods have attempted to utilize the instance-level contextual image semantics to guide the 3D object detection. In this paper, we propose a simple and effective Painting Adaptive Instance-prior for 3D object detection (PAI3D) to fuse instance-level image semantics flexibly with point cloud features. PAI3D is a multi-modal sequential instance-level fusion framework. It first extracts instance-level semantic information from images, the extracted information, including objects categorical label, point-to-object membership and object position, are then used to augment each LiDAR point in the subsequent 3D detection network to guide and improve detection performance. PAI3D outperforms the state-of-the-art with a large margin on the nuScenes dataset, achieving 71.4 in mAP and 74.2 in NDS on the test split. Our comprehensive experiments show that instance-level image semantics contribute the most to the performance gain, and PAI3D works well with any good-quality instance segmentation models and any modern point cloud 3D encoders, making it a strong candidate for deployment on autonomous vehicles.
Professional adaptation of university students in the changing world of professions

Introduction. The relevance of the problem of the article is determined by the need to improve approaches to the professional adaptation of university students in the conditions of rapid changes in the world of work and professions. The subject of the study is various forms of professional adaptation of students. Aim. The present research aims to identify the forms of university students’ professional adaptation, which have the greatest potential for adapting students to the future profession. The hypothesis of the study was the assumption that one of the new and effective forms of professional adaptation of students at the university can be their project activities. Methodology and research methods . The research methodology was mainly based on the community-based and activity-based approaches. The empirical basis of the article is the results of the research on the adaptation of students in Russian universities (2022 May–June). The conducted research was implemented using the methods of online survey, focus group, semi-formalised interview. Results. The main results of the study are the following: characteristics of the forms of professional adaptation of students at the university, the assessment of its main background factors, as well as the development by students of the norms of the academic environment as a predictor of mastering work and professional ethics, the problems of implementing project activities as a new form of adaptation to the profession. The scope of application of the research results is the practice of university project training and the development of programmes for professional students’ adaptation. In conclusion, project-based learning is considered as one of the most promising forms of students’ professional adaptation, the potential of which is not fully used. An important conclusion of the authors is that there is the need to focus student projects on an external customer in order to create a situation of students’ real immersion in the professional environment. Scientific novelty . The authors considered new forms of professional adaptation of students at the university and justified the significance of the revealed adaptation forms. Practical significance . The practical significance of the article is the possibility of using the results and conclusions of the study in the elaboration of university adaptation programmes for students and the improvement of their professional adaptation practices.
Gas Gauge: A Security Analysis Tool for Smart Contract Out-of-Gas Vulnerabilities

In recent years, we have witnessed a dramatic increase in the adoption and application of smart contracts in a variety of contexts. However, security vulnerabilities pose a significant challenge to the continued adoption of smart contracts. An important and pervasive class of security vulnerabilities that afflicts Ethereum smart contracts is the gas limit DoS on a contract via unbounded operations. These vulnerabilities result in a failed transaction with an “out-of-gas” error and are often present in contracts containing loops whose bounds are affected by end-user input. To address this issue, we present Gas Gauge, a tool aimed at detecting Out-of-Gas DoS vulnerabilities in Ethereum smart contracts. The Gas Gauge tool has three major components: The Detection Phase, Identification Phase, and Correction Phase. The Detection Phase component consists of an accurate static analysis approach that finds and summarizes all the loops in a smart contract. The Identification Phase component uses a white-box fuzzing approach to generate a set of inputs that causes the contract to run out of gas. Lastly, the Correction Phase component uses static analysis and run-time verification to predict the maximum loop bounds consistent with allowable gas usage and suggest appropriate repairs to the tool’s users. Each part of Gas Gauge can be used separately or all together to detect, identify and help repair contracts vulnerable to Out-of-Gas DoS vulnerabilities. Gas Gauge was tested on 1,000 real-world solidity smart contracts. When compared to seven state-of-the-art tools, we show that Gas Gauge is the most effective (i.e., has no false positives and false negatives) while being competitive in terms of efficiency.