Reviewerly/exHarmony huggingface dataset:
A novel <scp>TCNN–Bi‐LSTM</scp> deep learning model for predicting sentiments of tweets about COVID‐19 vaccines

Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.
Various Diseases’ Prediction Based on Symptom by Using Machine Learning

An overwhelming number of disease surveys capture patient records of the severity of their illness indications that enable differentiation of patient ailment from typical indication. This paper aims to forecast illnesses in users based on their symptoms. To achieve our aim, we use the XGBoost Classifier, which aids in regulating the victim's health condition then acquiring the symptoms to predict the condition. The data carries 250 variables and 140 corporal evaluation instances (diseases). In addition, we provide medicine for illness and gather the corresponding victim EH to summarize the diagnostic described by the NLTK.
DieRoll: A Unique Key Generation and Encryption Technique

Advanced Encryption Standard (AES) or Data Encryption Standard (DES) are standard block cipher techniques mostly used for text or numbers but not preferred for multimedia data. Many researchers presented their work for grayscale image cryptography. However, little work has been reported for RGB image cryptography. This paper presents DieRoll, an encryption technique for color images. The key generation provides a novel approach with various stages of dividing and scrambling followed by using a fair die to transpose, reintegrate and form a new key. This newly generated key is used to encrypt RGB images. The implementation and performance evaluation of DieRoll is also given. The paper compares DieRoll encryption techniques with other techniques found in the literature. Quantitative and visual evaluation is also presented to analyze the resistance against modern threats. In the end attack analysis is given to support the claims.
Optimal quantum reservoir computing for the noisy intermediate-scale quantum era

Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to perform challenging computations on today's available machines. In this Letter, we provide a criterion to select optimal quantum reservoirs, requiring few and simple gates. Our findings demonstrate that they render better results than other commonly used models with significantly less gates and also provide insight on the theoretical gap between quantum reservoir computing and the theory of quantum states' complexity.
Research of Maritime Object Detection Method in Foggy Environment Based on Improved Model SRC-YOLO

An improved maritime object detection algorithm, SRC-YOLO, based on the YOLOv4-tiny, is proposed in the foggy environment to address the issues of false detection, missed detection, and low detection accuracy in complicated situations. To confirm the model's validity, an ocean dataset containing various concentrations of haze, target angles, and sizes was produced for the research. Firstly, the Single Scale Retinex (SSR) algorithm was applied to preprocess the dataset to reduce the interference of the complex scenes on the ocean. Secondly, in order to increase the model's receptive field, we employed a modified Receptive Field Block (RFB) module in place of the standard convolution in the Neck part of the model. Finally, the Convolutional Block Attention Module (CBAM), which integrates channel and spatial information, was introduced to raise detection performance by expanding the network model's attention to the context information in the feature map and the object location points. The experimental results demonstrate that the improved SRC-YOLO model effectively detects marine targets in foggy scenes by increasing the mean Average Precision (mAP) of detection results from 79.56% to 86.15%.
Hybridizations of evolutionary algorithms with Large Neighborhood Search

Recent developments of evolutionary algorithms (EAs) for discrete optimization problems are often characterized by the hybridization of EAs with local search methods, in particular, with Large Neighborhood Search. In this survey, we consider some of the most promising directions of this kind of hybridization and provide examples in the context of well-known optimization problems. We distinguish different approaches by the algorithmic components in which they make use of Large Neighborhood Search: initialization, recombination and the local improvement stages of hybrid EAs.
Learning Salient Feature for Salient Object Detection Without Labels

Supervised salient object detection (SOD) methods achieve state-of-the-art performance by relying on human-annotated saliency maps, while unsupervised methods attempt to achieve SOD by not using any annotations. In unsupervised SOD, how to obtain saliency in a completely unsupervised manner is a huge challenge. Existing unsupervised methods usually gain saliency by introducing other handcrafted feature-based saliency methods. In general, the location information of salient objects is included in the feature maps. If the features belonging to salient objects are called salient features and the features that do not belong to salient objects, such as background, are called nonsalient features, by dividing the feature maps into salient features and nonsalient features in an unsupervised way, then the object at the location of the salient feature is the salient object. Based on the above motivation, a novel method called learning salient feature (LSF) is proposed, which achieves unsupervised SOD by LSF from the data itself. This method takes enhancing salient feature and suppressing nonsalient features as the objective. Furthermore, a salient object localization method is proposed to roughly locate objects where the salient feature is located, so as to obtain the salient activation map. Usually, the object in the salient activation map is incomplete and contains a lot of noise. To address this issue, a saliency map update strategy is introduced to gradually remove noise and strengthen boundaries. The visualization of images and their salient activation maps show that our method can effectively learn salient visual objects. Experiments show that we achieve superior unsupervised performance on a series of datasets.
Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications

Human-machine interfaces for hand gesture recognition across multiple sessions and days of doffing and re-donning while maintaining acceptable recognition accuracy are still challenging. In this paper, a flexible wristband, which was integrated with a highly sensitive capacitive pressure sensing array, was used for inter-day hand gesture recognition. The performance of the entire system was further improved by utilizing a triplet network for deep feature embedding. Seven hand gestures were included into the gesture set, and inter-day experiments which lasted for five consecutive days with three sessions on each day were conducted. Five healthy subjects participated in the experiment. Between each session, the wristband was doffed, and re-donned before the next session. The triplet network achieved an average recognition accuracy of 91.98% across all the sessions of all the subjects, and yielded a higher classification result (p < 0.05) over the convolutional neural network trained with softmax-cross-entropy loss (with an average accuracy of 84.65%). Furthermore, we also found that the capacitive array size had an evident influence on the inter-day classification result. The array with the full size (thirty-two channels) achieved a higher average recognition accuracy over all the down-sampled arrays. This work demonstrated the feasibility of improving the hand gesture recognition performance over days of usage by fabricating a wearable, flexible multi-channel capacitive wristband and implementing the triplet network.
Translation-invariant optical neural network for image classification

The classification performance of all-optical Convolutional Neural Networks (CNNs) is greatly influenced by components' misalignment and translation of input images in the practical applications. In this paper, we propose a free-space all-optical CNN (named Trans-ONN) which accurately classifies translated images in the horizontal, vertical, or diagonal directions. Trans-ONN takes advantages of an optical motion pooling layer which provides the translation invariance property by implementing different optical masks in the Fourier plane for classifying translated test images. Moreover, to enhance the translation invariance property, global average pooling (GAP) is utilized in the Trans-ONN structure, rather than fully connected layers. The comparative studies confirm that taking advantage of vertical and horizontal masks along GAP operation provide the best translation invariance property, compared to the alternative network models, for classifying horizontally and vertically shifted test images up to 50 pixel shifts of Kaggle Cats and Dogs, CIFAR-10, and MNIST datasets, respectively. Also, adopting the diagonal mask along GAP operation achieves the best classification accuracy for classifying translated test images in the diagonal direction for large number of pixel shifts (i.e. more than 30 pixel shifts). It is worth mentioning that the proposed translation invariant networks are capable of classifying the translated test images not included in the training procedure.
i-SISSO: Mutual information-based improved sure independent screening and sparsifying operator algorithm

Symbolic regression is a method to extract quantitative expressions from dataset and has already been applied in different research fields. The sure independence screening and sparsifying operation (SISSO) is a powerful data-driven method widely applied in various fields of material research or other research directions and can handle large candidate descriptor spaces and generate prediction models in the form of equations with high accuracy. However, SISSO is confronted by the combinatorial explosion phenomenon when enumerating each case to decide the sparsing representation with an ensured L0-norm. This study uses the Max-Relevance and Min-Redundancy (mRMR) algorithm to solve the problem arising from the combinatorial explosion in SISSO by reducing the candidate descriptor space before the SO phase and shrinking the possible search space without losing excess information. Experimental results on six different public datasets and 12 predictive targets show that, when compared with SISSO, mutual information based SISSO significantly reduced time consumption and maintained the error of the model close to the model generated by SISSO, demonstrating that the optimal strategy works on it. Hence, the time cost decreases to approximately 11000, and the predictive accuracy remains steady.
Assessing certificate validation user interfaces of WPA supplicants

WPA (Wi-Fi Protected Access) Enterprise is the de facto standard for safeguarding enterprise-level wireless networks. It relies on Transport Layer Security (TLS) to establish a secure tunnel during its authentication process, and thus the notoriously error-prone certificate validation may haunt it. Incorrect validation may lead to the SSL/TLS man-in-the-middle attack, or the evil twin attack in the context of wireless networking, where the supplicant connects and unwittingly sends authentication credentials to a fake access point.
Network-side digital contact tracing on a large university campus

We describe a study conducted at a large public university campus in the United States which shows the efficacy of network log information for digital contact tracing and prediction of COVID-19 cases. Over the period of January 18, 2021 to May 7, 2021, more than 216 million client-access-point associations were logged across over 11,000 wireless access points (APs). The association information was used to find potential contacts for approximately 30,000 individuals. Contacts are determined using an AP colocation algorithm, which supposes contact when two individuals connect to the same WiFi AP at approximately the same time. The approach was validated with a truth set of 350 positive COVID-19 cases inferred from the log data by observing associations with APs in isolation residence halls reserved for individuals with a confirmed (clinical) positive COVID-19 test result. The network log data and AP-colocation have a predictive value of greater than 10%; more precisely, the contacts of an individual with a confirmed positive COVID-19 test have greater than a 10% chance of testing positive in the following 7 days (compared with a 0.79% chance if chosen at random, a relative risk ratio of 12.6). A cumulative exposure score is computed to account for exposure to multiple individuals that test positive. Over the duration of the study, the exposure score predicts positive cases with a true positive rate of 16.5% and missed detection rate of 79% at a specified operating point.
Code Vulnerability Detection Based on Deep Sequence and Graph Models: A Survey

With the flourishing of the open-source software community, the problem of software vulnerabilities is becoming more and more serious. Hence, it is urgent to come up with an effective and efficient code vulnerability detection method. Source code vulnerability detection techniques used in practice today like symbolic execution and fuzz testing suffer from high false positives and low code coverage, respectively. Traditional machine-learning-based solutions fail to cope with the diversity of vulnerabilities. To overcome these drawbacks, a large number of deep-learning-based code vulnerability detection works have emerged, aiming at building powerful neural network models to fully learn code semantics and vulnerability patterns. In this survey, we mainly focus on code vulnerability detection approaches based on deep sequence modeling and graph modeling technologies. Our goal is to investigate how these two methods are applied to facilitate code vulnerability detection. We also go over current prevailing datasets that are used to evaluate detection models. At last, we identify the current challenges in this field and share our views on future work.
Weakly-Supervised Online Hashing with Refined Pseudo Tags

With the rapid development of social media, various types of tags uploaded by social users are attached to the images. Compared to clean labels marked by experts, although user-provided tags are imperfect, e.g., wrong tags, reduplicative tags, or missing tags, they are more diverse, fine-grained, and informative. Currently, there exist several weakly-supervised hashing methods attempting to learn hash codes using tags as supervision. Although they could benefiting from the rich information contained in tags, most of them may defy the nature of social media data. In real scenarios, social media data appears in streaming fashion, but most weakly-supervised hashing methods are just batch-based which cannot effectively handle streaming data. To this end, only one weakly-supervised online hashing method has been proposed, but it is still far from enough to alleviate the negative effects of tags.
Cascade Variational Auto-Encoder for Hierarchical Disentanglement

While deep generative models pave the way for many emerging applications, decreased interpretability for larger model sizes and complexities hinders their generalizability to wide domains such as economy, security, healthcare, etc. Considering this obstacle, a common practice is to learn interpretable representations through latent feature disentanglement, aiming for exposing a set of mutually independent factors of data variations. However, existing methods either fail to catch the trade-off between the synthetic data quality and model interpretability, or consider the first-order feature disentangling only, overlooking the fact that a subset of salient features can carry decomposable semantic meanings and hence be of high-order in nature. Hence, we in this paper propose a novel generative modeling paradigm by introducing a Bayesian network-based regularize on a cascade Variational Auto-Encoder (VAE). Specifically, this regularizer guides the learner to discover a representation space that comprises both first-order disentangled features and high-order salient features, with the feature interplay captured by the Bayesian structure. Experiments demonstrate that this regularizer gives us free control over the representation space and can guide the learner to discover decomposable semantic meanings by capturing the interplay among independent factors. Meanwhile, we benchmark extensive experiments on six widely-used vision datasets, and the results exhibit that our approach outperforms the state-of-the-art VAE competitors in terms of the trade-off between the synthetic data quality and model interpretability. Although our design is framed in the VAE regime, it in effect is generic and can be better amenable to both GANs and VAEs in terms of letting them concurrently enjoy both high model interpretability and high synthesis quality.
Joint Clothes Detection and Attribution Prediction via Anchor-free Framework with Decoupled Representation Transformer

Clothes attribution prediction is the key technology for users to automatically describe clothing characteristics. Most current methods are first to detect the multiple clothes, and then crop out the clothes and feed to a certain network for clothes attribution prediction. But this two-stage approach is time- and resource- consuming; on the other hand, one-stage approach can provide an effective and efficient solution by integrating clothes detection and attribution prediction into an end-to-end framework. But the one-stage approach tends to explore anchor-based detectors causing high sensitivity to the hyperparameters and high computational complexity from dense anchors. In addition, it may also confront with optimization contradiction problem in the training procedure, as the clothes detection and attribution prediction branches demand diverse optimization. In this work, to handle the above problems, we aim to develop an end-to-end anchor-free framework by involving an additional branch for joint clothes detection and attribution prediction. To handle the optimization contradiction in two branches, we encode the backbone feature map as pixel-level dense queries and decode them via deformable transformer as the output features that are fed into detection and prediction branches, respectively. In this way, the features of detection and prediction branches can be decoupled and the optimization contradiction can be naturally solved. To further enhance the prediction accuracy, we in the prediction branch also develop a special attention strategy and loss function to adaptively integrate the peer attribution relationships into feature learning as well as to avoid mutual suppression for hierarchical attributions. Extensive simulation results verify the effectiveness of the proposed work.
MetaRule: A Meta-path Guided Ensemble Rule Set Learning for Explainable Fraud Detection

Machine learning methods for fraud detection have achieved impressive prediction performance, but often sacrifice critical interpretability in many applications. In this work, we propose to learn interpretable models for fraud detection as a simple rule set. More specifically, we design a novel neural rule learning method by building a condition graph with an expectation to capture the high-order feature interactions. Each path in this condition graph can be regarded as a single rule. Inspired by the key idea of meta learning, we combine the neural rules with rules extracted from the tree-based models in order to provide generalizable rule candidates. Finally, we propose a flexible rule set learning framework by designing a greedy optimization method towards maximizing the recall number of fraud samples with a predefined criterion as the cost. We conduct comprehensive experiments on large-scale industrial datasets. Interestingly, we find that the neural rules and rules extracted from tree-based models can be complementary to each other to improve the prediction performance.
A Preliminary Exploration of Extractive Multi-Document Summarization in Hyperbolic Space

Summary matching is a recently proposed paradigm for extractive summarization. It aims to calculate similarities between candidate summaries and their corresponding document and extract summaries by ranking similarities. Due to natural languages often exhibiting the inherent hierarchical structures ingrained with complex syntax and semantics, the latent hierarchical structures between candidate summaries and their corresponding document should be considered when calculating the summary-document similarities. However, the above structural property is hard to model in the Euclidean space. Inspired by the above issues, we explore extractive summarization in the hyperbolic space and propose a new Hyperbolic Siamese Network for the matching-based extractive summarization (HyperSiameseNet). Specifically, HyperSiameseNet projects candidate summaries and their corresponding document representations from the Euclidean space to the Hyperbolic space and then models the summary-document similarities via the squared poincaré distance. Finally, the summary-document similarities are optimized by the margin-based triplet loss for extracting the final summary. The results on the Multi-News dataset have shown the superiority of our model HyperSiameseNet by comparing with the state-of-the-art baselines.