Reviewerly/exHarmony huggingface dataset:
Appearance-Based Gaze Estimation Method Using Static Transformer Temporal Differential Network

Gaze behavior is important and non-invasive human–computer interaction information that plays an important role in many fields—including skills transfer, psychology, and human–computer interaction. Recently, improving the performance of appearance-based gaze estimation, using deep learning techniques, has attracted increasing attention: however, several key problems in these deep-learning-based gaze estimation methods remain. Firstly, the feature fusion stage is not fully considered: existing methods simply concatenate the different obtained features into one feature, without considering their internal relationship. Secondly, dynamic features can be difficult to learn, because of the unstable extraction process of ambiguously defined dynamic features. In this study, we propose a novel method to consider feature fusion and dynamic feature extraction problems. We propose the static transformer module (STM), which uses a multi-head self-attention mechanism to fuse fine-grained eye features and coarse-grained facial features. Additionally, we propose an innovative recurrent neural network (RNN) cell—that is, the temporal differential module (TDM)—which can be used to extract dynamic features. We integrated the STM and the TDM into the static transformer with a temporal differential network (STTDN). We evaluated the STTDN performance, using two publicly available datasets (MPIIFaceGaze and Eyediap), and demonstrated the effectiveness of the STM and the TDM. Our results show that the proposed STTDN outperformed state-of-the-art methods, including that of Eyediap (by 2.9%).
A Novel Technique for Handwritten Digit Recognition Using Deep Learning

Handwritten digit recognition (HDR) shows a significant application in the area of information processing. However, correct recognition of such characters from images is a complicated task due to immense variations in the writing style of people. Moreover, the occurrence of several image artifacts like the existence of intensity variations, blurring, and noise complicates this process. In the proposed method, we have tried to overcome the aforementioned limitations by introducing a deep learning- (DL-) based technique, namely, EfficientDet-D4, for numeral categorization. Initially, the input images are annotated to exactly show the region of interest (ROI). In the next phase, these images are used to train the EfficientNet-B4-based EfficientDet-D4 model to detect and categorize the numerals into their respective classes from zero to nine. We have tested the proposed model over the MNIST dataset to demonstrate its efficacy and attained an average accuracy value of 99.83%. Furthermore, we have accomplished the cross-dataset evaluation on the USPS database and achieved an accuracy value of 99.10%. Both the visual and reported experimental results show that our method can accurately classify the HDR from images even with the varying writing style and under the presence of various sample artifacts like noise, blurring, chrominance, position, and size variations of numerals. Moreover, the introduced approach is capable of generalizing well to unseen cases which confirms that the EfficientDet-D4 model is an effective solution to numeral recognition.
CommonGraph: Graph Analytics on Evolving Data

We consider the problem of graph analytics on evolving graphs (i.e., graphs that change over time). In this scenario, a query typically needs to be applied to different snapshots of the graph over an extended time window, for example to track the evolution of a property over time. Solving a query independently on multiple snapshots is inefficient due to repeated execution of subcomputation common to multiple snapshots. At the same time, we show that using streaming, where we start from the earliest snapshot and stream the changes to the graph incrementally updating the query results one snapshot at a time is also inefficient. We propose CommonGraph, an approach for efficient processing of queries on evolving graphs. We first observe that deletion operations are significantly more expensive than addition operations for many graph queries (those that are monotonic). CommonGraph converts all deletions to additions by finding a common graph that exists across all snapshots. After computing the query on this graph, to reach any snapshot, we simply need to add the missing edges and incrementally update the query results. CommonGraph also allows sharing of common additions among snapshots that require them, and breaks the sequential dependency inherent in the traditional streaming approach where snapshots are processed in sequence, enabling additional opportunities for parallelism. We incorporate the CommonGraph approach by extending the KickStarter streaming framework. We implement optimizations that enable efficient handling of edge additions without resorting to expensive in place graph mutations, significantly reducing the streaming overhead, and enabling direct reuse of shared edges among different snapshots. CommonGraph achieves 1.38x-8.17x improvement in performance over Kickstarter across multiple benchmarks.
Learning speaker-independent multimodal representation for sentiment analysis

Multimodal sentiment analysis is an actively growing research area that utilizes language, acoustic and visual signals to predict sentiment inclination. Compared to language, acoustic and visual features carry a more evident personal style which may degrade the model generalization capability. The issue will be exacerbated in a speaker-independent setting, where the model will encounter samples from unseen speakers during the testing stage. To mitigate personal style's impact, we propose a framework named SIMR for learning speaker-independent multimodal representation. This framework separates the nonverbal inputs into style encoding and content representation with the aid of informative cross-modal correlations. Besides, in terms of integrating cross-modal complementary information, the classical transformer-based approaches are inherently inclined to discover compatible cross-modal interactions but ignore incompatible ones. In contrast, we suggest simultaneously locating both through an enhanced cross-modal transformer module. Experimental results show that the proposed model achieves state-of-the-art performance on several datasets.
CoMix: Collaborative filtering with mixup for implicit datasets

Collaborative filtering (CF) is the prevalent solution to mitigate massive information overload in modern recommender systems. However, it usually suffers from data sparsity and popularity bias problems. Existing studies exploit auxiliary information or data augmentation, requiring additional data collection or expensive computational overheads. Inspired by Mixup used in the classification problem, we propose a simple-yet-effective data augmentation method for CF, namely Collaborative Mixup (CoMix), for implicit feedback datasets. The underlying idea of CoMix is to generate virtual users/items by logically combining random users/items. Unlike the original Mixup, we synthesize virtual users/items to complement weak collaborative signals by distinguishing the intersection and non-overlapping parts between two users/items. Despite its simplicity, extensive experimental results show that various CF models equipped with CoMix consistently improve the base models on four benchmark datasets.
Performance Analysis of Averaged Perceptron Machine Learning Classifier for Breast Cancer Detection

Breast cancer is the primary cause of women's death due to cancer; if detected in the early stage, it is a curable disease. Machine learning classification techniques are helpful in breast cancer detection. The research aims to investigate the averaged-perceptron machine-learning classifier performance on the Wisconsin original breast cancer dataset (WBC); the work has focused on two points; first, does the averaged-perceptron classifier has the quality to gain a higher accuracy than the other classifiers? Second, does it help to reduce false-negative or false-positive breast cancer predictions? The averaged-perceptron model recorded an accuracy score of 0.984 with zero false-negative predictions. The investigation has also signified the effect of threshold on false-negative or false-positive prediction. Applying the averaged-perceptron classifier in a computer-aided-diagnosis system can improve breast cancer recognition accuracy with zero false-positive or false-negative forecasts.
Classifying Transactional Addresses using Supervised Learning Approaches over Ethereum Blockchain

Ethereum is a digital asset whose transactions are kept on a decentralized, globally accessible ledger. An Ethereum Blockchain owner's real identity is concealed behind a pseudonym termed an address. Because of this, Ethereum is frequently used in illegal activities like gambling and ransomware attacks because it is popularly believed to offer the highest level of anonymity. As a result, it is necessary to categorize the various malicious cybercriminal users' activities and addresses in the Ethereum Blockchain. The Blockchain's public data enables an in-depth analysis. Using supervised machine learning models including linear, non-linear, and ensemble learning models based on malicious and non-malicious activities, the classification of Ethereum Blockchain addresses is carried out in this paper. In this research work, cross-validation accuracy, recall, precision, and f1-score have been employed for the assessment. Findings indicate that linear and non-linear machine learning approaches are superior to ensemble learning for classifying Ethereum Blockchain addresses. The results also show that it is possible to discover the Ethereum Blockchain addresses of malicious users.
Sarcasm Detection Using Bidirectional Encoder Representations from Transformers and Graph Convolutional Networks

The Internet has become a crucial space for customer feedback and the budding of various ideologies across different cultures. But some people provide their opinions whose sole meaning is different from what their figurative meanings imply. This sophisticated form of sentiment expression through mockery or irony is called sarcasm. Sarcastic form of texts has taken a prominent place in social media and their growth has become exponential in recent years through messages and posts. Sarcastic comments, tweets, or feedback can be misleading in data mining activities and may result in wrong predictions since the boundaries of the sarcastic context are not well defined. There is a need to model new systems that can precisely define the boundaries of the sentence and detect sarcasm in them. Different methods of classification have been used for sarcasm detection in various works including deep learning, neural networks, and other architectures. This paper focuses on combining the capabilities of both Bidirectional Encoder Representations from Transformers (BERT) and Graph Convolutional Networks (GCN) for detecting sarcastic content from text. A BERT-GCN architecture is proposed which takes as input, the graph, and text representations and learns the complex structural and semantic patterns in the text, thereby detecting sarcastic content. The efficiency of BERT-GCN is compared with various baseline methods.
Design and Analysis of Novel Chaotic Pelican-Optimization Algorithm for Feature-Selection of Occupational Stress

Pelican-Optimization Algorithm is a revolutionary meta-heuristic algorithm influenced by societal activities and stalking ability of pelicans. The ingenious chaotic variations of POA have been created to address the challenge of feature-selection for occupational stress diagnosis. In this manuscript, six different chaotic maps have been deployed to POA to enhance its performance. Moreover, the chaotic variants' performance has been contrasted with the original POA. It has been observed from the results that CPOA_4(Chaotic Pelican Optimization Algorithm with Iterative map) delivers the best performance in terms of average accuracy. Whereas, in the case of average execution time except for CPOA_1(Chaotic Pelican Optimization Algorithm with Chebyshev map) all other chaotic variants outperformed the original POA. However, the original POA surpasses other chaotic variants when only the number of features are of prime concern. Additionally, the performance of the six chaotic variants and original POA has been evaluated based on a multi-criterion decision (MCD) matrix.
Classification accuracy improvement using conditioned Latin Hypercube Sampling in Supervised Machine Learning

The present study aims to elaborate on advanced sampling mechanisms employment into bleeding edge machine learning processes. Application of such approaches are considered in two stages. First, selection of a fit for purpose subset of the initially accumulated data to train the selected machine learning algorithm. Second, replacement of random sampling with samples drawn using Latin Hypercube Design (LHD) mechanisms. Latin Hypercube Sampling (LHS) was recently proposed as a sampling method based on covariates. The method provides full coverage of the vary of every variable by maximal stratification of the marginal distribution. Initial results on classification tasks are promising.
Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes

Cancer subtyping delivers valuable insights into the study of cancer heterogeneity and fulfills an essential step toward personalized medicine. For example, studies in breast cancer have shown that cancer subtypes based on molecular differences are associated with different patient survival and treatment responses. However, recent studies have suggested inconsistent breast cancer subtype classifications using alternative approaches, suggesting that current methods are yet to be optimized. Existing computation-based methods have also been limited by their dependency on incomplete prior knowledge and ineffectiveness in handling high-dimensional data beyond gene expression. Here, we propose a novel deep-learning-based algorithm, Moanna, that is trained to integrate multi-omics data for predicting breast cancer subtypes. Moanna’s architecture consists of a semi-supervised Autoencoder attached to a multi-task learning network for generalizing the combination of gene expression, copy number and somatic mutation data. We trained Moanna on a subset of the METABRIC breast cancer dataset and evaluated the performance on the remaining hold-out METABRIC samples and a fully independent cohort of TCGA samples. We evaluated our use of Autoencoder against other dimensionality reduction techniques and demonstrated its superiority in learning patterns associated with breast cancer subtypes. The overall Moanna model also achieved high accuracy in predicting samples’ ER status (96%), differentiating basal-like samples (98%), and classifying samples into PAM50 subtypes (85%). Moreover, Moanna’s predicted subtypes show a stronger correlation with patient survival when compared to the original PAM50 subtypes.
Improvement of Min-Entropy Evaluation Based on Pruning and Quantized Deep Neural Network

In the field of information security, the unpredictability of random numbers plays determinant role according to the security of cryptographic systems. However, limited by the capability of pattern recognition and data mining, statistical-based methods for random number security assessment can only detect whether there are obvious statistical flaws in random sequences. In recent years, some machine learning-based techniques such as deep neural networks and prediction-based methods applied to random number security have exhibited superior performance. Concurrently, the proposed deep learning models bring out issues of large number of parameters, high storage space occupation and complex computation. In this paper, for the challenge of random number security analysis: building high-performance predictive models, we propose an effective analysis method based on pruning and quantized deep neural network. Firstly, we train a temporal pattern attention-based long short-term memory (TPA-LSTM) model with complex structure and good prediction performance. Secondly, through pruning and quantization operations, the complexity and storage space occupation of the TPA-LSTM model were reduced. Finally, we retrain the network to find the best model and evaluate the effectiveness of this method using various simulated data sets with known min-entropy values. By comparing with related work, the TPA-LSTM model provides more accurate estimates: the relative error is less than 0.43%. In addition, the model weight parameters are reduced by more than 98% and quantized to 2 bits (compression over 175x) without accuracy loss.
A novel Machine learning technique for fake smart watches advertisement detection

Fake social media advertisements for items such as smart watches and other types of media are common and a major subject of concern due to their potential to cause considerable social and national harm. This paper examines the research on fake advertisement detection and investigates machine learning models to select the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake advertisement as true or false, by using tools like python scikit-learn, NLP for textual analysis, and CNN, LSTM, RNN deep learning models for image analysis. We divided our data into 30 and 70 ratios. We trained our algorithm using 70% of the data. The remaining 30% of the data was examined using assessment measures such as accuracy, recall, and fscore. Out of 404 total advertisements, our algorithm identified 372 genuine ads and 32 fake ads.
A hybrid deep learning classifier and Optimized Key Windowing approach for drift detection and adaption

The generation of huge data with high velocity creates alteration in the distribution of the stream data, which is defined as the concept of drifts. The concept drifts negatively influence the classification accuracy and the stability of the data streams. Numerous machine learning-based models are developed to detect the concept drift in machine learning techniques. Yet, these models are inadequate for real-time applications due to time and memory constraints. Hence, this research devises dynamic streaming data analytics depending on the optimized hybrid deep learning classifier and Optimized Key Windowing (OKW) approach to effectively handle the time and memory constraints. An optimized hybrid deep learning classifier is the base classifier model developed by integrating deep Long short-term memory (LSTM) and deep Recurrent Neural Networks (RNN) to detect the concept drifts in streaming data. The model’s main advantage lies in enhanced accuracy in drift detection by a hybrid classifier due to optimal hyper parameter tuning, which is performed by the proposed intelligent preying algorithm. The other advantage of the proposed model lies in adapting the classifier with varying data patterns, which is performed using OKW. The experimentation is done using the benchmark dataset, such as Apache, Hadoop, Linux, Spark, and Cloud monitoring. The experimental analysis demonstrates the proposed model attaining higher sensitivity, accuracy, specificity, precision, and recall for Hadoop data. This shows a higher result than that of the competent techniques.
Robust feature matching via progressive smoothness consensus

Feature matching is a long-standing fundamental and critical problem in computer vision and photogrammetry. The indirect matching strategy has become a popular choice because of its high precision and generality, but it finds only a limited number of correct matches, and the mismatch removal phase does not utilize the critical feature descriptors. To this end, this paper proposes a novel and effective feature matching method, named Progressive Smoothness Consensus (PSC). Our PSC designs an objective function to directly construct correct matches from two feature point sets. To optimize the objective, we introduce a stepwise strategy, where a small but reliable match set with the smooth function is used as initialization, and then the correct match set is iteratively enlarged and optimized by match expansion and smooth function estimation, respectively. In addition, the local geometric constraint is added to the compact representation with a Fourier basis, thus improving the estimation precision. We perform the match expansion as a Bayesian formulation to exploit both the spatial distribution and feature description information, thus finding feasible matches to expand the match set. Extensive experiments on feature matching, homography & fundamental matrix estimation, and image registration are conducted, which demonstrate the advantages of our PSC against state-of-the-art methods in terms of generality and effectiveness. Our code is publicly available at https://github.com/XiaYifan1999/Robust-feature-matching-via-Progressive-Smoothness-Consensus.
Quantum Data Compression for Efficient Generation of Control Pulses

In order to physically realize a robust quantum gate, a specifically tailored laser pulse needs to be derived via strategies such as quantum optimal control. Unfortunately, such strategies face exponential complexity with quantum system size and become infeasible even for moderate-sized quantum circuits. In this paper, we propose an automated framework for effective utilization of these quantum resources. Specifically, this paper makes three important contributions. First, we utilize an effective combination of register compression and dimensionality reduction to reduce the area of a quantum circuit. Next, due to the properties of an autoencoder, the compressed gates produced are robust even in the presence of noise. Finally, our proposed compression reduces the computation time of quantum control. Experimental evaluation using popular quantum algorithms demonstrates that our proposed approach can enable efficient generation of noise-resilient control pulses while state-of-the-art fails to handle large-scale quantum systems.
A Privacy Protection Framework for Medical Image Security without Key Dependency Based on Visual Cryptography and Trusted Computing

The development of mobile Internet and the popularization of intelligent sensor devices greatly facilitate the generation and transmission of massive multimedia data including medical images and pathological models on the open network. The popularity of artificial intelligence (AI) technologies has greatly improved the efficiency of medical image recognition and diagnosis. However, it also poses new challenges to the security and privacy of medical data. The leakage of medical images related to users’ privacy is emerging one after another. The existing privacy protection methods based on cryptography or watermarking often bring a burden to image transmission. In this paper, we propose a privacy-preserving recognition network for medical images (called MPVCNet) to solve these problems. MPVCNet uses visual cryptography (VC) to transmit images by sharing. Benefiting from the secret-sharing characteristics of VC, MPVCNet can securely transmit images in clear text, which can both protect privacy and mitigate performance loss. Aiming at the problem that VC is easy to forge, we combine trusted computing environments (TEE) and blind watermarking technologies to embed verification information into sharing images. We further leverage the transfer learning technology to abate the side effect resulting from the use of visual cryptography. The results of the experiment show that our approach can maintain the trustworthiness and recognition performance of the recognition networks while protecting the privacy of medical images.