Reviewerly/exHarmony huggingface dataset:
Multi-label emotion classification based on adversarial multi-task learning

In this paper, we focus on the task of multi-label emotion classification and aim to tackle two problems of this task. First, few studies try to exploit the correlation among different emotions, which motivates us to introduce the task-specific information into the shared hidden layer. Second, the public multi-label emotion datasets for low-resource languages are limited. To overcome these problems, we propose a novel multi-task multi-label emotion classification. Our approach consists of three components: general representation module, emotion representation module and adversarial classifier. The model applies emotion descriptors to incorporate the correlation among different emotions, and then uses adversarial training to prevent too much emotion-relevant information from being injected into the shared layer. Extensive experiments demonstrate that our approach outperforms the state-of-the-art baselines across a variety of evaluation metrics, achieving macro-average F1 scores of 50.21%, 41.33% and 40.24% on the Chinese, English, and Indonesian datasets, respectively. In addition, an Indonesian dataset and an English one containing 4207 and 26,019 samples respectively, are constructed for the multi-label emotion classification task. The datasets will be publicly available and we believe this can support the future study of Indonesian multi-label emotion recognition resources, which are limited for the related research fields now. We make our codes and resources in this work publicly available on: https://github.com/GKLMIP/MLEC-AML.
Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization

Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification. One aspect of this AI approach is the application of population search algorithms to optimise hyperparameters for enhanced prediction performance. For the first time, Bayesian optimization is deployed to determine the optimal learning parameters for more accurate tree and tree ensemble lithology classifiers. The aim is to rely on the ability of Bayesian optimization to consider previous classification results to improve the output of decision and ensemble tree lithology models using well logs as inputs. The proposed Bayesian optimised decision tree (BODT) generated the best classification accuracy of 89.8% as compared to 86.9%, 83.3% and 81.2% for fine, medium and coarse trees. For the ensembled trees, the Bayesian optimised AdaBoost (BO-AdaBoost) classifier generated the highest improved prediction accuracy of 94.2% while Bayesian optimised Bagged (BO-Bagged) and Bayesian optimised RUSBoost (BO-RUSBoost) had a lower accuracy rate of 94.0% and 77.1% respectively. Additionally, the performance of the Bayesian optimised classifiers offered higher reliability when compared with particle swarm optimization-based artificial neural networks (PSO-ANN). Hence, incorporating Bayesian optimization as a hyperparameter search algorithm will improve litholofacies recognition, leading to a higher accuracy rate and thereby provide an improved alternative for intelligent lithology identification.
Validation of a moving base driving simulator for motion sickness research

Increasing levels of vehicle automation are envisioned to allow drivers to engage in other activities but are also likely to increase the incidence of Carsickness or Motion Sickness (MS). Ideally, MS is studied in a safe and controlled environment, such as a driving simulator. However, only few studies address the suitability of driving simulators to assess MS. In this study, we validate a moving base driving simulator for MS research by comparing the symptoms and time course of MS between a real-road driving scenario and a rendition of this scenario in a driving simulator, using a within-subjects design. 25 participants took part as passengers in an experiment with alternating sections (slaloming, stop-and-go) with normal and provocative driving styles. Participants performed Sudoku puzzles (eyes-off-road) during both scenarios and reported MIsery SCale (MISC) scores at 30 s intervals. Motion Sickness Assessment Questionnaire (MSAQ) scores were collected upon completion of either scenario. Overall, the results indicate that MS was more severe in the car than in the simulator. Nevertheless, significant correlations were found between individual MS in the car and simulator for 3 out of 4 MSAQ symptom categories (0.48 < r < 0.73, p < 0.02), with a strong overall correlation (r = 0.57, p = 0.004). MS onset times were similar between the car and the simulator, and sickness fluctuations as a result of driving style showed a similar pattern between scenarios, albeit more pronounced in the car. Based on observed similarities in MS, we conclude these simulator results to have relative validity. We attribute the observed reduction of MS severity in the simulator to the downscaling of the motion by the Motion Cueing Algorithm (MCA). These results suggest that, at least in eyes-off-road conditions, findings on MS from simulator studies may generalize to real vehicles after application of a conversion factor. This conversion factor is likely to depend on simulator and MCA characteristics.
<scp>COSCO2</scp> : <scp>AI</scp> ‐augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers

Abstract Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. Therefore, in this article, a tree hierarchical deep convolutional neural network (T‐CNN) optimized with sheep flock optimization algorithm based work load prediction is proposed for sustainable cloud data centers. Initially, the historical data from the cloud data center is preprocessed using kernel correlation method. The proposed T‐CNN approach is used for workload prediction in dynamic cloud environment. The weight parameters of the T‐CNN model are optimized by sheep flock optimization algorithm. The proposed COSCO2 method has accurately predicts the upcoming workload and reduces extravagant power consumption at cloud data centers. The proposed approach is evaluated utilizing two benchmark datasets: (i) NASA, (ii) Saskatchewan HTTP traces. The simulation of this model is implemented in java tool and the parameters are calculated. From the simulation, the proposed method attains 20.64%, 32.95%, 12.05%, 32.65%, 26.54% high accuracy, and 27.4%, 26%, 23.7%, 34.7%, 36.5% lower energy consumption for validating NASA dataset, similarly 20.75%, 19.06%, 29.09%, 23.8%, 20.5% high accuracy, 20.84%, 18.03%, 28.64%, 30.72%, 33.74% lower energy consumption for validating Saskatchewan HTTP traces dataset than the existing approaches, like auto adaptive differential evolution algorithm BiPhase adaptive learning‐based neural network, error preventive score in time series forecasting models, time series forecasting methods for cloud data workload prediction, and self‐directed workload forecasting method.
A novel meaningful image encryption algorithm based on newly-designed coupled map lattice and adaptive embedding

In this paper, a novel visually meaningful encryption algorithm is proposed by combining ILCM-Dynamic Coupled Map Lattice (IDCML), compressive sensing (CS), and adaptive embedding. First, the 2D discrete wavelet transform (DWT) is adopted to sparse the plain image. Then the sparse matrix after threshold processing is scrambled by Fisher-Yates confusion, and the key-controlled partial Hadamard matrix is used for measurement. Finally, the gray level co-occurrence matrix (GLCM) is used to analyze the texture complexity of the carrier image for adaptive embedding, and the final visually meaningful cipher image is obtained. Compared with traditional encryption algorithms that generate noise-like cipher images, visual image encryption achieves the dual protection of digital images in content and vision, and adaptive embedding also avoids the problem that improper embedding location will affect the quality of encryption and decryption. To achieve higher security, a new combined ICMIC and Logistic chaotic map (ILCM) is first presented, then a novel ILCM dynamic coupling mapping lattice with ILCM as dynamic coupling coefficient is proposed and used in the process of compression, encryption and embedding. The experimental results and comprehensive analyses demonstrate that the proposed encryption algorithm has superior visual security and decryption quality.
SCICERO: A deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain

Science communication has a number of bottlenecks that include the rising number of published research papers and its non-machine-accessible and document-based paradigm, which makes the exploration, reading, and reuse of research outcomes rather ine cient.Recently, Knowledge Graphs (KG), i.e., semantic interlinked networks of entities, have been proposed as a new core technology to describe and curate scholarly information with the goal to make it machine readable and understandable.However, the main drawback of the use of such a technology is that researchers are asked to manually annotate their research papers and add their contributions within the KGs.To address this problem, in this paper we propose SCICERO, a novel KG generation approach that takes in input text from research articles and generates a KG of research entities.SCICERO uses Natural Language Processing techniques to parse the content of scientific papers to discover entities and relationships, exploits state-of-the-art Deep Learning Transformer models to make sense and validate extracted information, and uses Semantic Web best
Healthcare data analysis by feature extraction and classification using deep learning with cloud based cyber security

The rapid rise of information technology and Internet technology has resulted in the emergence of electronic health records (EHR). This research proposes novel techniques in cyber security prevention-based electronic health records in feature selection as well as classification using DL methods. Here input EHR (electronic health record) data is processed for noise removal as well as removing of null values. This processed data is selected based on their features utilizing kernel based gradient boosting NN architecture integrated with classification using stochastic convolutional neural network. The data security of the network has been enhanced using a Cryptographic cloud based cyber blockchain model. The classification of EHR is analyzed in terms of accuracy of 97%, precision of 93%, recall of 91%, f-measure of 90%, RMSE of 88%. The security of the network is analyzed based on Data Transmission rate of 85%, Time Consumption of 55%, Data Privacy Rate of 95%.
Where Are You Looking?

360{\deg} videos in recent years have experienced booming development. Compared to traditional videos, 360{\deg} videos are featured with uncertain user behaviors, bringing opportunities as well as challenges. Datasets are necessary for researchers and developers to explore new ideas and conduct reproducible analyses for fair comparisons among different solutions. However, existing related datasets mostly focused on users' field of view (FoV), ignoring the more important eye gaze information, not to mention the integrated extraction and analysis of both FoV and eye gaze. Besides, users' behavior patterns are highly related to videos, yet most existing datasets only contained videos with subjective and qualitative classification from video genres, which lack quantitative analysis and fail to characterize the intrinsic properties of a video scene. To this end, we first propose a quantitative taxonomy for 360{\deg} videos that contains three objective technical metrics. Based on this taxonomy, we collect a dataset containing users' head and gaze behaviors simultaneously, which outperforms existing datasets with rich dimensions, large scale, strong diversity, and high frequency. Then we conduct a pilot study on user's behaviors and get some interesting findings such as user's head direction will follow his/her gaze direction with the most possible time interval. A case of application in tile-based 360{\deg} video streaming based on our dataset is later conducted, demonstrating a great performance improvement of existing works by leveraging our provided gaze information. Our dataset is available at https://cuhksz-inml.github.io/head_gaze_dataset/
Immersive virtual reality for the fruition of ancient contexts: The case of the archaeological and Naturalistic Park of Santa Maria d’Agnano in Ostuni

The work presents Agnano RiVive, an immersive Virtual Reality application developed to be used within the Museum of Preclassical Civilizations of the Southern Murgia in Ostuni, which preserves the findings of the Archaeological and Naturalistic Park of Santa Maria di Agnano. The application gives the user the possibility to explore the Upper Paleolithic settlement and interact with the old manufacts. The goal is to conduct the user to assist in the burial ritual of Ostuni 1, the world's oldest mother found in this site. A fundamental element is the paleoenvironment representation made possible by archaeobotanical studies. User experience was evaluated by submitting the Presence Questionnaire to 25 users. The results showed high levels of Involvement and Visual/Audio Fidelity and a strong correlation between them. Starting from this preliminary work, we propose to expand language and content to make the application educational and attractive to all types of Museum visitors.
Bi-graph attention network for aspect category sentiment classification

Aspect category sentiment classification (ACSC) aims to determine the sentiment polarities of sentences under given aspect categories, which can be used to infer finer-grained information in text sequences. It is widely used in consumer services, healthcare, and elections. Most models ignore the interaction of global sequence context and syntactic structure information in sentences and fail to fully learn the rich relations between word nodes related to specific aspect categories. To tackle these problems, this paper introduces a bi-graph attention network (BiGAT) for ACSC, which constructs two graphs to describe the sequential context information and syntactic structure information in sentences. It utilizes the graph attention network to aggregate neighbor information from each node within a single graph and uses biaffine modules to coordinate heterogeneous information between the sequential- and syntactic-based graphs. The model uses the aspect-specific mask operation and retrieval-based attention mechanism to reduce the effect of noise created by useless information in sentences. Experimental results on the SemEval 2015, SemEval 2016, and MAMS datasets show that BiGAT outperforms other state-of-the-art ACSC models.
An oversampling method for imbalanced data based on spatial distribution of minority samples SD-KMSMOTE

With the rapid expansion of data, the problem of data imbalance has become increasingly prominent in the fields of medical treatment, finance, network, etc. And it is typically solved using the oversampling method. However, most existing oversampling methods randomly sample or sample only for a particular area, which affects the classification results. To solve the above limitations, this study proposes an imbalanced data oversampling method, SD-KMSMOTE, based on the spatial distribution of minority samples. A filter noise pre-treatment is added, the category information of the near-neighbouring samples is considered, and the existing minority class sample noise is removed. These conditions lead to the design of a new sample synthesis method, and the rules for calculating the weight values are constructed on this basis. The spatial distribution of minority class samples is considered comprehensively; they are clustered, and the sub-clusters that contain useful information are assigned larger weight values and more synthetic sample numbers. The experimental results show that the experimental results outperform existing methods in terms of precision, recall, F1 score, G-mean, and area under the curve values when the proposed method is used to expand the imbalanced dataset in the field of medicine and other fields.
One-particle Green's functions from the quantum equation of motion algorithm

Many-body Green's functions encode all the properties and excitations of interacting electrons. While these are challenging to be evaluated accurately on a classical computer, recent efforts have been directed towards finding quantum algorithms that may provide a quantum advantage for this task, exploiting architectures that will become available in the near future. In this work we introduce a novel near-term quantum algorithm for computing one-particle Green's functions via their Lehmann representation. The method is based on a generalization of the quantum equation of motion algorithm that gives access to the charged excitations of the system. We demonstrate the validity of the present proposal by computing the Green's function of a two-site Fermi-Hubbard model on a IBM quantum processor.
Hybrid Transformer Network for Deepfake Detection

Deepfake media is becoming widespread nowadays because of the easily available tools and mobile apps which can generate realistic looking deepfake videos/images without requiring any technical knowledge. With further advances in this field of technology in the near future, the quantity and quality of deepfake media is also expected to flourish, while making deepfake media a likely new practical tool to spread mis/disinformation. Because of these concerns, the deepfake media detection tools are becoming a necessity. In this study, we propose a novel hybrid transformer network utilizing early feature fusion strategy for deepfake video detection. Our model employs two different CNN networks, i.e., (1) XceptionNet and (2) EfficientNet-B4 as feature extractors. We train both feature extractors along with the transformer in an end-to-end manner on FaceForensics++, DFDC benchmarks. Our model, while having relatively straightforward architecture, achieves comparable results to other more advanced state-of-the-art approaches when evaluated on FaceForensics++ and DFDC benchmarks. Besides this, we also propose novel face cut-out augmentations, as well as random cut-out augmentations. We show that the proposed augmentations improve the detection performance of our model and reduce overfitting. In addition to that, we show that our model is capable of learning from considerably small amount of data.
Security analysis of a QAM modulated quantum noise stream cipher under a correlation attack

Quantum noise stream cipher where encrypted signals are masked by quantum noise and ASE noise provides a physical layer of security. It requires the transmitter and the receiver to share a stream cipher that is generated from a PRNG. Yet a correlation attack threatens its security due to the mathematical properties of PRNG. This paper discusses the security of QNSC system under correlation attacks. Our experiment results find that the security of the whole system depends on the cycle to refresh the seed key and the correlation between the incepted running key, original running key, and seed key. Furthermore, it is important to provide security for the QNSC system by maintaining low optical power. Besides, this new analytical method provides quantitative security analysis for a QNSC system under a correlation attack.
Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking.
A Novel Method of Small Object Detection in UAV Remote Sensing Images Based on Feature Alignment of Candidate Regions

To solve the problem of low detection accuracy of small objects in UAV optical remote sensing images due to low contrast, dense distribution, and weak features, this paper proposes a small object detection method based on feature alignment of candidate regions is proposed for remote sensing images. Firstly, AFA-FPN (Attention-based Feature Alignment FPN) defines the corresponding relationship between feature mappings, solves the misregistration of features between adjacent levels, and improves the recognition ability of small objects by aligning and fusing shallow spatial features and deep semantic features. Secondly, the PHDA (Polarization Hybrid Domain Attention) module captures local areas containing small object features through parallel channel domain attention and spatial domain attention. It assigns a larger weight to these areas to alleviate the interference of background noise. Then, the rotation branch uses RROI to rotate the horizontal frame obtained by RPN, which avoids missing detection of small objects with dense distribution and arbitrary direction. Next, the rotation branch uses RROI to rotate the horizontal box obtained by RPN. It solves the problem of missing detection of small objects with dense distribution and arbitrary direction and prevents feature mismatch between the object and candidate regions. Finally, the loss function is improved to better reflect the difference between the predicted value and the ground truth. Experiments are conducted on a self-made dataset. The experimental results show that the mAP of the proposed method reaches 82.04% and the detection speed reaches 24.3 FPS, which is significantly higher than that of the state-of-the-art methods. Meanwhile, the ablation experiment verifies the rationality of each module.
A process variation resilient spintronic true random number generator for highly reliable hardware security applications

True random number generators (TRNGs) are critical in cryptography and security systems. This paper presents a reliable TRNG relying on the stochastic switching of the spin-transfer torque magnetic tunnel junction (STT-MTJ) in the sub-critical current regime. Using an XOR-based structure in our proposed design eliminates the need for succeeding post-processing units while ensuring a reliable generation of random numbers, even in the presence of major variations. Moreover, utilizing gate-all-around (GAA) carbon nanotube field-effect transistors (CNTFETs) and the efficient structure of the proposed design leads to a low-power and high-performance operation. Comprehensive transient simulations verify the functionality of the proposed TRNG in fabrication process corners. According to the simulation results, our proposed TRNG has, on average, 53% and 63% lower power consumption and energy per bit, respectively, and a 70% smaller area compared to its counterparts. A statistical randomness test provided by the U.S. National Institute of Standards and Technology (NIST) validates the randomness quality of the generated random bitstreams.