Reviewerly/exHarmony huggingface dataset:
Traffic Data-Empowered XGBoost-LSTM Framework for Infectious Disease Prediction

Large-scale infectious diseases pose a tremendous risk to humans, with global outbreaks of COVID-19 causing millions of deaths and trillions of dollars in economic losses. To minimize the damage caused by large-scale infectious diseases, it is necessary to develop infectious disease prediction models to provide assistance for prevention. In this paper, we propose an XGBoost-LSTM mixed framework that predicts the spread of infectious diseases in multiple cities and regions. According to big traffic data, it was found that population flow is closely related to the spread of infectious diseases. Clustering and dividing cities according to population flow can significantly improve prediction accuracy. Meanwhile, an XGBoost is used to predict the transmission trend based on the key features of infection. An LSTM is used to predict the transmission fluctuation based on infection-related multiple time series features. The mixed model combines transmission trends and fluctuations to predict infections accurately. The proposed method is evaluated on a dataset of highly pathogenic infectious disease transmission published by Baidu and compared with other advanced methods. The results show that the model has an excellent predictive effect and practical value for large-scale infectious disease prediction.
AMFuse: Add–Multiply-Based Cross-Modal Fusion Network for Multi-Spectral Semantic Segmentation

Multi-spectral semantic segmentation has shown great advantages under poor illumination conditions, especially for remote scene understanding of autonomous vehicles, since the thermal image can provide complementary information for RGB image. However, methods to fuse the information from RGB image and thermal image are still under-explored. In this paper, we propose a simple but effective module, add–multiply fusion (AMFuse) for RGB and thermal information fusion, consisting of two simple math operations—addition and multiplication. The addition operation focuses on extracting cross-modal complementary features, while the multiplication operation concentrates on the cross-modal common features. Moreover, the attention module and atrous spatial pyramid pooling (ASPP) modules are also incorporated into our proposed AMFuse modules, to enhance the multi-scale context information. Finally, in the UNet-style encoder–decoder framework, the ResNet model is adopted as the encoder. As for the decoder part, the multi-scale information obtained from our proposed AMFuse modules is hierarchically merged layer-by-layer to restore the feature map resolution for semantic segmentation. The experiments of RGBT multi-spectral semantic segmentation and salient object detection demonstrate the effectiveness of our proposed AMFuse module for fusing the RGB and thermal information.
Improving cross-platform binary analysis using representation learning via graph alignment

Cross-platform binary analysis requires a common representation of binaries across platforms, on which a specific analysis can be performed. Recent work proposed to learn low-dimensional, numeric vector representations (i.e., embeddings) of disassembled binary code, and perform binary analysis in the embedding space. Unfortunately, however, existing techniques fall short in that they are either (i) specific to a single platform producing embeddings not aligned across platforms, or (ii) not designed to capture the rich contextual information available in a disassembled binary.
Approximation of restrictions of <i>q</i>-valued logic functions to linear manifolds by affine analogues

Abstract For a finite q -element field F q , we established a relation between parameters characterizing the measure of affine approximation of a q -valued logic function and similar parameters for its restrictions to linear manifolds. For q &gt; 2, an analogue of the Parseval identity with respect to these parameters is proved, which implies the meaningful upper estimates q n −1 ( q − 1) − q n /2−1 and q r −1 ( q − 1) − q r /2−1 , for the nonlinearity of an n -place q -valued logic function and of its restrictions to manifolds of dimension r . Estimates characterizing the distribution of nonlinearity on manifolds of fixed dimension are obtained.
Fusion-GCN: Multimodal Action Recognition Using Graph Convolutional Networks

In this paper we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Network (GCNs). Action recognition methods based around Graph Convolutional Network (GCNs) recently yielded state-of-the-art performance for skeleton-based action recognition. With Fusion-GCN, we propose to integrate various sensor data modalities into a graph that is trained using a GCN model for multi-modal action recognition. Additional sensor measurements are incorporated into the graph representation either on a channel dimension (introducing additional node attributes) or spatial dimension (introducing new nodes). Fusion-GCN was evaluated on two publicly available datasets, the UTD-MHAD- and MMACT datasets, and demonstrates flexible fusion of RGB sequences, inertial measurements and skeleton sequences. Our approach gets comparable results on the UTD-MHAD dataset and improves the baseline on the large-scale MMACT dataset by a significant margin of up to 12.37% (F1-Measure) with the fusion of skeleton estimates and accelerometer measurements.
Artificial Intelligence Wireless Network Data Security System For Medical Records Using Cryptography Management

So we&#x0027;re going to study here. Wireless technology is amongst the most quickly expanding and active technological disciplines in the world of communication. Connectivity communication that does not need the use of wires, cables, and perhaps other physical media. Encryption algorithm has always been widely used in various these decades to secure information from potential danger and using wireless technologies can also make the information more sheltered therefore these network infrastructure which focuses on providing replaying intervention against many issues, also can analyze the information from almost anywhere through these wireless communication and it will safeguard the health information from potential danger by storing the data in multiple servers in everything using a few real cryptographies This way, only authorized personnel may access the data and no one else can, ensuring that the data is protected from threats. To monitor environmental conditions, wireless sensor networks employ a variety of sensors, which transmit data to a central destination. These wireless sensor nodes are primarily motivated by military applications, and they are now widely used in many automotive and commercial areas, as well as in promising industries such as medicine. Nowadays, remote health monitoring wireless sensor networks unquestionably increase the quality of treatment. dropping and impersonating are two common security vulnerabilities to healthcare applications.
PC VR vs Standalone VR Fully-Immersive Applications: History, Technical Aspects and Performance

It is well known that Virtual Reality technology has the potential to immerse the user in virtual real-life scenarios with solid interaction capabilities. However, most of the mission-oriented application scenarios require complex interaction capabilities and feature-rich virtual environments which certainly require processing power. It is a fact that a modern higher-performance PCs can render the 2D and 3D assets in the VR space with higher quality allowing large polygon numbers, greater shadows, post-processing, and other advantages in comparison with the standalone VR headset. From the other side, the PCVR is significantly affecting the mobility, price and development complexity. In this paper, we are focusing on the analyses of the technical aspects of PC VR and Standalone Fully-Immersive VR applications, their characteristics and performance evaluation of real VR application realized as PC VR and Standalone VR. As a conclusion, we prove that acceptable performance and user experience can be achieved with proper and simple optimization for Standalone VR without significantly affecting the VR application usability and tracking accuracy.
Gender Diversity and Community Smells: A Double-Replication Study on Brazilian Software Teams

Social debts in software teams are gaining increasing attention from the research community due to their potential adverse effects on software quality. For instance, community smells are indicators of sub-optimal organizational structures and may well lead to the emergence of social debt. Previous studies analyzed which factors influence the emergence/mitigation of such smells. In particular, studies by Catolino et al. showed how factors related to team composition, particularly gender diversity, correlated to the mitigation of community smells. However, a confirmation survey on 60 practitioners suggested that these results were not aligned with the experts&#x0027; perceptions. In a separate survey, Catolino et al. collected the most common team refactoring strategies for those community smells. In this work we replicate two studies by those authors, focusing on the Brazilian software teams; culture-specific expectations on the behavior of people of different genders might have affected the perception of the importance of gender diversity and refactoring strategies when mitigating community smells. We translated the survey instrument used by Catolino et al. to Brazilian Portuguese and recruited 184 Brazilian developers. Re-sults did not show significant differences from the original study; indeed, participants perceived gender diversity as less valuable to mitigate community smells than such factors like experience or team size. Additionally, we performed a qualitative analysis of an open question within the questionnaire for the refactoring strategies. Brazilian developers agree with the original studies for most smells, mainly promoting restructuring communities, creating a communication plan and mentoring. We believe these results provide further evidence on the problem and its implications when managing software teams, avoiding technical debt and maintenance issues due to team communication and coordination problems.
Disentanglement of Emotional Style and Speaker Identity for Expressive Voice Conversion

Expressive voice conversion performs identity conversion for emotional speakers by jointly converting speaker identity and emotional style. Due to the hierarchical structure of speech emotion, it is challenging to disentangle the emotional style for different speakers. Inspired by the recent success of speaker disentanglement with variational autoencoder (VAE), we propose an any-to-any expressive voice conversion framework, that is called StyleVC. StyleVC is designed to disentangle linguistic content, speaker identity, pitch, and emotional style information. We study the use of style encoder to model emotional style explicitly. At run-time, StyleVC converts both speaker identity and emotional style for arbitrary speakers. Experiments validate the effectiveness of our proposed framework in both objective and subjective evaluations.
2D and 3D Angles-Only Target Tracking Based on Maximum Correntropy Kalman Filters

In this paper, angles-only target tracking (AoT) problem is investigated in the non Gaussian environment. Since the conventional minimum mean square error criterion based estimators tend to give poor accuracy in the presence of large outliers or impulsive noises in measurement, a maximum correntropy criterion (MCC) based framework is presented. Accordingly, three new estimation algorithms are developed for AoT problems based on the conventional sigma point filters, termed as MC-UKF-CK, MC-NSKF-GK and MC-NSKF-CK. Here MC-NSKF-GK represents the maximum correntropy new sigma point Kalman filter realized using Gaussian kernel and MC-NSKF-CK represents realization using Cauchy kernel. Similarly, based on the unscented Kalman filter, MC-UKF-CK has been developed. The performance of all these estimators is evaluated in terms of root-mean-square error (RMSE) in position and % track loss. The simulations were carried out for 2D as well as 3D AoT scenarios and it was inferred that, the developed algorithms performed with improved estimation accuracy than the conventional ones, in the presence of non Gaussian measurement noise.
Comparison of color imaging vs. hyperspectral imaging for texture classification

Many approaches of texture analysis by color or hyperspectral imaging are based on the assumption that the image of a texture can be viewed as a multi-component image, where spatial interactions within and between components are jointly considered (opponent component approach) or not (marginal approach). When color images are coded in multiple color spaces, texture descriptors are based on Multi Color Channel (MCC) representations. By extension, a Multi Spectral Band (MSB) representation can be used to characterize the texture of material surfaces in hyperspectral images. MSB and MCC representations are compared in this paper for texture classification issues. The contribution of each representation is investigated with marginal and/or opponent component strategies. For this purpose, several relevant texture descriptors are considered. Since MSB and MCC representations generate high-dimensional feature spaces, a dimensionality reduction is applied to avoid the curse of dimensionality. Experimental results carried out on three hyperspectral texture databases (HyTexiLa, SpecTex and an original dataset extracted from the Timbers database) show that considering between component interactions in addition to the within ones significantly improves the classification accuracies. The proposed approaches allow also to outperform state of the art hand-designed descriptors and color texture descriptors based on deep learning networks. This study highlights the contribution of hyperspectral imaging compared to color imaging for texture classification purposes but also the advantages of color imaging depending on the considered texture representation.
Multimodal Fusion of Voice and Gesture Data for UAV Control

To enable unmanned aerial vehicle (UAV) operators to efficiently and intuitively convey their commands to a swarm of UAVs, we propose the use of natural and human-centric input modalities, such as voices and gestures. This paper addresses the fusion of input modalities such as voice and gesture data, which are captured through a microphone and a Leap Motion controller, respectively, to control UAV swarms. The obtained experimental results are presented, and the achieved performance (accuracy) is analyzed. Finally, combined human factor ergonomics test with a questionnaire to verify the method’s validity.
Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud

The method of building and deploying applications through the combination of container virtualization technology and a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps and a lack of coordination mechanisms between the Internet-of-Things and cloud computing. This study constructs a resource management platform, which is based on application container virtualization technology and combined with the microservices framework. The platform provide a support environment for the construction and deployment of Internet-of-Things cloud applications. However, there is no unified specification for the microservices templates. Therefore, a new service model called tool service was designed. The invocation relationship between services is studied, and developers can combine services through the invocation relationship between services to form a service function chain. However, container-based service deployment remains an unresolved issue. The deployment method of a container involves the quality of service of end users and the profit of cloud providers. To balance the profits of both parties, it is necessary to minimize the service response time and improve the resource utilization of the cloud data center. To address this problem, an accelerated particle swarm optimization strategy is proposed to realize service deployment. Through the invocation relationship between services, the execution containers are aggregated, so as to reduce the service transmission overhead and improve resource utilization. Compared with the experimental results of existing deployment strategies, the proposed optimization strategy has significantly improved performance parameters such as service transmission overhead, container aggregation, and resource utilization.
Patch-wise Contrastive Style Learning for Instagram Filter Removal

Image-level corruptions and perturbations degrade the performance of CNNs on different downstream vision tasks. Social media filters are one of the most common resources of various corruptions and perturbations for real-world visual analysis applications. The negative effects of these dis-tractive factors can be alleviated by recovering the original images with their pure style for the inference of the downstream vision tasks. Assuming these filters substantially inject a piece of additional style information to the social media images, we can formulate the problem of recovering the original versions as a reverse style transfer problem. We introduce Contrastive Instagram Filter Removal Network (CIFR), which enhances this idea for Instagram filter removal by employing a novel multi-layer patch-wise contrastive style learning mechanism. Experiments show our proposed strategy produces better qualitative and quantitative results than the previous studies. Moreover, we present the results of our additional experiments for proposed architecture within different settings. Finally, we present the inference outputs and quantitative comparison of filtered and recovered images on localization and segmentation tasks to encourage the main motivation for this problem.
AI-Empowered Trajectory Anomaly Detection and Classification in 6G-V2X

The immense growth of Autonomous Vehicles (AVs) and networking technologies have paved the way for advanced Intelligent Transportation Systems (ITS). AVs increase data demands from in-vehicle users, which pose a significant risk to the vehicular trajectory data and are extremely vulnerable to security threats. It is challenging to describe and detect the trajectory anomalies in urban motion behavior due to the enormous coverage and complexity of ITS in the V2X environment. Most existing systems rely on a restricted number of single detection strategies, such as determining frequent patterns and have limited accuracy in detecting anomalous trajectories. However, they focus only on outlier detection, failing to consider different patterns of anomalous trajectories. This paper proposes Efficient Trajectory Anomaly Detection and Classification (ETADC) framework in a 6G-V2X environment. The proposed ETADC framework employs the Deep Deterministic Policy Gradient algorithm (DDPG) to improve accuracy and efficiency by analyzing multiple strategies, namely driving speed, driving distance, driving direction, and driving time. The result analysis shows that the proposed ETADC technique outperforms the existing systems by 97% accuracy.
Child Speech Recognition on End-to-End Neural ASR Models

Automatic speech recognition (ASR) is an area which is having maximum improvement and is developing to a very high extent as far as adult speech is concerned. Nevertheless, child speech recognition is an area which is least explored. This is because of the scarcity of a neatly labelled corpora. Precisely, to train a speech recognition model from scratch and to make it work child speech is a challenging task as the availability of child speech data is limited. In this context, exploring child speech recognition can also help us improve the current speech recognition systems since on validating it in such a manner that it will be useful for child speech may help the systems work in a good way such that we will be able to validate it for everyone irrespective of the age variation in which a speech utterance is produced. State-of-art models in this domain perform various complex speech recognition tasks such as machine translation and ASR on multilingual datasets as well as it is useful in performing self supervised speech recognition tasks. This process has proved to work well for speech data which consists of recordings with adult data and it is not yet validated on child speech. So the present work tries to validate the performance six state-of-the-art ASR models on children speech and report the corresponding word error rate.
Synthetic Data Augmentation of MRI using Generative Variational Autoencoder for Parkinson’s Disease Detection

Madan, Yamini Veetil, Iswarya Kannoth V, Sowmya EA Gopalakrishnan KP, SomanMachine learning models are being increasingly proposed for the automated classification of Parkinson's disease from brain imaging data such as magnetic resonance imaging (MRI). However, the problem of class imbalance is a major setback in deriving the maximum benefit from using these techniques. We propose in overcoming the class imbalance between Parkinson's disease (PD) and normal cohorts (NC) subjects by using variational autoencoders (VAEs) as the generative model that follows a probabilistic regeneration and aim to experiment over the effect of latent variables in generating new MR images of the subjects to improve the detection of PD. The efficiency of the proposed method with and without data augmentation is compared and evaluated using a deep learning classifier model at the subject level. The results obtained using a model trained with data augmentation show a significant increase of 6% in the performance of the classification model.
Rectified DenseNet169-based automated criminal recognition system for the prediction of crime prone areas using face recognition

With the increase in the crime rate, it becomes difficult for police officials to catch criminals. Face recognition is a technique that helps police officials to catch suspects easily with the help of CCTV footage. The constant checking of surveillance video is a tiresome approach that involves substantial visual consideration and is not mentally captivating, making it increasingly inclined to errors. Therefore, the authors proposed an automated criminal recognition system in the present paper. The present paper comprised three phases; the initial phase investigated the performance of various existing face detection algorithms. The second phase proposed a rectified fine-tuned DenseNet169 model for face recognition. In the third phase, the authors proposed an automated criminal recognition system in which the identification of crime-prone areas is made, based on the data collected where most of the criminals are identified. The proposed rectified model outperformed 1.02% to DeepID, 1% to deep convolutional neural network (CNN), and 1.05% to self-learning CNN on the LFW dataset, 1.09% to VGGFace2, 1.13% to SphereFace, and 1.03% to lightweight CNN on the CPLFW dataset, and achieved 88.7% recognition accuracy on a self-created dataset by meticulously selecting the hyperparameters and customizing the layers of the model.