Reviewerly/exHarmony huggingface dataset:
Security and Privacy of Cloud-Based Online Online Social Media: A Survey

The social network is a big forum where people communicate with each other everywhere in a digital word based on a connection. Users may share anything with their contacts, such as profiles, images, posts, videos, etc. Data sharing makes communication easier on such platforms. Also, with increasing use of social networking sites, information exchange across social networks has also increased. The protection and rights of shared information are being threatened because more and more data is being shared. Security and privacy, is therefore posing significant questions. All the data in the online social media must be protected from unauthorized access. As users of social media are growing significantly, it is difficult to store this very large amount of information. As a remedy, Cloud offers a space to store data created by social media. So as the data is very large their maintenance is very costly, so cloud-based services are used to handle such large amounts of data as cloud provides many services like platform-based services, storage based, and many more. However, the problems of protecting users’ private information in cloud-based social media remain. Users are ignorant of such problems. They post mostly on media sites, different photographs, videos, and private details that persist even after deletion. Though some of the leaked info is supposed to be a secret, data from social media have contributed to a possibility of persons revealing sensitive information. That’s also due to the fact that they gather enormous private information and clients accept the risk of believing them. When enough personalized data is exchanged with the public, it becomes even simpler to violate the confidentiality of a target user. Therefore, one of the biggest problems of cloud-based social media is the protection of social media data stored in the cloud. In this paper, we present an overview of privacy and security issues in cloud-based online social media. We’re adding privacy and security threat taxonomy in online social media, evaluating current methods to minimize those threats, and highlighting problems that are yet to be addressed.
Intrusion Monitoring in Military Surveillance Applications using Wireless Sensor Networks (WSNs) with Deep Learning for Multiple Object Detection and Tracking

Terrestrial Wireless Sensor Networks (WSNs) are used in military environments for region surveillance, healthcare systems for soldiers, and, smart transport, and logistics, etc. In surveillance applications, the sensor nodes are deployed randomly in the field to observe the events of interest, movement of humans, or vehicles. In these sensor networks, the image or video is captured by the camera module. Many times it becomes difficult to correctly detect the intrusion or anomalous activity in the field because the image being captured maybe not clear enough due to prevailing weather conditions, the amount of light, and other reasons. In this paper, in addition to a WSN Surveillance System for military applications, we have used Convolutional Neural Network (CNN) for analyzing and understanding the content of the captured images and videos. CNN is a deep learning neural network that detects and tracks automatically the important features without any human supervision. The distinctive layers of each class are learned by themselves and have the highest accuracy of prediction. The results of the implementation for four test images captured in different conditions show an accuracy of 92%. The results of the video tracking yield the Object Tracking Efficiency of 80.35%.
Depth Completion Using Geometry-Aware Embedding

Exploiting internal spatial geometric constraints of sparse LiDARs is beneficial to depth completion, however, has been not explored well. This paper proposes an efficient method to learn geometry-aware embedding, which encodes the local and global geometric structure information from 3D points, e.g., scene layout, object's sizes and shapes, to guide dense depth estimation. Specifically, we utilize the dynamic graph representation to model generalized geometric relationship from irregular point clouds in a flexible and efficient manner. Further, we joint this embedding and corresponded RGB appearance information to infer missing depths of the scene with well structure-preserved details. The key to our method is to integrate implicit 3D geometric representation into a 2D learning architecture, which leads to a better trade-off between the performance and efficiency. Extensive experiments demonstrate that the proposed method outperforms previous works and could reconstruct fine depths with crisp boundaries in regions that are over-smoothed by them. The ablation study gives more insights into our method that could achieve significant gains with a simple design, while having better generalization capability and stability. The code is available at https://github.com/Wenchao-Du/GAENet.
CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise

Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have been made to improve the model robustness against the class-conditional noise. However, they typically focus on the single label case by assuming that only one label is corrupted. In real applications, an instance is usually associated with multiple labels, which could be corrupted simultaneously with their respective conditional probabilities. In this paper, we formalize this problem as a general framework of learning with Class-Conditional Multi-label Noise (CCMN for short). We establish two unbiased estimators with error bounds for solving the CCMN problems, and further prove that they are consistent with commonly used multi-label loss functions. Finally, a new method for partial multi-label learning is implemented with the unbiased estimator under the CCMN framework. Empirical studies on multiple datasets and various evaluation metrics validate the effectiveness of the proposed method.
A Deep Generative–Discriminative Learning for Multimodal Representation in Imaging Genetics

Imaging genetics, one of the foremost emerging topics in the medical imaging field, analyzes the inherent relations between neuroimaging and genetic data. As deep learning has gained widespread acceptance in many applications, pioneering studies employed deep learning frameworks for imaging genetics. However, existing approaches suffer from some limitations. First, they often adopt a simple strategy for joint learning of phenotypic and genotypic features. Second, their findings have not been extended to biomedical applications, e.g., degenerative brain disease diagnosis and cognitive score prediction. Finally, existing studies perform insufficient and inappropriate analyses from the perspective of data science and neuroscience. In this work, we propose a novel deep learning framework to simultaneously tackle the aforementioned issues. Our proposed framework learns to effectively represent the neuroimaging and the genetic data jointly, and achieves state-of-the-art performance when used for Alzheimer's disease and mild cognitive impairment identification. Furthermore, unlike the existing methods, the framework enables learning the relation between imaging phenotypes and genotypes in a nonlinear way without any prior neuroscientific knowledge. To demonstrate the validity of our proposed framework, we conducted experiments on a publicly available dataset and analyzed the results from diverse perspectives. Based on our experimental results, we believe that the proposed framework has immense potential to provide new insights and perspectives in deep learning-based imaging genetics studies.
DroneSense: The Identification, Segmentation, and Orientation Detection of Drones via Neural Networks

The growing ubiquity of drones has raised concerns over the ability of traditional air-space monitoring technologies to accurately characterise such vehicles.Here, we present a CNN using a decision tree and ensemble structure to fully characterise drones in flight.Our system determines the drone type, orientation (in terms of pitch, roll, and yaw), and performs segmentation to classify different body parts (engines, body, and camera).We also provide a computer model for the rapid generation of large quantities of accurately labelled photo-realistic training data and demonstrate that this data is of sufficient fidelity to allow the system to accurately characterise real drones in flight.Our network will provide a valuable tool in the image processing chain where it may build upon existing drone detection technologies to provide complete drone characterisation over wide areas.
Understanding what patients think about hospitals: A deep learning approach for detecting emotions in patient opinions

Most hospital assessment systems are based on the study of objective statistical variables as well as patient opinions on their experiences with respect to the services offered by each hospital. Nevertheless, studies have indicated that most of these assessment systems fail to detect patient emotions when they are assessing their stays in a hospital. This information is vital to understanding most of the patient reviews, which are very complex and convey several emotions per review. Therefore, this study aimed to address the problem of detecting multiple emotions from patient reviews.First, a large set of patient opinions was collected from a website that allowed patients to publish their experiences when visiting hospitals. Second, each opinion was labeled with the corresponding conveyed emotions. Third, a deep learning architecture based on a bidirectional gated recurrent unit with a multichannel convolutional neural network layer was proposed to detect multiple emotions from these reviews. Finally, the hyperparameters of this architecture were fine-tuned and different pretrained word embedding models were configured to test its performance.The results confirmed that our proposed method outperformed other deep learning and machine learning-based algorithms and achieved an average accuracy of 95.82%. Furthermore, the experiments show that clinical-domain word embedding slightly outperforms other general-domain word embeddings, although general-domain embeddings are larger in terms of dimensions.The combination of the gated recurrent unit and the multichannel convolutional neural network is able to exploit both semantic and syntactic characteristics of patient opinions. The findings of this study identify research gaps related to areas such as opinion-based hospital recommendations, thereby providing future research directions.
Graph-Enabled Intelligent Vehicular Network Data Processing

Intelligent vehicular network (IVN) is the underlying support for the connected vehicles and smart city, but there are several challenges for IVN data processing due to the dynamic structure of the vehicular network. Graph processing, as one of the essential machine learning and big data processing paradigm, which provide a set of big data processing scheme, is well-designed to processing the connected data. In this paper, we discussed the research challenges of IVN data processing and motivated us to address these challenges by using graph processing technologies. We explored the characteristics of the widely used graph algorithms and graph processing frameworks on GPU. Furthermore, we proposed several graph-based optimization technologies for IVN data processing. The experimental results show the graph processing technologies on GPU can archive excellent performance on IVN data.
Leaf: Multiple-Choice Question Generation

Testing with quiz questions has proven to be an effective way to assess and improve the educational process. However, manually creating quizzes is tedious and time-consuming. To address this challenge, we present Leaf, a system for generating multiple-choice questions from factual text. In addition to being very well suited for the classroom, Leaf could also be used in an industrial setting, e.g., to facilitate onboarding and knowledge sharing, or as a component of chatbots, question answering systems, or Massive Open Online Courses (MOOCs). The code and the demo are available on GitHub ( https://github.com/KristiyanVachev/Leaf-Question-Generation ).
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving

Optical flow estimation is an essential task in self-driving systems, which helps autonomous vehicles perceive temporal continuity information of surrounding scenes. The calculation of all-pair correlation plays an important role in many existing state-of-the-art optical flow estimation methods. However, the reliance on local knowledge often limits the model’s accuracy under complex street scenes. In this paper, we propose a new deep network architecture for optical flow estimation in autonomous driving——CSFlow, which consists of two novel modules: Cross Strip Correlation module (CSC) and Correlation Regression Initialization module (CRI). CSC utilizes a striping operation across the target image and the attended image to encode global context into correlation volumes, while maintaining high efficiency. CRI is used to maximally exploit the global context for optical flow initialization. Our method has achieved state-of-the-art accuracy on the public autonomous driving dataset KITTI-2015. Code is publicly available at https://github.com/MasterHow/CSFlow.
A Soldering Motion Analysis System for Danger Detection Considering Object Detection and Attitude Estimation

It is important for the society to support the employment of people with disabilities. However, at the work sites, it is necessary to ensure safety while soldering for persons with disabilities and to support instructors. In this paper, in order to solve these problems, we propose a soldering motion analysis system for danger detection based on object detection and attitude estimation. Also, we show the experimental results for dangerous detection during soldering iron. The experimental results show that the proposed system has a good accuracy for detecting dangerous situations.
A Predictive Method to Improve the Effectiveness of Twitter Communication in a Cultural Heritage Scenario

Museums are embracing social technologies in an attempt to broaden their audience and to engage people. Although social communication seems an easy task, media managers know how hard it is to reach millions of people with a simple message. Indeed, millions of posts are competing every day to get visibility in terms of likes and shares and very little research focused on museums communication to identify best practices. In this article, we focus on Twitter and we propose a novel method that exploits interpretable machine learning techniques to: (a) predict whether a tweet will likely be appreciated by Twitter users or not; (b) present simple suggestions that will help to enhance the message and increase the probability of its success. Using a real-world dataset of around 40,000 tweets written by 23 world famous museums, we show that our proposed method allows identifying tweet features that are more likely to influence the tweet success.
Deep Channel Prediction: A DNN Framework for Receiver Design in Time-Varying Fading Channels

In time-varying fading channels, channel coefficients are estimated using pilot symbols that are transmitted every coherence interval. For channels with high Doppler spread, the rapid channel variations over time will require considerable bandwidth for pilot transmission, leading to poor throughput. In this paper, we propose a novel receiver architecture using deep recurrent neural networks (RNNs) that learns the channel variations and thereby reduces the number of pilot symbols required for channel estimation. Specifically, we design and train an RNN to learn the correlation in the time-varying channel and predict the channel coefficients into the future with good accuracy over a wide range of Dopplers and signal-to-noise ratios (SNR). The proposed training methodology enables accurate channel prediction through the use of techniques such as teacher-force training, early-stop, and reduction of learning rate on plateau. Also, the robustness of prediction for different Dopplers and SNRs is achieved by adapting the number of predictions into the future based on the Doppler and SNR. Numerical results show that good bit error performance is achieved by the proposed receiver in time-varying fading channels. We also propose a data decision driven receiver architecture using RNNs that further reduces the pilot overhead while maintaining good bit error performance.
Temporal Moment Localization via Natural Language by Utilizing Video Question Answers as a Special Variant and Bypassing NLP for Corpora

Temporal moment localization using natural language (TMLNL) is an emerging issue in computer vision for localizing a specific moment inside a long, untrimmed video. The goal of TMLNL is to obtain the video’s output moment, which is related to the input query in a substantial way. Previous research focused on the visual portion of TMLNL, such as objects, backdrops, and other visual attributes, but natural language processing (NLP) techniques were largely used for the textual portion. A long query requires sufficient context to properly localize moments within a long untrimmed video. Thus, as a consequence of not completely understanding how to handle queries, performances deteriorated, especially when the query was longer. In this paper, we treat the TMLNL challenge as a unique variation of VQA, which equally considers the visual elements by using our proposed VQA joint visual-textual framework (JVTF). However, we also manage complex and long input queries without employing natural language processing (NLP) by improving poorly graded to finely graded distinct granularity representations. Our suggested BCPN searches for insufficient context for long input queries using an approach called query handler (QH) and helps the JVTF find the most relevant moment. Previously, a recurrence of words was caused by increasing the number of encoding layers in transformers, LSTMs, and other NLP techniques; however, our QH ensured that repetition of word locations was reduced. The output of BCPN is combined with JVTF’s guided attention to further improve the end outcome. Therefore, we propose a novel bidirectional context predictor network (BCPN), in addition to a VQA joint visual-textual framework (JVTF), to address the equal importance of videos and queries. Through extensive experiments on three benchmark datasets, we show that the proposed BCPN outperforms the state-of-the-art methods by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$IoU = 0.3 (2.65 \%) $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$IoU = 0.5 (2.49 \%)$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$IoU = 0.7 (2.06 \%) $ </tex-math></inline-formula> .
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy

Vision transformers (ViTs) have gained increasing popularity as they are commonly believed to own higher mod-eling capacity and representation flexibility, than traditional convolutional networks. However, it is questionable whether such potential has been fully unleashed in prac-tice, as the learned ViTs often suffer from over-smoothening, yielding likely redundant models. Recent works made pre-liminary attempts to identify and alleviate such redundancy, e.g., via regularizing embedding similarity or re-injecting convolution-like structures. However, a “head-to-toe as-sessment” regarding the extent of redundancy in ViTs, and how much we could gain by thoroughly mitigating such, has been absent for this field. This paper, for the first time, systematically studies the ubiquitous existence of re-dundancy at all three levels: patch embedding, attention map, and weight space. In view of them, we advocate a principle of diversity for training ViTs, by presenting cor-responding regularizers that encourage the representation diversity and coverage at each of those levels, that enabling capturing more discriminative information. Extensive ex-periments on ImageNet with a number of ViT backbones validate the effectiveness of our proposals, largely eliminating the observed ViT redundancy and significantly boosting the model generalization. For example, our diversified DeiT obtains 0.70% ~ 1.76% accuracy boosts on ImageNet with highly reduced similarity. Our codes are fully available in https://github.com/VITA-Group/Diverse-ViT.
Constrained Sequence-to-Tree Generation for Hierarchical Text Classification

Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy. The majority of prior studies consider HTC as a flat multi-label classification problem, which inevitably leads to ''label inconsistency'' problem. In this paper, we formulate HTC as a sequence generation task and introduce a sequence-to-tree framework (Seq2Tree) for modeling the hierarchical label structure. Moreover, we design a constrained decoding strategy with dynamic vocabulary to secure the label consistency of the results. Compared with previous works, the proposed approach achieves significant and consistent improvements on three benchmark datasets.
A Light-Weight Strategy for Restraining Gender Biases in Neural Rankers

In light of recent studies that show neural retrieval methods may intensify gender biases during retrieval, the objective of this paper is to propose a simple yet effective sampling strategy for training neural rankers that would allow the rankers to maintain their retrieval effectiveness while reducing gender biases. Our work proposes to consider the degrees of gender bias when sampling documents to be used for training neural rankers. We report our findings on the MS MARCO collection and based on different query datasets released for this purpose in the literature. Our results show that the proposed light-weight strategy can show competitive (or even better) performance compared to the state-of-the-art neural architectures specifically designed to reduce gender biases.