Miracle-dz/UniFAR-FTUs huggingface dataset:
On Learning Disentangled Representation for Acoustic Event Detection

Polyphonic Acoustic Event Detection (AED) is a challenging task as the sounds are mixed with the signals from different events, and the features extracted from the mixture do not match well with features calculated from sounds in isolation, leading to suboptimal AED performance. In this paper, we propose a supervised β-VAE model for AED, which adds a novel event-specific disentangling loss in the objective function of disentangled learning. By incorporating either latent factor blocks or latent attention in disentangling, supervised β-VAE learns a set of discriminative features for each event. Extensive experiments on benchmark datasets show that our approach outperforms the current state-of-the-arts (top-1 performers in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 AED challenge). Supervised β-VAE has great success in challenging AED tasks with a large variety of events and imbalanced data.
A Secure Privacy Preserving Proxy re-encryption Scheme for IoT Security using Near-ring

In current years, Internet of Things (IoT) has attained tremendous growth which includes all the aspects of private and public sectors. The resource constrained nature of IoT devices enables the IoT devices to store and access their sensitive data across the cloud computing platforms. The outsourced data do not only include users personal information's but also contain various information's such as sensor data, device data and several other confidential information's. Thus, the property of security remains to be the major concern across IoT based cloud system. In this article, we describe a secure privacy preserving proxy re- encryption scheme for IoT security using near-ring. The proposed approach solves DLP based factor problem using near-ring. It is observed from the security analysis that the proposed approach is highly secure with lesser computational overheads
The Completeness Assessment on Product Reviews based on User-concerned Information Requirements

More and more stakeholders such as consumers, sales clerks, manufacturers and administrative staffs are gravitated to wade through product reviews prior to making decisions, and they often try to get more helpful concerned information with a few reviews. However, the information items included in different reviews are often limited and different users often concern different aspects, and the existing research does not achieve the completeness assessment of reviews in the form of information items. Therefore, we identify the user-concerned information items from the reviews published or clicked by a user, and develop the assessment models to assess the completeness of reviews based on the identified information items, and finally show a method for ranking review with the requirements in information completeness.
A Survey on Representation Learning Efforts in Cybersecurity Domain

In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.
Cost-free Transfer Learning Mechanism: Deep Digging Relationships of Action Categories

End-to-end deep learning for video action recognition is always a data-hungry task because training data with detailed annotations will never be sufficient for such a difficult problem. To fully explore the potential of existing labeled categories, we propose a new transfer learning mechanism deeply digging the local and global relationships between different action categories. As for local relation mining, a weakly-constrained hierarchy structure is built to prioritize the correlation for action pairs. And for global, the consistency of joint distribution of visual and semantic feature for all action categories is measured by the maximum mean discrepancy. As far as we know, this is the first time to explicitly explore the local and global relationships between video action categories. The experimental results show that the proposed relationships of action categories can improve the classification results on most representative models without any extra annotation. Surprisingly, the boost is even more obvious when the training samples are few. We achieve state-of-art experimental results on well-known datasets.
CRA-Net: Composed Relation Attention Network for Visual Question Answering

The task of Visual Question Answering (VQA) is to answer a natural language question tied to the content of a visual image. Most existing VQA models either apply attention mechanism to locate the relevant object regions and/or utilize the off-the-shelf methods of the relation reasoning to detect object relations. However, they 1) mostly encode the simple relations which cannot sufficiently provide sophisticated knowledge for answering complicated visual questions; 2) seldom leverage the harmony cooperation of the object appearance feature and relation feature. To address these problems, we propose a novel end-to-end VQA model, termed Composed Relation Attention Network (CRA-Net ). In specific, we devise two question-adaptive relation attention modules that can extract not only the fine-grained and precise binary relations but also the more sophisticated trinary relations. Both kinds of question-related relations can reveal deeper semantics, thereby enhancing the reasoning ability in question answering. Furthermore, our CRA-Net also combines the object appearance feature with the relation feature under the guidance of the corresponding question, which can reconcile the two types of features effectively. Extensive experiments on two large benchmark datasets, VQA-1.0 and VQA-2.0, demonstrate that our proposed model outperforms state-of-the-art approaches.
Dataset: Horse Movement Data and Analysis of its Potential for Activity Recognition

We describe and analyze a dataset that comprises horse movement. Data was collected during horse riding sessions and when the horses freely roamed the pasture over 7 days. The dataset comprises 1.8 million 2-second data samples from 18 individual horses, of which 93303 samples from 11 subjects were labeled. Sensor devices were attached to a collar around the neck of the horses while the orientation was not fixed. The devices contained a 3-axis accelerometer, gyroscope, and magnetometer that were sampled at 100 Hz. To demonstrate how this dataset can be used, we evaluated a Naive Bayes classifier with leave-one-out validation. Our results show that a performance of 90 % accuracy can be achieved using only the 3D acceleration vector as input. Furthermore, we demonstrate the effect of increased complexity, parameter tuning, and class balancing on classification performance and identify open research challenges. The complete dataset is available online with open access at the 4TU.Centre for Research Data [9].
Towards Personalizing Participation in Health Studies

There is substantial evidence on the relevant factors that motivate participation in human subject studies and the expectations of participants when sharing their health data for research. However, most human subject studies focus on participant eligibility and data collection, omitting even a rudimentary use of the factors that motivate participation. We illustrate an approach to use motivation to construct personalized stories and exemplify it by using a chatbot under development towards monitoring, analyzing, and influencing health study participation, engagement, and retention. Additionally, we discuss the new advantages, challenges, and unexplored avenues for research stemming from our approach.
A Heuristic Guided Optimized Strategy for Non-Deterministic Mutation

AFL (American Fuzzy Lop) is one of the most popular fuzzy test tools. Aiming at the problem of insufficient path coverage caused by complete random mutation in the non-deterministic mutation stage, this paper proposes a heuristic guided optimized strategy for non-deterministic mutation, and implements AFLCAI on the basis of AFL. AFLCAI uses the effector map mechanism to obtain the approximation of metadata, and improves the branch coverage and the number of path coverage by heuristic guided mutation. The comparison experiment proves that AFLCAI can effectively improve the code coverage without affecting the running speed. The branch coverage rate is increased by 3.79% and the new path is increased by 9.90%, which confirms the effectiveness and advantages of the proposed method.
Application of Modal Decomposition Technique in Network Traffic Prediction

Network traffic prediction is an important means of network security monitoring, and modal decomposition technology is the key to improve the accuracy of network traffic prediction. Therefore, it is imperative to study modal decomposition technology. In this paper, the advantages of Variational Mode Decomposition (VMD) are explored by summarizing and reviewing the application of modal decomposition in network traffic prediction. The findings show that the performance of VMD mainly depends on its decomposition layers k, penalty factor C and Lagrange multiplier Θ. We propose a novel algorithm structure based on square root difference and minimum Theil inequality coefficient to optimize the performance of VMD by finding the best value for these parameters. Optimized Variational Mode Decomposition (OVMD) has improved the network traffic prediction accuracy in network security management.
GAIN: Gradient Augmented Inpainting Network for Irregular Holes

Image inpainting, which aims to fill the missing holes of the images, is a challenging task because the holes may contain complicated structures or different possible layouts. Deep learning methods have shown promising performance in image inpainting but still, suffer from generating poor-structured artifacts when the holes are large and irregular. Some existing methods use edge inpainting to help image inpainting, with binary edge map obtained from image gradient. However, by only using the binary edge map, these methods discard the rich information in image gradient and thus leave some critical issues (e.g. , color discrepancy) unattended. In this paper, we propose Gradient Augmented Inpainting Network (GAIN), which uses image gradient information instead of edge information to facilitate image inpainting. Specifically, we formulate a multi-task learning framework which performs image inpainting and gradient inpainting simultaneously. A novel GAI-Block is designed to encourage the information fusion between the image feature map and the gradient feature map. Moreover, gradient information is also used to determine the filling priority, which can guide the network to construct more plausible semantic structures for the holes. Experimental results on public datasets CelebA-HQ and Places2 show that our proposed method outperforms state-of-the-art methods quantitatively and qualitatively.
Processing Historical Film Footage with Photogrammetry and Machine Learning for Cultural Heritage Documentation

Historical film footages in many cases represent the only remaining traces of Cultural Heritage that has been lost or changed over time. Photogrammetry is a powerful technique to document the heritage transformations, but its implementation is technically challenging due to the difficulty in finding the historical data suitable to be process. This paper aims to examine the possibility to extract metric information of historic buildings from historical film footage for their 3D virtual reconstruction. In order to make automatic the research of a specific monument to document, in the first part of the study an algorithm for the detection of architectural heritage in historical film footage was developed using Machine Learning. This algorithm allowed the identification of the frames in which the monument appeared and their processing with photogrammetry. In the second part, with the implementation of open source Structure-from-Motion algorithms, the 3D virtual reconstruction of the monument and its metric information were obtained. The results were compared with a benchmark for evaluate the metric quality of the model, according to specific camera motion. This research, analysing the metric potentialities of historical film footage, provides fundamental support to documentation of Cultural Heritage, creating tools useful for both geomatics and historians.
Insider Threats: It's the HUMAN, Stupid!

Insider threats refer to threats posed by individuals who intentionally or unintentionally destroy, exfiltrate, or leak sensitive information, or expose their organization to outside attacks. Surveys of organizations in government and industry consistently show that threats posed by insiders rival those posed by hackers, and that insider attacks are even more costly. Emerging U.S. government guidelines and policies for establishing insider threat programs tend to specify only minimum standards for insider threat monitoring, analysis, and mitigation programs. Arguably, one of the most serious challenges is to identify and integrate behavioral (sociotechnical) indicators of insider threat r isk in addition to cyber/technical indicators. That is, in focusing on data that are most readily obtained, insider threat programs most often miss the human side of the problem. This talk briefly describes research aiming to catalog human as well as technical factors associated with insider threat risk and summarizes several recent studies that seek to inform the development of more comprehensive, proactive approaches to insider threat assessment.
Organizing Cultural Heritage with Deep Features

In recent years, the preservation and diffusion of culture in the digital form has been a priority for the governments in different countries, as in Mexico, with the objective of preserving and spreading culture through information technologies. Nowadays, a large amount of multimedia content is produced. Therefore, more efficient and accurate systems are required to organize it. In this work, we analyze the ability of a pre-trained residual network (ResNet) to describe information through the extracted deep features and we analyze its behavior by grouping new data into clusters by the K-means method at different levels of compression with the PCA algorithm showing that the structuring of new input data can be done with the proposed method.
Facial Image-to-Video Translation by a Hidden Affine Transformation

There has been a prominent emergence of work on video prediction, aiming to extrapolate the future video frames from the past. Existing temporal-based methods are limited to certain numbers of frames. In this paper, we study video prediction from a single still image in the facial expression domain, a.k. a, facial image-to-video translation. Our main approach, dubbed AffineGAN, associates each facial image with an expression intensity and leverages an affine transformation in the latent space. AffineGAN allows users to control the number of frames to predict as well as the expression intensity for each of them. Unlike previous intensity-based methods, We derive an inverse formulation to the affine transformation, enabling automatic inference of the facial expression intensities from videos --- manual annotation is not only tedious but also ambiguous as people express in various ways and have different opinions about the intensity of a facial image. Both quantitative and qualitative results verify the superiority of AffineGAN over the state of the arts. Notably, in a Turing test with web faces, more than 50% of the facial expression videos generated by AffineGAN are considered real by the Amazon Mechanical Turk workers. This work could improve users' communication experience by enabling them to conveniently and creatively produce expression GIFs, which are popular art forms in online messaging and social networks.
Social Games Are Fun: Exploring Social Interactions on Smart Speaker Platforms for People with Disabilities

Social environments can be challenging for many adults with disability. Most existing social skills programs tend to be delivered by paid staff and are therefore costly and time limited and there are few studies that have explored playful interventions to address social skills training. We believe that the natural language processing capability of conversational voice interfaces and the engaging qualities of interactive storytelling games can provide people with social interaction difficulties with a playful, accessible, and cost-effective training platform to improve their social skills. This paper reports on the conceptualization of the social stories game, the implementation process, test plans, preliminary results, and future scope.
Fewer-Shots and Lower-Resolutions: Towards Ultrafast Face Recognition in the Wild

Is it possible to train an effective face recognition model with fewer shots that works efficiently on low-resolution faces in the wild? To answer this question, this paper proposes a few-shot knowledge distillation approach to learn an ultrafast face recognizer via two steps. In the first step, we initialize a simple yet effective face recognition model on synthetic low-resolution faces by distilling knowledge from an existing complex model. By removing the redundancies in both face images and the model structure, the initial model can provide an ultrafast speed with impressive recognition accuracy. To further adapt this model into the wild scenarios with fewer faces per person, the second step refines the model via few-shot learning by incorporating a relation module that compares low-resolution query faces with faces in the support set. In this manner, the performance of the model can be further enhanced with only fewer low-resolution faces in the wild. Experimental results show that the proposed approach performs favorably against state-of-the-arts in recognizing low-resolution faces with an extremely low memory of 30KB and runs at an ultrafast speed of 1,460 faces per second on CPU or 21,598 faces per second on GPU.
User-Adaptive Editing for 360 degree Video Streaming with Deep Reinforcement Learning

The development through streaming of 360\degree\ videos is persistently hindered by how much bandwidth they require. Adapting spatially the quality of the sphere to the user's Field of View (FoV) lowers the data rate but requires to keep the playback buffer small, to predict the user's motion or to make replacements to keep the buffered qualities up to date with the moving FoV, all three being uncertain and risky. We have previously shown that opportunistically regaining control on the FoV with active attention-driving techniques makes for additional levers to ease streaming and improve Quality of Experience (QoE). Deep neural networks have been recently shown to achieve best performance for video streaming adaptation and head motion prediction. This demo presents a step ahead in the important investigation of deep neural network approaches to obtain user-adaptive and network-adaptive 360 degree video streaming systems. In this demo, we show how snap-changes, an attention-driving technique, can be automatically modulated by the user's motion to improve the streaming QoE. The control of snap-changes is made with a deep neural network trained on head motion traces with the Deep Reinforcement Learning strategy A3C.
Inferring Mood Instability via Smartphone Sensing: A Multi-View Learning Approach

A high correlation between mood instability (MI), the rapid and constant fluctuation in mood, and mental health has been demonstrated. However, conventional approaches to measure MI are limited owing to the high manpower and time cost required. In this paper, we propose a smartphone-based MI detection that can automatically and passively detect MI with minimal human involvement. The proposed method trains a multi-view learning classification model using features extracted from the smartphone sensing data of volunteers and their self-reported moods. The trained classifier is then used to detect the MI of unseen users efficiently, thereby reducing the human involvement and time cost significantly. Based on extensive experiments conducted with the dataset collected from 68 volunteers, we demonstrate that the proposed multi-view learning model outperforms the baseline classifiers.
Application of a Multi-source Data Warehouse for Tailings Ponds

As a major hazardous source of high potential energy, tailings ponds have always been an important part of China's safety management work. In 2007, China began a series of special rectification and comprehensive treatment measures to strengthen the safety management of tailings ponds and curb the extraordinary accidents involving them. There are a large number of tailings ponds, and they are widely distributed. Monitoring their parameters is a complex task, which increases the difficulty of daily safety supervision. It is of great significance to improve the level of safety management and management efficiency of tailings ponds nationally. This paper aims to strengthen the informatization of tailings ponds by building a data warehouse through searching the basic national tailings pond data in real time using keyword retrieval, statistical analyses and the special management of similar tailings ponds. This paper uses the multi-channel basic statistical information of the tailings reservoirs to construct a multi-source data warehouse for tailings ponds based on the ETL/ELT (extract-convert-load/extract-load-convert) data warehouse architecture and maintenance technology. This approach provides the information technology support for the current situation of tailings ponds in China and can improve the safety management level of tailings ponds.
Intelligent Aircraft Maneuvering Decision Based on CNN

Aiming at the maneuvering decision of aircraft in air combat, an intelligent maneuvering decision model based on convolutional neural network(CNN) is proposed in this paper. Firstly, the situation data, maneuvering decision variables and evaluation indexs are given, and a CNN model that can realize intelligent maneuvering decision is established. Then, according to the evaluation indexes, the structure and parameters of the CNN model are adjusted through the simulation experiments to improve the accuracy and robustness of the maneuvering decision. After that, the validity of the intelligent maneuvering decision model proposed in this paper is verified through comparative experiments that the CNN model can make stable maneuvering decisions with high accuracy. Finally, the flight path in an air combat process is presented.