Reviewerly/exHarmony huggingface dataset:
PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing

Person re-identification is a critical privacy breach in publicly shared healthcare data. We investigate the possibility of a new type of privacy threat on publicly shared privacy insensitive large scale wearable sensing data. In this paper, we investigate user specific biometric signatures in terms of two contextual biometric traits, physiological (photoplethysmography and electrodermal activity) and physical (accelerometer) contexts. In this regard, we propose PhysioGait, a context-aware phyiological signal model that consists of a Multi-Modal Siamese Convolutional Neural Network (mmSNN) which learns the spatial and temporal information individually and performs sensor fusion in a Siamese cost with the objective of predicting a person's identity. We evaluated PhysioGait attack model using 4 real-time collected datasets (3 collected datasets and one publicly available data) and two combined datasets achieving 89% - 93% accuracy of re-identifying persons.
Statistical Feature Transformation Methods

In this chapter, we introduce statistical feature transformation methods for transfer learning. This kind of approaches is extremely popular in existing literature with good results. Especially, they are often implemented in deep neural networks in recent research, demonstrating remarkable performance. Thus, it is important that we understand its very basic knowledge. Note that we will focus on its basics and will not introduce its deep learning extensions, which will be introduced in later sections.
A Spectrum Dependent Depth Layered Model for Optimization Rendering Quality of Light Field

Light field rendering technology is an important tool, which is applied a set of multi view image to render realistic novel views and experiences through some simple interpolation. However, the rendered novel views often have various distortions or low quality due to complexity of the scene, e.g., occlusion and non Lambertian. The distortion of novel views in the spectrum of light field signal can be reflected in periodic aliasing. In this paper, we propose a spectrum dependent depth layered (SDDL) model to eliminate the spectrum aliasing of light field signals, so as to improve the rendering quality of novel views. The SDDL model is about taking advantage of the characteristics that light field signal spectrum structure is only limited by the minimum depth and the maximum depth. So we in manner of increasing the depth of the layer between the minimum and maximum depth, it will reduce the sampling interval between cameras, and the adjacent two sampling during the period of the spectrum interval will become bigger. Thus, the aliasing of novel views will become smaller, it can be eliminated by this method to achieve the purpose of aliasing. In fact, the result of experiment prove our method can improve the rendering quality of light field.
Performance Analysis of Convolutional Neural Networks and Multilayer Perceptron in Generative Adversarial Networks

A Generative Adversarial Networks (GAN) model has a generative and discriminative network, in which they compete to obtain the desired outcome to make the fake generated data look real. To evaluate the functionality of a GAN, a dataset with 8431 male images and 11596 female images is introduced in this paper as the sample set. Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP) are used as generators and discriminators. The dataset is divided into two subsets, with one only having male images and the other female images. Then each set is used as a sample set to evaluate the network using CNN for both generator and discriminator, CNN for generator and MLP for discriminator, and MLP for generator and CNN for the discriminator. The result for each experiment is different, but they all indicate that the generator serves a more critical role than the discriminator because of the functionality of a GAN. Also, a dataset with a narrow interval between each element of the set with permutations of eigenvalues, instead of a larger or smaller sample size, indicates a better performance of a GAN.
DPSD: Dynamic Private Spatial Decomposition Based on Spatial and Temporal Correlations

IoT data collected in a physical space have intrinsic values, such as the spatial and temporal correlation of people’s activities. As such spatio-temporal data inevitably includes private information like trajectory and waypoints, privacy exposure becomes a serious problem. Local Differential Privacy (LDP) has been gaining attention as a privacy protection procedure on a device collecting spatio-temporal data. However, LDP cannot retain spatial and temporal properties which are essential for cyber-physical systems. The is because LDP makes each data indistinguishable and inevitably removes spatial and temporal properties as well. In this paper, we propose a method enabling LDP to keep spatial and temporal properties on privacy protection process. Our method dynamically changes the strength of privacy protection (called privacy budget) for each of device groups who has resemble spatial and temporal behavior. This makes data of each device in a group indistinguishable within the group but a set of data made by a group distinguishable between groups in terms of spatial and temporal domains. As the whole data merged in a data store will consists of modified data with wide variety of privacy budgets, we arrange every privacy budgets so that merged data keeps particular strength of privacy protection. We call this process as Dynamic Private Spatial Decomposition (DPSD). The experimental results show that our LDP preserves the data utility while maintaining the privacy protection of the entire client because of DPSD.
Dry Reactor Fault Early Warning Model and Simulation

This paper proposes a modeling and fault early warning method for dry reactor based on multi-physics coupling technology. Based on the calculation and analysis of the electromagnetic field and temperature field of the dry power station, the early warning model of the fault of the dry power station is established. At the same time, the modeling and fault warning method were applied to 35 kV dry air core reactor, and analyzed and verified. The fusion algorithm of PSO-SVM is used to reduce the order of the three-dimensional fluid-temperature field finite element model. Based on the above model, a variety of inter-turn short circuit faults are simulated to realize fault identification and early-warning discrimination. The fault warning model’s accuracy can be improved by the simulation results.
A Length-Sensitive Language-Bound Recognition Network for Multilingual Text Recognition

Due to the widespread use of English, considerable attention has been paid to scene text recognition with English as the target language, rather than multilingual scene text recognition. However, it is increasingly necessary to recognize multilingual texts with the continuous advancement of global integration. In this paper, a Length-sensitive Language-bound Recognition Network (LLRN) is proposed for multilingual text recognition. LLRN follows the traditional encoder-decoder structure. We improve the encoder and decoder respectively to better adapt to multilingual text recognition. On the one hand, we propose a Length-sensitive Encoder (LE) to encode features of different scales for long-text images and short-text images respectively. On the other hand, we present a Language-bound Decoder (LD). LD leverages language prior information to constrain the original output of the decoder to further modify the recognition results. Moreover, to solve the problem of multilingual data imbalance, we propose a Language-balanced Data Augmentation (LDA) approach. Experiments show that our method outperforms English-oriented mainstream models and achieves state-of-the-art results on MLT-2019 multilingual recognition benchmark.
CMFG: Cross-Model Fine-Grained Feature Interaction for Text-Video Retrieval

As a fundamental task in the multimodal domain, text-to-video retrieval task has received great attention in recent years. Most of the current research focuses on the interaction between cross-modal coarse-grained features. However, the feature granularity of retrieval models has not been fully explored. Therefore, we introduce video internal region information into cross-modal retrieval and propose a cross-model fine-grained feature retrieval framework. Videos are represented as video-frame-region triple features, texts are represented as sentence-word dual features, and the cross-similarity between visual features and text features is computed through token-wise interaction. It effectively extracts the detailed information in the video, guides the model to pay attention to the effective video region information and keyword information in the sentence, and reduces the adverse effects of redundant words and interfering frames. On the most popular retrieval dataset MSRVTT, the framework achieves state-of-the-art results (51.1@1). Excellent experimental results demonstrate the superiority of fine-grained feature interaction.
Application of Negative Learning Ant Colony Optimization to the Far from Most String Problem

We propose the application of a recently introduced version of ant colony optimization—negative learning ant colony optimization—to the far from most string problem. This problem is a notoriously difficult combinatorial optimization problem from the group of string selection problems. The proposed algorithm makes use of negative learning in addition to the standard positive learning mechanism in order to achieve better guidance for the exploration of the search space. In addition, we compare different versions of our algorithm characterized by the use of different objective functions. The obtained results show that our algorithm is especially successful for instances with specific characteristics. Moreover, it becomes clear that none of the existing state-of-the-art methods is best for all problem instances.
Data Augmentation for building QA Systems based on Object Models with Star Schema

Enterprises produce massive amounts of data every day. Data records generated in various formats are usually classified into structured, semi-structured, and unstructured data [1]. Many transactional data records are crucial and need to be exchanged between systems, thus data conversion becomes necessary and even tedious. Moreover, decision-makers always make "ad hoc" requests that require searching within large volumes of data. Therefore, an intelligent system is needed to respond rapidly to the demands of modern enterprises. In this paper, we purpose a novel method to build a question-answering (QA) system from a transactional system. We use object models that are translated from a star schema to represent the transactional system, such that it can generate questions (Natural Language, NL) and answers (SQL statements) as the training data. Then, we use an end-to-end (E2E) neural network to train a QA system with the generated data. Our experiments show that the Long Short-Term Memory (LSTM) network with a 0.95 BLEU value is more accurate than the Gated Recurrent Unit (GRU) network with a 0.90 BLEU value. Consequently, the proposed method can automatically generate training data from object models, and the trained artificial intelligence (AI) model can further become a QA system for us to ask questions directly.
An atomic member addition mechanism for permissioned blockchain based on autonomous rollback

As a distributed ledger technology, the addition of new members in permissioned blockchain is usually composed of several steps among distributed nodes. The addition can not be considered successful until all of the steps are completed. In other words, these steps are an atomic operation. However, there is no solution for the atomic operation in existing permissioned blockchain, leading to an inconsistent state when the addition of new members is partially completed. To implement the atomic member addition in permissioned blockchain, we propose a method targeting at the atomic addition of new members based on distributed and autonomous rollback. After member addition starts, distributed nodes of existing members detect the new node and decide whether to rollback or not, instead of getting commands from the coordinator. After deciding to rollback, a new configuration block is added to achieve rollback of the uncompleted member addition. In order for the new configuration block to pass the policy validation of orderers, we set a rollback mode for orderers. The evaluation results show that our method can actually implement atomic member addition and has little impact on performance.
Manga Text Detection with Manga-Specific Data Augmentation and Its Applications on Emotion Analysis

We especially target at detecting text in atypical font styles and in cluttered background for Japanese comics (manga). To enable the detection model to detect atypical text, we augment training data by the proposed manga-specific data augmentation. A generative adversarial network is developed to generate atypical text regions, which are then blended into manga pages to largely increase the volume and diversity of training data. We verify the importance of manga-specific data augmentation. Furthermore, with the help of manga text detection, we fuse global visual features and local text features to enable more accurate emotion analysis.
Enabling Heterogeneous Domain Adaptation in Multi-inhabitants Smart Home Activity Learning

Domain adaptation for sensor-based activity learning is of utmost importance in remote health monitoring research. However, many domain adaptation algorithms suffer with failure to operate adaptation in presence of target domain heterogeneity (which is always present in reality) and presence of multiple inhabitants dramatically hinders their generalizability producing unsatisfactory results for semi-supervised and unseen activity learning tasks. We propose AEDA, a novel deep auto-encoder- based model to enable semi-supervised domain adaptation in the existence of target domain heterogeneity and how to incorporate it to empower heterogeneity to any homogeneous deep domain adaptation architecture for cross-domain activity learning. Ex-perimental evaluation on 18 different heterogeneous and multi- inhabitants use-cases of 8 different domains created from 2 publicly available human activity datasets (wearable and ambient smart homes) shows that AEDA outperforms (max. 12.8% and 8.9 % improvements for ambient smart home and wearables) over existing domain adaptation techniques for both seen and unseen activity learning in a heterogeneous setting.
LAE-Net: Light and Efficient Network for Compressed Video Action Recognition

Action recognition is a crucial task in computer vision and video analysis. The Two-stream network and 3D ConvNets are representative works. Although both of them have achieved outstanding performance, the optical flow and 3D convolution require huge computational effort, without taking into account the need for real-time applications. Current work extracts motion vectors and residuals directly from the compressed video to replace optical flow. However, due to the noisy and inaccurate representation of the motion, the accuracy of the model is significantly decreased when using motion vectors as input. Besides the current works focus only on improving accuracy or reducing computational cost, without exploring the tradeoff strategy between them. In this paper, we propose a light and efficient multi-stream framework, including a motion temporal fusion module (MTFM) and a double compressed knowledge distillation module (DCKD). MTFM improves the network's ability to extract complete motion information and compensates to some extent for the problem of inaccurate description of motion information by motion vectors in compressed video. DCKD allows the student network to gain more knowledge from teacher with less parameters and input frames. Experimental results on the two public benchmarks(UCF-101 and HMDB-51) outperform the state of the art on the compressed domain.
Application of Cloud Theory in Evaluation of Driving Skills of Armored Vehicles

In view of the existing problems and application deficiencies of the current armored vehicle driving skills assessment methods, cloud theory was introduced into the application of armored vehicle driving skills assessment. Firstly, the index system of cloud model was established, and the index value was obtained by collecting the driving data of real vehicles. Then determine the index weight, and finally get the evaluation result. The results show that the armored vehicle driving skills evaluation method based on cloud theory can accurately evaluate the driving process of armored vehicles, and provide a technical path to solve the problem of relying on experience teaching in the driving evaluation of armored vehicles.
Exploring Encrypted Keyboards to Defeat Client-Side Scanning in End-to-End Encryption Systems

End-to-End Encryption (E2EE) aims to make all messages impossible to read by anyone except you and your intended recipient(s). Many well-known and widely used Instant-Messaging (IM) applications (such as Signal, WhatsApp, Apple’s iMessage, and Telegram) claim to provide an E2EE functionality. However, a recent technique called client-side scanning (CSS), which could be implemented by these IM applications, makes these E2EE claims grandiose and hollow promises. The CSS is a technology that scans all sending and receiving messages from one end to the other, including text, images, audio, and video files. Some in industry and government now advocate this CSS technology to combat the growth of malicious child pornography, terrorism, and other illicit communication. Even though combating the spread of illegal and morally objectionable content is a laudable effort, it may open further backdoors that impact the user’s privacy and security. Therefore, it is not end-to-end encryption when there are censorship mechanisms and backdoors in end-to-end encrypted applications. In this paper, we shed light on this hugely problematic issue by introducing an encrypted keyboard that works as a system keyboard and can be enabled on the user’s phone device as a default system keyboard. Therefore, it works on every application on the user’s phone device when the user is asked to enter some data. To avoid the CSS system, users can use this encrypted keyboard to encrypt and decrypt their messages locally on their phone devices when sending and receiving them via IM applications. We first design and implement our encrypted keyboard as a custom keyboard application, and then we evaluate the effectiveness and security of our encrypted keyboard. Our study results show that our encrypted keyboard can successfully encrypt and decrypt all sending and receiving messages through IM applications, and therefore, it can successfully defeat the CSS technology in end-to-end encrypted systems. We also show that our encrypted keyboard can be used to add another layer of E2EE functionality on top of the existing E2EE functionality implemented by many end-to-end encrypted applications.
Traffic Sign Detection Based on YOLO v3

Machine vision has penetrated into every field of our daily life and is being used more and more widely, especially in the field of automatic driving in recent years, computer vision in ImageNet Dataset will show very strong accuracy and robustness. The research objective of this paper is to analyze the network structure of YOLO V3 and summarize the loss function, use the GTSDB dataset to train and test the YOLO V3 algorithm, get the recall rate, accuracy and average accuracy of YOLO V3 in identifying traffic signs, and compare the results with R-CNN series algorithms. After testing, we find that YOLO algorithm is faster and more accurate than RCNN algorithm in traffic sign recognition, which is exactly what automatic driving needs. This provides a new way for the traffic sign recognition algorithm in the future, and also has potential application value.
Toward Certified Robustness of Graph Neural Networks in Adversarial AIoT Environments

Graph neural networks (GNNs) have transformed network analysis, leading to state-of-the-art performance across a variety of tasks. Especially, GNNs are increasingly been employed as detection tools in the AIoT environment in various security applications. However, GNNs have also been shown vulnerable to adversarial graph perturbation. We present the first approach for certifying robustness of general GNNs against attacks that add or remove graph edges either at training or prediction time. Extensive experiments demonstrate that our approach significantly outperforms prior art in certified robust predictions. In addition, we show that a noncertified adaptation of our method exhibits significantly better robust accuracy against state-of-the-art attacks that past approaches. Thus, we achieve both the best certified bounds and best practical robustness of GNNs to structural attacks to date.
Anomaly Detection for Reoccurring Concept Drift in Smart Environments

Many crowdsensing applications today rely on learning algorithms applied to data streams to accurately classify information and events of interest in smart environments. Unfor-tunately, the statistical properties of the input data may change in unexpected ways. As a result, the definition of anomalous and normal data can vary over time and machine learning models may need to be re-trained incrementally. This problem is known as concept drift, and it has often been ignored by anomaly detection systems, resulting in significant performance degradation. In addition, the statistical distribution of past data often tends to repeat itself, and thus old learning models could be reused, avoiding costly retraining phases on new data, which would waste computational and energy resources. In this paper, we propose a hybrid anomaly detection system for streaming data in smart environments that accounts for concept drift and minimize the number of machine learning models that need to be retrained when shifts in incoming data distribution are detected. The system is multi-tier and relies on two different concept drift detection modules and an ensemble of anomaly detection models. An extensive experimental evaluation has been carried out, using two real datasets and a synthetic one; results show the high performance achieved by the system using common metrics such as F1-score and accuracy.