Reviewerly/exHarmony huggingface dataset:
ESPADE: An Efficient and Semantically Secure Shortest Path Discovery for Outsourced Location-Based Services

With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. Specifically, our paper aims to address the following problem: Given a user’s location s, a target destination t, and a graph G stored in a cloud, can users retrieve the shortest path route from s to t in a privacy-preserving manner? Although there exist a few solutions to this problem, they are either inefficient or insecure. For example, existing solutions either leak intermediate results to untrusted cloud providers or incur significant costs on the end-user. To address this gap, we propose an efficient and secure solution based on homomorphic encryption properties combined with a novel data aggregation technique. We formally show that our solution achieves semantic security guarantees under the semi-honest model. Additionally, we provide complexity analysis and experimental results to demonstrate that the proposed protocol is significantly more efficient than the current state-of-the-art techniques.
Context‐aware emergency detection method for edge computing‐based healthcare monitoring system

Abstract When taking physical exercise, healthcare monitoring systems are needed to detect emergencies, such as sychnosphygmia, and so on. However, heterogeneity and privacy make it more challenging to collect and process multi‐source healthcare data in a real time manner. To support healthcare monitoring, in this research, a multi‐layer edge computing‐based framework is designed to detect emergencies in low latency and secure ways. First, a universal data model is defined to process heterogeneous data from multi‐sources such as sensors or smart devices. Then, Gated Recurrent Units‐based (GRU) model is utilized to capture and process physiological data series. Fine‐grained detection models are built and distinguished according to scenario information and individual health persona. In the proposed edge computing‐based framework, detection algorithm computation and physiological index data storage are close to end user sides to support low latency. Meanwhile encryption algorithm is used to protect data privacy. Finally, a case study in the Physical Education Class scenario is implemented to demonstrate the feasibility of our methods. The experimental result shows that our GRU model is more accurate and is three times faster for emergency identification compared with Support Vector Classification model.
Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

Abstract In order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the membership functions of intensity are generated. In the second one, fuzzy inference systems are established and the contextual dissimilarity of each pixel is calculated. In the third module, the contextual dissimilarity histograms are clipped and equalized. The fuzzy system established in this paper not only fully considers the uncertainty source of pixel gray level expression, but also has adaptability. The parameter selection of fuzzy inference system membership function in this algorithm does not need human intervention, but is automatically obtained based on the statistical information of image pixel gray. Its adaptability makes the algorithm more widely used and convenient. Experiments are conducted using four typical medical images and 800 images from the KNIX dataset and BrainWeb dataset. The performance of the proposed method was compared to a series of enhancement algorithms based on both subjective judgment and image quality measurement indexes. Experimental results demonstrate that the proposed algorithm has a better contrast enhancement ability and yields better performance.
Cryptoparties: empowerment in internet security

Cryptoparties (CPs) are a global movement of forums where citizens can come to learn how to improve their digital privacy and security.The present paper is one of the few empirical studies on CPs and is based on participant observation of three CPs.I demonstrate that the organisers of CPs strive for an egalitarian space for teaching and learning.Even though this goal is not always achieved, CPs might still serve as an example of citizen education in a technological society where every citizen needs to deal with complex technological issues.In addition, this paper contributes to the emerging debate on 'doing internet governance' , broadening our focus to include user-based and decentred practices.I argue for the political relevance of CPs showing how they enact decentred threat-scenarios to a non-expert public.
ARSketch

Hand gesture interaction is a key component in Augmented Reality (AR) / Mixed Reality (MR). Users usually interact with AR/MR devices, e.g., Microsoft HoloLens, etc., via hand gestures to express their intentions and the devices will recognize the gestures and respond accordingly to users. However, the use of such technique so far is limited to only a few less-expressive hand gestures, which, unfortunately, are insufficient or inadequate to input complex information.
Neural network-based blockchain decision scheme

Blockchain is a game-changer for many applications such as storage, consensus, authentication, and many others. Although blockchain’s contribution is undeniable, but it is not always the best solution. Professionals struggle to decide whether to adopt blockchain and when they do, they are faced with another challenge to choose the suitable type. Decision schemes are developed to help professionals select the best technology. Existing decision schemes are of two types questionnaire forms and flow diagrams. All existing schemes share a main drawback that the user is limited to a predefined set of answers without having the capacity to fine-grain his preferences in addition to per scheme-specific drawbacks. In this paper, we will propose a neural network-based decision scheme that gives the user an advanced decision support feature of specifying proportional weights between characteristics that does not exist in any other blockchain type decision scheme and solve the drawbacks available in existing schemes.
On line sum optimization

We show that the column sum optimization problem, of finding a (0,1)-matrix with prescribed row sums which minimizes the sum of evaluations of given functions at its column sums, can be solved in polynomial time, either when all functions are the same or when all row sums are bounded by any constant. We conjecture that the more general line sum optimization problem, of finding a matrix minimizing the sum of given functions evaluated at its row sums and column sums, can also be solved in polynomial time.
Improving arm segmentation in sign language recognition systems using image processing

BACKGROUND: For a traditional vision-based static sign language recognition (SLR) system, arm segmentation is a major factor restricting the accuracy of SLR. OBJECTIVE: To achieve accurate arm segmentation for different bent arm shapes, we designed a segmentation method for a static SLR system based on image processing and combined it with morphological reconstruction. METHODS: First, skin segmentation was performed using YCbCr color space to extract the skin-like region from a complex background. Then, the area operator and the location of the mass center were used to remove skin-like regions and obtain the valid hand-arm region. Subsequently, the transverse distance was calculated to distinguish different bent arm shapes. The proposed segmentation method then extracted the hand region from different types of hand-arm images. Finally, the geometric features of the spatial domain were extracted and the sign language image was identified using a support vector machine (SVM) model. Experiments were conducted to determine the feasibility of the method and compare its performance with that of neural network and Euclidean distance matching methods. RESULTS: The results demonstrate that the proposed method can effectively segment skin-like regions from complex backgrounds as well as different bent arm shapes, thereby improving the recognition rate of the SLR system.
Towards Modality Transferable Visual Information Representation with Optimal Model Compression

Compactly representing the visual signals is of fundamental importance in various image/video-centered applications. Although numerous approaches were developed for improving the image and video coding performance by removing the redundancies within visual signals, much less work has been dedicated to the transformation of the visual signals to another well-established modality for better representation capability. In this paper, we propose a new scheme for visual signal representation that leverages the philosophy of transferable modality. In particular, the deep learning model, which characterizes and absorbs the statistics of the input scene with online training, could be efficiently represented in the sense of rate-utility optimization to serve as the enhancement layer in the bitstream. As such, the overall performance can be further guaranteed by optimizing the new modality incorporated. The proposed framework is implemented on the state-of-the-art video coding standard (i.e., versatile video coding), and significantly better representation capability has been observed based on extensive evaluations.
SRHEN

The crux of homography estimation is that the homography is characterized by the geometric correspondences between two related images rather than appearance features, which differs from typical image recognition tasks. Existing methods either decompose the task of homography estimation into several individual sub-problems and optimize them sequentially, or attempt to tackle it in an end-to-end manner by delegating the whole task to deep convolutional networks (CNNs). However, it is quite arduous for CNNs to learn the mapping function from appearance features of related images to the homography directly. In this paper, we propose to parse the geometric correspondences between related images explicitly to bridge the gap between deep appearance features and the homography. Furthermore, we propose a coarse-to-fine estimation framework to capture different scale of homography transformations and thus predict the homography in a stepwise-refining manner. Additionally, we propose a pyramidal supervision scheme to leverage an important prior concerning the homography estimation. Extensive experiments on two large-scale datasets demonstrate that our model advances the state-of-the-art performance significantly.
Task Decoupled Knowledge Distillation For Lightweight Face Detectors

Face detection is a hot topic in computer vision. The face detection methods usually consist of two subtasks, i.e. the classification subtask and the regression subtask, which are trained with different samples. However, current face detection knowledge distillation methods usually couple the two subtasks, and use the same set of samples in the distillation task. In this paper, we propose a task decoupled knowledge distillation method, which decouples the detection distillation task into two subtasks and uses different samples in distilling the features of different subtasks. We firstly propose a feature decoupling method to decouple the classification features and the regression features, without introducing any extra calculations at inference time. Specifically, we generate the corresponding features by adding task-specific convolutions in the teacher network and adding adaption convolutions on the feature maps of the student network. Then we select different samples for different subtasks to imitate. Moreover, we also propose an effective probability distillation method to joint boost the accuracy of the student network. We apply our distillation method on a lightweight face detector, EagleEye. Experimental results show that the proposed method effectively improves the student detector's accuracy by 5.1%, 5.1%, and 2.8% AP in Easy, Medium, Hard subsets respectively.
Thematic-Based Big Book Learning Media as a Facility of Visual Learning Styles for Students

This study aimed to develop a thematic-based big book media as a means of visual learning styles of students in grade IV Elementary Schools. This research was conducted in Elementary School in class IV several 20 students with details of 10 students in a limited trial. In the learning process still using conventional methods, the use of media is not optimal and there are no media that contains some learning material. This type of research uses a model modified by Sugiyono. This model consists of 6 stages, namely problem analysis, data collection, product design, design validation, design revision, and product trials. The instrument used was a questionnaire sheet for media validation by lecturers and teachers as well as student questionnaire sheets. Based on data analysis, it can be concluded that the quality of the developed big book media is in the proper category with a percentage of 80% of media experts, a very feasible category with a percentage of 88% from 2 material experts, and 93.33% of linguists. Student responses to the big book media in the trial with 10 respondents amounted to 85.4% with a very good category. This big book media is said to be appropriate because it can overcome the problems that exist in elementary schools. very feasible category with a percentage of 88% from 2 material experts, and 93.33% of linguists. Student responses to the big book media in the trial with 10 respondents amounted to 85.4% with a very good category. This big book media is said to be appropriate because it can overcome the problems that exist in elementary schools. very feasible category with a percentage of 88% from 2 material experts, and 93.33% of linguists. Student responses to the big book media in the trial with 10 respondents amounted to 85.4% with a very good category. This big book media is said to be appropriate because it can overcome the problems that exist in elementary schools.
Virtual reality research and development in NTU

In 1981, Nanyang Technological Institute was established in Singapore to train engineers and accountants to keep up with the fast-growing economy of the country. In 1991, the institute was upgraded to Nanyang Technological University (NTU). NTU holds the rank for world's top young university for six consecutive years according to the Quacquarelli Symonds (QS) world university ranking. Virtual Reality (VR) research began in NTU in the late 1990s. NTU's colleges, schools, institutes, and centers have contributed toward the excellence of VR research. This article briefly describes the VR research directions and activities in NTU.
Revisiting hierarchy: Deep learning with orthogonally constrained prior for classification

Deep learning has attracted significant attention for its applications to a variety of classification problems, such as handwritten recognition, image classification and document categorization. One reason behind the success of deep learning can be attributed to its strong representation power with multiple layers of hidden variables. However, complex models are often encompassed with overfitting problems when limited training data is available. In this paper, we are interested in deep learning for classification with prior, where the set of labels are expressed in a hierarchy. In particular, we attempt to leverage knowledge transfer and parameter sharing among classes. We introduce the orthogonally constrained prior into deep learning, which exploits the following information among different classes: (1) introduce weight decomposition to model parameter sharing along the path from the root to leafs; (2) incorporate the orthogonal restrictions among adjacent nodes in the hierarchy. We test our method on challenge datasets in the case of a few training examples available, and show promising results compared to support vector machines and other deep learning methods.
An Interactive Design for Visualizable Person Re-Identification

Although person Re-Identification (ReID) is widely applied in a variety of multimedia systems, most of its essentially multifaceted output is evaluated and visualized inflexibly only using a list of images ranked by the similarity of image content, while the correlations between samples of different IDs and the spatial-temporal features of the images are underinvestigated. As system operators need a comfortable access to these important elements, we introduce an interactive design of a person ReID system to visualize these quantities. We demonstrate that a system offering these visual representations can effectively expedite and improve a person re-identification analysis and make it a much user-friendly experience.
VONAS: Network Design in Visual Odometry using Neural Architecture Search

The end-to-end VO (visual odometry) is a complicated task with the property of highly temporal dependency, but the design of its deep networks lacks thorough investigation. Meanwhile, NAS (Neural architecture search) has been widely searched and applied in many computer vision fields due to its advantage in automatic network design. However, most of the existing NAS frameworks only consider single image tasks such as image classification, lacking the consideration of the video (multi-frames) tasks such as VO. Therefore, this paper explores the network design for the VO task and proposes a more general single path based one-shot NAS, named VONAS, which can model sequential information for video-related tasks. Extensive experiments prove that the network architecture is significant for the (un)supervised VO. The models obtained by VONAS are lightweight and achieve SOTA performance with good generalization.
Towards Purely Unsupervised Disentanglement of Appearance and Shape for Person Images Generation

There have been a fairly of research interests in exploring the disentanglement of appearance and shape from human images. Most existing endeavours pursuit this goal by either using training images with annotations or regulating the training process with external clues such as human skeleton, body segmentation or cloth patches etc. In this paper, we aim to address this challenge in a more unsupervised manner---we do not require any annotation nor any external task-specific clues. To this end, we formulate an encoder-decoder-like network to extract both the shape and appearance features from input images at the same time, and train the parameters by three losses: feature adversarial loss, color consistency loss and reconstruction loss. The feature adversarial loss mainly impose little to none mutual information between the extracted shape and appearance features, while the color consistency loss is to encourage the invariance of person appearance conditioned on different shapes. More importantly, our unsupervised framework utilizes learned shape features as masks which are applied to the input itself in order to obtain clean appearance features. Without using fixed input human skeleton, our network better preserves the conditional human posture while requiring less supervision. Experimental results on DeepFashion and Market1501 demonstrate that the proposed method achieves clean disentanglement and is able to synthesis novel images of comparable quality with state-of-the-art weakly-supervised or even supervised methods.
Learning Visual Features from Product Title for Image Retrieval

There is a huge market demand for searching for products by images in e-commerce sites. Visual features play the most important role in solving this content-based image retrieval task. Most existing methods leverage pre-trained models on other large-scale datasets with well-annotated labels, e.g. the ImageNet dataset, to extract visual features. However, due to the large difference between the product images and the images in ImageNet, the feature extractor trained on ImageNet is not efficient in extracting the visual features of product images. And retraining the feature extractor on the product images is faced with the dilemma of lacking the annotated labels. In this paper, we utilize the easily accessible text information, that is, the product title, as a supervised signal to learn the features of the product image. Specifically, we use the n-grams extracted from the product title as the label of the product image to construct a dataset for image classification. This dataset is then used to fine-tuned a pre-trained model. Finally, the basic max-pooling activation of convolutions (MAC) feature is extracted from the fine-tuned model. As a result, we achieve the fourth position in the Grand Challenge of AI Meets Beauty in 2020 ACM Multimedia by using only a single ResNet-50 model without any human annotations and pre-processing or post-processing tricks. Our code is available at: \urlhttps://github.com/FangxiangFeng/AI-Meets-Beauty-2020.
BMXNet 2: An Open Source Framework for Low-bit Networks - Reproducing, Understanding, Designing and Showcasing

Binary and quantized neural networks are a promising technique to run convolutional neural networks on mobile or embedded devices. BMXNet 2 is an open-source framework that provides a broad basis for academia and industry. It provides a modern implementation of binary and quantized layers with a wide array of implemented state-of-the-art models. Our implementation fosters reproducibility of other works and our own work through publishing model code, hyperparameters, detailed model graphs, and training logs. Furthermore, we implement several applications for BNNs, including demo applications, which can run on a smartphone or a Raspberry Pi. The code can be found online: https://github.com/hpi-xnor/BMXNet-v2