Reviewerly/exHarmony huggingface dataset:
Variational shape prior segmentation with an initial curve based on image registration technique

In general images, it is practically hard to distinguish only the desired object using the conventional image segmentation methods. In many cases, we can segment the desired object by using the shape information of the object in addition to the standard image segmentation. Chan and Zhu's model is not robust to the intensity changes of objects. In this paper, we propose a novel model for the shape prior segmentation that produces robust results using the hierarchical image segmentation and an attraction term. Moreover, we adopt an image registration technique and a multi-region image segmentation to get an initial for a given shape prior. Finally, we consider the free-form deformation in obtaining the shape function from the reference shape prior for real-world images. Numerical experiments demonstrate the results independent of intensities of objects and the location of the reference shape prior. All numerical calculations are automatic and progress without any user input.
F2P-ABS: A Fast and Secure Attribute-Based Signature for Mobile Platforms

Attribute-based signature (ABS) is a promising cryptographic primitive. It allows the signer to generate a signature with attributes satisfying the predicate without leaking more information, so as to provide message authenticity in an anonymous manner. However, drawbacks concerning security and efficiency hinder its applications for authentication in mobile platforms. Here, we present F2P-ABS, an escrow-free and pairing-free attribute-based signature supporting perfect signer privacy for mobile anonymous authentication. To enhance its adaptiveness to mobile platforms, a novel key extraction is proposed so that the key escrow problem is mitigated over the single authority setting. It also helps to remarkably reduce the size of the signing key. Different from existing schemes, F2P-ABS is free from pairing operations. It performs no pairing operation for verification. Without the loss of security, we prove that F2P-ABS achieves signer privacy in perfect sense. It is also proven to guarantee existential unforgeability under corrupted and adaptive chosen predicate and message attack, which is securer than existing schemes.
User Multi-Modal Emotional Intelligence Analysis Method Based on Deep Learning in Social Network Big Data Environment

In order to accurately analyze the emotional tendency of social media users' evaluation and better promote the work of emotional analysis and recommendation algorithm. This paper presents a new text emotion classification model, which integrates content features and user features, by representing the sentence, content features and user features of microblog as vector matrix and inputting them into the text emotion classification model which integrates content features and user features. Firstly, it analyzes the content information and user information related to the sentence emotion of the target microblog, and constructs the content characteristics and user characteristics respectively; Then, a text emotion classification model is constructed, which integrates content features and user features; Then, the method of feature level fusion and decision-making level fusion is designed for microblog emotion analysis of image and text fusion. Maxout neuron is also introduced to solve the problem of gradient dispersion in the training process and optimize the training process. The experimental results show that: Compared with other models, the accuracy of the proposed model is improved by more than 2.5%, and it is better than other models in recall rate and F value.
Radar Emitter Signal Detection with Convolutional Neural Network

In this paper, we propose a deep convolutional neural network (CNN) based automatic detection algorithm for recognizing radar emitter signals. The algorithm leverages on the structure estimation power of deep CNN and the capability of time-frequency image processing for radio signal representation. We transform raw radio signals into time-frequency image using the Choi-Williams distribution function. We compare the proposed method with Belief Propagation (BP) and Support Vector Machine (SVM) based methods in terms of recognition accuracy versus signal-to-noise-ratio. The experiments demonstrate that the proposed CNN network with time-frequency image processing achieves very competitive results on the testing datasets.
New public auditing protocol based on homomorphic tags for secure cloud storage

Summary Outsourcing datum to the cloud servers is more and more popular for most data owners and enterprises. However, how to ensure the outsourced datum to be kept secure is very important. Especially, how to check the outsourced datum's integrity is a very challenge problem. Until now, there are many cryptographic protocols proposed to solve this problem, such as (dynamic) provable data position protocol, (dynamic) proof of retrievability protocol, etc. Recently, Tian et al proposed a dynamic‐hash‐table‐based public auditing scheme for secure cloud storage, which aims at simultaneously supporting secure dynamic data updating and secure public auditing for cloud storage. However, we find a security flaw in this protocol; concretely, the signature algorithm for the data blocks in their protocol is not secure; the cloud servers can easily modify the outsourced data blocks without detecting. Finally, we give a new protocol by using homomorphic tags based on their protocol and roughly analysis its security.
Performance Analysis of Optimization Algorithms on Stacked Autoencoder

Stacked autoencoder (SAE) model, which is one of the deep learning methods, has been widely used in one dimensional data sets in recent years. In this study, a comparative performance analysis was performed using the five most commonly used optimization techniques and two well-known activation functions in SAE architecture. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RmsProp), Adaptive Moment Estimation (Adam), Adaptive Delta (Adadelta) and Nesterov-accelerated Adaptive Moment Estimation (Nadam) and Softmax and Sigmoid were used as optimization techniques. In this study, two different data sets in public UCI database were used. In order to verify the performance of the SAE model, experimental studies were performed by using the obtained data sets together with optimization and activation techniques separately. As a result of the experimental studies, the success rate of 88.89%, 85.19% in Cryotherapy and Immunotherapy data set was achieved by using Softmax activation function with SGD optimization method on three-layer SAE. After a successful training phase, adaptive optimization techniques Adam, Adadelta, Nadam and RmsProp methods were observed to have a weaker learning process than the stochastic method SGD.
Data-Driven Colormap Optimization for 2D Scalar Field Visualization

Colormapping is an effective and popular visual representation to analyze data patterns for 2D scalar fields. Scientists usually adopt a default colormap and adjust it to fit data in a trial-and-error process. Even though a few colormap design rules and measures are proposed, there is no automatic algorithm to directly optimize a default colormap for better revealing spatial patterns hidden in unevenly distributed data, especially the boundary characteristics. To fill this gap, we conduct a pilot study with six domain experts and summarize three requirements for automated colormap adjustment. We formulate the colormap adjustment as a nonlinear constrained optimization problem, and develop an efficient GPU-based implementation accompanying with a few interactions. We demonstrate the usefulness of our method with two case studies.
Flow-guided feature enhancement network for video-based person re-identification

Video-based person re-identification associates sequences of the same person among surveillance camera network. Most existing works explore motion and inter-frame information on the features corrupted by spatial noises such as occlusion, blur, posture changes, etc, leading to degraded representation and matching performance. Enhancing features of each frame guarantees a more robust and discriminative final feature representation. In this paper, we propose a novel flow-guided feature enhancement network that leverages flow information to enhance low-level features. Specifically, it improves per-frame features by aggregating with the warped feature under the guidance of optical flow and the enhanced feature of previous frame in spatial attention mechanism. Then, a part-based loss is directly employed on the enhanced features to supervise the aggregation process, which can exert full capability of the network. Experiments on three widely used benchmark datasets: iLIDS-VID, PRID-2011 and MARS, demonstrate that the proposed model achieves superior performance and outperforms most of the recent state-of-the-art methods.
Breaking Permutation-Based Mesh Steganography and Security Improvement

Permutation-based steganography in polygonal meshes can provide considerably large embedding capacities for hiding secret messages. However, corresponding steganalysis techniques against such methods have never been studied. This paper identifies the essential differences between naturally generated meshes and meshes produced by permutation-based steganography methods. It is found that the two types of meshes differ significantly in the distribution of topological distances between consecutive mesh elements. Therefore, by measuring the orderliness of the vertex list and the face list of meshes, we develop solutions for several mesh steganalysis problems. These solutions are effective, leading to high detection accuracy; and they are also universal, requiring no knowledge such as which steganography method is used and what data embedding rate is adopted for the detection mechanism to work. Moreover, this paper also presents a security-improved permutation-based mesh steganography approach, by taking advantage of the connectivity information of polygonal meshes and establishing a good trade-off between embedding capacity and undetectability. Without bringing global changes, our approach embeds secret messages into local neighborhoods on meshes. As a result, meshes generated by the proposed steganography approach tend to have natural structures that are unlikely to draw suspicions to steganalyzers.
Simplified calibration method for multibeam footprint displacements due to non-concentric arrays

When the offset between the transmitter and the receiver in the multibeam is large, neglecting the offset will introduce depth errors into the multibeam footprints. Although the current non-concentric algorithm (NCCA) considers this offset, its model is complicated and time-consuming. To solve the issues, a simplified method is developed. First, the offset is considered by constructing two separated intersecting triangles projected by the transmitted and received cones under the array reference frame (ARF); second, two groups of the footprint coordinates under the ARF are calculated with slant ranges from the transmitter and receiver to the footprint, respectively; then, the transmitted and received vectors, and their corresponding one-way travel times (OWTTs) are calculated by the two coordinates; finally, an iteration is used to adjust the two slant ranges according to the measured and the calculated time, and the optimal location of the footprint will be obtained. To verify the proposed method, comparisons between existing methods and ours are made. The results indicate that when the depth is about 457 m, the average depth deviation between the NCCA and ours is only 0.03 m. And our new method saves about 17% of computation time compared to that of the NCCA.
Ridge extraction based on adaptive variable-bandwidth cost functions by edge detection of time frequency images

Ridge extraction of a time frequency image (TFI) can serve to estimate an instantaneous frequency of a signal. However, there is a considerable difficulty in ridge extraction of a multi-ridge TFI due to mutual interference between ridges. Some traditional methods for ridge extraction fail to resolve this difficulty. The one-step cost function (OSCF), a typical method for processing a multi-ridge TFI, lacks self-adaptation in determining searching regions of a targeted ridge. To overcome this question, this paper proposes an adaptive variable-bandwidth cost function (AVBCF) for ridge extraction. Firstly, a TFI is acquired by time frequency analysis of a signal. Afterwards, the TFI is filtered into a binary image by a Canny detector. Also, some dispersive ridges in the binary image serve to form a synthetic dispersive ridge. Next, searching regions of a targeted ridge are estimated by projecting statistics of edges of the synthetic dispersive ridge to the targeted ridge. Next, the AVBCF is compared with the OSCF and some widespread methods numerally and experimentally. The results demonstrate that the AVBCF performs better than the other methods.
Face-based Gender recognition Analysis for Nigerians Using CNN

Abstract Estimating human gender from faces in images is an important area of research as many applications rely on it. Facial recognition is a branch of biometrics that uses the face which is a physical trait to uniquely identify individuals. Gender recognition using face analysis is also an important task in computer vision as it helps in visual surveillance, intelligent user interfaces, demographic studies etc. The fundamental of gender recognition and other similar classification problem is broken into four stages i.e. the image to be examined to the pre – processing of the image, feature extraction and lastly classification. Several approaches including the deep learning approach which is a representation of the learning procedure that discover multiple levels of representations using neural network has been explored for gender recognition. This work is essential in creating a face-based recognition for gender analysis for Nigerians. The face database consists of over 6000 images of Nigerians with different variations. The created database was used to analyze gender by pre-processing the images, extracting necessary features and classification using the Convolutional Neural Network (CNN). An overall recognition accuracy of 98.72% was achieved demonstrating the feasibility and research potential in such direction.
A categorization of RDF triplestores

The management of data produced by multiple services of our daily life necessitates to be done intelligently to come up with smart services and intelligent answers to the challenges our world is facing. RDF has proven to provide a good solution for the data modeling in various application fields. Various RDF data management systems with various characteristics have been proposed to handle RDF. For each specific enterprise application domain, it is therefore a necessity to choose the adequate system that helps to deal with associated data in an efficient way. To help enterprise applications developers in such task, we give a categorization of the existing RDF management systems, also called triplestores, according to their main characteristics. The categorization is motivated by the reasons behind the adoption of one or the other system for handling RDF data. Existing categorization works only treat a limited number of categories related to a relational or a native database classification and consider only a small number of triple stores. However, our categorization takes into account various factors related among others to RDF objectives, data organization, query processing, dynamicity and scalability and in accordance with the diverse developments in data modeling and manipulation technologies. This allows users to choose the most appropriate RDF system depending on their needs.
Collaboration on large interactive displays: a systematic review

Large Interactive Displays (LIDs), such as tabletops or interactive walls, are promising innovations, which are increasingly used to support co-located collaboration. Yet the current evidence base on the impact of LID use on collaborative processes and outcomes, and associated influencing factors, is fragmented, particularly in comparison with other media. To address this gap, a systematic review was carried out in the databases Web of Science, Psych.Info, ACM, Elsevier, JSTOR and Springer and in the ACM CHI conference database. A corpus of 38 articles with experimental study designs met the eligibility criteria and was analyzed in-depth. With regard to collaboration processes, the findings suggest a relatively clear advantage of the use of LIDs over classic forms of collaboration, in particular over single-user environments (e.g. laptops). With attention to collaborative outcomes, positive effects of LIDs were identified for knowledge gains and social encounters, and mixed effects for task-related outcomes. The analysis further shows relevant influencing factors of LID, such as the separation of personal and joint work spaces and the deployment of horizontal instead of vertical displays. Conceptual and practice implications are discussed.
A Novel Scale Insensitive KCF Tracker Based on HOG and Color Features

Kernelized Correlation Filters (KCF) for visual tracking have received much attention due to their fast speed and outstanding performances in real scenarios. However, the KCF sometimes still fails to track the targets with different scales, and it may drift because the target response is fixed and the original histogram of orientation gradient (HOG) features cannot represent the targets well. In this paper, we propose a novel fast tracker, which is based on KCF and insensitive to scale changes by learning two independent correlation filters (CFs) where one filter is designed for position estimation and the other is for scale estimation. In addition, it can adaptively change the target response and multiple features are integrated to improve the performance for our tracker. Finally, we employ an adaptive high confidence filters updating scheme to avoid errors. Evaluated on the popular OTB50 and OTB100 datasets, our proposed trackers show superior performances in terms of efficiency and accuracy compared to the existing methods.
Using context analysys for providing real time recommendations in e-tourism mobile location-based recommender systems

This paper describes main modern tendencies for the design and development of e-tourism recommender systems with real time support. This paper focuses on development of e-tourism mobile location-based recommender systems with context analysys function. Main goal is to enable better real time recommendations for trip support in time-location amplitude.
Cost-Effective Testing of a Deep Learning Model through Input Reduction

With the increasing adoption of Deep Learning (DL) models in various applications, testing DL models is vitally important. However, testing DL models is costly and expensive, e.g., manual labelling is widely-recognized to be costly. To reduce testing cost, we propose to select only a subset of testing data, which is small but representative enough for a quick estimation of the performance of DL models. Our approach, DeepReduce, adopts a two-phase strategy. At first, our approach selects testing data for the purpose of satisfying testing adequacy. Then, it selects more testing data to approximate the distribution between the whole testing data and the selected data by leveraging relative entropy minimization. We evaluate DeepReduce on four widely-used datasets (with 15 models in total). We find that DeepReduce reduces the whole testing data to 7.5% on average and can reliably estimate the performance of DL models.
A Writing Activities Monitoring System for Preschoolers Using a Layered Computing Infrastructure

Growing advancements in sensor based smart devices allow monitoring the activities of preschoolers in classroom. The existing systems for monitoring the writing activity of preschoolers use the local processing and Cloud based solutions which require high processing time and seamless connectivity, respectively. Such systems are not feasible for low cost and low power smart devices. This work presents a writing activity monitoring system that monitors the gripping, holding, and orientation of the pencil and transfers the results to their parents and teachers. The system uses a layered computing infrastructure - Edge, Fog and Cloud layers, that allows energy consumption reduction and provides the solution closer to the user. Specially, the system selects the appropriate layers for processing and recognition of writing activities which provide the desired level of accuracy and prolongs the connectivity of the system. A prototype of the system is developed and examined on real user data to validate the accuracy and energy consumption of the system.
Image Local Features Description Through Polynomial Approximation

This work introduces a novel local patch descriptor that remains invariant under varying conditions of orientation, viewpoint, scale, and illumination. The proposed descriptor incorporate polynomials of various degrees to approximate the local patch within the image. Before feature detection and approximation, the image micro-texture is eliminated through a guided image filter with the potential to preserve the edges of the objects. The rotation invariance is achieved by aligning the local patch around the Harris corner through the dominant orientation shift algorithm. Weighted threshold histogram equalization (WTHE) is employed to make the descriptor in-sensitive to illumination changes. The correlation coefficient is used instead of Euclidean distance to improve the matching accuracy. The proposed descriptor has been extensively evaluated on the Oxford’s affine covariant regions dataset, and absolute and transition tilt dataset. The experimental results show that our proposed descriptor can categorize the feature with more distinctiveness in comparison to state-of-the-art descriptors.
Search task success evaluation by exploiting multi-view active semi-supervised learning

Search task success rate is an important indicator to measure the performance of search engines. In contrast to most of the previous approaches that rely on labeled search tasks provided by users or third-party editors, this paper attempts to improve the performance of search task success evaluation by exploiting unlabeled search tasks that are existing in search logs as well as a small amount of labeled ones. Concretely, the Multi-view Active Semi-Supervised Search task Success Evaluation (MA4SE) approach is proposed, which exploits labeled data and unlabeled data by integrating the advantages of both semi-supervised learning and active learning with the multi-view mechanism. In the semi-supervised learning part of MA4SE, we employ a multi-view semi-supervised learning approach that utilizes different parameter configurations to achieve the disagreement between base classifiers. The base classifiers are trained separately from the pre-defined action and time views. In the active learning part of MA4SE, each classifier received from semi-supervised learning is applied to unlabeled search tasks, and the search tasks that need to be manually annotated are selected based on both the degree of disagreement between base classifiers and a regional density measurement. We evaluate the proposed approach on open datasets with two different definitions of search tasks success. The experimental results show that MA4SE outperforms the state-of-the-art semi-supervised search task success evaluation approach.