Reviewerly/exHarmony huggingface dataset:
Research of Contextual Semantic Reasoning Model Based on Domain Ontology

With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are divided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.
Multi-Scale Ship Detection in SAR Images Based on Multiple Attention Cascade Convolutional Neural Networks

with the development of synthetic aperture radar (SAR) technology, accurate detection of target in SAR images has become a challenging task, such as multi-scale ship detection. Detection of different scale ship target in SAR images is widely used in military and civilian field, but for small ships with few pixels and low contrast, the traditional detection algorithms are difficult to accurately detection. In order to solve the problem of multi-scale ship detection, the multiple attention cascade convolutional neural networks (MAC-CNNs) is proposed. This algorithm based on the YOLOv3 network and attention mechanism, introduces channel attention and spatial attention during the feature extraction stage, and then uses the filtered weighted feature vector to replace the original feature vector for residual fusion. Experiments on the SAR ship detection datasets which including multi-scale ships in various SAR images, and the results shown that the proposed algorithm can detect multi-scale ships in SAR images with extremely high accuracy.
Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network

Splicing is the most common operation in image forgery, where the tampered background regions are imported from different images. Illumination maps are inherent attribute of images and provide significant clues when searching for splicing locations. This paper proposes an end-to-end dual-stream network for splicing location, where the illumination stream, which includes Grey-Edge (GE) and Inverse-Intensity Chromaticity (IIC), extract the inconsistent features, and the image stream extracts the global unnatural tampered features. The dual-stream feature in our network is fused through Multiple Feature Pyramid Network (MFPN), which contains richer context information. Finally, a Cluster Region Proposal Network (C-RPN) with spatial attention and an adaptive cluster anchor are proposed to generate potential tampered regions with greater retention of location information. Extensive experiments, which were evaluated on the NIST16 and CASIA standard datasets, show that our proposed algorithm is superior to some state-of-the-art algorithms, because it achieves accurate tampered locations at the pixel level, and has great robustness in post-processing operations, such as noise, blur and JPEG recompression.
Preference access of users' cancer risk perception using disease-specific online medical inquiry texts

Substantial real cases can be formed in current online medical platforms, constituting potentially rich commercial medical value. In order to obtain the value, it is necessary to mine the preference for user perceived cancer risk in online medical platforms. However, user preference in the platforms varies with medical inquiry text environments, and a user's disease-specific online medical inquiry text environment would also affect his/her behavioral decisions in real time. In this sense, considering the inner relations between different contexts and user preferences under different diseases-specific inquiry text environments and integrating early cancer texts will facilitate the exploration on the law of preference for user perceived cancer risk. Therefore, in this paper, the matrix decomposition and Labeled-LDA models are expanded to propose a context-based method to access the preference for user perceived cancer risk. Firstly, modeling on the relationship between user preferences and information in multi-dimensional context is carried out, and the universal method of integrating multi-dimensional contextual information with user preferences is analyzed. Moreover, more accurate user references were obtained under the multi-dimensional text space and multi-dimensional disease space. Secondly, the similarity relationships between all disease-specific online medical inquiries and early cancer texts are used to obtain user perceived cancer risk, thus knowing the online medical inquiry texts of user cognized diseases and perceiving the cancer risk. Lastly, by accessing the user preferences under different disease topics and user perceived cancer risk in multi-dimensional contexts, the preference for user perceived cancer risk is obtained in a more accurate way. Based on the large-volume real-world dataset, the relationship between each context and user preferences is assessed, and it is concluded that the method proposed in this paper is superior to MF-LDA method in obtaining the preference for user perceived cancer risk. This indicates that the proposed method not only expresses user perceived risk, but also clearly expresses the characteristics of user's preference. Furthermore, it is verified that the integration of context with early cancer text and the establishment of user preference model are feasible and effective.
On-Device System for Device Directed Speech Detection for Improving Human Computer Interaction

Voice assistants (VA) are finding a place in many households, with increasing numbers. Nevertheless, for every interaction user invokes VA using a key or wake-up word, which is too common and hinders natural conversation. To solve this, we propose an On-Device solution that listens to the user continuously for only predefined period and classifies the utterance into device-directed or non-device-directed using a deep learning-based model. Since our solution is On-Device, the privacy of the user is maintained. We tried to solve false acceptance of utterance as a command and incorrect rejection of the command as background noise to improve user experience. We calculated the False Acceptance Rate (FAR) and False Rejection Rate (FRR) for different thresholds to evaluate our model. Then we calculated Equal Error Rate (EER) results with the FRR and FAR data. We achieved 3.6% of EER on our best optimized Stage-1 model and EER of 5.1% on Stage-2 model. We have also used accuracy as evaluation metrics during training and testing. We achieved an accuracy of 96.8% on testing data with our Stage-1 model and 95.3% on Stage-2 model.
An Improved Gaussian Particle Filtering Algorithm for Fixed Single Station Passive Location and Tracking

In this paper, we propose an improved Gaussian particle filtering algorithm to improve the convergence performance and the location accuracy of single-station passive location tracking system. Moreover, in the proposed algorithm, the result of the unscented Kalman filtering is used as importance density function to avoid the resampling process in the particle filtering. Finally, Simulation experiments show that the proposed algorithm can be applied to a fixed single-station passive location system, can effectively improve the filtering accuracy, and the tracking performance is better than the Gaussian particle filter.
Video Identification Based on Watermarking Schemes and Visual Cryptography

Related to the growth of data sharing on the Internet and the wide - spread use of digital media, multimedia security and copyright protection have become of broad interest. Visual cryptography () is a method of sharing a secret image between a group of participants, where certain groups of participants are defined as qualified and may combine their share of the image to obtain the original, and certain other groups are defined as prohibited, and even if they combine knowledge of their parts, they can’t obtain any information on the secret image. The visual cryptography is one of the techniques which used to transmit the secrete image under the cover picture. Human vision systems are connected to visual cryptography. The black and white image was originally used as a hidden image. In order to achieve the owner’s copy right security based on visual cryptography, a watermarking algorithm is presented. We suggest an approach in this paper to hide multiple images in video by meaningful shares using one binary share. With a common share, which we refer to as a smart key, we can decrypt several images simultaneously. Depending on a given share, the smart key decrypts several hidden images. The smart key is printed on transparency and the shares are involved in video and decryption is performed by physically superimposing the transparency on the video. Using binary, grayscale, and color images, we test the proposed method.
SwingNet

Sports analytics in the wild (i.e., ubiquitously) is a thriving industry. Swing tracking is a key feature in sports analytics. Therefore, a centimeter-level tracking resolution solution is required. Recent research has explored deep neural networks for sensor fusion to produce consistent swing-tracking performance. This is achieved by combining the advantages of two sensor modalities (IMUs and depth sensors) for golf swing tracking. Here, the IMUs are not affected by occlusion and can support high sampling rates. Meanwhile, depth sensors produce significantly more accurate motion measurements than those produced by IMUs. Nevertheless, this method can be further improved in terms of accuracy and lacking information for different domains (e.g., subjects, sports, and devices). Unfortunately, designing a deep neural network with good performance is time consuming and labor intensive, which is challenging when a network model is deployed to be used in new settings. To this end, we propose a network based on Neural Architecture Search (NAS), called SwingNet, which is a regression-based automatic generated deep neural network via stochastic neural network search. The proposed network aims to learn the swing tracking feature for better prediction automatically. Furthermore, SwingNet features a domain discriminator by using unsupervised learning and adversarial learning to ensure that it can be adaptive to unobserved domains. We implemented SwingNet prototypes with a smart wristband (IMU) and smartphone (depth sensor), which are ubiquitously available. They enable accurate sports analytics (e.g., coaching, tracking, analysis and assessment) in the wild. Our comprehensive experiment shows that SwingNet achieves less than 10 cm errors of swing tracking with a subject-independent model covering multiple sports (e.g., golf and tennis) and depth sensor hardware, which outperforms state-of-the-art approaches.
IoT Honeypot Scanning and Detection System Based on Authorization Mechanism

In this paper, an Internet of Things (IoT) honeypot scanning and detection system is proposed based on an authorization mechanism. For the functional characteristics of different devices existing in the IoT environment, an authorization and authentication system was designed based on device MAC and randomly generated key for requesting permissions from devices with asset management and traffic monitoring. Subsequently, an authorized access network model was constructed between devices and the authorization system, which inveigles the scanning requests from unauthorized devices into the IoT honeypot based on the authorized authentication algorithm. Specifically, an IoT honeypot system was built and a data collection module, a data preprocessing module, and a scan detection module were installed in it to perform detection and output feedback on the traffic redirected to the honeypot. The experimental results show that our designed system can efficiently identify whether the device is authorized or not in the IoT system and successfully detect the illegal scanning requests from non-authorized devices.
Leveraging Association Rules in Feature Selection to Classify Text

Appropriate featureAghbari, Zaher Al selection is an importantSaeed, Mozamel M. aspect in the fields of data mining and machine learning. Feature selection reduces data dimensionality and produces simpler classification models that have lower variance. In this paper, we propose a robust feature selection method that produces smaller and yet more effective set of features. The proposed feature selection method leverages association rules to select the effective features for text classification. Our experiment shows that the proposed method outperforms its pears in terms of execution time and classification accuracy.
BabelCalib: A Universal Approach to Calibrating Central Cameras

Existing calibration methods occasionally fail for large field-of-view cameras due to the non-linearity of the underlying problem and the lack of good initial values for all parameters of the used camera model. This might occur because a simpler projection model is assumed in an initial step, or a poor initial guess for the internal parameters is pre-defined. A lot of the difficulties of general camera calibration lie in the use of a forward projection model. We side-step these challenges by first proposing a solver to calibrate the parameters in terms of a back-projection model and then regress the parameters for a target forward model. These steps are incorporated in a robust estimation framework to cope with outlying detections. Extensive experiments demonstrate that our approach is very reliable and returns the most accurate calibration parameters as measured on the downstream task of absolute pose estimation on test sets. The code is released at https://github.com/ylochman/babelcalib.
Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming

Abstract We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which emerge in numerous applications including finance, manufacturing, power systems and, recently, deep neural networks. We propose an active-set stochastic sequential quadratic programming (StoSQP) algorithm that utilizes a differentiable exact augmented Lagrangian as the merit function. The algorithm adaptively selects the penalty parameters of the augmented Lagrangian, and performs a stochastic line search to decide the stepsize. The global convergence is established: for any initialization, the KKT residuals converge to zero almost surely . Our algorithm and analysis further develop the prior work of Na et al. (Math Program, 2022. https://doi.org/10.1007/s10107-022-01846-z ). Specifically, we allow nonlinear inequality constraints without requiring the strict complementary condition; refine some of designs in Na et al. (2022) such as the feasibility error condition and the monotonically increasing sample size; strengthen the global convergence guarantee; and improve the sample complexity on the objective Hessian. We demonstrate the performance of the designed algorithm on a subset of nonlinear problems collected in CUTEst test set and on constrained logistic regression problems.
Dynamic Scene Deblurring Using Enhanced Feature Fusion and Multi - Distillation Mechanism

Despite the surges of deep learning-based method in dynamic scene deblurring achieves good performance, the challenges still remain a lot: (a) the running speed is far from the requirement of processing; (b) there will be inevitable information loss along with the deepening of network layers, which will further lead to the deterioration of the quality of the restored pictures. To deal with these challenges, we propose a novel learning-based model. In our method, we integrate two mechanisms for the generator based on the Generative Adversarial Nets (GAN). First, we develop the Enhanced Feature Fusion (EFF) mechanism which aims at providing multi-layer feature information to assist the image restoration. We further design Feature Multi-distillation (FMD) mechanism to filter and well fuse the multi-scale feature maps. By integrating the two mechanisms, the high-level feature maps can be progressively refined and the detailed semantic information can be properly utilized. In addition, we use the double-scale discriminator architecture which could enables the network to observe the image from the perspective of local and global respectively and obtain overall information for the whole image restoration. Extensive experimental results on the GOPRO and Kohler datasets show that our method can approach comparably to the state-of-the-arts in terms of accuracy while consuming much less inference time, which demonstrates that our method acquiring a better trade off between image restoration quality and running speed.
A Medical Liquid Varifocal Endoscope for Abdominal Cavity and Its Parallax Estimation Algorithm Compatible With WBANs

Due to the rapid development of science and technology over recent years, such as Virtual Reality (VR) and low power wireless transmission, electronic image endoscope has gained an important role in the early disease diagnosis as one of the most significant medical sensors. In this paper, we introduced a varifocal medical endoscope with a wide field of view for VR-assisted diagnosis. Inspired by the artificial compound eye, a multi-channel probe was designed to establish wide FOV imaging. A voltage was applied for the realization of the varifocal lens so as to reduce the gap between membrane and substrate, and a filled conductive was squeezed so as to change the curvature of the membrane. Besides, we proposed an improved SIFT algorithm by introducing a secondary projection, while the algorithm&#x2019;s feasibility was quantitatively verified. Moreover, an SSIM index of 0.945, K-Blur index of 0.8927, and PSNR value of 29.271 were obtained over a test set of images, which were used to verify the algorithm&#x2019;s effectiveness. At the same time, we proposed the parallax estimation algorithm of binocular endoscopic images based on HS-ResNet, thus achieving the three-dimensional display of endoscopic image parallax. Mean SSIM index of 0.945 and Mean PSNR value of 29.271 were obtained over a test set of images, which proved that the algorithm had good robustness. Zoom Medical Endoscope 3D reconstruction algorithm of endoscopic images can obtain real-time pathological information and transmit it to the medical terminal via Wireless Body Area Network (WBAN).
Participatory Evaluation of Water Management Options for Climate Change Adaptation in River Basins

Climate and other human-induced changes will increase water scarcity in world areas such as in the Mediterranean. Adaptation principles need to be urgently incorporated into water management and stakeholder engagement needs to be strengthened at all steps of the management cycle. This study aimed to analyse and compare stakeholder-preferred water management options (WMOs) to face climate change related challenges and to foster adaptation in four Mediterranean river basins. The challenges and WMOs of the four river basins identified by stakeholders were analysed examining to what extent the WMOs tackled the identified challenges. The impact of the WMOs resulting from a participatory modelling method was included in a comparative analysis of the stakeholders’ WMOs preferences. The results indicate the participatory approach that was applied allowed local priorities and real-world challenges to be defined with adequate detail as well as the definition of tailored responses. The participatory impact analysis provided an integrated view of the river basin as an interrelated system. The participatory evaluation of the WMOs was able to consider a wide range of elements and was able reflect the combined preferences of the stakeholders. Moreover, it allowed groups of basin actors with highly diverse profiles and concerns to further promote sets of these WMOs as input into decision making processes.
Learning-Based Template Synthesis for Groupwise Image Registration

Groupwise image registration (GIR), as a fundamental task during medical image processing, aims to transform a group of images into a common space simultaneously. Most GIR methods consist of creating one template image and registering all group subjects to the template space. This paper presents a novel learning-based template synthesis method that can produce sharper and unbiased template images in shorter runtime. The method is based on the generative adversarial network (GAN) scheme, which is expected to generate authentic images. Besides GAN, we use a registration network to warp images and an auxiliary segmentor to take advantage of semantic information and improve the quality of generated images. First, the label mask will be combined with medical images as the input of the discriminator. Second, we calculate the entropy of pixel-wise label probability distribution as a new loss term. We compare our method with three baseline methods on 2D and 3D brain MR images in the experiments. We evaluate the performance using metrics corresponding to accuracy, smoothness, and unbiasedness. Results illustrate that our method outperforms the baseline methods and can achieve the overall state-of-the-art performance.
Sequential Self-Attentive Model for Knowledge Tracing

With the ongoing development of online education platforms, knowledge tracing (KT) has become a critical task that can help online education platforms provide personalized education. KT aims to find out students’ knowledge states and predict whether students can correctly answer the question according to their exercise history. However, existing works fail to incorporate question information and ignore some useful contextual information. In this paper, we propose a novel Sequential Self-Attentive model for Knowledge Tracing (SSAKT). SSAKT utilizes question information based on Multidimensional Item Response Theory (MIRT) which can capture the relations between questions and skills. Then SSAKT uses a self-attention layer to capture the relations between questions. Unlike traditional self-attention networks, the self-attention layer in SSAKT uses Long Short-Term Memory networks (LSTM) to perform positional encoding. Moreover, a context module is designed to capture the contextual information. Experiments on four real-world datasets show that SSAKT outperforms existing KT models. We also conduct a case study that shows our model can effectively capture the relations between questions and skills.