Reviewerly/exHarmony huggingface dataset:
Novel models to estimate hourly diffuse radiation fraction for global radiation based on weather type classification

The diffuse radiation is well recognized as a key variable in solar energy assessment, albeit with sorely lacking ground-based measurements. Here, we proposed two novel models to estimate hourly diffuse radiation using the typical meteorological year's radiation data in Beijing as training samples. Model 1 was a combination of four classical models, including Liu&Jordan, Orgill&Hollands, Erbs and Reindl, in which the weight or coefficient was determined by weather types that were derived from clearness index. In Model 2, the weather type classification was refined by total cloud cover, and the principal component analysis (PCA) was further applied to determine the major meteorological variables for each weather type as model's input, along with linear fitting. Using sub-typical year's radiation data as testing samples, the proposed models showed strong extrapolation ability with three statistical metrics: lower mean absolute percentage error and normalized root mean square error but relatively higher correlation coefficient, compared with other models. Finally, these models were verified by the observations in Wuhan. The results indicated that weather type classification and PCA effectively improved model's performance by eliminating the collinearity between meteorological and environmental variables. Furthermore, both models performed better than any single classical model, irrespective of large-scale weather patterns.
Towards Usable Cloud Storage Auditing

Cloud storage security has gained considerable research efforts with the wide adoption of cloud computing. As a security mechanism, researchers have been investigating cloud storage auditing schemes that enable a user to verify whether the cloud keeps the user's outsourced data undamaged. However, existing schemes have usability issues in compatibility with existing real world cloud storage applications, error-tolerance, and efficiency. To mitigate this usability gap, this article proposes a new general cloud storage auditing scheme that is more usable. The proposed scheme uses the idea of integrating linear error correcting codes and linear homomorphic authentication schemes together. This integration uses only one additional block to achieve error tolerance and authentication simultaneously. To demonstrate the power of the general construction, we also propose one detailed scheme based on the proposed general construction using the Reed Solomon code and the universal hash based MAC authentication scheme, both of which are implemented over the computation-efficient Galois field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ). We also show that the proposed scheme is secure under the standard definition. Moreover, we implemented and open-sourced the proposed scheme. Experimental results show that the proposed scheme is orders of magnitude more efficient than the state-of-the-art scheme.
Virtual reality and dementia: A bibliometric analysis

Some studies have shown that the Virtual Reality (VR) has emerged as a promising tool for the diagnosis, treatment and cognitive improvement in people with dementia. The objective was to make a bibliometric analysis to reflect on VR and dementia research from 1998 until 2018, identifying its intellectual structure and emerging trends. Data encompassing 356 documents and 19,238 citations collated between 1998 and 2018 from Web-of-Science and Scopus databases was analyzed through CiteSpace. There is an increasing growth of VR research as an instrument of diagnosis and intervention in dementia. Both databases show differences in terms of scope, the volume of data, and coverage policies. The Journal of Alzheimer's Disease is the top source. Giuseppe Riva is the most productive author. The USA and the UK are leading contributors. There is a dynamic shift in the focus of research over time and some new topics are now catching the attention of researchers. The results showed the relevance of VR in improving the health of patients with dementia and they found that the use of VR in people with dementia had a precious applicability, fundamentally, as a tool in diagnosis, treatment, cognitive improvement, or rehabilitation. The co-citation and co-authorship network analysis, the identification of critical papers, and the development of new emerging tendencies highlights the priorities in this field, pointing to new opportunities to guide researchers.
FibeR-CNN: Expanding Mask R-CNN to improve image-based fiber analysis

Fiber-shaped materials (e.g. carbon nano tubes) are of great relevance, due to their unique properties but also the health risk they can impose. Unfortunately, image-based analysis of fibers still involves manual annotation, which is a time-consuming and costly process. We therefore propose the use of region-based convolutional neural networks (R-CNNs) to automate this task. Mask R-CNN, the most widely used R-CNN for semantic segmentation tasks, is prone to errors when it comes to the analysis of fiber-shaped objects. Hence, a new architecture – FibeR-CNN – is introduced and validated. FibeR-CNN combines two established R-CNN architectures (Mask and Keypoint R-CNN) and adds additional network heads for the prediction of fiber widths and lengths. As a result, FibeR-CNN is able to surpass the mean average precision of Mask R-CNN by 33% (11 percentage points) on a novel test data set of fiber images.
Joint 3D Reconstruction and Object Tracking for Traffic Video Analysis Under IoV Environment

Benefits from artificial intelligence and the Internet of Vehicles (IoV), Management of modern transportation have great progress, especially in urban areas. However, traditional traffic video analysis and visualization are usually conducted in offsite and textural environments, i.e., text and number, which do not promote user's sensorial perception and interaction. Thus, the problem that how to use modern novel techniques to analyze traffic video for improving intelligent transportation is so emergency. In this paper, we introduce a joint 3D reconstruction and object tracking approach to traffic video analysis under the IoV environment, which is an integrative framework and consists of 3D reconstruction, object detection, and visual tracking. The 3D reconstruction system is connected to the Internet of Vehicles and integrated into the system to retrieve image data for recovering the 3D model of vehicles, and then, visualizing vehicle trajectories in real-time by augmented reality. And the system can also locate the vehicle's position in real-time. The experiments in both laboratory and practice show great feedback, which will effectively contribute to intelligent transportation.
A Semantic Focused Web Crawler Based on a Knowledge Representation Schema

The Web has become the main source of information in the digital world, expanding to heterogeneous domains and continuously growing. By means of a search engine, users can systematically search over the web for particular information based on a text query, on the basis of a domain-unaware web search tool that maintains real-time information. One type of web search tool is the semantic focused web crawler (SFWC); it exploits the semantics of the Web based on some ontology heuristics to determine which web pages belong to the domain defined by the query. An SFWC is highly dependent on the ontological resource, which is created by domain human experts. This work presents a novel SFWC based on a generic knowledge representation schema to model the crawler’s domain, thus reducing the complexity and cost of constructing a more formal representation as the case when using ontologies. Furthermore, a similarity measure based on the combination of the inverse document frequency (IDF) metric, standard deviation, and the arithmetic mean is proposed for the SFWC. This measure filters web page contents in accordance with the domain of interest during the crawling task. A set of experiments were run over the domains of computer science, politics, and diabetes to validate and evaluate the proposed novel crawler. The quantitative (harvest ratio) and qualitative (Fleiss’ kappa) evaluations demonstrate the suitability of the proposed SFWC to crawl the Web using a knowledge representation schema instead of a domain ontology.
Threshold Multi-Keyword Search for Cloud-Based Group Data Sharing

Searchable Encryption (SE) is a popular cryptographic primitive for building ciphertexts retrieval systems with far-reaching applications. However, existing SE schemes generally do not support threshold access control (i.e., data users must collaboratively issue search and decryption operations over encrypted cloud data) in a group-oriented cloud data sharing setting, which is increasingly receiving much attention in the research community. Thus, in this article, we first propose a Threshold Multi-keyword Search (TMS) scheme for cloud-based group data sharing (referred to as basic TMS scheme) by utilizing Shamir’s secret sharing technique, to achieve threshold multi-keyword search, threshold decryption, and short record ciphertext size. Then, we extend this basic TMS to realize threshold result verification and threshold traceability (referred to as enhanced TMS). Furthermore, the enhanced TMS is extended to support public result verification and dynamic operations with the public verifier and improved hash tables, respectively. Our formal security analysis proves that both basic TMS and enhanced TMS are semi-adaptively secure and can resist Chosen-Keyword Attack (CKA). Our theoretical evaluation and empirical experiments demonstrate the potential utility of both schemes.
HSTF-Model: An HTTP-based Trojan detection model via the Hierarchical Spatio-temporal Features of Traffics

HTTP-based Trojan is extremely threatening, and it is difficult to be effectively detected because of its concealment and confusion. Previous detection methods usually are with poor generalization ability due to outdated datasets and reliance on manual feature extraction, which makes these methods always perform well under their private dataset, but poorly or even fail to work in real network environment. In this paper, we propose an HTTP-based Trojan detection model via the Hierarchical Spatio-Temporal Features of traffics (HSTF-Model) based on the formalized description of traffic spatio-temporal behavior from both packet level and flow level. In this model, we employ Convolutional Neural Network (CNN) to extract spatial information and Long Short-Term Memory (LSTM) to extract temporal information. In addition, we present a dataset consisting of Benign and Trojan HTTP Traffic (BTHT-2018). Experimental results show that our model can guarantee high accuracy (the F1 of 98.62% ~ 99.81% and the FPR of 0.34% ~ 0.02% in BTHT-2018). More importantly, our model has a huge advantage over other related methods in generalization ability. HSTF-Model trained with BTHT-2018 can reach the F1 of 93.51% on the public dataset ISCX-2012, which is 20+% better than the best of related machine learning methods.
A New Simulation Environment Based on Airsim, ROS, and PX4 for Quadcopter Aircrafts

In recent years, quadcopter aircrafts are widely used in various fields. Nowadays, the test methods for quadcopter can't meet the test requirements well. There are many shortcomings, such as it is inconducive for construction, not real enough, long development cycle, and so on. Aiming at these problems, this paper constructs a quadcopter aircrafts simulation environment based on AirSim, ROS, and PX4. Firstly, this paper introduces AirSim, ROS(Robot Operating System) and PX4 SITL (Software in the Loop). Secondly, this article describes the framework of this work and the communication way between different parts. Thirdly, two similar experiments were separately carried out in the simulation environment and the real environment. Finally, through the comparison of the test results, it proves that this simulation environment is real and efficient.
Spatio-temporal Saliency-based Motion Vector Refinement for Frame Rate Up-conversion

A spatio-temporal saliency-based frame rate up-conversion (FRUC) approach is proposed, which achieves better quality of interpolated frames and invalidates existing texture variation-based FRUC detectors. A spatio-temporal saliency model is designed to select salient frames. After obtaining initial motion vector field by texture- and color-based bilateral motion estimation, two motion vector refining (MVR) schemes are adopted for high and low saliency frames to hierarchically refine the motion vectors, respectively. To produce high-quality interpolated frames, image enhancement are performed for salient frames after frame interpolation. Due to distinct MVR schemes, there are different degrees of texture information in interpolated frames. Some edge and texture information is supplemented into salient frames as post-processing, which can invalidate existing texture variation-based FRUC detectors. Experimental results show that the proposed approach outperforms state-of-the-art works in both objective and subjective qualities of interpolated frames, and achieves the purpose of FRUC anti-forensics.
Multi-component transfer metric learning for handling unrelated source domain samples

Transfer learning (TL) is a machine learning paradigm designed for the problem where the training and test data are from different domains. Existing TL approaches mostly assume that training data from the source domain are collected from multiple views or devices. However, in practical applications, a sample in a target domain often only corresponds to a specific view or device. Without the ability to mitigate the influence of the many unrelated samples, the performance of existing TL approaches may deteriorate for such learning tasks. This problem will be exacerbated if the intrinsic relationships among the source domain samples are unclear. Currently, there is no mechanism for determining the intrinsic characteristics of samples in order to treat them differently during TL. The source domain samples that are not related to the test data not only incur computational overhead, but may result in negative transfer. We propose the multi-component transfer metric learning (MCTML) method to address this challenging research problem. Unlike previous metric-based transfer learning which are only capable of using one metric to transform all the samples, MCTML automatically extracts distinct components from the source domain and learns one metric for each component. For each component, MCTML learns the importance of that component in terms of its predictive power based on the Mahalanobis distance metrics. The optimized combination of components are then used to predict the test data collaboratively. Extensive experiments on public datasets demonstrates its effectiveness in knowledge transfer under this challenging condition.
Towards Understanding Gender Bias in Relation Extraction

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards automated knowledge base construction.While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems.In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems.We find that when extracting spouse and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different.However, such disparity does not appear when extracting relations such as birth date or birth place.We also analyze two existing bias mitigation techniques, word embedding debiasing and data augmentation.Unfortunately, due to NRE models relying heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE.Our analysis lays groundwork for future quantifying and mitigating bias in relation extraction.
VocalLock

Recent years have witnessed the surge of biometric-based user authentication for mobile devices due to its promising security and convenience. As a natural and widely-existed behavior, human speaking has been exploited for user authentication. Existing voice-based user authentication explores the unique characteristics from either the voiceprint or mouth movements, which is vulnerable to replay attacks and mimic attacks. During speaking, the vocal tract, including the static shape and dynamic movements, also exhibits the individual uniqueness, and they are hardly eavesdropped and imitated by adversaries. Hence, our work aims to employ the individual uniqueness of vocal tract to realize user authentication on mobile devices. Moreover, most voice-based user authentications are passphrase-dependent, which significantly degrade the user experience. Thus, such user authentications are pressed to be implemented in a passphrase-independent manner while being able to resist various attacks. In this paper, we propose a user authentication system, VocalLock, which senses the whole vocal tract during speaking to identify different individuals in a passphrase-independent manner on smartphones leveraging acoustic signals. VocalLock first utilizes FMCW on acoustic signals to characterize both the static shape and dynamic movements of the vocal tract during speaking, and then constructs a passphrase-independent user authentication model based on the unique characteristics of vocal tract through GMM-UBM. The proposed VocalLock can resist various spoofing attacks, while achieving a satisfactory user experience. Extensive experiments in real environments demonstrate VocalLock can accurately authenticate user identity in a passphrase-independent manner and successfully resist various attacks.
Minimally Supervised Categorization of Text with Metadata

Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in conventional supervised document classification, they are less concerned with two real problems: (1)the presence of metadata : in many domains, text is accompanied by various additional information such as authors and tags. Such metadata serve as compelling topic indicators and should be leveraged into the categorization framework; (2)label scarcity: labeled training samples are expensive to obtain in some cases, where categorization needs to be performed using only a small set of annotated data. In recognition of these two challenges, we propose MetaCat, a minimally supervised framework to categorize text with metadata. Specifically, we develop a generative process describing the relationships between words, documents, labels, and metadata. Guided by the generative model, we embed text and metadata into the same semantic space to encode heterogeneous signals. Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity. We conduct a thorough evaluation on a wide range of datasets. Experimental results prove the effectiveness of MetaCat over many competitive baselines.
Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact

Detecting anomalies is an important task in a wide variety of applications and domains. Deep learning methods have achieved state-of-the-art performance in anomaly detection in recent years; unsupervised methods being particularly popular. However, deep learning methods can be fragile to small perturbations in the input data. This can be exploited by an adversary to deliberately hinder model performance; an adversarial attack. This phenomena has been widely studied in the context of supervised image classification since its discovery, however such studies for an anomaly detection setting are sorely lacking. Moreover, the plethora of defense mechanisms that have been proposed are often not applicable to unsupervised anomaly detection models. In this work, we study the effect of adversarial attacks on the performance of anomaly-detecting autoencoders using real data from a Cyber physical system (CPS) testbed with intervals of controlled, physical attacks as anomalies. An adversary would attempt to disguise these points as normal through adversarial perturbations. To combat this, we propose the Approximate Projection Autoencoder (APAE), which incorporates two defenses against such attacks into a general autoencoder. One of these involves a novel technique to improve robustness under adversarial impact by optimising latent representations for better reconstruction outputs.
Vehicle Detection Using Different Deep Learning Algorithms from Image Sequence

Image processing has become a very popular topic in recent years with images obtained by photogrammetry, remote sensing and computer vision.Deep learning practices are progressing rapidly with this innovation.Object detection, one of the new subjects of deep learning, is applied to high resolution aerial or remote sensing images to ex.tract information from these images.Traditional convolutional neural network (CNN) methods perform estimates in two stages but remain slow in terms of speed performance.You Only Look Once (YOLO) method that is used for real-time object detection, is quickly performed using a single convolutional neural network.In this study, YOLO-v3, YOLO-v3-spp and YOLO-v3-tiny models were applied in the Google Colab environment using python programming language.The comparison of YOLO models trained on COCO data was performed on the video obtained separately from a UAV and the terrestrial camera to identify the vehicles.As a result of the study, the highest results were obtained in Yolov3-spp method with average IoU 84,88% and precision value as 72,02%.
Mitigating Cache-Based Side-Channel Attacks through Randomization: A Comprehensive System and Architecture Level Analysis

Cache hierarchy was designed to allow CPU cores to process instructions faster by bridging the significant latency gap between the main memory and processor. In addition, various cache replacement algorithms are proposed to predict future data and instructions to boost the performance of the computer systems. However, recently proposed cache-based Side-Channel Attacks (SCAs) have shown to effectively exploiting such a hierarchical cache design. The cache-based SCAs are exploiting the hardware vulnerabilities to steal secret information from users by observing cache access patterns of cryptographic applications and thus are emerging as a serious threat to the security of the computer systems. Prior works on mitigating the cache-based SCAs have mainly focused on cache partitioning techniques and/or randomization of mapping between main memory. However, such solutions though effective, require modification in the processor hardware which increases the complexity of architecture design and are not applicable to current as well as legacy architectures. In response, this paper proposes a lightweight system and architecture level randomization technique to effectively mitigate the impact of side-channel attacks on last-level caches with no hardware redesign overhead for current as well as legacy architectures. To this aim, by carefully adapting the processor frequency and prefetchers operation and adding proper level of noise to the attackers' cache observations we attempt to protect the critical information from being leaked. The experimental results indicate that the concurrent randomization of frequency and prefetchers can significantly prevent cache-based side-channel attacks with no need for a new cache design. In addition, the proposed randomization and adaptation methodology outperforms the stat-of-the-art solutions in terms of the performance and execution time by reducing the performance overhead from 32.66% to nearly 20%.