Reviewerly/exHarmony huggingface dataset:
VTDP: Privately Sanitizing Fine-grained Vehicle Trajectory Data with Boosted Utility

With the rapidly growing deployment of intelligent transportation systems (ITS) and smart traffic applications, vehicle trajectory data are ubiquitously generated, e.g., from GPS navigation systems, mobile applications, and urban traffic cameras. Analyzing such fine-grained data would greatly benefit the development of ITS and smart cities, yet pose severe privacy risks due to the recorded drivers’ visited locations, routes, and driving habits. Recently, some privacy enhancing techniques were proposed to sanitize such data. However, such schemes have some major limitations–they either lack formal privacy notions to quantify and bound the privacy risks, or result in very limited utility, e.g., only a sequence of locations or aggregated information can be released (without retaining the speeds, accelerations and the timestamps of vehicles). In this article, we propose a novel framework to sanitize the fine-grained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vehicle trajectories with differential privacy</i> (VTDP), which provides rigorous privacy protection against adversaries who possess arbitrary background knowledge. Our VTDP technique involves three phases of differentially private sampling, which sequentially generate all the three categories of data (besides a pseudo identity for each vehicle)– <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">position, moving,</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">timestamps</i> . It also includes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vehicle trajectory interpolation</i> procedure to further improve the output utility with the properties of fine-grained vehicle trajectory data. We conducted experiments on real vehicle trajectory datasets to validate the performance of our approach.
Scheduling in CloudSim of Interdependent Tasks for SLA Design

One of the most recent concepts in the framework of today's distributed systems is Cloud Computing.A very difficult problem that needs to be addressed is the management of the Cloud.When designing a Cloud scheduling strategy, the design trade-offs of the Cloud architecture should be evaluated.The easiest way to evaluate this infrastructure is to use a simulation tool (in this case CloudSim simulation toolkit).This article examines three different algorithms that consider the scheduling of tasks in Cloud, which are described as Directed Acyclic Graphs (DAGs) (interdependent tasks).The results of the scheduling algorithms are valuable because they are helpful for the design of the Cloud Computing scheduling algorithms and for the design of a Service Level Agreement (SLA) contract whose members are the Cloud Service Provider (CSP) and the Cloud Service Customers (CSCs).This paper shows that the selection of the scheduling algorithms is useful in the design of the SLA contract.
Egyptian Social Insurance Big Data Mining Using Supervised Learning Algorithms

Social insurance is an important way to defend individuals from the dangers of retirement, sickness, and financial despondency.However, the social insurance data of Egypt are extremely massive, diverse and complex, due to the intricate provisions in Egyptian laws on social insurance.This calls for an effective method to distinguish between the insured and the uninsured based on the big data.Therefore, this paper fully demonstrates the capability of supervised learning algorithms in predicting the insured and the uninsured based on some of the data on social insurance.The big dataset of Egyptian social insurance was preprocessed in details, including replacing the missing values with mean values, normalization, and removal of outliers and extreme values.Next, three supervised learning algorithms, namely, Naive Bayes, Decision Tree Algorithm, and CN2 Rule Induction, were applied to classify the Egyptians based on the preprocessed data.The results show that all three algorithms achieved highly accuracy classification results.The research provides a new way to mine useful information from the big data of social insurance.
Illumination Correction in a Comparative Analysis of Feature selection for Rear-View Vehicle Detection

One of the most frequent infractions on the road is the act of a vehicle crossing to the wrong side of the road to pass another vehicle traveling in the same direction.Automatic detection of this violation can be a challenging issue.Thus, we aim to develop a computer vision system to robustly detect forbidden overtaking observed from a fixed camera.Our approach is based on two main phases: Line Detection and Vehicle Detection.In this work, we focus on the vehicle detection stage.Here, features are extracted from the image and then classified using machine learning algorithms.In a first experiment, we constructed different models using features such as HOG (Histogram of Oriented Gradient), SURF (Speeded up Robust Features), Gabor filter and LBP (Local Binary Patterns), and machine learning classifiers as SVM (Support Vector Machines), kNN (k-Nearest Neighbor) and Decision Tree.Then, we merged the best descriptors to combine the advantages of their different robustness in order to build a strong vehicle detection model.The performances of all constructed models are evaluated on the GTI database.In a second experiment, illumination normalization techniques were applied to image database for the effective models.Thus, the first experimental results reveal that the combination HOG+ LBP+ Gabor Phase performs well with SVM.The second experimental results show that the Variational Retinex algorithm-based illumination correction provides a significant improvement in the detection rate.
A Novel Fitting H-Infinity Kalman Filter for Nonlinear Uncertain Discrete-Time Systems Based on Fitting Transformation

The classical Kalman-based filtering algorithm, such as extended Kalman filter or unscented Kalman filter, commonly assumes that the accurate system model or precise noise statistics is known for using. Hence, these filters are not robust estimation to practical systems and always with poor stability, low precision or even divergence since uncertain items exist. In order to tackle these issues, a novel scheme referred to as the fitting H-infinity Kalman filter (FHKF) is proposed and used for robust estimation of the nonlinear uncertain systems. The hardcore of FHKF is the fitting transformation, which is a numerical approximation approach to get the estimation values of coefficient matrix based on least weighted squares. Moreover, FHKF is proposed by applying the coefficient matrix to the structure of the extended H-infinity Kalman filter. Based on the stochastic stability lemma, the stability analysis is presented to verify the error boundness of the proposed filtering. Its efficiency is demonstrated by taking Monte Carlo simulation for the uncertain system and practical experiments in the INS/GPS integrated navigation.
Quantum-optical realization of an Ornstein-Uhlenbeck-type process via simultaneous action of white noise and feedback

We establish an important connection between coherent quantum feedback and the Ornstein-Uhlenbeck process in quantum optics. We show that an emitter with fluctuating energy levels in front of a mirror results in an Ornstein-Uhlenbeck process for electronic populations, although the fluctuation of the energy levels is assumed to be uncorrelated in time and space. Based on a Heisenberg equation of motion description of the quantum feedback dynamics, we discuss additionally the impact of phase noise on the population dynamics and provide examples in which noise itself is not detrimental but supports and enhances typical features of quantum feedback such as self-stabilization.
Novel design of a fractional wavelet and its application to image denoising

This paper proposes a new wavelet family based on fractional calculus. The Haar wavelet is extended to fractional order by a generalization of the associated conventional low-pass filter using the fractional delay operator Z-D. The high-pass fractional filter is then designed by a simple modulation of the low-pass filter. In contrast, the scaling and wavelet functions are constructed using the cascade Daubechies algorithm. The regularity and orthogonality of the obtained wavelet basis are ensured by a good choice of the fractional filter coefficients. An application example is presented to illustrate the effectiveness of the proposed method. Thanks to the flexibility of the fractional filters, the proposed approach provides better performance in term of image denoising.
Big Map R-CNN for object detection in large-scale remote sensing images

Detecting sparse and multi-sized objects in very high resolution (VHR) remote sensing images remains a significant challenge in satellite imagery applications and analytics. Difficulties include broad geographical scene distributions and high pixel counts in each image: a large-scale satellite image contains tens to hundreds of millions of pixels and dozens of complex backgrounds. Furthermore, the scale of the same category object can vary widely (e.g., ships can measure from several to thousands of pixels). To address these issues, here we propose the Big Map R-CNN method to improve object detection in VHR satellite imagery. Big Map R-CNN introduces mean shift clustering for quadric detecting based on the existing Mask R-CNN architecture. Big Map R-CNN considers four main aspects: 1) big map cropping to generate small size sub-images; 2) detecting these sub-images using the typical Mask R-CNN network; 3) screening out fragmented low-confidence targets and collecting uncertain image regions by clustering; 4) quadric detecting to generate prediction boxes. We also introduce a new large-scale and VHR remote sensing imagery dataset containing two categories (RSI LS-VHR-2) for detection performance verification. Comprehensive evaluations on RSI LS-VHR-2 dataset demonstrate the effectiveness of the proposed Big Map R-CNN algorithm for object detection in large-scale remote sensing images.
Deep Learning-based Automatic Modulation Recognition Algorithm in Non-Cooperative Communication systems

Automatic modulation recognition (AMR) is considered one of most important topics in non-cooperative communication systems. Traditional algorithms, e.g., high order cumulants (HOC) and support vector machine (SVM), are hard to achieve reliable performance. In this paper, we propose an effective AMR algorithm based on deep learning (DL) with capabilities of automatically extracting representative and effective features. The proposed method resorts to in-phase and quadrature (IQ) samples that are in-phase and quadrature components of received baseband signal, respectively. We adopt convolutional neural networks (CNN) and recurrent neural networks (RNN) to classify six types of signal modulations in additive white Gaussian noise (AWGN) channel and Rayleigh channel, respectively. Simulation results show that the features automatically extracted by DL algorithm is more suitable and effective for AMR and its performance is far beyond that of traditional algorithms in two channels.
The pertinent single-attribute-based classifier for small datasets classification

Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In this paper, we revealed the power of a single attribute by introducing the pertinent single-attribute-based-heterogeneity-ratio classifier (SAB-HR) that used a pertinent attribute to classify small datasets. The SAB-HR’s used feature selection method, which used the Heterogeneity-Ratio (H-Ratio) measure to identify the most homogeneous attribute among the other attributes in the set. Our empirical results on 12 benchmark datasets from a UCI machine learning repository showed that the SAB-HR classifier significantly outperformed the classical OneR classifier for small datasets. In addition, using the H-Ratio as a feature selection criterion for selecting the single attribute was more effectual than other traditional criteria, such as Information Gain (IG) and Gain Ratio (GR).
Recognizing patterns of student’s modeling behaviour patterns via process mining

Abstract Evaluating student learning effect plays an essential role in education, which is typically done by assessing student’s final deliverables. However, the student’s learning process has not been properly explored in the past.In this paper, we propose an interactive student learning effect evaluation framework which focuses on in-process learning effect evaluation. In particular, our proposal analyzes students modeling assignment based on their operation records by using techniques of frequent sequential pattern mining, user behavior analysis, feature engineering, and process mining. A comprehensive online modeling platform has been developed to enable data collection. We have carried out a case study, in which we applied our approach to a real teaching scenario, consisting of student online modeling behavior data collected from 24 students majoring in computer science. We also associate our process mining results with the numeric evaluation values. The preliminary result of case analysis has shown good potential to mine student modeling patterns and interpret their behaviors, contributing to student learning effect evaluation.
News Title Classification Based on Sentence-LDA Model and Word Embedding

Due to the severe data sparsity problem, conventional text classification methods are difficult to achieve good results in news title classification. In this paper, we design a novel news title classification method, referred as Word-Embedding-based Sentence-LDA (WESL) model. WESL employs the Sentence-LDA model to distill topic vectors from news titles, and FastText is used to learn word distributed representations. Furthermore, the proposed model expands the features of news titles by combining word and topic vectors. Finally, we utilize a Support Vector Machine (SVM) to classify news titles, and the classification results are evaluated using the precision, recall and F1-value. The experimental results illustrate that our method can significantly enhance classification performance.
Image Steganography using Inverted LSB based on 2<sup>nd</sup>, 3<sup>rd</sup> and 4<sup>th</sup> LSB pattern

Steganography is one method of concealing data, research on this topic always aims to improve image quality, message payload and security. The Inverted Least Significant Bit (LSB) method is one technique that can be used to optimize the quality of the steganography method. This study proposes a modified inverted LSB technique. In the previous study the inverted LSB method used a two-bit LSB pattern, on the second bit and the third bit LSB. Based on the calculation pattern the calculation of bit changes in each pattern will determine whether or not the inverted LSB process is performed. This research proposes the use of the LSB three-bit pattern, where the pattern is taken from the second, third and fourth bits of LSB. The aim is to minimize LSB changes in each pixel of the cover image. Based on the tests that have been conducted, it has been proven that the inverted LSB method using three LSB patterns produces better results than the two-bit LSB pattern. Eight of the ten cover images tested, showed the results of an increase in the quality of the stego image. Chaotic map-based message encryption methods are also applied and combined with the LSB inverted method proposed to increase message security.
Tell Me What They're Holding: Weakly-Supervised Object Detection with Transferable Knowledge from Human-Object Interaction

In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable knowledge from human-object interactions (HOI). While WSOD shows lower performance than full supervision, we mainly focus on HOI as the main context which can strongly supervise complex semantics in images. Therefore, we propose a novel module called RRPN (relational region proposal network) which outputs an object-localizing attention map only with human poses and action verbs. In the source domain, we fully train an object detector and the RRPN with full supervision of HOI. With transferred knowledge about localization map from the trained RRPN, a new object detector can learn unseen objects with weak verbal supervision of HOI without bounding box annotations in the target domain. Because the RRPN is designed as an add-on type, we can apply it not only to the object detection but also to other domains such as semantic segmentation. The experimental results on HICO-DET dataset show the possibility that the proposed method can be a cheap alternative for the current supervised object detection paradigm. Moreover, qualitative results demonstrate that our model can properly localize unseen objects on HICO-DET and V-COCO datasets.
Video Face Super-Resolution with Motion-Adaptive Feedback Cell

Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN). Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames. However, duo to the complex temporal dependency between frames, with the number of LR input frames increase, the performance of the reconstructed HR frames become worse. The reason is in that these methods lack the ability to model complex temporal dependencies and hard to give an accurate motion estimation and compensation for VSR process. Which makes the performance degrade drastically when the motion in frames is complex. In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way. Our approach efficiently utilizes the information of the inter-frame motion, the dependence of the network on motion estimation and compensation method can be avoid. In addition, benefiting from the excellent nature of MAFC, the network can achieve better performance in the case of extremely complex motion scenarios. Extensive evaluations and comparisons validate the strengths of our approach, and the experimental results demonstrated that the proposed framework is outperform the state-of-the-art methods.
From Textural Inpainting to Deep Generative Models: An Extensive Survey of Image Inpainting Techniques

Image inpainting is an evolving discipline of image processing with the objective of reconstructing an image by removing unwanted information, adding missing information or presenting the information appealing to the human visual system.In the presented manuscript, we have exhibited an extensive survey of various image inpainting techniques.The effectiveness of the techniques is together summarized with significant comparisons and assessed by analyzing the merits and demerits.For applicability of image inpainting imparting optimum results in the field of loss concealment, object removal, image restoration or disocclusion, the information from nearby regions is seeked to acquire an image with restored absent information.The inpainted image result can be evaluated using subjective and objective analysis, with emphasis on subjective analysis as a dedicated tool for evaluation.
Cross-Domain Cascaded Deep Translation

In recent years we have witnessed tremendous progress in unpaired image-to-image translation, propelled by the emergence of DNNs and adversarial training strategies. However, most existing methods focus on transfer of style and appearance, rather than on shape translation. The latter task is challenging, due to its intricate non-local nature, which calls for additional supervision. We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation between these deep features. Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we first translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones. We further demonstrate the effectiveness of using pre-trained deep features in the context of unconditioned image generation. Our code and trained models will be made publicly available.
An Ordinal Data Clustering Algorithm with Automated Distance Learning

Clustering ordinal data is a common task in data mining and machine learning fields. As a major type of categorical data, ordinal data is composed of attributes with naturally ordered possible values (also called categories interchangeably in this paper). However, due to the lack of dedicated distance metric, ordinal categories are usually treated as nominal ones, or coded as consecutive integers and treated as numerical ones. Both these two common ways will roughly define the distances between ordinal categories because the former way ignores the order relationship and the latter way simply assigns identical distances to different pairs of adjacent categories that may have intrinsically unequal distances. As a result, they may produce unsatisfactory ordinal data clustering results. This paper, therefore, proposes a novel ordinal data clustering algorithm, which iteratively learns: 1) The partition of ordinal dataset, and 2) the inter-category distances. To the best of our knowledge, this is the first attempt to dynamically adjust inter-category distances during the clustering process to search for a better partition of ordinal data. The proposed algorithm features superior clustering accuracy, low time complexity, fast convergence, and is parameter-free. Extensive experiments show its efficacy.
A governance framework with permissioned blockchain for the transparency in e-tendering process

The electronic tendering process is rapidly growing popularity among private and public sectors for its efficiency and convenience.An end to end fair and transparent tendering process is desirable for all stakeholders and the proper business environment.Researchers and business entities are continuously working to improve its quality.In general, while offering a tender, an enterprise usually maintains the following steps [1].1.An enterprise or a tendering organization opens a tender, and once it is available for bidding it remains unchanged till the end of the process.2. Each tender includes a list of evaluation criteria based on legal, technical and financial requirements for selecting the best possible bidder.3.Only the authorized bidding organizations can submit a bid, the contents of their bid will remain confidential throughout the tendering process and intact.