Miracle-dz/UniFAR-FTUs huggingface dataset:
An Effective Security Model for Removing Distrustful Macros from Office Documents

Macros of Microsoft Office documents are used in many organizations to improve efficiency of operations on the office documents. However, when created with malicious intentions, macros contain viruses that steal sensitive information of users or cause damage to files and systems of users. Although antivirus programs can remove the office documents containing macro viruses from users’ systems, it can’t detect new viruses that are not registered in its virus definition databases. Although users pay attention not to activate macros of the office documents come from unknown senders, macro virus creators have successfully used social engineering techniques that lead users of Microsoft office to run the infected macros. Therefore, viruses in macros of the office documents are still dangerous threats for the organizations. The objective of this paper is to propose an effective security model that solves this problem. This proposed model employs digital signature technology for examining trusted macro creators and detecting uncertified modification of macros of Microsoft Office documents. Keywords— Documents; macro; virus; XML; digital signature; certificates
Antecedents of Project Agility in Analytics Projects

Agile project management methods have been widely adopted since the publication of the Agile manifesto. However, the nature of project agility needs further theoretical development and empirical support. Consequently, this study defines project agility and then explores the factors that influence project teams’ achievement of agility. Complex Adaptive Systems (CAS) theory is adopted as a theoretical lens suitable to address the emerging, co-evolving nature of projects. A case-based research methodology is used to examine several analytics projects, the activities and interactions associated with each project, and the outcomes. Overall, the proposed study is designed to contribute to the project agility literature by applying CAS theory in the context of business analytics.
Fragmentation Guided Human Shape Reconstruction

We present a novel semantic-driven multi-view reconstruction technique for producing realistic 3D human models. Our approach borrows the fragmentation concept in Cubism style painting, where the human body is decomposed into semantically meaningful fragments for conveying space and movement. We first employ deep learning-based skeleton estimation for warping a proxy human model under the canonical pose to the target multi-view input. It also conducts 3D fragment labeling on the warped model to separate different human body parts. Finally, we utilize the normal, depth, and fragment label of the proxy model as priors in the multi-view stereo reconstruction process. The comprehensive experiments have shown that our reconstruction technique outperforms the state-of-the-art methods in robustness and accuracy, especially near occlusion boundaries and on textureless regions. In particular, it manages to significantly reduce the “adhesive” artifacts commonly observed in MVS that incorrectly stitches different body parts.
TDARMA Model Estimation Using the MLS and the TF Distribution

An approach for modeling linear time-dependent auto-regressive moving-average (TDARMA) systems using the time-frequency (TF) distribution is presented. The proposed method leads to an extension of several well-known techniques of linear timeinvariant (LTI) systems to process the linear, time-varying (LTV) case. It can also be applied in the modeling of non-stationary signals. In this paper, the well-known modified least square (MLS) and the Durbin's approximation methods are adapted to this nonstationary context. A simple relationship between the generalized transfer function and the time-dependent parameters of the LTV system is derived and computer simulation illustrating the effectiveness of our method is presented, considering that the output of the LTV system is corrupted by additive noise.
An Improved H.264 Encoded Algorithm Based on Weber-Fechner Law

With the study of human visual system (HVS), people find that the human eye shows different degree of sensitivity to different light signals. A higher background brightness region of the human eye for the distortion, the degree of sensitivity will be greatly reduced. The human eye’s sensitivity will be greatly reduced to the distortion of a higher brightness background region. This finding is discribed in Weber-Fechner law. Therefore, the encoder can be improved by employing this characteristic of human visual system. In this paper, the authors use this characteristic to improve the H.264 video coding algorithm. In order to improve the compression efficiency of encoder, give the brightness of different regions with different levels of quantification by adjusting the quantization step size (QP), without affecting the subjective quality of video. Experimental results show that the output bit rate decrease up to 10% -20% by using this improved algorithm in H.264 reference encoder, and the subjective quality of decoded image by using this improved algorithm is as good as the standard H.264 encoders.
Deep Reinforcement Learning for Pellet Eating in Agar.io

The online game Agar.io has become massively popular on the internet due to its intuitive game design and its ability to instantly match players with others around the world. The game has a continuous input and action space and allows diverse agents with complex strategies to compete against each other. In this paper we focus on the pellet eating task in the game, in which an agent has to learn to optimize its navigation strategy to grow maximally in size within a specific time period. This work first investigates how different state representations affect the learning process of a Q-learning algorithm combined with artificial neural networks which are used for representing the Q-function. The representations examined range from raw pixel values to extracted handcrafted feature vision grids. Secondly, the effects of using different resolutions for the representations are examined. Finally, we compare the performance of different value function network architectures. The architectures examined are two convolutional Deep Q-networks (DQN) of varying depth and one multilayer perceptron. The results show that the use of handcrafted feature vision grids significantly outperforms the direct use of raw pixel input. Furthermore, lower resolutions of 42×42 lead to better performances than larger resolutions of 84 × 84.
Multi-target Tracking Algorithm Based on TLD under Dynamic Background

The tracking of multi-target under dynamic background is a challenging issue in computer vision. We improve TLD algorithm by introducing multi-threading mechanism to expand the number of the tracking targets. Thus the excellent framework of TLD for accurate long-time single target tracking is kept, and the tracking of multiple moving targets is realized at the same time. The time cost of tracking also reduces effectively by this algorithm to meet the real-time demand in many dynamic scenes.
Highway traffic data: macroscopic, microscopic and criticality analysis for capturing relevant traffic scenarios and traffic modeling based on the highD data set.

This work provides a comprehensive analysis on naturalistic driving behavior for highways based on the highD data set. Two thematic fields are considered. First, some macroscopic and microscopic traffic statistics are provided. These include the traffic flow rate and the traffic density, as well as velocity, acceleration and distance distributions. Additionally, the dependencies to each other are examined and compared to related work. The second part investigates the distributions of criticality measures. The Time-To-Collision, Time-Headway and a third measure, which couples both, are analyzed. These measures are also combined with other indicators. Scenarios, in which these measures reach a critical level, are separately discussed. The results are compared to related work as well. The two main contributions of this work can be stated as follows. First, the analysis on the criticality measures can be used to find suitable thresholds for rare traffic scenarios. Second, the statistics provided in this work can also be utilized for traffic modeling, for example in simulation environments.
Recognising Actions for Instructional Training using Pose Information: A Comparative Evaluation

Humans perform many complex tasks involving the manipulation of multiple objects. Recognition of the constituent actions of these tasks can be used to drive instructional training systems. The identities and poses of the objects used during such tasks are salient for the purposes of recognition. In this work, 3D object detection and registration techniques are used to identify and track objects involved in an everyday task of preparing a cup of tea. The pose information serves as input to an action classification system that uses LongShort Term Memory (LSTM) recurrent neural networks as part of a deep architecture. An advantage of this approach is that it can represent the complex dynamics of object and human poses at hierarchical levels without the need for design of specific spatio-temporal features. By using such compact features, we demonstrate the feasibility of using the hyperparameter optimisation technique of Tree-Parzen Estimators to identify optimal hyperparameters as well as network architectures. The results of 83% recognition show that this approach is viable for similar scenarios of pervasive computing applications where prior scene knowledge exists.
Security-Aware Task Mapping Reducing Thermal Side Channel Leakage in CMPs

Chip multiprocessor (CMP) suffers from growing threats on hardware security in recent years, such as side channel attack, hardware Trojan infection, chip clone, etc. In this paper, we propose a security-aware (SA) task mapping method to reduce the information leakage from CMP thermal side channel. First, we construct a mathematical function that can estimate the CMP security cost corresponding to a given mapping result. Then, we develop a greedy mapping algorithm that automatically allocates all threads of an application to a set of proper cores, such that the total security cost is optimized. Finally, we perform extensive experiments to evaluate our method. The experimental results show that our SA mapping effectively decreases the CMP side channel leakage. Compared to the two existing task mapping methods, Linux scheduler (LS; a standard Linux scheduler) and NoC-Sprinting (NS; a thermal-aware mapping technique), our method reduces side-channel vulnerability factor by up to 19% and 7%, respectively. Moreover, our method also gains higher computational efficiency, with improvement in million instructions per second achieving up to 100% against NS and up to 33% against LS.
Are contributions of emissions to ozone a matter of scale? – a study using MECO(n) (MESSy v2.50)

Abstract. Anthropogenic and natural emissions influence the tropospheric ozone budget, thereby affecting air quality and climate. To study the influence of different emission sources on the ozone budget, often source apportionment studies with a tagged tracer approach are performed. Studies investigating air quality issues usually rely on regional models with a fine spatial resolution, while studies focusing on climate-related questions often use coarsely resolved global models. It is well known that simulated ozone mixing ratios depend on the resolution of the model and the resolution of the emission inventory. Whether the contributions simulated using source apportionment approaches also depend on the model resolution, however, is still unclear. Therefore, this study attempts for the first time to analyse the impact of the model, the model resolution, and the emission inventory resolution on simulated ozone contributions using a diagnostic tagging method. The differences in the ozone contributions caused by these factors are compared with differences that arise from the usage of different emission inventories. To do so, we apply the MECO(n) (MESSy-fied ECHAM and COSMO models nested n times) model system which couples online a global chemistry-climate model with a regional chemistry-climate model equipped with a tagging scheme for source apportionment. The results of the global model (at 300 km horizontal resolution) are compared with the results of the regional model at 50 km (Europe) and 12 km (Germany) resolutions. Besides model-specific differences and biases that are discussed in detail, our results have important implications for other modelling studies and modellers applying source apportionment methods. First, contributions from anthropogenic emissions averaged over the continental scale are quite robust with respect to the model, model resolution, and emission inventory resolution. Second, differences on the regional scale caused by different models and model resolutions can be quite large, and regional models are indispensable for source apportionment studies on the subcontinental scale. Third, contributions from stratospheric ozone transported to the surface differ strongly between the models, mainly caused by differences in the efficiency of the vertical mixing. As stratospheric ozone plays an important role for ground level ozone, but the models show large differences in the amount of downward transported ozone, source apportionment methods should account for this source explicitly to better understand inter-model differences.
The Impact of Environmental Factors on Heart Failure Decompensations

Heart failure (HF) is defined as the incapacity of the heart to pump sufficiently to maintain blood flow to meet the body's needs. Often, this causes sudden worsening of the signs and symptoms of heart failure (decompensations), which may lead on hospital admissions, deteriorating patients’ quality of life and causing an increment on the healthcare cost. Environmental exposure is an important but underappreciated risk factor contributing to the development and severity of cardiovascular diseases, such as HF. In this paper, we describe the development and results of a methodology to determine the effect of environmental factors on HF decompensations by means of hospital admissions. For that, a total number of 8338 hospitalizations of 5343 different patients, and weather and air quality information from open databases have been considered. The results demonstrate that several environmental factors, such as weather temperature, have an impact on the HF related hospital admissions rate, and hence, on HF decompensations and patientś quality of life. The next steps are first to predict the number of hospital admissions based on the presented study, and second, the inclusion of these environmental factors on predictive models to assess the risk of decompensation of an ambulatory patient in real time.
Cascaded convolutional neural networks for spine chordoma tumor segmentation from MRI

Chordoma is a rare type of tumor that usually appears in the bone near the spinal cord and skull base. Due to their location in the skull base and diverse appearance in size and shape, automatic segmentation of chordoma tumors from magnetic resonance images (MRI) is a challenging task. In addition, similar MR intensity distributions of different anatomical regions, specifically sinuses, make the segmentation task from MRI more challenging. In comparison, most of the state-of-the-art lesion segmentation methods are designed to segment pathologies inside the brain. In this work, we propose an automatic chordoma segmentation framework using two cascaded 3D convolutional neural networks (CNN) via an auto-context model. While the first network learns to detect all potential tumor voxels, the second network fine-tunes the classifier to distinguish true tumor voxels from the false positives detected by the first network. The proposed method is evaluated using multi-contrast MR images of 22 longitudinal scans from 8 patients. Preliminary results showed a linear correlation of 0.71 between the detected and manually outlined tumor volumes, compared to 0.40 for a random forest (RF) based method. Furthermore, the response of tumor growth over time, i.e. increasing, decreasing, or stable, is evaluated according to the response evaluation criteria in solid tumors with an outcome of 0.26 kappa coefficient, compared to 0.13 for the RF based method.
Detection of Control Points for UAV-Multispectral Sensed Data Registration through the Combining of Feature Descriptors

The popularization of the Unmanned Aerial Vehicle (UAV) and the development of new sensors has enabled the acquisition and use of multispectral and hyperspectral images in precision agriculture. However, performing the image registration process is a complex task due to the lack of image characteristics among the various spectra and the distortions created by the use of the UAV during the acquisition process. Therefore, the objective of this work is to evaluate different techniques for obtaining control points in multispectral images of soybean plantations obtained by UAVs and to investigate if combining features obtained by different techniques generates better results than when used individually. In this work Were evaluated 3 different feature detection algorithms (KAZE, MEF and BRISK) and their combinations. Results shown that the KAZE technique, achieve better results.
Multifeature Human Authentication using Score Level Fusion of Ear and Helix

With the advancement of technology in every field, security can be enhanced by means of using biometrics through which a person can be uniquely identified. Distinguishing biological traits can be used as unique identifiers. Biometric identifiers should satisfy properties such as universality, uniqueness, permanence and collectability. Among various biometrics, ear biometric plays a vital role since it has a rich, stable and unique structure which varies among individuals even among twins, triplets, etc. Unlike other traits ear does not vary much with age and does not vary with any facial expressions. This paper deals with unique feature such as ear and tragus feature. Gabor filter can be used to extract the ear and tragus feature. Gabor filter is a linear filter which is used for edge detection whose representations are similar to human visual system. In this paper, ear and helix feature can be extracted at different frequencies and orientations using SIFT descriptor and Modified Hessian Transform which are then fused using score level fusion. Finally they are matched and classified using Artificial Neural Networks (ANN).
Study of OpenCL processing models for FPGA device

In our study, we present the results of the implementation of SHA-512 algorithm in FPGA. The distinguished element of our work is that we conducted the work using OpenCL for FPGA which is a relatively new development method for reconfigurable logic. We examine the loop unrolling; as the OpenCL performance optimisation method, and compare the efficiency of the different kernel implementation types: NDRange, Single-Work Item, and SIMD kernels. In conclusions, we compare metrics of the created FPGA accelerator to the corresponding GPGPU solutions. Also, our paper is accompanied by the source code repository to allow the reader to follow and extend our survey.
Learning to Remove Rain in Traffic Surveillance by using Synthetic Data

: Rainfall is a problem in automated trafﬁc surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artiﬁcially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on trafﬁc surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a trafﬁc surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we ﬁnd that high PSNR or SSIM scores do not imply good object detection performance.
An Extensive Game-Based Resource Allocation for Securing D2D Underlay Communications

Device-to-device (D2D) communication has been increasingly attractive due to its great potential to improve cellular communication performance. While resource allocation optimization for improving the spectrum efficiency is of interest in the D2D-related work, communication security, as a key issue in the system design, has not been well investigated yet. Recently, a few studies have shown that D2D users can actually serve as friendly jammers to help enhance the security of cellular user communication against eavesdropping attacks. However, only a few studies considered the security of D2D communications. In this paper, we consider the secure resource allocation problem, particularly, how to assign resources to cellular and the D2D users to maximize the system security. To solve this problem, we propose an extensive game-based algorithm aiming at strengthening the security of both cellular and the D2D communications via system resource allocation. Finally, the simulation results show that the proposed method is able to efficiently improve the overall system security when compared to existing studies.
Image Segmentation Based Vehicle Number Identification in Toll Booth

Vehicle number plate recognition is the most interesting and challenging research topic from past few years. It is shown that the number plates are different shape and size and also have different color in different countries. In India the most common vehicle number plate used yellow or white as background and black used as foreground color. In this paper we proposed a system to localization of number plate mainly for the vehicles in West Bengal (India) and segmented the numbers as to identify each number separately. This presents an approach based on simple and efficient morphological operation and sobel edge detection method. We also presents a simple approach to segmented all the letters and numbers used in the number plate. After reducing noise from the input image we try to enhance the contrast of the binarized image using histogram equalization. We mainly concentrate on two steps; one is to locate the number plate and second is to segment all the number and letters to identify each number separately. Key Word : Vehicle Number Plate Recognition ,Character Recognition
Identifying Linkage Across Social Network Platforms based on Heterogeneous Modeling

People pose with different identities on various social media, this is of critical importance for most of the business that target social media to gain popularity as well as for the cyber security. Here we propose a framework, HYDRA, which has the following ways to provide a solution (I) we use the long time behavior of the profile of the users and match the information missing along with the behavior of the user (II) Since user’s have account in various platforms we have to get the accurate information so the model should be built for consistency (III) We learn the profiles using the function formulation and linkage to get the parento similarity. Here we design a model that can handle various problems such as consistency, information missing along with the real-linkage across the different platforms