Miracle-dz/UniFAR-FTUs huggingface dataset:
Botnet homology method based on symbolic approximation algorithm of communication characteristic curve

The IRC botnet is the earliest and most significant botnet group that has a significant impact. Its characteristic is to control multiple zombies hosts through the IRC protocol and constructing command control channels. Relevant research analyzes the large amount of network traffic generated by command interaction between the botnet client and the C&C server. Packet capture traffic monitoring on the network is currently a more effective detection method, but this information does not reflect the essential characteristics of the IRC botnet. The increase in the amount of erroneous judgments has often occurred. To identify whether the botnet control server is a homogenous botnet, dynamic network communication characteristic curves are extracted. For unequal time series, dynamic time warping distance clustering is used to identify the homologous botnets by category, and in order to improve detection. Speed, experiments will use SAX to reduce the dimension of the extracted curve, reducing the time cost without reducing the accuracy.
The Implementation of Halal Tourism in Indonesia National Park

Halal tourism is a new phenomenon based on demographic factors and needs to be taken into consideration by tourism destinations. Nature tourism, especially tourism in national parks, in this case, also has the potential to become a halal tourism destination. However, the parks’ halal value still requires special consideration in some areas such as the provision of halal tour packages, worship facilities, visitor status and interaction with local communities. Moreover, considering that recreation is the fundamental activity of tourism that has no boundary, including religion so that the development of halal tourism in national parks still needs aware to the consumers' consumptive behavior.
Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability

It is well known that electrocardiogram heartbeats are substantial for cardiac disease diagnosis. In this paper, the best time scale was investigated to recognize congestive heart failure (CHF) based on heart rate variability (HRV) measures. The classifications were performed on seven different time scales with a support vector machine classifier. Nine HRV measures, including three time-domain measures, three frequency-domain measures, and three nonlinear-domain measures, were taken as feature vectors for classifier on each time scale. A total of 83 subjects with RR intervals were analyzed, of which 54 cases were normal and 29 patients were suffering from CHF in PhysioNet databases. The classifying results using tenfold cross-validation method achieved the best performance of a sensitivity, specificity, and accuracy of 86.7%, 98.3%, and 94.4%, respectively, on the 2-h time scale. Moreover, by introducing only three nonstandard HRV features extracted from the trends of HRV measures on time scales, it achieved a better performance of a sensitivity of 93.3%, specificity of 98.3%, and an accuracy of 96.7%. The impressive performance of discrimination power on the 2-h time scale and the trends of HRV measures on time scales indicate that multiple time scales play significant roles in detecting CHF and can be valuable in expressing useful knowledge in medicine.
CovNN: A Covariance Neural Network Extended from CNN

Convolutional neural networks (CNNs) show commendable performance in computer vision, approaching high accuracy in a broad number of application domains. However, the training process of feature kernels in CNNs is easily affected by illumination intensity and feature interaction, which leads to over-fitting. In this paper, we propose a covariance neural network (CovNN), which replaces the original convolutional operation with our covariance algorithm, to make the learned kernels more robust to different illumination conditions and irrelevant features. This covariance layer uses the 3D covariance between all the input feature maps and the corresponding group of kernels by sliding window method, and regularizes them without additional parameters. Moreover, the covariance layer can be seamlessly transplanted to a variety of neural network architectures extended from CNNs (e.g., ResNet, Faster R-CNN). We evaluate the proposed CovNN on several popular datasets for image recognition (MNIST, Fashion-MNIST, CIFAR 10 and AR) and classification of organs (Abdominal Ultrasound Dataset). Experimental results demonstrate that CovNN achieves significant improvements over the state-of-the-art on most of them.
Use of a Confidence Map Towards Improved Multi-layer Stixel Segmentation

We propose the use of a reliable confidence map for multi-layer stixel segmentation; our confidence map uses a calibrated collinear trinocular vision model. It is generated from three conjugate synchronized stereo images for evaluating the consistency of disparity values. The evaluation measure is referred to as transitivity error in disparity space. Multi-layer stixels are commonly generated from a single disparity map which make them merely dependent on the applied stereo matcher. A multi-map fusion is proposed to achieve more reliable stixel segmentation for disparity values. Moreover, another advantage of our work is to provide a new and effective ground-detection technique (ground-manifold detection) which benefits from the confidence map. Experimental results show a significant improvement on average of 12.6% using our method compared with conventional stixels detected by binocular vision only.
Representative Measurement Point Selection to Monitor Software-defined Networks

Network state monitoring is a fundamental task for network management. However, determining the full network state in Software defined Networks requires disproportionately too many resources. This stems from the discrepancy between the established methods used for state monitoring compared to the varying contribution in terms of information obtained from every additionally monitored network node. This relationship may even become more complicated depending on the network state information of interest. One solution to overcome bottlenecks by reducing the overall monitoring footprint is the use of spatial sampling, which allows the estimation of the network state based a fraction of the overall state.In this work, we propose schemes to place a small number of measurement points in the SDN network to maximize the obtained network state information. Considering different conditions, we utilize routing information and graph theoretic centrality metrics, respectively, to estimate the amount of information a node provides. Based on this knowledge, we, furthermore, develop a mechanism to place multiple measurement points while avoiding redundant measurements. For demonstration purpose, we use the developed mechanisms to estimate the Flow Size Distribution in SDN environments. An emulative evaluation taking several known topologies shows the effectiveness of spatial sampling using the proposed scheme.
Automatic Visual Features for Writer Identification: A Deep Learning Approach

Identification of a person from his writing is one of the challenging problems; however, it is not new. No one can repudiate its applications in a number of domains, such as forensic analysis, historical documents, and ancient manuscripts. Deep learning-based approaches have proved as the best feature extractors from massive amounts of heterogeneous data and provide promising and surprising predictions of patterns as compared with traditional approaches. We apply a deep transfer convolutional neural network (CNN) to identify a writer using handwriting text line images in English and Arabic languages. We evaluate different freeze layers of CNN (Conv3, Conv4, Conv5, Fc6, Fc7, and fusion of Fc6 and Fc7) affecting the identification rate of the writer. In this paper, transfer learning is applied as a pioneer study using ImageNet (base data-set) and QUWI data-set (target data-set). To decrease the chance of over-fitting, data augmentation techniques are applied like contours, negatives, and sharpness using text-line images of target data-set. The sliding window approach is used to make patches as an input unit to the CNN model. The AlexNet architecture is employed to extract discriminating visual features from multiple representations of image patches generated by enhanced pre-processing techniques. The extracted features from patches are then fed to a support vector machine classifier. We realized the highest accuracy using freeze Conv5 layer up to 92.78% on English, 92.20% on Arabic, and 88.11% on the combination of Arabic and English, respectively.
An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures

Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials’ signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene. Recently, investigating HU under the non-negligible endmember variability (EV) and outlier effects (OE) has drawn extensive attention. Some state-of-the-art works either consider EV or consider OE, but none of them considers both EV and OE simultaneously. In this paper, we propose a novel HU algorithm, referred to as the variability/outlier-insensitive multi-convex unmixing (VOIMU) algorithm, which is robust against both EV and OE. Considering two suitable regularizers, a nonconvex minimization problem is formulated for which the perturbed linear mixing model proposed by Thouvenin et al., is used for modeling EV, while OE is implicitly handled by applying a $p$ quasi-norm to the data fitting with $0 < p < 1$ . Then, we reformulate it into a multi-convex problem which is then solved by the block coordinate descent method, with convergence guarantee by casting it into the block successive upper bound minimization framework. The proposed VOIMU algorithm can yield a stationary-point solution with convergence guarantee, together with some intriguing information of potential outlier pixels though outliers are neither physically modeled in the above problem nor detected in the algorithm operation. Finally, we provide some simulation results and experimental results using real data to demonstrate the efficacy and practical applicability of the proposed VOIMU algorithm.
Adaptive Synchronization of Chaotic Brushless DC Motors with Uncertain System Parameters Based on Lyapunov Stability theory

The adaptive synchronization problem of chaotic Brushless DC motors with uncertain system parameters via controller and update law is investigated by using Lyapunov stability theory. The adaptive control are firstly proposed, and then by using numerical simulations we proofed the validity and reliability of the control method. Our study can solve the synchronization and the motors coordination problem in manufacturing industry.
Positive Streamer Development in Selected Dielectric Liquids in a Point-to-Sphere Electrode System with Pressboard Barrier

This article presents the results of the studies on positive streamers behavior in an insulation system of non-uniform electric field distribution where point-to-sphere electrode system was divided on two parts by placing the pressboard barrier in the middle of oil gap. The studies were performed for two synthetic esters as per IEC 61099, natural ester as per 62770, blended low viscosity ester and mineral oil as per IEC 60296. The experimental techniques used in the measurements were both electrical and optical ones. The results showed that independently of the liquids tested lightning strength of the insulation system increased circa 3 times in relation to the system without pressboard barrier. In turn in the waveforms of light emitted by the streamers propagating in esters no differences were observed between them. However in comparison to the mineral oil duration of streamer propagation is longer in esters and intensity of emitted light is higher. In general the results indicated quite similar properties of the streamers developing in all the liquids tested in the condition of the experiment.
A Multistage Binarization Technique for the Degraded Document Images

This paper presents a multistage binarization technique for the degraded document images. The proposed technique can deal with many types of the degradations, where we propose edge detection methods to find the edges of the objects in the image even if this image suffers from inhomogeneities. Then, to find the rest of the objects contents, we propose a combination of Niblack’s method, integral image, and a machine learning technique, where Markov random field is applied in an energy minimization framework using graph cut. Also we propose new formulas to measure the binarization efficiently and a new method for the binarization measurement. The results of extensive experiments on many datasets show the robustness of the proposed technique on various types of degradations in the document images where our technique demonstrates superior performance against also many other methods.
Selection of Related Stocks using Financial Text Mining

We propose a method to select and rank stocks related to a given theme. The proposed method has two flows; obtaining related words, and selecting related stocks based on obtained related words. First, on the basis of the given theme word, the proposed method selects words with high similarity using an ensemble of word2vec models. Then, we modify the similarity based on the results of the word matches in information from companies including investor relations documents and homepages. Second, the top-10 similar words are matched to the company data, and we extract sentences related to the given theme from the data of each company. We then calculate company similarity by summing the modified similarity of related words in the extracted sentences as a final similarity measure of each company. Finally, we select the top-n related stocks based on the obtained final similarity. Targeting the Japanese documents, companies, and stocks, we achieved 0.49 accuracy (precision, recall, and F1-value), which is better than the result of randomly selecting. In addition, by comparing the results obtained using a completely different theme, we verified that the proposed method works correctly and can filter related stocks effectively.
Detection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning

Software-defined radios (SDRs) with substantial cognitive (computing) and networking capabilities provide an opportunity for observing radio communications in an area and potentially identifying malicious rogue agents. Assuming a prevalence of encryption methods, a cognitive network of such SDRs can be used as a low-cost and flexible scanner/sensor array for distributed detection of anomalous communications by focusing on their statistical characteristics. Identifying rogue agents based on their wireless communications patterns is not a trivial task, especially when they deliberately try to mask their activities. We address this problem using a novel framework that utilizes adversarial learning, non-linear data transformations to minimize the rogue agent’s attempts at masking their activities, and game theory to predict the behavior of rogue agents and take the necessary countermeasures.
Secure and Reliable Resource Allocation and Caching in Aerial-Terrestrial Cloud Networks (ATCNs)

Aerial-terrestrial cloud networks (ATCNs), global integration of air and ground communication systems, pave a way for a large set of applications such as surveillance, on-demand transmissions, data-acquisition, and navigation. However, such networks suffer from crucial challenges of secure and reliable resource allocation and content-caching as the involved entities are highly dynamic and there is no fine-tuned strategy to accommodate their connectivity. To resolve this quandary, cog-chain, a novel paradigm for secure and reliable resource allocation and content-caching in ATCNs, is presented. Various requirements, key concepts, and issues with ATCNs are also presented along with basic concepts to establish a cog-chain in ATCNs. Feed and fetch modes are utilized depending on the involved entities and caching servers. In addition, a cog-chain communication protocol is presented which avails to evaluate the formation of a virtual cog-chain between the nodes and the content-caching servers. The efficacy of the proposed solution is demonstrated through consequential gains observed for signaling overheads, computational time, reliability, and resource allocation growth. The proposed approach operates with the signaling overheads ranging between 30.36 and 303.6 bytes*hops/sec and the formation time between 186 and 195 ms. Furthermore, the overall time consumption is 83.33% lower than the sequential-verification model and the resource allocation growth is 27.17% better than the sequential-verification model.
A Two-Stage Energy-Efficient Approach for Joint Power Control and Channel Allocation in D2D Communication

A large number of mobile multimedia terminals are prominent features of smart cities. Device-to-device (D2D) communication takes advantage of the limited bandwidth resources of cellular networks to accommodate more mobile devices. However, when D2D pairs reuse cellular users channels, serious interference leads to energy consumption, which dissatisfies the requirements of green communication. This paper focuses on energy efficiency maximization of D2D communication under the constraints of both D2D pairs and cellular users quality of service. The formulated resource allocation problem is NP-hard, which is usually difficult to solve within polynomial time. To make the problem easy to handle, we divide it into power control and channel allocation sub-problems. In particular, we propose a power control algorithm based on the Lambert W function to maximize the energy efficiency of a single D2D pair. The preference values of D2D pairs and cellular users are calculated using the power control results, respectively. A channel allocation scheme based on the Gale–Shapley algorithm utilizes preference values to match two sides, which aims at maximizing the signal to interference plus noise ratio of cellular users and the energy efficiency of D2D pairs. The simulation results show that the proposed algorithm could not only guarantee the transmission rate of cellular users but also improve the system and D2D pairs energy efficiency.
Leveraging Reconstructive Profiles of Users and Items for Tag-Aware Recommendation

It is an effective recommendation method by revealing user preferences and extracting latent semantic information of items through social tag information. Recent research shows impressive recommendation performance by using neural network-based methods to transform tag-based user or item profiles to abstract feature representations. However, in the process of training a neural network, these methods need an more effective measurement to balance the tag-based profiles and the abstract representations to further improve item recommendation. This paper proposes a method based on Generative Adversarial Networks to tackle this issue. In this method, abstract features of users and items are extracted from their tag-based profiles by a disentangling network. These abstract features are then used to calculate the probability of a user preferring an item, and are also used to reconstruct new user and item profiles by a generative network. Furthermore, the discriminative network is introduced to identify generated profiles for enforcing smoothness in the representation of users and items. Experiments on two real-world data-sets demonstrate the state-of-the-art performance of the proposed method.
From Geolocation-Based Only to Semantically-Aware Digital Advertising: A Neural Embedding Approach

The increasing availability of geo-location data in digital advertising businesses, like Valassis Digital, provides a vast amount of trajectories for mobile devices representing users location histories. As part of a unique massive data collection and analysis pipeline, a mapping stage from the geo-location data of mobile devices coming from advertisement exchange platforms to meaningful location atoms, such as geo-hashes, is included. Currently, location atoms are merely pieces of land in which their geographical properties connect them. However, online activities of mobile devices over time would impose an additional layer to the location data, which we call Semantical Location Layer (SLL). In fact, users online activities observed in different locations connect the location atoms not only by their geographical properties but also based on their sequences and co-occurrences. In this paper, we describe the developed SLL using a neural embedding algorithm to learn a latent representation of location atoms. Following this semantical layer, we develop a scalable collaborative filtering method to predict plausible location atoms for the devices. We show the results of the proposed method on very large geo-location data with a training set of size 200 million instances, and illustrate the impact of the proposed solution on the advertising business.
A Novel Approach for Handling Soft Error in Conjugate Gradients

Soft errors or bit flips have recently become an important challenge in high performance computing. In this paper, we focus on soft errors in a particular algorithm: conjugate gradients (CG). We present a series of techniques to detect soft errors in CG. We first derive a mathematical quantity that is monotonically decreasing. Next, we add a set of heuristics and combine our approach with previously established methods. We have extensively evaluated our method considering three distinct dimensions. First, we show that the F-score of our detection is significantly better than two other methods. Second, we show that for soft errors that are not detected by our method, the resulting inaccuracy in the final results are small, and better than those with other methods. Finally, we show that the runtime overheads of our method are lower than for other methods.
Predicting Soft Biometric Attributes from 30 Pixels: A Case Study in NIR Ocular Images

In this work, we investigate the possibility of extracting soft biometric attributes, viz., gender, race and eye color, from down-sampled near-infrared ocular images. In particular, we evaluate the possibility of deducing gender, race and eye color from ocular images as small as 56 pixels. Our preliminary analysis yields the surprising result that gender, race and eye color cues are still available in such low-resolution near-infrared images. This research bolsters the previously made assertion in the literature that certain soft biometric attributes can be deduced from poor quality biometric data.
Detecting Moving Shadow using a Fusion of Local Binary Pattern and Gabor Features

Detecting moving objects in the video sequences is an important issue for visual surveillance. It is needed to extract the moving shadows to describe the moving targets correctly. This paper proposes a method for detecting shadow by exploiting the similarity of texture between the segmented regions of moving objects and the corresponding regions in the background image. The proposed method generates texture similarity maps using Local Binary Pattern (LBP) and Gabor features. Principal Component Analysis (PCA) fuses the feature maps to generate an integrated texture similarity map to classify shadow pixels. HSV color model is then exploited for validation of the shadow pixels and reduce the misclassifications of both shadow and moving objects pixels. In the end, post processing step of morphological operations to determine the final shadow regions. Experiments show the effectiveness of the proposed method in shadow detection process and provide high-performance rates.