Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine-Learning-Driven Evolutionary Approach for Testing Web Application Firewalls

Web application firewalls (WAFs) are an essential protection mechanism for online software systems. Because of the relentless flow of new kinds of attacks as well as their increased sophistication, WAFs have to be updated and tested regularly to prevent attackers from easily circumventing them. In this paper, we focus on testing WAFs for SQL injection attacks, but the general principles and strategy we propose can be adapted to other contexts. We present ML-Driven, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them. Initially, ML-Driven automatically generates a diverse set of attacks and submits them to the system being protected by the target WAF. Then, ML-Driven selects attacks that exhibit patterns (substrings) associated with bypassing the WAF and evolves them to generate new successful bypassing attacks. Machine learning is used to incrementally learn attack patterns from previously generated attacks according to their testing results, i.e., if they are blocked or bypass the WAF. We implemented ML-Driven in a tool and evaluated it on ModSecurity, a widely used open-source WAF, and a proprietary WAF protecting a financial institution. Our empirical results indicate that ML-Driven is effective and efficient at generating SQL injection attacks bypassing WAFs and identifying attack patterns.
A Comparative Study of Global and Deep Features for the Analysis of User-Generated Natural Disaster Related Images

The paper addresses the problem of adverse events (natural disasters) recognition in user-generated images from social media, addressing the problem from two complementary perspectives. On one side, we aim to provide a comprehensive comparative analysis of different feature extraction and classification algorithms, relying on two different families of feature extraction algorithms, namely (i) Global features and (ii) Deep features. On the other hand, we demonstrate that the fusion of different feature extraction and classification strategies can outperform the single methods by jointly exploiting the capabilities of individual feature descriptors. The evaluation of the methods are carried out on two datasets, including a benchmark and a self-collected dataset.
Selecting and Evaluating Representative Days for Generation Expansion Planning

This work applies feature engineering and machine learning to design a rigorous algorithm to select representative daily profiles of net load for Generation Expansion Planning (GEP). This algorithm incorporates Principal Component Analysis and clustering techniques to produce multiple sets of representative days. Guidelines to determine the proper number of representative days and the number of times the clustering process should be repeated are also proposed. These sets are then assessed using a rolling horizon unit commitment (RHUC) on a test system. Using RHUC as a metric to select the best set of representative days ensures that the selection is based on a criterion that closely approximates the operation of a power system with a large penetration of renewable generation.
Optimizing Load Forecasting Configurations of Computational Neural Networks

Load demand forecasting is a broad branch of electric power systems engineering. In the last few decades, hundreds of methods have been suggested by many brilliant researchers around the world to improve the current forecasting tools. These tools are categorized as: deterministic, probabilistic, stochastic, and artificial intelligent (AI) algorithms. Among these approaches, artificial neural networks (ANNs) proved themselves as very competitive and highly precise methods to accurately forecast energy. ANNs are divided into two main types called: feed-forward networks and recurrent/feedback networks, where each type of them has multiple sub-types. This study tries to improve the performance of any existing type/sub-type of ANNs by optimizing its configuration through using the biogeography-based optimization (BBO) algorithm. The number of input variables, layers, neurons, and the types of activation functions and training algorithm all are optimized. The goal is to preserve the simplicity, so only very simple multi-layer feed-forward ANNs are used instead of using time-series-based feed-forward/feedback ANNs. To prove the effectiveness of hybridizing ANNs with evolutionary algorithms (EAs), numerical simulations are carried-out on some Nova Scotia's loads. The results obtained from these optimally configured ANN s are highly significant, and thus they confirm that allegation.
LSTM Based Short Message Service (SMS) Modeling for Spam Classification

The Short Message Service (SMS) has widely extended in the modern methods of communication technology. The classification of the spam message is an interesting and prominent issue. Classifying availability of spam in SMS is a challenging task, a plenty of research has been carried out in this direction employing Machine Learning techniques such as Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) for Spam Classification. Although these methods have shown adequate performance, but are not efficient enough in terms of spam classification. Hence, a rigorous study is needed to find a more accurate and robust method. To address this, we proposed a novel method Long Short-Term Memory (LSTMs), which is an advanced structure of Recurrent Neural Network (RNN) that has gating mechanism including memory cells. Additionally, Word2Vec tool has been employed in this study, which converts simplified text into representation of words in a vector space. To evaluate the effectiveness of our method, SMS datasets have been used which are freely available. Experimental results prove that proposed method outperformed state-of-the-art Machine Learning methods like Random Forest (RF), SVM, kNN (k Nearest Neighbor), Decision Tree, and providing 97.5 percent accuracy.
Reducing VR Sickness Through Peripheral Visual Effects

This paper proposes and evaluates two novel visual effects that can be applied to Virtual Reality (VR) applications to reduce VR sickness with head-mounted displays (HMD). Unlike other techniques that pursue the same goal, our approach allows a user to move continuously through a virtual environment without reducing the perceived field of view (FOV). A within-design study with 18 users compares reported sickness between the two effects and baseline. The results show lower means of sickness in the two novel effects; however, the difference is not statistically significant across all users, replicating large variety in individual reactions found in previous studies. In summary, reducing optical flow in peripheral vision is a promising approach. Future potential lies in adjusting visual effect parameters to maximize impact for large user groups.
Study of Optimization of Air Route Networks and Locations of Crossing Waypoints for SEA

This study was conducted in three stages through a number of single and multi-objective Particle Swarm Optimization (PSO) simulations. In the first two stages, simulations were performed to assess the feasibility and effectiveness of the optimization algorithms and to study the efficiency of the Air Route Networks (ARNs) based on these algorithms. In the third stage, these PSO algorithms were applied to a South East Asia focused ARN. The ARN's efficiency indicators used were two principle objectives. Findings from this study can be used as a basis for future ARN designs. The optimization algorithms studied can serve as fundamental templates for future ARN simulation analyses.
Automatic Segmentation of Relevant Sections of the Conjunctiva for Non-Invasive Anemia Detection

Anemia is diagnosed by measuring the blood concentration of hemoglobin (Hb). In the literature, many studies have aimed to diagnose anemia with non-invasive methods, for example, estimating the pallor of the conjunctiva by means of digital images. In this way, this paper aims to identify a procedure for the automatic segmentation and optimization of conjunctiva sections. Therefore, image analysis algorithms have been applied to optimize the area of interest in terms of correlation with the estimated Hb value by blood sampling. Optimization was also possible through the study of the influence of image brightness on the correct Hb estimation by means of digital images of the conjunctiva. In conclusion, interesting experimental results were reported.
Detecting Stealthy False Data Injection Attacks in Power Grids Using Deep Learning

The electric power grid, as a critical national infrastructure, is under constant threat from cyber-attacks. State estimation (SE) is at the foundation of a series of critical control processes in a power transmission system. A false data injection (FDI) attack against SE can disrupt these control processes, crippling a power system and wreaking havoc in a region. With knowledge of the system topology, a cyber-attacker can formulate and execute stealthy FDI attacks that are very difficult to detect. Statistical and, more recently, machine learning approaches have been undertaken to detect FDI attacks on SE of the power grid. In this paper, we propose a Deep Learning (DL) based method to accurately detect stealthy FDI attacks on the SE of power grid. We compare the performance of the DL method with three popular machine learning algorithms, which are: gradient boosting machines (GBM), generalized linear modelings (GLM) and distributed random forests (DRF). All four algorithms analyze a dataset simulating the IEEE 14-bus system. The results demonstrate that these algorithms perform well in accurately and precisely detecting stealthy FDI attacks on the smart grid, with the DL-based approach showing best results.
Performance Evaluation of a Proposed On-Demand Recharging Policy in Wireless Sensor Networks

In order to tackle the energy hole problem in wireless sensor networks, various recharging policies have been proposed attempting to keep the average energy level high and the recharging distance (i.e., the distance covered by a mobile recharger) low. As shown in the literature, the recharging distance is minimized when the basis of the mobile recharger is located at the solution of a median problem that takes into account both distance and traffic demands. In this paper, a novel on-demand recharging policy is proposed based on local information, where the mobile recharger moves-upon request-to a target node of reduced energy level and replenishes its battery. Under the proposed policy, the mobile recharger moves in a hop-by-hop manner to the neighbor nodes of the lowest energy level, starting from the target node. The effectiveness of this policy is investigated here using simulation results and compared against an existing well-known on-demand recharging policy that exploits global knowledge (i.e., knowledge of both the energy level of all nodes and the network topology). It is shown that the proposed policy, even though based on local information, maintain the average energy level and termination time higher than that under the existing one that exploits global knowledge. Furthermore, it is observed that the network's lifetime is maximized when the basis of the mobile recharger is located at the solution of the mentioned median problem for all studied policies. Index Terms-Recharging Policy, Local Information, Wireless Sensor Networks.
The opportunity to choose enhances long-term episodic memory

ABSTRACT Episodic memory is typically studied under conditions that treat participants as passive agents. Here we sought to explore how actively engaging in ongoing naturalistic occurrences affects long-term episodic memory. Participants viewed 40 short movie clips that depicted a protagonist that conversed with the participants. In each clip, they were either offered the chance to (supposedly) determine the clip’s continuation (active condition), or let the computer decide for them (passive condition). Participants returned either two days or one week after the experience to undergo a true/false memory test for the clips’ details and a two-alternative recognition test for the choices made. Memory performance for both groups was superior for information and choices conveyed in the active vs. passive condition. These findings suggest that the sense of actively influencing the unfolding of events is beneficial to long-term memory of the experience at large, baring potential interventions in the fields of education and cognitive enhancement.
Automated Lubrication Systems Prognostics Using Long - Term Recurrent Convolutional Networks

In many mining processes, wheel loaders are used to extract high-grade ore from the seam. Unscheduled downtime in these primary digging units causes production delays and financial losses. An automatic lubrication system on-board the machine reduces friction wear on critical components and increases maintenance intervals. Blockages and leaks occur in this system, which will continue degrading until system failure. A sensor measures the grease pump pressure, from which distinct failure patterns can be extracted. These patterns indicate the system condition and remaining useful life. This data can be utilized to monitor system health using machine learning and artificial neural networks (ANNs). We present a Long-Term Recurrent Convolutional Network (LRCN) architecture applied to detecting and classifying abnormal pressure patterns in grease pumps on wheel loaders. We find that the LRCN extracts and classifies symptomatic patterns, and propose a method for monitoring remaining useful life in cyclic systems with highly variable failure trajectories.
Virtual Buzzwire: Assessment of a Prototype VR Game for Stroke Rehabilitation

We created a VR version of the Buzzwire children's toy as part of a project to develop tools for assessment and rehabilitation of upper-body motor skills for people with dexterity impairment after stroke. In two pilot studies, participants wearing a HMD used a hand-held wand with precision tracking to traverse virtual ‘wires'. In the first study, we compared able-bodied participant's performance with and without binocular viewing to establish a connection with previous experiments using physical versions of the game. Furthermore, we show that our extended measures were could also discern differences between subjects' dominant versus non-dominant hand. In a second study, we assessed the usability of the system on a small sample of subjects with post-stroke hemiparesis. There was positive acceptance of the technology with no fatigue or nausea and measurements highlighted the differences between the hemiparetic and unaffected hand.
Evaluation of Hand-Based Interaction for Near-Field Mixed Reality with Optical See-Through Head-Mounted Displays

Hand-based interaction is one of the most widely-used interaction modes in the applications based on optical see-through head-mounted displays (OST-HMDs). In this paper, such interaction modes as gesture-based interaction (GBI) and physics-based interaction (PBI) are developed to construct a mixed reality system to evaluate the advantages and disadvantages of different interaction modes for near-field mixed reality. The experimental results show that PBI leads to a better performance of users regarding their work efficiency in the proposed tasks. The statistical analysis of T-test has been adopted to prove that the difference of efficiency between different interaction modes is significant.
Popularity-Based Cache Placement for Fog Networks

Cache placement is a critical issue in fog networks. It is essential to simultaneously consider the quality of network connection, the demand of contents, and the users’ activities. This paper proposes an efficient cache placement by placing the files based on popularity categories within a fog node cluster. Requested files are categorized into three popularity levels and strategically cached in fog nodes of various activity levels This work aims to reduce energy consumption by reducing the number of cells to serve the users based on content popularity. Another contribution of this paper is the clustering method for the fog nodes to select the node to host for the cache contents. The effectiveness of the algorithm is tested using the simulation regarding energy efficiency.
Listener-Position Adaptive Crosstalk Cancelation Using a Parameterized Superdirective Beamformer

We present a method for cancelation of the acoustic crosstalk between the ears of a listener using a linear loudspeaker array. This allows for controlling the signals that arise at listener's ear independently of each other so that binaural cues can be imposed. We employ superdirective near-field beamforming (SDB) over the vast part of the frequency range. Allowing the listener to move in front of the array requires the computation of a new SDB solution for each possible position of the listener, which is a computationally expensive procedure. We show that the beamformer weights exhibit a very smooth evolution for listening positions along a line parallel to the array. This makes it straightforward to parameterize pre-computed beamformer weights with a few parameters for each frequency. Upon realtime execution, the beamformer weights can be determined efficiently for any arbitrary position along the listener contour from the parameters with negligible error. Simulation results show that the proposed beamformer provides a higher channel separation than previously published solutions while maintaining robustness.
A Taxonomy on Recent Mobile Malware: Features, Analysis Methods, and Detection Techniques

Smartphones, tablets, and other mobile devices have quickly become ubiquitous due to their highly personal and powerful attributes. Android has been the most popular mobile operating system. Such popularity, however, also extends to attackers. The amount of Android malware has risen steeply during the last few years, making it the most targeted mobile operating system. Although there have been numerous studies reviewing the current analysis and detection methods, they are unable to fully cover this research domain. Therefore, in this paper, we group the current analysis and detection methods in mobile malware detection. In addition, we review the Android features available in mobile malware detection, and several trusted and widely used datasets.
Anonymous Image Data Generation from Gastric X-Ray Images for Improving Gastritis Recognition Performance

This paper presents an anonymous gastritis image generation method for improving gastritis recognition performance. We realize the generation of realistic gastritis images by considering label information. Experimental results showed that anonymous images generated by our method had a potential for a gastritis recognition task. Concretely, the recognition performance of a classifier constructed with the anonymous images outperformed the performance of the conventional image generation method-based classifier.
Region Proposal Approach for Human Detection on Aerial Imagery

In this paper we evaluated region proposal based CNN approach in human body detection from aerial perspective. Particular emphasis is on the automation of the detection for supporting search and rescue missions. This challenging task is characterized by two important requirements. The first requirement for proposed algorithm is real-time speed of execution and the other is exceptional detection quality on complex natural environment images. Evaluation is performed on high spatial resolution images with high level of details that were collected by UAV platforms. Evaluated method based on FasterRCNN detection model showed promising preliminary results, as well as fast processing of high resolution images. Overall, detection model achieved 88.3% recall with precision of 67.3%.
Effective Scale-Invariant Feature Transform Based Iris Matching Technology for Identity Identification

In this work, the effective scale-invariant feature transform (SIFT) based iris matching method is developed for identity identification. To avoid the eyelid and eyelash interferences, the retrieved iris region in the proposed design only locates near the pupil around the ring area for the recognition. The iris features are enhanced by the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gabor filtering processes. Then the SIFT-based method is applied for iris features matching. The SIFT method uses the local features of images, and it keeps the feature invariance for the changes of rotation, scaling, and brightness. Finally, the Random Sample Consensus (RANSAC) skill is used to increase the matching efficiency. In experimental results, the accuracy of iris recognition is up to 96%. Compared with the other methods by using the same iris database and the SIFT-based technology, the recognition accuracy of the proposed design is suitable for the consumer identity identification application.
Optimal Signalling for Massive SIMO Channels

This paper focuses on a noncoherent flat Rayleigh fading wireless communication system with a single transmitter antenna and $N$ receiver antennas, i.e., a single-input-multiple-output (SIMO) system, in which the channel coefficients vary in every one time slot. For such a system, a fast noncoherent maximum likelihood (ML) detection algorithm and a corresponding closed form symbol error probability (SEP) formula are derived. In addition, the lower and upper bounds on SEP are established to quantitatively characterize how quickly SEP decays when $N$ goes to infinity, with two important concepts, full receiver diversity gain and geometrical coding gain, being introduced, showing that any nonnegative pulse amplitude modulation (PAM) constellation enables full receiver diversity for the massive SIMO system. Furthermore, we find that an optimal received constellation that maximizes the geometrical coding gain is the geometrical constellation. Comprehensive computer simulations validate our theoretical analysis and demonstrate that our proposed optimal constellation attains significant performance gains over the currently available constellation design the the same massive SIMO system.
Fast Content Updating Algorithm for an SRAM-Based TCAM on FPGA

Static random-access memory (SRAM)-based ternary content-addressable memory (TCAM), an alternative to traditional TCAM, where inclusion of SRAM improves the memory access speed, scalability, cost, and storage density compared to conventional TCAM. In order to confidently use the SRAM-based TCAMs in application, an update module (UM) is essential. The UM replaces the old TCAM contents with fresh contents. This letter proposes a fast update mechanism for an SRAM-based TCAM and implements it on Xilinx Virtex-6 field-programmable gate array. To the best of authors’ knowledge, this is the first ever proposal on content-update-module in an SRAM-based TCAM, which consumes least possible clock cycles to update a TCAM word.