Miracle-dz/UniFAR-FTUs huggingface dataset:
I see EK: A lightweight technique to reveal exploit kit family by overall URL patterns of infection chains

: The prevalence and nonstop evolving technical sophistication of exploit kits (EKs) is one of the most challenging shifts in the modern cybercrime landscape. Over the last few years, malware infections via drive-by download attacks have been orchestrated with EK infrastructures. Malicious advertisements and compromised websites redirect victim browsers to web-based EK families that are assembled to exploit client-side vulnerabilities and finally deliver evil payloads. A key observation is that while the webpage contents have drastic differences between distinct intrusions executed through the same EK, the patterns in URL addresses stay similar. This is due to the fact that autogenerated URLs by EK platforms follow specific templates. This practice in use enables the development of an efficient system that is capable of classifying the responsible EK instances. This paper proposes novel URL features and a new technique to quickly categorize EK families with high accuracy using machine learning algorithms. Rather than analyzing each URL individually, the proposed overall URL patterns approach examines all URLs associated with an EK infection automatically. The method has been evaluated with a popular and publicly available dataset that contains 240 different real-world infection cases involving over 2250 URLs, the incidents being linked with the 4 major EK flavors that occurred throughout the year 2016. The system achieves up to 100% classification accuracy with the tested estimators.
A machine learning approach to detecting malware in TLS traffic using resilient network features

The growth of malware utilizing encrypted channels makes it challenging to detect malicious activity using current Network Intrusion Detection Systems (NIDSs). The current network intrusion detection systems utilize pattern matching algorithms to identify malware artifacts in the network traffic. In Transport Layer Security (TLS) encrypted networks, limited amount of data are available for the NIDS. This obstacle is exploited by malware authors to evade detection. In this thesis, we are looking into using machine learning classification algorithms to recognize malware communication within TLS channels without having to decrypt the network traffic. In the last few years, an increase in research has been looking into solutions for this problem using classification algorithms. We extend the existing research by identifying features in the TLS traffic that is resilient to evasion techniques used by more advanced types of malware. Advanced malware is more problematic to detect with traditional NIDSs since they try to evade these systems by generating traffic that is similar to ordinary corporate traffic. Features identified as resilient towards these evasion techniques are those describing the malware behavior in TLS encrypted traffic. Extracting behavior artifacts are performed with a mature NIDS and Network Security Monitoring (NSM) system called Suricata and Metadata Collector, a tool developed as a part of this thesis. With efficient classification algorithms, we can overcome some major challenges of NIDS face today.
Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks: A Q-Learning Approach for Graph Routing

Industrial wireless sensor networks usually have a centralized management approach, where a device known as network manager is responsible for the overall configuration, definition of routes, and allocation of communication resources. Graph routing is used to increase the reliability of communication through path redundancy. Some of the state-of-the-art graph-routing algorithms use weighted cost equations to define preferences on how the routes are constructed. The characteristics and requirements of these networks complicate to find a proper set of weight values to enhance network performance. Reinforcement learning can be useful to adjust these weights according to the current operating conditions of the network. In this article, we present the Q-learning reliable routing with a weighting agent approach, where an agent adjusts the weights of a state-of-the-art graph-routing algorithm. The states of the agent represent sets of weights, and the actions change the weights during network operation. Rewards are given to the agent when the average network latency decreases or the expected network lifetime increases. Simulations were conducted on a WirelessHART simulator considering industrial monitoring applications with random topologies. Results show, in most cases, a reduction of the average network latency while the expected network lifetime and the communication reliability are at least as good as what is obtained by the state-of-the-art graph-routing algorithms.
Joint Modulus Zero-Forcing MIMO Detector

We propose an improvement over a modulus zero-forcing (MZF) detector proposed earlier in [1] for multi-input multi-output (MIMO) detection, namely, the joint MZF (JMZF) detector. The MZF detector provides better detection-performance than a traditional ZF detector through preserving controllable interferences, which are mitigated later by nonlinear modulus arithmetic operation. However, the MZF detector has a constraint that the effective MIMO channel after modulus operation is diagonal, which yields separate detections on different transmit layers and is suboptimal. In this paper, we propose the JMZF detector by considering a general effective channel which can further boost the detection-performance.
Distance transform and template matching based methods for localization of barcodes and QR codes

Visual codes play an important role in automatic identification, which became an inseparable part of industrial processes. Thanks to the revolution of smartphones and telecommunication, it also becomes more and more popular in everyday life, containing embedded web addresses or other small informative texts. While barcode reading is straightforward in images having optimal parameters (focus, illumination, code orientation, and position), localization of code regions is still challenging in many scenarios. Every setup has its own characteristics, therefore many approaches are justifiable. Industrial applications are likely to have more fixed parameters like illumination, camera type and code size, and processing speed and accuracy are the most important requirements. In everyday use, like with smartphone cameras, a wide variety of code types, sizes, noise levels and blurring can be observed, but the processing speed is often not crucial, and the image acquisition process can be repeated in order for successful detection. In this paper, we address this problem with two novel methods for localization of 1D barcodes based on template matching and distance transformation, and a third method to detect QR codes. Our proposed approaches can simultaneously localize several different types of codes. We compare the effectiveness of the proposed methods with several approaches from the literature using public databases and a large set of synthetic images as a benchmark. The evaluation shows that the proposed methods are efficient, having 84.3 % Jaccard accuracy, superior to other approaches. One of the presented approaches is an improvement on our previous work. Our template matching based method is computationally more complex, however, it can be adapted to specific code types providing high accuracy. The other method uses distance transformation, which is fast and gives rough regions of interests that can contain valid visual code candidates.
AUDIO STEGANOGRAPHY TECHNIQUE USING CIRCULAR BIT REPLACEMENT

Audio steganography is one of the important methods to transit hidden information by modifying an audio signal in an imperceptible manner. In audio steganography, determining the best possible position for embedding the secret message with minimum alteration of the cover audio remains a challenging issue. In this technique we hide secrete text or audio information in a host message such that no one except the intended receiver is aware of its existence and prevents unauthorised access. Various techniques have been used for embedding information in digital audio. Least significant bit (LSB) technique is most commonly used technique for secure data transfer. This paper presents replacement of bits of samples to hide a sequence of bits of secrete text. The bits of samples are selected in a circular manner from LSB to MSB or vice versa. The results reveal satisfactory performance of the system and may be used for secure movement of data.
Automatic Extraction of Dependencies between Web Components and Database Resources in Java Web Applications

Web applications typically interact with databases. Therefore, it is very crucial to understand which web components access which database resources when maintaining web apps. Existing research identifies interactions between Java web components, such as JavaServer Pages and servlets but does not extract dependencies between the web components and database resources, such as tables and attributes. This paper proposes a dynamic analysis of Java web apps, which extracts such dependencies from a Java web app and represents them as a graph. The key responsibility of our analysis method is to identify when web components access database resources. To fulfill this responsibility, our method dynamically observes the database-related objects provided in the Java standard library using the proxy pattern, which can be applied to control access to a desired object. This study also experiments with open source web apps to verify the feasibility of the proposed method
Object Detection for Accident Prevention using Convolution Neural Network

: The growth of automobile population and the dearth of appropriate infrastructure have been a serious issue causing road accidents on a large scale all around the globe. Detection of objects for assisting drivers is a fascinating but challenging dilemma in the arena of pattern recognition. It is the most intricate study that has attracted the eye of several researchers. Convolution Neural Network is achieving popularity due to its performance and ability to classify complex images. In this paper we have designed our own CNN architecture to detect an object from an image. This CNN model was tested with different sized datasets of images with and without noise. The results obtained using different random filter initialization functions are presented.
Flowprints: A revised method for visualising flow in rap

ABSTRACT With scholarship on the musical dimensions of rap still in its infancy, there are still analytical tools that need to be developed and refined. We have a lot of data: the timing and articulation of hundreds of songs has been transcribed, thanks to a few very dedicated scholars. This data, however, exists in lengthy, largely neglected spreadsheets. By tracking recent developments in flow visualisation and utilising insights from each in a new approach, the paper contributes a working system for visualising flow aimed at nuance, automation, integration with existing corpuses, and accessibility for a lay audience.
S-UNet: A Bridge-Style U-Net Framework With a Saliency Mechanism for Retinal Vessel Segmentation

Deep learning methods have been successfully applied in medical image classification, segmentation and detection tasks. The U-Net architecture has been widely applied for these tasks. In this paper, we propose a U-Net variant for improved vessel segmentation in retinal fundus images. Firstly, we design a minimal U-Net (Mi-UNet) architecture, which drastically reduces the parameter count to 0.07M compared to 31.03M for the conventional U-Net. Moreover, based on Mi-UNet, we propose Salient U-Net (S-UNet), a bridge-style U-Net architecture with a saliency mechanism and with only 0.21M parameters. S-UNet uses a cascading technique that employs the foreground features of one net block as the foreground attention information of the next net block. This cascading leads to enhanced input images, inheritance of the learning experience of previous net blocks, and hence effective solution of the data imbalance problem. S-UNet was tested on two benchmark datasets, DRIVE and CHASE_DB1, with image sizes of <inline-formula> <tex-math notation="LaTeX">$584\times 565$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$960\times 999$ </tex-math></inline-formula>, respectively. S-UNet was tested on the TONGREN clinical dataset with image sizes of <inline-formula> <tex-math notation="LaTeX">$1880\times 2816$ </tex-math></inline-formula>. The experimental results show superior performance in comparison to other state-of-the-art methods. Especially, for whole-image input from the DRIVE dataset, S-UNet achieved a Matthews correlation coefficient (MCC), an area under curve (AUC), and an F1 score of 0.8055, 0.9821, and 0.8303, respectively. The corresponding scores for the CHASE_DB1 dataset were 0.8065, 0.9867, and 0.8242, respectively. Moreover, our model shows an excellent performance on the TONGREN clinical dataset. In addition, S-UNet segments images of low, medium, and high resolutions in just 33ms, 91ms and 0.49s, respectively. This shows the real-time applicability of the proposed model.
An Exploration of Evolutionary Algorithms for a Bi-objective Competitive Facility Location Problem in Congested Systems

This paper presents a bi-objective competitive facility location model for congested systems in which the entering facilities compete with the competitors’ facilities for capturing the market share. In the proposed model, customers can choose which facility to patronize based on the gravity function that depends on both the quality of service provider and the travel time to facilities. The proposed model attempts to simultaneously maximize the captured demand from each facility and minimize the total waiting time in the system. Two multi-objective evolutionary algorithms, involving a multi-objective harmony search algorithm (MOHS) and a non-dominated sorting genetic algorithm-II (NSGA-II), are designed to solve the proposed model. The performance of solution procedures are compared on the basis of different performance metrics including generational distance, spacing metric, diversification metric, and number of nondominated solution. The computational results based on different test problems show that in general MOHS outperforms NSGA-II.
Local Symmetry of Non-Coding Genetic Sequences

The simplest hypothesis of DNA strand symmetry states that proportions of nucleotides of the same base pair are approximately equal within single DNA strands. Results of extensive empirical studies using asymmetry measures and various visualization tools show that for long DNA sequences (approximate) strand symmetry generally holds with rather rare exceptions. In the paper, a formal definition of DNA strand local symmetry is presented, characterized in terms of generalized logits and tested for the longest non-coding sequences of bacterial genomes. Validity of a special regression-type probabilistic structure of the data is supposed. This structure is compatible with probability distribution of random nucleotide sequences at a steady state of a context-dependent reversible Markov evolutionary process. The null hypothesis of strand local symmetry is rejected in majority of bacterial genomes suggesting that even neutral mutations are skewed with respect to leading and lagging strands.
Leap Motion- and Mouse-Based Target Selection: Productivity, Perceived Comfort and Fatigue, User Preference, and Perceived Usability

ABSTRACT Remote pointing is an input method that involves in-air gesticulation. Target selection via Leap Motion-based remote pointing and mouse-based pointing were compared while addressing methodological limitations that were present in previous studies. Participants completed the ISO 9241-9 multidirectional tapping test for pointing device evaluation with a wide range of target size-distance combinations and a standardized arm posture. Dependent variables included movement time, errors, throughput, perceived comfort, perceived fatigue, user preference, and perceived usability. Leap Motion-based input led to (a) worse user productivity, (b) more finger, wrist, shoulder, and neck fatigue, (c) lower preference ratings, and (d) lower usability ratings than mouse-based input. The experiment (i) confirmed findings about remote pointing-related productivity and fatigue, and did so while controlling methodological limitations present in prior studies, (ii) revealed significant discomfort and fatigue despite the presence of an arm rest, and (iii) revealed new findings about preference and usability.
Scale-invariant MFCCs for speech/speaker recognition

The feature extraction process is a fundamental part of speech processing. Mel frequency cepstral coefficients (MFCCs) are the most commonly used feature types in the speech/speaker recognition literature. However, the MFCC framework may face numerical issues or dynamic range problems, which decreases their performance. A practical solution to these problems is adding a constant to filter-bank magnitudes before log compression, thus violating the scale-invariant property. In this work, a magnitude normalization and a multiplication constant are introduced to make the MFCCs scale-invariant and to avoid dynamic range expansion of nonspeech frames. Speaker verification experiments are conducted to show the effectiveness of the proposed scheme.
The sharing economy: promises and challenges

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Generating Chinese Ci with Designated Metrical Structure

Ci is a lyric poetry form that follows highly restrictive metrical structures. This makes it challenging for a computer to compose Ci subject to a specified metrical requirement. In this work, we adapt the CVAE framework to automated Ci generation under metrical constraints. Specifically, we present the first neural model that explicitly encodes the designated metrical structure for Ci generation. The proposed model is shown experimentally to generate Ci with nearly perfect metrical structures.
Exploring the Application of Information and Communication Technologies in the Acquisition of Information Resources in Three Academic Libraries in North-West Nigeria: Preliminary Findings

Abstract Information and Communication Technologies (ICTs) incursion has brought a lot of changes to libraries activities. ICTs are used to manage libraries more efficiently and in meeting customers’ demands more appropriately at the right time. Despite the contribution of ICTs to library and information work, many libraries are yet to adopt these technologies especially in the acquisitions of library materials. This study was carried out in three selected university libraries in North West Nigeria. The objectives of the study were to: determine the influence of performance expectancy on academic librarians’ intention to use ICT for acquisition; establish the influence of effort expectancy on academic librarians’ intention to use ICT for acquisition; find out the extent of social influence and facilitating conditions on academic librarians’ intention to use ICT for acquisition and lastly to proffer recommendations. This study adopted a positivistic approach and used a questionnaire administered to 224 librarians to collect data in the three selected libraries. The study is guided by the Unified Theory Acceptance and Use of Technology (UTAUT) developed by Venkatesh. Findings from the study reveals that academic librarians are supportive on the use of ICT in their work; ICTs are significantly important, trainings were organized mainly for librarians on general use of ICT not only on acquisition, but also on the general issues related to library activities; librarians also have the knowledge necessary to use ICT for acquisition in their libraries. The study recommends among others that the parent universities should increase the funding of their libraries with adequate supervision, so that such funds are well utilized for ICTs application; university libraries should continually sponsor their academic staff on modern computerized ways of using ICTs to source for materials and the acquisition of information resources in academic libraries should be given all the seriousness it deserves by enacting legislation to allocate a reasonable percentage of the university’s budget to it.
Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between a manipulator and an obstacle. The distance calculations are based on Gilbert–Johnson–Keerthi algorithm, which calculates the distance between a manipulator and an obstacle by directly using their three-dimensional geometries, which also implies that our algorithm works for a manipulator and an arbitrarily shaped obstacle. Theoretical treatment proves the stability and convergence, and simulations results using an LBR IIWA seven-DOF manipulator are presented to analyze the performance of the proposed framework.