The enhanced suffix array is an indexing data structure used for a wide range of applications in Bioinformatics. It is basically the suffix array but enhanced with extra tables that provide extra information to improve the performance in theory and in practice. In this paper, we present a number of improvements to the enhanced suffix array: 1) We show how to find a pattern of length m in O(m) time, i.e., independent of the alphabet size. 2) We present an improved representation of the bucket table. 3) We improve the access time of addressing the LCP (longest common prefix) table when one byte

Background: Comparative genomics is the analysis and comparison of genomes from different species. This area of research is driven by the large number of sequenced genomes and heavily relies on efficient algorithms and software to perform pairwise and multiple genome comparisons. Results: Most of the software tools available are tailored for one specific task. In contrast, we have developed a novel system CoCoNUT (Computational Comparative geNomics Utility Toolkit) that allows solving several different tasks in a unified framework: (1) finding regions of high similarity among multiple genomic

Wireless Sensor Networks (WSNs) consist of a collection of cheap, easy to deploy Sensor nodes arranged together to fulfill a specific purpose (monitoring, tracking...etc.). A WSN network is composed of a Base Station (BS) and collection of sensors. There are a lot of approaches for the network construction. Amongst them is the hierarchical structure, where the network is divided into clusters and the node inside this cluster communicates with BS through a chosen leader called Cluster Head (CH). In this paper, we present cluster-Head election algorithms for WSNs. We will discuss the operations

Recently, digital forensics analysis got a great attention in IT security. This is especially after cyber incidents are getting new form of organized crime which introduced Advanced Persistent Threats (APT), and hacking Kill Chain definitions. The threat intense rises when it is affecting the healthcare organization where it will be life-threatening. Handling such incidents is a great challenge for handlers to uncover the attack steps. With various sources of evidential data that require analysis, one analysis technique can be more beneficial than another, comparing to the time and resources

Optic Disc (OD) localization is an important pre-processing step that significantly simplifies subsequent segmentation of the OD and other retinal structures. Current OD localization techniques suffer from impractically-high computation times (few minutes per image). In this work, we present a fast technique that requires less than a second to localize the OD. The technique is based upon obtaining two projections of certain image features that encode the x- and y- coordinates of the OD. The resulting 1-D projections are then searched to determine the location of the OD. This avoids searching

Reputation systems are an emerging area of research in ad-hoc networks. They have been introduced as a security solution for nodes' misbehaving problem. A reputation system should cope with any kind of misbehavior. It enables honest nodes to make fair decisions about their neighbors. This may encourage nodes to behave well and cooperate in order to avoid being penalized or isolated. In this paper, we propose a new reputation system for Misbehavior Detection And Control in ad hoc Networks (MDAC). It aims to overcome some of the unsolved issues of other reputation systems, and it is customizable

Vehicular Ad-hoc Networks (VANETs) are a subset of Mobile Ad-hoc Networks (MANETs). They are deployed to introduce the ability of inter-communication among vehicles in order to guarantee safety and provide services for people while driving. VANETs are exposed to many types of attacks like denial of service, spoofing, ID disclosure and Sybil attacks. In this paper, a novel lightweight approach for preventing Sybil attack in VANETs is proposed. The presented protocol scheme uses symmetric key encryption and authentication between Road Side Units (RSUs) and vehicles on the road so that no

Recently, unmanned aerial vehicles (UAVs), also known as drones, have gained widespread interest in civilian and military applications, which has led to the development of novel UAVs that can perform various operations. UAVs are aircraft that can fly without the need of a human pilot onboard, meaning they can fly either autonomously or be remotely piloted. They can be equipped with multiple sensors, including cameras, inertial measurement units (IMUs), LiDAR, and GPS, to collect and transmit data in real time. Due to the demand for UAVs in various applications such as precision agriculture

Federated learning (FL) has recently been proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global

Motion and depth provide critical information in autonomous driving and they are commonly used for generic object detection. In this paper, we leverage them for improving semantic segmentation. Depth cues can be useful for detecting road as it lies below the horizon line. There is also a strong structural similarity for different instances of different objects including buildings and trees. Motion cues are useful as the scene is highly dynamic with moving objects including vehicles and pedestrians. This work utilizes geometric information modelled by depth maps and motion cues represented by