Avoiding state enumeration in dynamic checking of distributed programs

Distributed programs are particularly vulnerable to software faults. Bugs in these programs are usually very hard to detect without automatic verification. The idea of checking an expected property in a given distributed program run (also referred to as runtime verification) has recently been attracting a great deal of attention for analyzing execution traces to ensure the reliability and dependability of distributed programs. Due to concurrency, the number of global states of a distributed program run tends to grow exponentially with respect to the number of program statements executed. As a result, checking the satisfaction of a property in a given distributed program run can incur significant overhead. This thesis introduces various ideas and exploits them to develop efficient dynamic property checking algorithms. These include the use of atom, introducing and exploiting the notion of serialization and finally proposing a methodology that exploits the concept of atoms and partial order semantics to specify and to check properties of distributed programs. The abstract specification of a distributed program can be mapped to the lower level implementation by labeling the code blocks that belong to the abstract functionalities of the program that are expected to be performed atomically. Each labeled code block is called an atom. Dynamically, an atom includes all the events that result from executing the selected statements from the corresponding code block. An efficient on-the-fly atomicity error detection algorithm has been developed. It is shown that if a run of a distributed program is atomic then the required properties can be checked on a reduced lattice, referred to as the atomic state lattice, which is significantly smaller than the original state lattice. Even with atomization, the number of global states can still grow exponentially in the number of atoms executed. However, when a number of processes has to maintain a property, we expect that each process will be, at some point in time, aware of the events of other processes that may affect the property. Consequently, it is not necessary to check the property in each state. Only synchronized states, where processes have already exchanged the information necessary to maintain the property, need to be considered. These states can be characterized by a synchronization predicate. Serialization of synchronized states is the minimal avenue for a set of processes to exchange the necessary information to maintain a property. Two efficient algorithms to check the satisfaction of a property in a distributed computation in cases where the synchronization predicate is conjunctive or disjunctive have been developed. Finally, a methodology based on the concept of atoms and a partially ordered multi-set (POMSET) model to specify and to check distributed programs properties has been proposed. The POMSET model promotes the separation of two different concerns in specifying and checking properties, namely, the ordering requirements and the computational requirements. A methodology to specify and to efficiently check the two requirements has been introduced

Artificial Bee Colony with Different Mutation Schemes: A comparative study

Artificial Bee Colony (ABC) is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of different mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR). The results on standard benchmark function suggest that the use of this operator improves performance in terms of convergence speed and quality of final obtained solution. It shows that Power and Polynomial mutations give best results. The fastest convergence was for the mutation rate value (MR=0.2)

Connecting Mobile Users Through Mobile Social Networks

Nowadays, social networks become popular with the emerging of web-based social networking services. Recently, several mobile services are developed to connect users to their favourite social networks such as Facebook, Twitter, Flickr, etc. However, these services depends upon the existing web-based social networks. In this paper, we present a mobile service for joining groups across communities. The originality of the work is that the framework of the service allows creating and joining social networks that are self-contained for mobile company servers. The service consists of several sub-services such as users invitation, group finding and others. Users, regardless of their disability, can use the service and its sub-services without the need to create their own accounts on social web sites and thus their own groups. We also propose a privacy control policy for mobile social networks

Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions

Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques

Connecting Mobile Users Through Mobile Social Networks

Nowadays, social networks become popular with the emerging of web-based social networking services. Recently, several mobile services are developed to connect users to their favourite social networks such as Facebook, Twitter, Flickr, etc. However, these services depends upon the existing web-based social networks. In this paper, we present a mobile service for joining groups across communities. The originality of the work is that the framework of the service allows creating and joining social networks that are self-contained for mobile company servers. The service consists of several sub-services such as users invitation, group finding and others. Users, regardless of their disability, can use the service and its sub-services without the need to create their own accounts on social web sites and thus their own groups. We also propose a privacy control policy for mobile social networks

Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3

Automatic detection and counting of crop circles in the desert can be of great use for large-scale farming as it enables easy and timely management of the farming land. However, so far, the literature remains short of relevant contributions in this regard. This letter frames the crop circles detection problem within a deep learning framework. In particular, accounting for their outstanding performance in object detection, we investigate the use of Mask R-CNN (Region Based Convolutional Neural Networks) as well as YOLOv3 (You Only Look Once) models for crop circle detection in the desert. In order to quantify the performance, we build a crop circles dataset from images extracted via Google Earth over a desert area in the East Oweinat in the South-Western Desert of Egypt. The dataset totals 2511 crop circle samples. With a small training set and a relatively large test set, plausible detection rates were obtained, scoring a precision of 1 and a recall of about 0.82 for Mask R-CNN and a precision of 0.88 and a recall of 0.94 regarding YOLOv3

Contrasting YOLOv5, Transformer, and EfficientDet Detectors for Crop Circle Detection in Desert

Ongoing discoveries of water reserves have fostered an increasing adoption of crop circles in the desert in several countries. Automatically quantifying and surveying the layout of crop circles in remote areas can be of great use for stakeholders in managing the expansion of the farming land. This letter compares latest deep learning models for crop circle detection and counting, namely Detection Transformers, EfficientDet and YOLOv5 are evaluated. To this end, we build two datasets, via Google Earth Pro, corresponding to two large crop circle hot spots in Egypt and Saudi Arabia. The images were drawn at an altitude of 20 km above the targets. The models are assessed in within-domain and cross-domain scenarios, and yielded plausible detection potential and inference response