Youssef Taher,Fouad Moussaoui and Brahim Lamrabti,Amal Azaroual, Kawtar Bouzoubaa, Hanaa Moussaoui
Center of guidance and planning of education -Rabat -Morocco
In the 21st century, and across the world, fighting crime is major concern, particularly the assessment of recidivism risk factors (the likelihood of a person convicted of a crime to offend again). Today, some criminal justice systems starting to use big data to predict how likely someone who has just been released from prison is to commit another crime within a few years. The main objective is to predict where and when crime will happen before it actually occurs. These new predictions of recidivism are often solved by dozens software based on data- driven approaches. These software solutions are limited in terms of cases handled and accuracy. In this context, and by combining theory-driven and data-driven approaches, the current work proposes an example of big data conceptual framework oriented to recidivism prediction. To predict, analyze and compare different possible cases of recidivism, we aim for integrating all three types of recidivism (general, violent, and sexual recidivism) in one design, and optimizing the critical features to predict accurately each type. The proposed framework is based in three important modules to integrate the prediction of the three types of recidivism, and three important theoretical digital libraries to support the decisions making by the data-driven approach.
Recidivism, conceptual framework, big data, recidivism prediction
Roberto Hernández Santander1 and Esperanza Camargo Casallas, PhD2, 1Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia and 2Faculty of Technology, Universidad Distrital Francisco José de Caldas, Bogotá
Empirical Mode Decomposition is an adaptive and local tool that extracts underlying analytical components of a non-linear and non-stationary process, in turn, is the basis of Hilbert Huang transform, however, there are problems such as interfering modes or ensuring the orthogonality of decomposition. Three variants of the algorithm are evaluated, with different experimental parameters and on a set of 10 time series obtained from surface electromyography. Experimental results show that obtaining low error in reconstruction with the analytical signals obtained from a process is not a valid characteristic to ensure that the purpose of decomposition has been fulfilled (physical significance and no interference between modes), in addition, freedom must be generated in the iterative processes of decomposition so that it has consistency and does not generate biased information. This project was developed within the framework of the research group DIGITI of the Universidad Distrital Francisco José de Caldas.
EMD, EEMD, CEEMDAN, mix of modes, non-linearity and non-stationarity
Jia-Chyi Wu, Department of Communications, Control and Navigation Engineering, National Taiwan Ocean University, Keelung, Taiwan, ROC
Rate-distortion formulation is the information-theoretic approach to the study of signal encoding systems. Since a more general approach to model the nonstationarity exhibited by real-world signals is to use appropriately fitted time varying autoregressive (TVAR) models, we have investigated the rate-distortion function R(D) for the class of time varying nonstationary signals. In this study, we present formulations of the rate-distortion function for the Gaussian TVAR processes. The R(D) function can serve as an information-theoretic bound on the performance achievable by source encoding techniques when the processing signal is represented exclusively by a Gaussian TVAR model.
Rate Distortion, Nonstationary, Time Varying Autoregressive (TVAR) Process
Gaseb, Alotibi1 Nathan Clarke2, Fudong Li2 and Steven Furnell2,3, 1Ministry of Interior, Public Security, IT Department Plymouth University, Plymouth, United Kingdom, 2Security Research Institute, Edith Cowan University, Western Australia and 3Center for Research in Information and Cyber Security, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa
Insider threat has become a serious issue to the many organizations. Various companies are increasingly deploying many information technologies to prevent unauthorized access to getting inside their system. Biometrics approaches have some techniques that contribute towards controlling the point of entry. However, these methods mainly are not able to continuously validate the users reliability. In contrast behavioral profiling is one of the biometrics technologies but it focusing on the activities of the users during using the system and comparing that with a previous history. This paper presents a comprehensive analysis, literature review and limitations on behavioral profiling approach and to what extent that can be used for mitigating insider misuse.
insider threat, behaviouial profiling, insider misuse
Befekadu G. Gebraselase, Bjarne E. Helvik, Yuming Jiang, NTNU – Norwegian University of Science and Technology
The underlying network infrastructure faces challenges from addressing maintenance, security, performance, and scalability to make the network more reliable and stable. Software-defined networking, blockchain, and network function virtualization were proposed and realized to address such issues in both academic and industry wise. This paper analyzes and summarizes works from implementing different categories of blockchains as an element or enabler of network functions to resolve the limitation. Blockchain as a network function has been proposed to give support to the underlying network infrastructure to provide services that have less lag, are more cost-effective, have better performance, guarantee security between participating parties, and protect the privacy of the users. This paper provides a review of recent work that makes use of blockchain to address such networking related challenges and the possible setbacks in the proposal.
Blockchain, Performance, Security, Network Function
Frederick Abban, Dr. Koudjo M. Koumadi, Computer Engineering Department, University of Ghana, Legon, Ghana
The universal exponential increase in the demand for high data rate for mobile devices has propelled lots of research in wireless communication. Deploying and implementing a wireless network in a particular geographical area requires proper planning since all existing propagation models are not “one size fit all” models. In this study, path losses of seven empirical propagation model were simulated and compared with results of measurements of received signal strength in Non-Line of Sight (NLOS) scenario for Accra, Ghana on 2300 MHz. The study terrain is similar to most cities on the coast of West Africa. Correction factors were computed and applied to original propagation model equations and the Ericsson 9999 model showed the best fit to the measurement data, thus it can be used to predict received signal strength for Accra and other environments with similar terrains.
Propagation Models, Received Signal Strength, LTE, Path Loss, NLOS
Pengfei Yue and Ru Li, Department of Computer Inner Mongolia University Hohhot, China
The Named Data networking (NDN) immunes to most of the attacks which exist in today’s Internet. However, this new born network architecture may still subject to Distributed Denial of Service (DDOS) attacks if less evaluation is paid. In this paper, we give a survey of the state of art works on the mitigations of the Content Poisoning Attack in NDN and discuss their limitations as well. After this, we give out our mitigation, named To Register First Before Publishing (RFBP), and the results from simulations show that with the implementation of our mitigation, the Interest Satisfaction Rate (ISR) of all Consumers maintains a highly acceptable rate even when network is under attack.
The Named Data networking (NDN), Denial of service attack (Dos), Content Poisoning Attack
Nicholas Patterson, Deakin University, Australia
Virtual worlds have become highly popular in recent years with reports of over a billion people accessing these environments and the virtual goods market growing to near 50 billion US dollars. An undesirable outcome to this popularity and market value is thriving criminal activity in these worlds. The most profitable cyber security problem in virtual worlds is named Virtual Property Theft. The aim of this study is to use an online survey to gain insight into how hackers (n=100) in these synthetic worlds conduct their criminal activity. This survey is the first to report an insight into the criminal mind of hackers (virtual thieves). Results showed a clear-cut profile of a virtual property thief, they appear to be mainly aged 20- 24 years of age, live in the United States of America, while using virtual worlds for 5-7 hours a day. These and the other key results of this study will provide a pathway for designing an effective anti-theft framework capable of abolishing this cyber security issue.
Virtual world environments, virtual property theft, cyber security, hackers, massively multiplayer online games
Carlos Martinez1, Ana Gonzalez2, 1Department of Research, Hispatel Ing. Srl, Murcia, Spain 2Institute of Photonics Technology, UPM University, Madrid, Spain
The evolving complexity of modern technologies brings new concepts, solutions, new tools, but also new needs and problems. This research reviews a model of computing inspired in physics, and seeing complex computing systems under the perspective of symmetry and conservation-laws. The aim is to help and control computing systems and designs. A proposal includes using physics-based principles like inertia to model communications. Seeing computing under a physics-conservation perspective allows accounting side effects on all process automatically, this proposed to help verification, security, and to gain reliability. We use Feynman-diagrams in computing, and their rotation to convey operation reversibility as well as design consistent with minimal formulas.
Unconventional Computing Model, Inertia, Data Communications, Symmetry, Reliability, Cloud
Jamolbek Mattiev1 and Branko Kavšek1,2, 1Department of Information Science and Technologies, University of Primorska, Koper, Slovenia, 2Artificial Intelligence Laboratory, Jozef Stefan Institute, Ljubljana, Slovenia
Existing well-known classification rule based algorithms use mainly greedy heuristic search to find regularities in datasets for classification. In recent years, extensive research on association rule mining was performed in the machine learning community on learning rules by using exhaustive search. The main aim of association rule mining is to find all rules in datasets that satisfy the user-specified minimum support and minimum confidence thresholds. Even though the all rules may not be used directly for effective classification, accurate and efficient classifiers have been built using these rules, so called, classification association rules. In this paper, we compare “classical” classification rule learning algorithms that uses greedy heuristic search with a class association rule(car) learner that uses constrained exhaustive search to find classification rules on real-life datasets. We propose a simple method to extract the reasonable number of class association rules by grouping them to form an accurate classifier. We have performed experiments on 12 datasets from UCI Machine Learning Database Repository and compared the results with well-known rule based and tree based classification algorithms. Experimental results show that our method was consistent and comparative with others, our proposed method achieves the fourth highest result among all classification algorithms (rule based and tree based although) on average accuracy with 82.16%. Although Random Forest, Ripper and PART algorithms gained the highest results on average accuracy, our method achieved the best accuracy (81%) on “Breast Cancer” dataset.
Attribute, frequent Itemset, Minimum Support, Minimum, Confidence, Class Association Rules, Classification.
Zeinab FARHAT1, Ali KAROUNI2, Bassam DAYA3 and Pierre CHAUVET4 1EDST,Lebanese University,Lebanon,Beirut,2,3University Institute of Technology,Lebanese University,Lebanon,Sidon,4LARIS EA, Angers University France, France, Angers
Nowadays, road traffic accidents are one of the leading causes of deaths in this world. It is a complex phenomenon leaving a significant negative impact on human’s life and properties. Classification techniques of data mining are found efficient to deal with such phenomena. After collecting data from Lebanese Internal Security Forces, data are split into training and testing sets using 10-fold cross validation. This paper aims to apply two different algorithms of Decision Trees C4.5 and CART, and various Artificial Neural Networks (MLP) in order to predict the fatality of road accidents in Lebanon. Afterwards, a comparative study is made to find the best performing algorithm. The results have shown that MLP with 2 hidden layers and 42 neurons in each layer is the best algorithm with accuracy rate of prediction (94.6%) and area under curve (AUC 95.71%).
Data mining, Fatal Road Accident Prediction, Neural Networks, Decision trees.
Christian Mancas, Department of Mathematics and Computer Science, Ovidius University, Constanta, Romania
MatBase is a prototype intelligent data and knowledge base management system based on the Relational, Entity-Relationship, and (Elementary) Mathematical Data Models, having two current versions (MS SQL Server and C#, MS Access and VBA). Users may work with it only at one or any combination of these conceptual levels, without any programming knowledge (be it SQL, C#, VBA, etc.), to create, populate, update, and delete databases and corresponding management software applications. The paper introduces the MatBase architecture and the principles used to transparently program while modelling data at these three conceptual levels with this tool. A real-life example illustrates them.
Conceptual Data Modelling, Automatic Code Generation, Relational Constraints, Non-relational Constraints, DBMS Engine Architectures, The (Elementary) Mathematic Data Model, MatBase
Rehab A. Rayan, Department of Epidemiology, High Institute of Public Health, Alexandria University, Alexandria, Egypt
Progress in computational sciences for cleaning, sorting, combining, digging, visualizing and managing data along with technological advancements in medical devices have urged needs for further extensive and consistent approaches to discuss the common key issues in medicine and health. Artificial Intelligence (AI) has significantly obtained grounds in everyday living in the era of information technology and it has now landed in healthcare. AI studies’ in healthcare are evolving swiftly. However, it could only be the start of observing how it will influence patient care. AI tries to simulate human cognitive capacities. It is carrying a transformation pattern to healthcare, strengthened by the escalating availability of clinical data and sped up advancement in analytics systems. Nonetheless, there is a similar doubt, including some pressing warning at these elevated anticipations. This review examines the present state of AI applications in health, major developments in health AI, and the disparate consequences of health AI and offers some directions for institutions and caregivers utilizing AI techniques.
Information Technology, Artificial Intelligence, Evidence-based Practice, Health & Medicine
Natarajan Meghanathan,Jackson State University, 1400 Lynch St, Jackson, MS, USA
Until now, we were determining stable data gathering (DG) trees for mobile sensor networks (MSNs) using a link stability metric (computationally-light or computationally-heavy) that is directly computed on the egocentric edge network. Among such DG trees, the BPI' (complement of bipartivity index)-based DG trees were observed to be the most stable, but the BPI' metric is also computationally-heavy. Hence, we seek to build a multi-variable linear regression model to predict the BPI' values for the egocentric networks of edges using three computationally-light metrics (neighborhood overlap: NOVER, one-hop two-hop neighborhood: OTH, and normalized neighbor degree: NND) that are also computed on the egocentric edge networks. The training and testing are conducted as part of a single simulation run (i.e., in-situ). The training dataset comprises of the BPI', NOVER, OTH and NND values of randomly sampled egocentric edge networks during the first phase of the simulation (1/5th of the total simulation time). We observe the R-square values for the prediction to be above 0.85 for both low density and high density networks. We also observe the lifetimes of the predicted BPI'-based DG trees to be 87-92% and 55-75% of the actual BPI'-based DG trees for low-moderate and moderate-high density networks respectively.
Multi-variable Regression, Bipartivity Index, Computationally-Light, Computationally-Heavy, Mobile Sensor Networks, Data Gathering Tree
Hichem KMIMECH1, Achraf Jabeur Telmoudi2, Layth Sliman1 and Lotfi Nabli1, 1University of Monastir, Research Laboratory of Automatic Signal and Image Processing and 2Efrei Cole D’ingnieur Des Technologies De L’information Et De La Communication, France
Computing the minimum initial marking (MIM) in labeled Petri nets (PN) while considering a sequence of labels constitutes a difficult problem. The existing solutions of such a problem suffer from diverse limitations.In this paper, we proposed a new approach to automatically compute the MIM in labeled PNs in a timely fashion. We adopted a grasp (Greedy Randomized Adaptive Search Procedure)-based algorithm to model the MIM problem. The choice of such an algorithm is justified by the nature of the MIM process which belongs to the NP-hard class. We experimentally showed the effectiveness of our approach and empirically studied the initial marking quality in particular.
Labeled Petri Nets;Minimum Initial Marking;Label sequence;GRASP algorithms