Sorting system for plastic garbage based on artificial intelligence
Janusz Bobulski and Mariusz Kubanek, Department of Computer Science, Czestochowa University of Technology, Poland
An important element of a complex recycling process that is an integral part of municipal waste management is the sorting of materials that can be re-used. Manual sorting of garbage is a tedious and expensive process, which is why scientists create and study auto-mated sorting techniques to improve the overall efficiency of the recycling process. An im-portant aspect here is the preliminary division of waste into various groups, from which de-tailed segregation of materials will take place. One of the most important contemporary en-vironmental problems is the recycling and utilization of plastic waste. The main problem under consideration in this article is the design of an automatic waste segregation system. A deep convoluted neural network will be used to classify images.
Convolutional Neural Network, Deep Learning, image processing, waste man-agement, environmental protection, recycling
Digital Image Forensics using Hexadecimal Image Analysis
Gina Fossati, Anmol Agarwal, Ebru Celikel Cankaya, Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA
Digital forensics is gaining increasing momentum today thanks to rapid developments in data editing technologies. We propose and implement a novel image forensics technique that incorporates hexadecimal image analysis to detect forgery in still images. The simple and effective algorithm we develop yields promising results with zero false positives. Moreover, it is comparable to other known image forgery detection algorithms w.r.t. runtime performance.
Forgery detection, image manipulation
Intelligent Recommender System Based on Sentiment Analysis
Mohamed Yacine GHERAIBIA1, Meryem AMMI2, Sohag Kabir3, Belkacem Chikhaoui4, Richard Hotte5, 1LICEF Research Institute, University of Teluq, Montreal, Canada, 2Naif Arab university for security sciences, Riyadh, KSA, 3Department of Computer Science and Technology, University of Bradford, UK, 4LICEF Research Institute, University of Teluq, Montreal, Canada and 5LICEF Research Institute, University of Teluq, Montreal, Canada
Recommender systems (RS) are widely used in web applications such as e-commerce, marketing campaigns and online publicity. The RS’s main objective is to provide recommendation to the users about products and content relevant to their interest. By the use of different algorithms and techniques, RS filters the available items to select the most relevant items to users. The provided recommendations are important for both users and organizations’ business. Thus, enhancing the recommendations to fall into the users’ interest is a challenging task. In this paper, we propose a new approach for enhancing the recommendations provided by RS by combining rating scores and the sentiment analysis of users’ reviews to generate new rating scores. The new raring scores are used to recommend items to users through the principal component analysis (PCA) and K-means methods. We experimentally demonstrated through extensive experiments the effectiveness of the proposed approach in enhancing the quality of recommendations.
Recommender Systems, Sentiment Analysis, Machine Learning, Information technology, Collaborative Filtering
Follow Then Forage Exploration: Improving A3c
James B. Holliday and T.H. Ngan Le, Department of Computer Science & Computer Engineering, University of Arkansas, Fayetteville, Arkansas, USA
Combining both value-iteration and policy-gradient, Asynchronous Advantage Actor Critic (A3C) by Google’s Deep Mind has successfully optimized deep neural network controllers on multi agents. In this work we propose a novel exploration strategy we call “Follow then Forage Exploration” (FFE) which aims to more effectively train A3C. Different from the original A3C where agents only use entropy as a means of improving exploration, our proposed FFE allows agents to break away from A3C's normal action selection which we call "following" and "forage" which means to explore randomly. The central idea supporting FFE is that forcing random exploration at the right time during a training episode can lead to improved training performance. To compare the performance of our proposed FFE, we used A3C implemented by Open AI’s Universe-Starter-Agent as baseline. The experimental results have shown that FFE is able to converge faster.
Reinforcement Learning, Multi Agents, Exploration
Public Authorities as Defendants: Using Bayesian Networks to determine the Likelihood of Success for Negligence claims in the wake of Oakden
Scott McLachlan1,2, Evangelia Kyrimi1, Norman E Fenton1, 1Risk and Information Management (RIM), Queen Mary University of London, UK and 2Health informatics and Knowledge Engineering Research (HiKER) Group
Several countries are currently investigating issues of neglect, poor quality care and abuse in the aged care sector. In most cases it is the State who license and monitor aged care providers, which frequently introduces a serious conflict of interest because the State also operate many of the facilities where our most vulnerable peoples are cared for. Where issues are raised with the standard of care being provided, the State are seen by many as a deep-pockets defendant and become the target of high-value lawsuits. This paper draws on cases and circumstances from one jurisdiction based on the English legal tradition, Australia, and proposes a Bayesian solution capable of determining probability for success for citizen plaintiffs who bring negligence claims against a public authority defendant. Use of a Bayesian network trained on case audit data shows that even when the plaintiff case meets all requirements for a successful negligence litigation, success is not often assured. Only in around one-fifth of these cases does the
plaintiff succeed against a public authority as defendant.
A Dynamic Approach for Managing Heterogeneous Wireless Networks in Smart Environments
Yara Mahfood Haddad and Hesham Ali, Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, USA
A Wireless Sensor Network (WSN) is a collection of sensors connected through a wireless infrastructure. Recently, WSNs have been evolving rapidly and likely to consist of heterogeneous sensors embedded in many objects performing various types of tasks. The emergence of the Internet of Things (IoT) represents a typical example of evolved WSNs, in which WSNs include sensors embedded in a variety of ‘things’ in a variety of environments. In this work, we propose a new approach for managing heterogeneous WSNs designed to accommodate variabilities associated with different environments. The proposed approach is implemented using genetic algorithms to achieve the flexibility needed to optimize different types of objective functions such as quality of coverage, redundancy and energy-awareness. We report the results of employing the proposed approach under different scenarios with different sets of assumptions and priorities for typical application domains. For assessment purposes, we compare our algorithm with two greedy algorithms used to manage WSNs in different applications. The proposed algorithm performs better than other methods and exhibits the ability to adjust to the different needs of each scenario.
Wireless Sensor Networks (WSNs), IoT, Genetic Algorithm, Homogeneous, Heterogeneous
An Enhanced Lucene Based System for Efficient Document/Information Retrieval
Alaidine Ben Ayed, Ismaïl Biskri and Jean-Guy Meunier, Université du Québec à Montréal (UQAM), Canada
In this paper we implement a document retrieval system using the Lucene tool and we conduct some experiments in order to compare the efficiency of two different weighting schema: the well-known TF-IDF and the BM25. Then, we expand queries using a comparable corpus (wikipedia) and word embeddings. Obtained results show that the latter method (word embeddings) is a good way to achieve higher precision rates and retrieve more accurate documents.
Internet and Web Applications, Data and knowledge Representation, Document Retrieval.
Wi-Fi Indoor localization using fingerprinting and Lateration
EL ABKARI Safae, EL MHAMDI Jamal, Department of Electrical Engineering, Ecole Normale Supérieure de l’Enseignement Technique, Mohamed V University, Rabat, Morocco
Indoor positioning has come under the spotlight as one of the upcoming applications due to its use in a variety of services. However, Wi-Fi based localization in indoor environment offers significant advantages utilizing current wireless infrastructures and good performances with low cost. The objective of this research is to provide a compromise between feasibility and accuracy for practical applications. We use filter to minimize Wi-Fi received signal strength (RSS) fluctuations and we combine two Wi-Fi approaches to locate a mobile user. Commonly known as fingerprinting, we exploit at first this technique that uses matching pre-recorded received signal strength (RSS) from nearby access point (AP) to the location data transmitted from the user in real time. The second technique is trilateration which is a distance-based approach using three known access points to determine positions. The combination of the two methods provides accuracy enhancement and wide indoor locating coverage.
Indoor positioning, Wi-Fi, fingerprinting, trilateration, Received Signal Strength.
Wireless Sensor Networks Simulators and Testbeds
Souhila Silmi1,2, Zouina Doukha1, Rebiha Kemcha2,3, Samira Moussaoui1, 1Department of Computer Science, USTHB University, RIMAA Lab., B P N°32 El Alia, 16000 Bab Ezzouar, Algiers, Algeria, 2Department of Computer Science, Normal Scool Superior, B P N°92 16308 Vieux-Kouba, Algiers, Algeria and 3Department of Computer Science, University of Boumerdes, LIMOSE Laboratory, Boumerdes, Algeria
Wireless sensor networks (WSNs) have emerged as one of the most promising technologies for the current era. They have been studied for years, but there is still remaining challenges for researchers since open opportunities to integrate new technologies are added to this field. One challenging task is WSN deployment. Yet, this is done by real deployment with testbeds platforms or by simulation tools when real deployment could be costly and time consuming. In this paper, we review the implementation and evaluation process in WSNs. We then describe relevant testbeds and simulation tools, and their features. Lastly, we conduct an experimentation study using these testbeds and simulations to highlight their pro and cons. As a use case, we implement a localization protocol. This paper opens the door for future work in achieving better implementations, in terms of reliability, accuracy and consumed time.
Wireless Sensor Network, Testbeds, Simulation Tools, Localization Protocol.
A New Intelligent Power Factor Corrector for Converter Applications
Hussain Attia, Department of Electrical, Electronics and Communications Engineering, School of Engineering, American University of Ras al khaimah, Ras Al Khaimah, UAE
This paper presents a new design of a unity power factor corrector for DC-DC converter applications based on an Artificial Neural Network algorithm. The controller firstly calculates the system power factor by measuring the phase shift between the grid voltage and drawn current. Secondly, the controller receives the absolute value of the grid voltage and the measured phase shift through the designed ANN, which predicts the duty cycle of the pulse width modulation (PWM) drive pulses, these PWM pulses used to drive the Boost DC-DC converter to enforce the drawn current to be fully in phase with the grid voltage as well as to improve the level of Total Harmonics Distortion (THD) of the drawn current. MATLAB/Simulink software is adopted to simulate the presented design. The analysis of the simulation results indicates the high performance of the proposed controller in terms of power factor correcting, and drawn current THD improving.
Power factor corrector, artificial neural network, Boost DC-DC converter, Total Harmonic Distortion, MATLAB/Simulink.
A Flow Simulation in the Foaming Process
Karel Frana1, Jörg Stiller2 and Iva Nová1, 1Technical University of Liberec, Studentska 2, 461 17 Liberec, Czech Republic and 2Technische Universität Dresden, Institut für Strömungsmechanik01062, Dresden, Germany
This paper deals with unsteady three-dimensional numerical calculations of the two-phase flow problem represents a gas bubble formation in the liquid in the container with the specific size. For calculations, the Volume of Fluids approach is adopted to resolve the shape of bubbles and their dynamics. The liquid phase is a mixture of water-ethanol and the gas phase is considered as air. The problem is treated as isothermal. The study is still limited to the lower flow rates at which bubbles are created and rising separately without any interaction, merging etc. However, this particular problem still required finer mesh especially in the domain in which bubbles are formed. The current results showed that air bubbles have a form of the ellipsoid and after they reach the liquid surface, they are moving towards to the side walls along this liquid level. This fact is interesting from the view of the foaming process and for the other investigation of the bubble behaviour at this phase interface. The flow study is calculated parallel using compressible multi-phase flow solver.
Multi-phase flow simulations, Computational mesh, Parallel calculations & Flow visualisation
A Stacked Ensemble Approach to the Security of Information System
OLASEHINDE Olayemi1, Alese B. K. 2 and OLAYEMI OLufunke C. 3, 1Department of Computer Science Federal Polytechnic, Ile Oluji, Ondo State, Nigeria, 2Department of Cyber Security, Federal University of Technology, Akure, Ondo State, Nigeria and 3Department of Computer Science, Joesph Ayo Babalola University, Ikeji, Osun State, Nigeria
Intrusion detection plays important role in the protection of information system against the growing activities of cyber attackers that seeks to compromise the availability, confidentiality and integrity of information system,Intrusion detection system (IDS) analyses network traffics to detect and alert any attempt to compromise computer systems and its resources, stacked ensemble build synergy among two or more IDS in order to improve and obtain amore accurate and improved intrusion detection accuracy, This research focuses on the application of Stacked Ensemble Approach to Intrusion Detection Systems(IDS). Three (3) filter-based feature selection methods comprising consistency-based, Information Gain-based and correlation-based methods were used to identifyrelevant features of the network traffic that identifies it as either a normal / attacks or attacks categories. Three (3) Supervised based-level machine learning algorithms comprising K Nearest Neighbor , Naïve Bayes’ and Decision Tree algorithms were used to build the Base-predictive models with all the features and reduced selected features. Information Gain-based method identifies the strongest and most efficient features for the network traffic and Decision Tree models gives the highest classification accuracy on evaluation with the testing dataset, the predictions of the base-level models were used to train the three (3) meta-level learning algorithms, namely; Meta Decision Tree (MDT), Multi Response Linear Regression (MLR) and Multiple Model Trees (MMT) to build the Stacked Ensemble models. The ensemble systems were implemented Python programming language, it was evaluated on Core i5, 6GB RAM and 500GB HDD laptop computer. The stacked ensemble records accuracy improvement of 3.0% and 5.11% over the best and least predictions of the base-level models respectively and a reduction of 0.89% and 3.29% over base-model least and highest false alarm rate respectively . The evaluation of this work shows a great improvement over reviewed work in literature.
Information Security, Intrusion, Detection accuracy, Base models, Ensemble, Detection Improvement.
Evaluating and Validating Cluster Results
Anupriya Vysala and Joseph Gomes, Department of Computer Science, Bowie State University, USA
Clustering is the technique to partition data according to their characteristics. Data that are similar in nature belong to same cluster . There are two type of evaluation methods to evaluate clustering quality. One is external evaluation where the truth labels in the data sets are known in advance and the other is internal evaluation in which the evaluation is done with data set itself without true labels. In this paper, both external evaluation and internal evaluation are performed on the cluster results of IRIS dataset. In case of external evaluation Homogeneity, Correctness and V-measure scores are calculated for the dataset. For internal performance measure, Silhouette Index and Sum of Square Errors are used. These internal performance measures along with the dendrogram (graphical tool from hierarchical Clustering) are used first to validate the number of clusters. Finally, as a statistical tool, we used the frequency distribution method to compare and provide a visual representation of the distribution of observations within a cluster result and the original data.
Hierarchical Agglomerative Clustering, K-means Clustering, Internal Evaluation, External Evaluation, Silhouette.
Sherry Yuan, Lionel Liang, Peter Potaptchik and Megan Boler, Computer Science, University of Toronto, Toronto, On, Canada
A major aim of this project is to integrate nuanced theoretical analysis of emotion with quantitative measurements of the latter so as to develop a multifaceted and interdisciplinary understanding of the role of emotion in politics, specifically around the time of the 2019 Canadian federal election. Elsewhere in this report we have discussed the concept of deep stories. Here we hope to take a more micro-level approach, which looks at the ways sentiments appear at the Tweet-level. So far this has involved using sentiment analysis on Twitter threads, where we have been interested in measuring the strength of sentiments (positive and negative) as conversations develop in the replies to popular tweets.
Sentiment Analysis, Canadian Politics, Emotion, Election, Deep Stories, Vader
What Comes After Shift-left? Shift-open with A Subsystem Firmware Design Framework
Rob Grant, AMD Inc, Markham, Ontario, Canada
This paper describes a development framework for pre-silicon verification of firmware within an SoC subsystem. The development framework integrates subsystem firmware into the RTL verification infrastructure of the subsystem to allow pre-silicon RTL/firmware co-simulations. The framework has been used to “shift-left” firmware development of subsystems developed in-house. This paper proposes that these techniques could also be applied to 3rd-party subsystem firmware to verify the proper integration of the subsystem. The ability to verify the integration of both RTL and firmware from a
subsystem provider enables a “shift open” in SoC design, allowing an increase in both the use and complexity of 3rd-party SoC subsystems containing firmware.
firmware co-simulation, shift-left, SoC verification