Thursday, April 29, 2021

MANAGING THE SYNTACTIC BLINDNESS OF LATENT SEMANTIC ANALYSIS

Author :  Raja Muhammad Suleman

Affiliation :  Edge Hill University, Ormskirk

Country :  United Kingdom

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 04, April, 2020

Abstract :

Natural Language Processing is a sub-field of Artificial Intelligence that is used for analysing and representing human language automatically. Natural Language Processing has been employed in many applications, such as information retrieval, information processing, automated answer grading etc. Several approaches have been developed for understanding the meaning of text, commonly known as semantic analysis. Latent Semantic Analysis is a widely used corpus-based approach that evaluates similarity of text on the basis of semantic relations among words. Latent Semantic Analysis has been used successfully in different language systems for calculating the semantic similarity of texts. However, Latent Semantic Analysis ignores the structural composition of sentences and therefore this technique suffers from the syntactic blindness problem. Latent Semantic Analysis fails to distinguish between sentences that contain semantically similar words but have completely opposite meaning. Latent Semantic Analysis is also blind to the syntactic structure of a sentence and therefore cannot differentiate between sentences and lists of keywords. In such a situation, the comparison between a sentence and a list of keywords without any syntactic structure gets a high similarity score. In this research we propose an algorithmic extension to Latent Semantic Analysis which focuses on syntactic composition of a sentence to overcome Latent Semantic Analysis’s syntactic blindness problems. We tested our approach on sentence pairs containing similar words but having different meaning. Our results showed that our extension provides more realistic semantic similarity scores.

Keyword :  Natural Language Processing, Natural Language Understanding, Latent Semantic Analysis, Semantic Similarity.

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100401.pdf

INFORMATION RETRIEVAL IN DATA SCIENCE CURRICULA

Author :  Duaa Bukhari

Affiliation :  Long Island University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

Data scientists are very much in demand as companies grapple with the challenge of making valuable discoveries from Big Data. Therefore, academic institutions have started to offer different kind of data science DS programs and they strive to prepare students to be data scientists who are capable to face the challenge of the new age. As an interdisciplinary field, DS programs should represent a combination of subject areas from several disciplines. Consequently, schools that host data science programs are diverse. Until now few studies have investigated data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that explore DS programs and investigate its curriculum structure across disciplines. This study conducted an exploratory content analysis of 30 United States’ DS programs from a variety of disciplines. The present study seeks to depict the current state of DS education in the U.S. to explore what discipline DS programs covers at the graduate level. The analysis was conducted on course titles and course descriptions. The results show that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. It can be said that all DS programs requires the basic knowledge of database design, representation, extraction and management. DS programs delivered information skills through their core courses. Results indicates that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Required courses also addressed communication visualization and mathematics skills.

Keyword :  Data Science, Information Retrieval, Curricula, Master’s Programs, DS curriculum

For More Details https://aircconline.com/csit/papers/vol10/csit100309.pdf

Tuesday, April 27, 2021

TWO STAGED PREDICTION OF GASTRIC CANCER PATIENT’S SURVIVAL VIA MACHINE LEARNING TECHNIQUES

Author :  Peng Liu

Affiliation :  Southeast University, Nanjing

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

Cancer is one of the most common causes of death in the world, while gastric cancer has the highest incidence in Asia. Predicting gastric cancer patients’ survivability can inform patients care decisions and help doctors prescribe personalized medicine. Classification techniques have been widely used to predict survivability of cancer patients. However, very few attention has been paid to patients who cannot survive. In this research, we consider survival prediction to be a twostaged problem. The first is to predict the patients’ five-year survivability. If the patient’s predicted outcome is death, the second stage predicts the remaining lifespan of the patient. Our research proposes a custom ensemble method which integrated multiple machine learning algorithms. It exhibits a significant predictive improvement in both stages of prediction, compared with the state-of-the-art machine learning techniques. The base machine learning techniques include Decision Trees, Random Forest, Adaboost, Gradient Boost Machine (GBM), Artificial Neural Network (ANN), and the most popular GBM framework--LightGBM. The model is comprehensively evaluated on open source cancer data provided by the Surveillance, Epidemiology, and End Results Program (SEER) in terms of accuracy, area under the curve, Fscore, precision, recall rate, training and predicting time in the classification stage, and root mean squared error, mean absolute error, coefficient of determination (R2 ) in the regression stage.

Keyword :  Gastric Cancer, Cancer Survival Prediction, Machine Learning, Ensemble Learning, SEER

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100308.pdf

ENTANGLEMENT IN SHOR'S FACTORING ALGORITHM

Author :  Jianing Tan

Affiliation :  Southeast University, Nanjing 210096

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

Quantum algorithms are well known for their higher efficiency compared to their classical counterparts. However, the origin of the speed-up offered by quantum algorithms is a debatable question. Using entanglement measure based on coefficient matrix, we investigate the entanglement features of the quantum states used in Shor’s factoring algorithm. The results show that if and only if the order r is 1, the algorithm generates no entanglement. Finally, compare with published studies results (Proceedings: Mathematical, Physical and Engineering Sciences, 459(2036): 2011-2032, 2003, Physical Review A, 72(6): 062308, 2005), we give counter examples to show that previous researches neglect partially entanglement.

Keyword :  Shor's factoring algorithm, entanglement measure, coefficient matrices

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100307.pdf

Sunday, April 25, 2021

PLAYING VIRTUAL MUSICAL DRUMS BY MEMS 3D ACCELEROMETER SENSOR DATA AND MACHINE LEARNING

Author :  Shaikh Farhad Hossain

Affiliation :  Graduate School of Information Science, Nara Institute of Science and Technology (NAIST)

Country :  Japan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

In our life, music is a vital entertainment part whose important elements are musical instruments. Forexample, the acoustic drum plays a vital role when a song is sung. With the modern era, the style of themusical instruments is changing by keeping identical tune such as an electronic drum. In this work, wehave developed "Virtual Musical Drums" by the combination of MEMS 3D accelerometer sensor data and machine learning. Machine learning is spreading in all arena of AI for problem-solving and the MEMS sensor is converting the large physical system to a smaller system. In this work, we have designed eight virtual drums for two sensors. We have found a 91.42% detection accuracy within the simulation environment and an 88.20% detection accuracy within the real-time environment with 20% windows overlapping. Although system detection accuracy satisfaction but the virtual drum sound was nonrealistic. Therefore, we implemented a 'multiple hit detection within a fixed interval, sound intensity calibration and sound tune parallel processing' and select 'virtual musical drums sound files' based on acoustic drum sound pattern and duration. Finally, we completed our "Playing Virtual Musical Drums" and played the virtual drum successfully like an acoustic drum. This work has shown a different application of MEMS sensor and machine learning. It shows a different application of data, sensor and machine learning as music entertainment with high accuracy.

Keyword :  Virtual musical drum, MEMS, SHIMMER, support vector machines (SVM) and k-Nearest Neighbors (kNN)

For More Details https://aircconline.com/csit/papers/vol10/csit100306.pdf

Friday, April 23, 2021

ONTOLOGY-BASED MODEL FOR SECURITY ASSESSMENT: PREDICTING CYBERATTACKS THROUGH THREAT ACTIVITY ANALYSIS

Author :  Pavel Yermalovich

Affiliation :  Université Laval Québec City

Country :  Canada

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

The prediction of attacks is essential for the prevention of potential risk. Therefore, risk forecasting contributes a lot to the optimization of the information security budget. This article focuses on the ontology and stages of a cyberattack. It introduces the main representatives of the attacking side and describes their motivation.

Keyword :  Cyberattack, cyberattack prediction, ontology, cyberattack ontology, information security, cybersecurity,IT security, data security, threat activity.

For More Details https://aircconline.com/csit/papers/vol10/csit100305.pdf

Thursday, April 22, 2021

ENHANCING NETWORK FORENSICS with PARTICLE SWARM and DEEP LEARNING: THE PARTICLE DEEP FRAMEWORK

Autor :  Nickolaos Koroniotis

Affiliation :  University of New South Wales Canberra

Country :  Australia

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

The popularity of IoT smart things is rising, due to the automation they provide and its effects on productivity. However, it has been proven that IoT devices are vulnerable to both well established and new IoT-specific attack vectors. In this paper, we propose the Particle Deep Framework, a new network forensic framework for IoT networks that utilised Particle Swarm Optimisation to tune the hyperparameters of a deep MLP model and improve its performance. The PDF is trained and validated using Bot-IoT dataset, a contemporary network-traffic dataset that combines normal IoT and non-IoT traffic, with well known botnet-related attacks. Through experimentation, we show that the performance of a deep MLP model is vastly improved, achieving an accuracy of 99.9% and false alarm rate of close to 0%.

Keyword :  Network forensics, Particle swarm optimization, Deep Learning, IoT, Botnets

For More Details https://aircconline.com/csit/papers/vol10/csit100304.pdf

PERFORMANCE EVALUATION OF PRINCE BASED GLITCH PUF WITH SEVERAL SELECTION PARTS

Author :  Yusuke Nozaki

Affiliation :  Meijo University

Country :  Japan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

To enhance the internet of things (IoT) security, lightweight ciphers and physically unclonable functions (PUFs) have attracted attention. Unlike standard encryption AES, lightweight ciphers can be implemented on embedded devices with strict constraints used in IoT. The PUF is a technology extracting manufacturing variations in LSI as device's unique ID. Since manufacturing variations cannot be cloned physically, the generated ID using PUF can be used for device's authentication. Actually, a method combining lightweight cipher (PRINCE) and PUF (glitch PUF) called PRINCE based glitch PUF has been proposed in recent years. However, PRINCE based glitch PUF was not optimized for PUF performances. Therefore, this study evaluates the detailed PUF performance of PRINCE based glitch PUF with changing the parameters. Experimental results using FPGAs clarified that PRINCE based glitch PUF had the relationship of trade-off between steadiness and uniqueness depending on the selected part as glitch generator.

Keyword :  Hardware Security, Physically Unclonable Function, Glitch PUF, PRINCE, Lightweight Cipher

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100303.pdf

Wednesday, April 21, 2021

Call for Papers - 7th International Conference on Networks & Communications (NCOM 2021)

 7th International Conference on Networks & Communications (NCOM 2021)

June 19 ~ 20, 2021, Copenhagen, Denmark

https://ccsea2021.org/ncom/index

Important Dates

Submission Deadline:April 24, 2021
Authors Notification:May 10, 2021
Final Manuscript Due:May 18, 2021
Contact Us: ncom@ccsea2021.org




THE IMPACT OF AI ON THE DESIGN OF RECEPTION ROBOT: A CASE STUDY

Author :  Nguyen Dao Xuan Hai

Affiliation :  HCMC University of Technology and Education Ho Chi Minh City

Country :  Viet Nam

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

Service robots have recently drawn a lot of attention from the public. Integrating with the artificial intelligence of computer science, modern service robots have great potential because they are capable of performing many sophisticated human tasks. In this paper, the service robot named "MiABot" as receptionist robot is described, it is a mobile robot with autonomous platform being used with a differential drive and controlled by mini PC. The MiABot could sense its surroundings with the aid of various electronic sensors while mechanical actuators were used to move it around. Robot's behaviour was determined by the program, which was loaded to the microcontrollers and PC with Artificial Intelligence. The experiment results demonstrated the feasibility and advantages of this predictive control on the trajectory tracking of a mobile robot. Service robots are designed to assist humans in reception tasks. Robots will interact closely with a group of people in their daily environment. This means that it is essential to create models for natural and intuitive communication between humans and robots.The theoretical basis of artificial intelligence and its application in the field of natural language processing. Besides, robot software architecture is designed and developed. Robot operation modes and implementation are addressed and discussed, they contain information on algorithm for human – robot interacting in natural language, thus a simple approach for generating robot response in arm gesture and emotion. Finally, system evaluation and testing is addressed.

Keyword :  AI, Artificial Intelligence, Service Robot, Receptionist Robot, NLP

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100302.pdf

Tuesday, April 20, 2021

TOPIC TRACKING AND VISUALIZATION METHOD USING INDEPENDENT TOPIC ANALYSIS

Author :  Takahiro Nishigaki

Affiliation :  Aoyama Gakuin University

Country :  Japan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 03, March, 2020

Abstract :

In this paper propose a topic tracking and visualization method using Independent Topic Analysis. Independent Topic Analysis is a method for extracting mutually independent topics from the documents data by using the Independent Component Analysis. In recent years, as the amount of information increases, there is often a desire to analyse topic transitions in time-series documents and track topics. For example, it is possible to analyse the causes of trend and hoaxes by SNS and predict future changes. However, there is no topic tracking method in Independent Topic Analysis. There is also no way to visualize topic tracking. So, topics in each periodwas extracted, and topic transition was analysed based on the similarity of topics. And, a method for tracking these four topics was proposed. In addition, this paper developed an interface that visualizes time-series changes of the tracked topics and obtained effective results through user experiments.

Keyword :  Data Mining, Independent Topic Analysis, Text Mining, Topic Tracking

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100301.pdf

Sunday, April 18, 2021

RESEARCH ON FARMLAND PEST IMAGE RECOGNITION BASED ON TARGET DETECTION ALGORITHM

Author :  Shi Wenxiu

Affiliation :  University of Jinan

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 02, March, 2020

Abstract :

In order to achieve the automatic identification of farmland pests and improve recognition accuracy, this paper proposes a method of farmland pest identification based on target detection algorithm .First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.

Keyword :  Object detection algorithm, Faster R-CNN, Inception network

For More Details https://aircconline.com/csit/papers/vol10/csit100210.pdf

Friday, April 16, 2021

DESIGN OF SOFTWARE TRUSTED TOOL BASED ON SEMANTIC ANALYSIS

Author :  Guofengli

Affiliation :  Beijing University of Technology

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 02, March, 2020

Abstract :

At present, the research on software trustworthiness mainly focuses on two parts: behavioral trustworthiness and trusted computing. The research status of trusted computing is in the stage of active immune of trusted 3.0. Behavioral trustworthiness mainly focuses on the detection and monitoring of software behavior trajectory. Abnormal behaviors are found through scene and hierarchical monitoring program call sequence, Restrict sensitive and dangerous software behavior. At present, the research of behavior trust mainly uses XML language to configure behavior statement, which constrains sensitive and dangerous software behaviors. These researches are mainly applied to software trust testing methods. The research of XML behavior statement file mainly uses the method of obtaining sensitive behavior set and defining behavior path to manually configure. It mainly focuses on the formulation of behavior statements and the generation of behavior statement test cases. There are few researches on behavior semantics trustworthiness. Behavior statements are all based on behavior set configuration XML format declaration files. There are complicated and time-consuming problems in manual configuration, including incomplete behavior sets. This paper uses the trusted tool of semantic analysis technology to solve the problem of behavior set integrity And can generate credible statement file efficiently The main idea of this paper is to use semantic analysis technology to model requirements, including dynamic semantic analysis and static semantic analysis. This paper uses UML model to automatically generate XML language code, behavioral semantic analysis and modeling, and formal modeling of non functional requirements, so as to ensure the credibility of the developed software trusted tools and the automatically generated XML files.

Keyword :  behavior declaration, behavior semantic analysis, trusted tool design, functional semantic set.

For More Details https://aircconline.com/csit/papers/vol10/csit100207.pdf

Thursday, April 15, 2021

FROM QUALITY ASSURANCE TO QUALITY ENGINEERING FOR DIGITAL TRANSFORMATION

Author :  Kiran Kumaar CNK

Affiliation :  Capgemini India Private Limited, Inside Divyasree Techno Park, Kundalahalli, Brookefield

Country :  India

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 02, March, 2020

Abstract :

Defects are one of the seven prominent wastes in lean process that arises out of the failure of a product or functionality from meeting customer expectations. These defects, in turn, can cause rework and redeployment of that product or functionality again, which costs valuable time, effort, and money. As per the survey, most of the clients invest much time, energy, and money in fixing production defects. This paper provides information about ways to move into quality engineering from quality assurance mode for digital transformation by diagnostic, Predictive & Prescriptive approaches, it also outlines the overall increase in quality observations, given QA shift left and continuous delivery through Agile with the integration of analytics and toolbox.

Keyword :  Diagnostic, Predictive & Prescriptive approaches, continuous delivery through Agile

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100206.pdf

Monday, April 12, 2021

TWO APPROACHES TOWARD GRAPHICAL DEFINITIONS OF KNOWLEDGE AND WISDOM

Author :  Mark Atkins

Affiliation :  Florida Institute of Technology

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 02, March, 2020

Abstract :

Two approaches are taken here in an endeavor to discover natural definitions of knowledge and wisdom that are justifiable with respect to both theory and practice, using graph theory: (1) The metrics approach is to produce graphs that force an increase in various graph metrics, whereas (2) the dimensions approach is based on the observation that the graphical representation of aggelia in the DIKW hierarchy seems to increase in dimension with each step up the hierarchy. The dimensions method produces far more cogent definitions than the metrics method, so that is the set of definitions proposed, especially for use in artificial intelligence.

Keyword :  Knowledge Representation, Artificial Intelligence, Graph Theory, DAG, DIKW

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100205.pdf

Sunday, April 11, 2021

DATA MINING AND MACHINE LEARNING IN EARTH OBSERVATION – AN APPLICATION FOR TRACKING HISTORICAL ALGAL BLOOMS

Author :  Alexandria Dominique Farias

Affiliation :  International Space University

Country :  France

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 02, March, 2020

Abstract :

The data produced from Earth Observation (EO) satellites has recently become so abundant that manual processing is sometimes no longer an option for analysis. The main challenges for studying this data are its size, its complex nature, a high barrier to entry, and the availability of datasets used for training data. Because of this, there has been a prominent trend in techniques used to automate this process and host the processing in massive online cloud servers. These processes include data mining (DM) and machine learning (ML). The techniques that will be discussed include: clustering, regression, neural networks, and convolutional neural networks (CNN). This paper will show how some of these techniques are currently being used in the field of earth observation as well as discuss some of the challenges that are currently being faced. Google Earth Engine (GEE) has been chosen as the tool for this study. GEE is currently able to display 40 years of historical satellite imagery, including publicly available datasets such as Landsat, and Sentinel data from Copernicus. Using EO data from Landsat and GEE as a processing tool, it is possible to classify and discover historical algal blooms over the period of ten years in the Baltic Sea surrounding the Swedish island of Gotland. This paper will show how these technical advancements including the use of a cloud platform enable the processing and analysis of this data in minutes.

Keyword :  Earth Observation, Remote Sensing, Satellite Data, Data Mining, Machine Learning, Google Earth Engine, Algal Blooms, Phytoplankton Bloom, Cyanobacteria

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100204.pdf

Friday, April 9, 2021

MERAMALNET: A DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK FOR BIOACTIVITY PREDICTION IN STRUCTUREBASED DRUG DISCOVERY

Author :  Hentabli Hamza

Affiliation :  Universiti Teknologi Malaysia

Country :  Malaysia

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 02, March, 2020

Abstract :

According to the principle of similar property, structurally similar compounds exhibit very similar properties and, also, similar biological activities. Many researchers have applied this principle to discovering novel drugs, which has led to the emergence of the chemical structure-based activity prediction. Using this technology, it becomes easier to predict the activities of unknown compounds (target) by comparing the unknown target compounds with a group of already known chemical compounds. Thereafter, the researcher assigns the activities of the similar and known compounds to the target compounds. Various Machine Learning (ML) techniques have been used for predicting the activity of the compounds. In this study, the researchers have introduced a novel predictive system, i.e., MaramalNet, which is a convolutional neural network that enables the prediction of molecular bioactivities using a different molecular matrix representation. MaramalNet is a deep learning system which also incorporates the substructure information with regards to the molecule for predicting its activity. The researchers have investigated this novel convolutional network for determining its accuracy during the prediction of the activities for the unknown compounds. This approach was applied to a popular dataset and the performance of this system was compared with three other classical ML algorithms. All experiments indicated that MaramalNet was able to provide an interesting prediction rate (where the highly diverse dataset showed 88.01% accuracy, while a low diversity dataset showed 99% accuracy). Also, MaramalNet was seen to be very effective for the homogeneous datasets but showed a lower performance in the case of the structurally heterogeneous datasets.

Keyword :  Bioactive Molecules, Activity prediction model, Convolutional neural network, Deep Learning, biological activities

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100203.pdf

LOCAL GRAY WORLD METHOD FOR SINGLE IMAGE DEHAZING

Author :  Vedran Stipetić

Affiliation :  University of Zagreb

Country :  Croatia

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 02, March, 2020

Abstract :

Images taken outdoors are often degraded by atmospheric conditions such as fog and haze. These degradations can reduce contrast, blur edges, and reduce saturation of images. In this paper we propose a new method for single image dehazing. The method is based on an idea from color constancy called the gray world assumption. This assumption states that the average values of each channel in a picture are the same. Using this assumption and a haze degradation model we can quickly and accurately estimate the haze thickness and recover a haze free image. The proposed method is validated on a synthetic and natural image dataset and compared to other methods. The experimental results have shown that the proposed method provides comparable results to other dehazing methods.

Keyword :  image restoration, image dehazing

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100202.pdf


Wednesday, April 7, 2021

PRIVACY-PRESERVING PATTERN RECOGNITION WITH IMAGE COMPRESSION

Author :  Takayuki Nakachi

Affiliation :  Nippon Telegraph and Telephone Corporation,Kanagawa

Country :  Japan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 02, March, 2020

Abstract :

In this paper, we propose a privacy-preserving pattern recognition scheme that well supports image com- pression. The proposed scheme is based on secure sparse coding using a random unitary transform. It offers the following two prominent features: 1) It is capable of pattern recognition in the encrypted image domain. Even if data leaks, privacy can be maintained because data remains encrypted. 2) It realizes Encryption-then-Compression (EtC) systems, where image encryption is conducted prior to compression. The pattern recognition can be carried out in the compressed signal domain using a few sparse coefficients. Based on the pattern recognition result, it can compress the selected images with high quality by estimat- ing sufficient number of sparse coefficients. We use the INRIA dataset to demonstrate its performance in detecting humans. The proposal is shown to realize human detection with encrypted images and efficiently compress the images selected in the image recognition stage.

Keyword :  Surveillance Camera, Pattern Recognition, Secure Computation, Sparse Coding, Random Unitary Trans- form

For More Details https://aircconline.com/csit/papers/vol10/csit100201.pdf

FILTER-BASED ACTIVE SUSPENSION SYSTEM WITH ADAPTED REFERENCE INPUT

 Author :  Adel Djellal

Affiliation :  Higher School for Industrial Technologies, Annaba

Country :  Algeria

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 01, January, 2020

Abstract :

In this paper, the Active Suspension system is controlled using a PID controller with an adapted reference point. After the derivation of the quarter car suspension model. Three approaches were applied: passive suspension system, Active Suspension system with constant reference and with adapted reference. The proposed approach was focusing on system life span; how to reduce brutal controller actions, that can cause car body damage, and assure a certain ride comfort? Simulation of three approaches has been done using the quarter car system and Matlab simulation model to implement the proposed technique and compare performance variation in different cases: road bump and other road disturbances.

Keyword :  Active Suspension system, PID controller, Quarter car model, Passive Suspension system

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100112.pdf


Sunday, April 4, 2021

RGBD BASED GENERATIVE ADVERSARIAL NETWORK FOR 3D SEMANTIC SCENE COMPLETION

 Author :  Jiahao Wang, Ling Pei

Affiliation :  Shanghai Jiao Tong University

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 01, January, 2020

Abstract :

3D scene understanding is of importance since it is a reflection about the real-world scenario. The goal of our work is to complete the 3d semantic scene from an RGB-D image. The state-ofthe-art methods have poor accuracy in the face of complex scenes. In addition, other existing 3D reconstruction methods use depth as the sole input, which causes performance bottlenecks. We introduce a two-stream approach that uses RGB and depth as input channels to a novel GAN architecture to solve this problem. Our method demonstrates excellent performance on both synthetic SUNCG and real NYU dataset. Compared with the latest method SSCNet, we achieve 4.3% gains in Scene Completion (SC) and 2.5% gains in Semantic Scene Completion (SSC) on NYU dataset.

Keyword :  Scene Completion, Semantic Segmentation, Generation Adversarial Network, RGB-D

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100111.pdf

Friday, April 2, 2021

COMPARISON OF TURKISH WORD REPRESENTATIONS TRAINED ON DIFFERENT MORPHOLOGICAL FORMS

Author :  Gökhan Güler

Affiliation :  Istanbul Technical University

Country :  Turkey

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 01, January, 2020

Abstract :

Increased popularity of different text representations has also brought many improvements in Natural Language Processing (NLP) tasks. Without need of supervised data, embeddings trained on large corpora provide us meaningful relations to be used on different NLP tasks. Even though training these vectors is relatively easy with recent methods, information gained from the data heavily depends on the structure of the corpus language. Since the popularly researched languages have a similar morphological structure, problems occurring for morphologically rich languages are mainly disregarded in studies. For morphologically rich languages, context-free word vectors ignore morphological structure of languages. In this study, we prepared texts in morphologically different forms in a morphologically rich language, Turkish, and compared the results on different intrinsic and extrinsic tasks. To see the effect of morphological structure, we trained word2vec model on texts which lemma and suffixes are treated differently. We also trained subword model fastText and compared the embeddings on word analogy, text classification, sentimental analysis, and language model tasks.

Keyword :  embedding, vector, morphology, Turkish, word2vec, fast

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100110.pdf

Thursday, April 1, 2021

EVALUATING VERBAL PRODUCTION LEVELS

 Author :  Fabio Fassetti

Affiliation :  University of Calabria

Country :  Italy

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 01, January, 2020

Abstract :

The paper presents a framework to evaluate the adequateness of a written text with respect to age or in presence of pathologies like deafness. This work aims at providing insights about verbal production level of an individual in order for a therapist to evaluate the adequateness of such level. The verbal production is analyzed by several points of view, categorized in six families: orthography, syntax, lexicon, lemmata, morphology, discourse. The proposed approach extract several features belonging to these categories through ad-hoc algorithms and exploits such features to train a learner able to classify verbal production in levels. This study is conducted in conjunction with a speech rehabilitation center. The technique is precisely designed for Italian language, however the methodology is more widely applicable. The proposed technique has a twofold aim. Other than the main goal of providing the therapist with an evaluation of the provided essay, the framework could spread lights on relationship between capabilities and ages. To the best of our knowledge, this is the first attempt to perform these evaluations through an automatic system.

Keyword :  Verbal production, Feature Extraction, Deep Learning.

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100109.pdf

FASE-AL — ADAPTATION OF FAST ADAPTIVE STACKING OF ENSEMBLES FOR SUPPORTING ACTIVE LEARNING

 Author :  Agustín Alejandro Ortiz-Díaz

Affiliation :  Santa Catarina State University

Country :  Brazil

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 01, January, 2020

Abstract :

Classification algorithms to mine data stream have been extensively studied in recent years. However, a lot of these algorithms are designed for supervised learning which requires labeled instances. Nevertheless, the labeling of the data is costly and time-consuming. Because of this, alternative learning paradigms have been proposed to reduce the cost of the labeling process without significant loss of model performance. Active learning is one of these paradigms, whose main objective is to build classification models that request the lowest possible number of labeled examples achieving adequate levels of accuracy. Therefore, this work presents the FASE-AL algorithm which induces classification models with non-labeled instances using Active Learning. FASE-AL is based on the algorithm Fast Adaptive Stacking of Ensembles (FASE). FASE is an ensemble algorithm that detects and adapts the model when the input data stream has concept drift. FASE-AL was compared with four different strategies of active learning found in the literature. Real and synthetic databases were used in the experiments. The algorithm achieves promising results in terms of the percentage of correctly classified instances.

Keyword :  Ensemble, active learning, data stream and concept drift.

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100108.pdf