Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

2017

Discipline
Institution
Keyword
Publication
Publication Type
File Type

Articles 1771 - 1800 of 2767

Full-Text Articles in Computer Sciences

Parallel, Cross-Platform Unit Testing For Real-Time Embedded Systems, Tosapon Pankumhang Jan 2017

Parallel, Cross-Platform Unit Testing For Real-Time Embedded Systems, Tosapon Pankumhang

Electronic Theses and Dissertations

Embedded systems are used in a wide variety of applications (e.g., automotive, agricultural, home security, industrial, medical, military, and aerospace) due to their small size, low-energy consumption, and the ability to control real-time peripheral devices precisely. These systems, however, are different from each other in many aspects: processors, memory size, develop applications/OS, hardware interfaces, and software loading methods. Unit testing is a fundamental part of software development and the lowest level of software testing, as it tests individual or groups of functions, methods, and classes, to increase confidence that the developed software satisfies both software specifications and user requirements. Although …

Go to article

An Annotated Corpus With Nanomedicine And Pharmacokinetic Parameters, Nastassja Lewinski, Ivan Jimenez, Bridget Mcinnes Jan 2017

An Annotated Corpus With Nanomedicine And Pharmacokinetic Parameters, Nastassja Lewinski, Ivan Jimenez, Bridget Mcinnes

Chemical and Life Science Engineering Publications

A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration’s Drugs@FDA online database. In this work, we present the development of the Engineered Nanomedicine Database corpus to support the …

Go to article

Parsing Metamap Files In Hadoop, Amy Olex, Alberto Cano, Bridget T. Mcinnes Jan 2017

Parsing Metamap Files In Hadoop, Amy Olex, Alberto Cano, Bridget T. Mcinnes

Computer Science Publications

The UMLS::Association CUICollector module identifies UMLS Concept Unique Identifier bigrams and their frequencies in a biomedical text corpus. CUICollector was re-implemented in Hadoop MapReduce to improve algorithm speed, flexibility, and scalability. Evaluation of the Hadoop implementation compared to the serial module produced equivalent results and achieved a 28x speedup on a single-node Hadoop system.

Go to article

Semi-Supervised Approach To Monitoring Clinical Depressive Symptoms In Social Media, Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth Jan 2017

Semi-Supervised Approach To Monitoring Clinical Depressive Symptoms In Social Media, Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth

Computer Science and Engineering Faculty Publications

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms …

Go to article

Road Accidents Bigdata Mining And Visualization Using Support Vector Machines, Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma Jan 2017

Road Accidents Bigdata Mining And Visualization Using Support Vector Machines, Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

Kno.e.sis Publications

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new …

Go to article

Relatedness-Based Multi-Entity Summarization, Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng Jan 2017

Relatedness-Based Multi-Entity Summarization, Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng

Kno.e.sis Publications

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and …

Go to article

An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed Jan 2017

An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and …

Go to article

Gpu-Pcc: A Gpu Based Technique To Compute Pairwise Pearson’S Correlation Coefficients For Big Fmri Data, Taban Eslami, Muaaz Gul Awan, Fahad Saeed Jan 2017

Gpu-Pcc: A Gpu Based Technique To Compute Pairwise Pearson’S Correlation Coefficients For Big Fmri Data, Taban Eslami, Muaaz Gul Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive brain imaging technique for studying the brain’s functional activities. Pearson’s Correlation Coefficient is an important measure for capturing dynamic behaviors and functional connectivity between brain components. One bottleneck in computing Correlation Coefficients is the time it takes to process big fMRI data. In this paper, we propose GPU-PCC, a GPU based algorithm based on vector dot product, which is able to compute pairwise Pearson’s Correlation Coefficients while performing computation once for each pair. Our method is able to compute Correlation Coefficients in an ordered fashion without the need to do post-processing reordering …

Go to article

Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller Jan 2017

Ai Education: Open-Access Educational Resources On Ai, Todd W. Neller

Computer Science Faculty Publications

Open-access AI educational resources are vital to the quality of the AI education we offer. Avoiding the reinvention of wheels is especially important to us because of the special challenges of AI Education. AI could be said to be “the really interesting miscellaneous pile of Computer Science”. While “artificial” is well-understood to encompass engineered artifacts, “intelligence” could be said to encompass any sufficiently difficult problem as would require an intelligent approach and yet does not fall neatly into established Computer Science subdisciplines. Thus AI consists of so many diverse topics that we would be hard-pressed to individually create quality learning …

Go to article

Privacy Setting Recommendation For Image Sharing, Jun Yu, Zhenzhong Kuang, Zhou Yu, Dan Lin, Jianping Fan Jan 2017

Privacy Setting Recommendation For Image Sharing, Jun Yu, Zhenzhong Kuang, Zhou Yu, Dan Lin, Jianping Fan

Computer Science Faculty Research & Creative Works

This paper aims to simultaneously consider two inseparable issues for privacy setting recommendation: (1) sensitiveness of visual content of the images being shared; and (2) trustworthiness of users being granted. First, an object-based approach is developed for image content sensitiveness (privacy) representation. Secondly, the users on a social network are clustered into a set of representative social groups to generate a discriminative dictionary for user trustworthiness characterization. Finally, a tree classifier is trained hierarchically to recommend appropriate privacy settings for image sharing.

Go to article

Using Consumer Accessible Mobile Devices To Collect Vehicle Emissions Compliance Data, Joshua N. Jensen Jan 2017

Using Consumer Accessible Mobile Devices To Collect Vehicle Emissions Compliance Data, Joshua N. Jensen

Regis University Student Publications (comprehensive collection)

The emissions inspection procedure has been largely stagnate for the last 20 years. Vehicle owners in the United States spend approximately 1.7 billion dollars annually for a technician to perform the simple task of plugging an emissions inspection computer into their car’s computer. Smart Emissions was developed as an Android application to provide a new procedure for emissions inspection utilizing consumer-accessible mobile devices and an ELM327 Bluetooth adapter. With Smart Emissions, vehicle owners utilize the Android devices they already own to connect to a Bluetooth adapter inserted into the diagnostic link connector port of their vehicle. The adapter communicates with …

Go to article

Visualization Of Carbon Monoxide Particles Released From Firearms, Sadan Suneesh Menon Jan 2017

Visualization Of Carbon Monoxide Particles Released From Firearms, Sadan Suneesh Menon

Browse all Theses and Dissertations

A number of soldiers have come forward to report discomfort, irritation and respiratory problems after taking part in a live firing session. These problems are caused due to the fumes and particulates emitted from the gun upon firing. There exists substantial research work focused on lead and other harmful metallic particulates expelled from a firearm, since they are the most harmful among the other emissions. However, our research focuses on visualizing the carbon monoxide (CO) particles released from a firearm in order to help understand adverse effects they may have on the human body. We use data provided by researchers …

Go to article

Accuracy Evaluation Of The Canadian Openstreetmap Road Networks, Hongyu Zhang, Jacek Malczewski Jan 2017

Accuracy Evaluation Of The Canadian Openstreetmap Road Networks, Hongyu Zhang, Jacek Malczewski

Geography & Environment Publications

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management. One of the reasons for popularity of VGI is its cost-effectiveness. However, the coverage and accuracy of VGI cannot be guaranteed. The issue of geospatial data quality in the OpenStreetMap (OSM) project has become a trending research topic because of the large size of the dataset and the multiple channels of data access. This paper provides details on a national study of the Canadian OSM street network data for the assessment ofcompleteness, positional accuracy, attribute accuracy, semantic accuracy and lineage. The …

Go to article

An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak Jan 2017

An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak

Electrical and Computer Engineering Publications

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework …

Go to article

A Gamification Framework For Sensor Data Analytics, Alexandra L'Heureux, Katarina Grolinger, Wilson A. Higashino, Miriam A. M. Capretz Jan 2017

A Gamification Framework For Sensor Data Analytics, Alexandra L'Heureux, Katarina Grolinger, Wilson A. Higashino, Miriam A. M. Capretz

Electrical and Computer Engineering Publications

The Internet of Things (IoT) enables connected objects to capture, communicate, and collect information over the network through a multitude of sensors, setting the foundation for applications such as smart grids, smart cars, and smart cities. In this context, large scale analytics is needed to extract knowledge and value from the data produced by these sensors. The ability to perform analytics on these data, however, is highly limited by the difficulties of collecting labels. Indeed, the machine learning techniques used to perform analytics rely upon data labels to learn and to validate results. Historically, crowdsourcing platforms have been used to …

Go to article

Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz Jan 2017

Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz

Electrical and Computer Engineering Publications

The increase in electrical metering has created tremendous quantities of data and, as a result, possibilities for deep insights into energy usage, better energy management, and new ways of energy conservation. As buildings are responsible for a significant portion of overall energy consumption, conservation efforts targeting buildings can provide tremendous effect on energy savings. Building energy monitoring enables identification of anomalous or unexpected behaviors which, when corrected, can lead to energy savings. Although the available data is large, the limited availability of labels makes anomaly detection difficult. This research proposes a deep semi-supervised convolutional neural network with confidence sampling for …

Go to article

The Document Similarity Network: A Novel Technique For Visualizing Relationships In Text Corpora, Dylan Baker Jan 2017

The Document Similarity Network: A Novel Technique For Visualizing Relationships In Text Corpora, Dylan Baker

HMC Senior Theses

With the abundance of written information available online, it is useful to be able to automatically synthesize and extract meaningful information from text corpora. We present a unique method for visualizing relationships between documents in a text corpus. By using Latent Dirichlet Allocation to extract topics from the corpus, we create a graph whose nodes represent individual documents and whose edge weights indicate the distance between topic distributions in documents. These edge lengths are then scaled using multidimensional scaling techniques, such that more similar documents are clustered together. Applying this method to several datasets, we demonstrate that these graphs are …

Go to article

Semantic Inference On Clinical Documents: Combining Machine Learning Algorithms With An Inference Engine For Effective Clinical Diagnosis And Treatment, Shuo Yang, Ran Wei, Jingzhi Guo, Lida Xu Jan 2017

Semantic Inference On Clinical Documents: Combining Machine Learning Algorithms With An Inference Engine For Effective Clinical Diagnosis And Treatment, Shuo Yang, Ran Wei, Jingzhi Guo, Lida Xu

Information Technology & Decision Sciences Faculty Publications

Clinical practice calls for reliable diagnosis and optimized treatment. However, human errors in health care remain a severe issue even in industrialized countries. The application of clinical decision support systems (CDSS) casts light on this problem. However, given the great improvement in CDSS over the past several years, challenges to their wide-scale application are still present, including: 1) decision making of CDSS is complicated by the complexity of the data regarding human physiology and pathology, which could render the whole process more time-consuming by loading big data related to patients; and 2) information incompatibility among different health information systems (HIS) …

Go to article

Qos Recommendation In Cloud Services, Xianrong Zheng, Li Da Xu, Sheng Chai Jan 2017

Qos Recommendation In Cloud Services, Xianrong Zheng, Li Da Xu, Sheng Chai

Information Technology & Decision Sciences Faculty Publications

As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. It can help a firm to satisfy and win its customers. As a result, how to assist cloud providers to promote their services and cloud consumers to identify services that meet their QoS requirements becomes an important problem. In this paper, we argue for QoS-based cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud …

Go to article

Gender Difference And Employees' Cybersecurity Behaviors, Mohd Anwar, Wu He, Ivan Ash, Xiaohong Yuan, Ling Li, Li Xu Jan 2017

Gender Difference And Employees' Cybersecurity Behaviors, Mohd Anwar, Wu He, Ivan Ash, Xiaohong Yuan, Ling Li, Li Xu

Information Technology & Decision Sciences Faculty Publications

Security breaches are prevalent in organizations and many of the breaches are attributed to human errors. As a result, the organizations need to increase their employees' security awareness and their capabilities to engage in safe cybersecurity behaviors. Many different psychological and social factors affect employees' cybersecurity behaviors. An important research question to explore is to what extent gender plays a role in mediating the factors that affect cybersecurity beliefs and behaviors of employees. In this vein, we conducted a cross-sectional survey study among employees of diverse organizations. We used structural equation modelling to assess the effect of gender as a …

Go to article

Probabilistic And More General Uncertainty-Based (E.G., Fuzzy) Approaches To Crisp Clustering Explain The Empirical Success Of The K-Sets Algorithm, Vladik Kreinovich, Olga Kosheleva, Shahnaz Shahbazova, Songsak Sriboonchitta Jan 2017

Probabilistic And More General Uncertainty-Based (E.G., Fuzzy) Approaches To Crisp Clustering Explain The Empirical Success Of The K-Sets Algorithm, Vladik Kreinovich, Olga Kosheleva, Shahnaz Shahbazova, Songsak Sriboonchitta

Departmental Technical Reports (CS)

Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algorithm. In this paper, we show that a natural uncertainty-based formalization of what is clustering automatically leads to the mathematical ideas and definitions behind this algorithm. Thus, we provide an explanation for this algorithm's empirical success.

Go to article

Industrial Wireless Sensor Networks 2016, Qindong Sun, Schancang Li, Shanshan Zhao, Hongjian Sun, Li Xu, Arumugam Nallamathan Jan 2017

Industrial Wireless Sensor Networks 2016, Qindong Sun, Schancang Li, Shanshan Zhao, Hongjian Sun, Li Xu, Arumugam Nallamathan

Information Technology & Decision Sciences Faculty Publications

The industrial wireless sensor network (IWSN) is the next frontier in the Industrial Internet of Things (IIoT), which is able to help industrial organizations to gain competitive advantages in industrial manufacturing markets by increasing productivity, reducing the costs, developing new products and services, and deploying new business models.

Go to article

Mdp: Minimum Delay Hot-Spot Parking, Peng Liu, Biao Xu, Guojun Dai, Zhen Jiang, Jie Wu Jan 2017

Mdp: Minimum Delay Hot-Spot Parking, Peng Liu, Biao Xu, Guojun Dai, Zhen Jiang, Jie Wu

Computer Science Faculty Publications

Hot-spot parking is becoming the Achilles' heel of the tourism industry. The more tourists that are attracted to the scenic site, the more often they will encounter a hassle of congestion to find a parking place; while those existing facilities for daily traffic are not supposed to support the excessive volume outburst. In this paper, we present a new parking guidance information system (PGI). By taking advantage of the technical advances of today in wireless communication of vehicular ad-hoc network, each vehicle will request and obtain a relatively fair opportunity to park. The competition and the corresponding allocation on the …

Go to article

Machine Learning And Natural Language Methods For Detecting Psychopathy In Textual Data, Andrew Stephen Henning Jan 2017

Machine Learning And Natural Language Methods For Detecting Psychopathy In Textual Data, Andrew Stephen Henning

Electronic Theses and Dissertations

Among the myriad of mental conditions permeating through society, psychopathy is perhaps the most elusive to diagnose and treat. With the advent of natural language processing and machine learning, however, we have ushered in a new age of technology that provides a fresh toolkit for analyzing text and context. Because text remains the medium of choice for most personal and professional interactions, it may be possible to use textual samples from psychopaths as a means for understanding and ultimately classifying similar individuals based on the content of their language usage. This paper aims to investigate natural language processing and supervised …

Go to article

Grace's Inheritance, James Noble, Andrew P. Black, Kim B. Bruce, Michael Homer, Timothy Jones Jan 2017

Grace's Inheritance, James Noble, Andrew P. Black, Kim B. Bruce, Michael Homer, Timothy Jones

Computer Science Faculty Publications and Presentations

This article is an apologia for the design of inheritance in the Grace educational programming language: it explains how the design of Grace’s inheritance draws from inheritance mechanisms in predecessor languages, and defends that design as the best of the available alternatives. For simplicity, Grace objects are generated from object constructors, like those of Emerald, Lua, and Javascript; for familiarity, the language also provides classes and inheritance, like Simula, Smalltalk and Java. The design question we address is whether or not object constructors can provide an inheritance semantics similar to classes.

Go to article

Application Of Nearly Linear Solvers To Electric Power System Computation, Lisa L. Grant Jan 2017

Application Of Nearly Linear Solvers To Electric Power System Computation, Lisa L. Grant

Doctoral Dissertations

"To meet the future needs of the electric power system, improvements need to be made in the areas of power system algorithms, simulation, and modeling, specifically to achieve a time frame that is useful to industry. If power system time-domain simulations could run in real-time, then system operators would have situational awareness to implement and avoid cascading failures, significantly improving power system reliability. Several power system applications rely on the solution of a very large linear system. As the demands on power systems continue to grow, there is a greater computational complexity involved in solving these large linear systems within …

Go to article

On The Security Of Nosql Cloud Database Services, Mohammad Ahmadian Jan 2017

On The Security Of Nosql Cloud Database Services, Mohammad Ahmadian

Electronic Theses and Dissertations

Processing a vast volume of data generated by web, mobile and Internet-enabled devices, necessitates a scalable and flexible data management system. Database-as-a-Service (DBaaS) is a new cloud computing paradigm, promising a cost-effective and scalable, fully-managed database functionality meeting the requirements of online data processing. Although DBaaS offers many benefits it also introduces new threats and vulnerabilities. While many traditional data processing threats remain, DBaaS introduces new challenges such as confidentiality violation and information leakage in the presence of privileged malicious insiders and adds new dimension to the data security. We address the problem of building a secure DBaaS for a …

Go to article

An Industrial Vision System To Analyze The Wear Of Cutting Tools, Christina Gillmann, Tobias Post, Benjamin Kirsch, Thomas Wischgoll, Jörg Hartig, Bernd Hamann, Hans Hagen, Jan C. Aurich Jan 2017

An Industrial Vision System To Analyze The Wear Of Cutting Tools, Christina Gillmann, Tobias Post, Benjamin Kirsch, Thomas Wischgoll, Jörg Hartig, Bernd Hamann, Hans Hagen, Jan C. Aurich

Computer Science and Engineering Faculty Publications

The wear behavior of cutting tools directly affects the quality of the machined part. The measurement and evaluation of wear is a time consuming process and is subjective. Therefore, an image-based wear measurement that can be computed automatically based on given image series of cutting tools and an objective way to review the resulting wear is presented in this paper. The presented method follows the industrial vision system pipeline where images of cutting tools are used as input which are then transformed through suitable image processing methods to prepare them for the computation of a novel image based wear measurement. …

Go to article

Panel: Teaching To Increase Diversity And Equity In Stem, Helen H. Hu, Douglas Blank, Albert Chan, Travis E. Doom Jan 2017

Panel: Teaching To Increase Diversity And Equity In Stem, Helen H. Hu, Douglas Blank, Albert Chan, Travis E. Doom

Computer Science and Engineering Faculty Publications

TIDES (Teaching to Increase Diversity and Equity in STEM) is a three-year initiative to transform colleges and universities by changing what STEM faculty, especially CS instructors, are doing in the classroom to encourage the success of their students, particularly those that have been traditionally underrepresented in computer science.Each of the twenty projects selected proposed new inter-disciplinary curricula and adopted culturally sensitive pedagogies, with an eye towards departmental and institutional change. The four panelists will each speak about their TIDES projects, which all involved educating faculty about cultural competency. Three of the panelists infused introductory CS courses with applications from other …

Go to article

A Novel Approach For Library Materials Acquisition Using Discrete Particle Swarm Optimization, Daniel A. Sabol Jan 2017

A Novel Approach For Library Materials Acquisition Using Discrete Particle Swarm Optimization, Daniel A. Sabol

Publications and Research

The academic library materials acquisition problem is a challenge for librarian, since library cannot get enough funding from universities and the price of materials inflates greatly. In this paper, we analyze an integer mathematical model by considering the selection of acquired materials to maximize the average preference value as well as the budget execution rate under practical restrictions. The objective is to improve the Discrete Particle Swarm Optimization (DPSO) algorithm by adding a Simulate Annealing algorithm to reduce premature convergence. Furthermore, the algorithm is implemented in multiple threaded environment. The experimental results show the efficiency of this approach.

Go to article

First Prev Next Last