Computer Sciences Commons^™

Research On Model Reuse Technology Based On Semantic Matching And Composition, Xingyu Tian, Guangxun Zeng, Yunbo Gao, Lili Ye, Guanghong Gong, Li Ni

Journal of System Simulation

Abstract: In order to solve the data barriers between the conceptual model and the simulation scenario of the combat system, the intelligent mapping and model reuse technology of the simulation scenario is researched. The conceptual model is analyzed using DOM technology. Based on the ontology theory, the knowledge base of the combat domain is constructed and the web crawler is customized to build the domain thesaurus. Through the SWRL rule library, the reasoning engine is called to realize the relational reasoning at the semantic level. An intelligent matching algorithm is designed to map the semantic relationship to the combination relationship …

Research On Cloud Tool Integration And Management Methods, Tianying Zhang, Ji Hang, Junhua Zhou, Tao Luan

Journal of System Simulation

Abstract: In response to the application needs of using professional tools to develop complex products in the fields of aerospace, aviation, weapons, ships, etc., it is urgent to implement centralized management of cloud tools and cross-professional sharing of tools through tool service-oriented methods, so as to solve issues such as inconsistent tool versions, cross-professional resource barriers, and high thresholds for tool mastery during the traditional model development process. By studying the integration and calling methods of cross-professional and different versions of self-developed tools, as well as methods of tool server operation control, authority management, etc., and taking the local …

Predictive Control Method Of Peak Hour Passenger Flow At Urban Rail Station, Xiaohe Li, Jianping Wu, Depin Peng

Journal of System Simulation

Abstract: With the rapid development of subway in China, the urban rail station, especially the transfer station, is prone to generate passenger congestion in the peak period. After analyzing the types of passenger flow in and out of the platform, a predictive control model of passenger flow is established based on the discrete linear quadratic optimal control theory. Taking Fuxingmen Station as an example, the simulation environment of the station is built by using the simulation software of Anylogic. The historical passenger flow data in peak period and the optimal passenger flow control sequence obtained by solving the passenger flow …

Study And Effect Evaluation On The Setting Of Contraflow Left-Turn At Intersections, Zhao Dan, Xuejun Niu, Shuhao Zhang, Jiaxu Wei

Journal of System Simulation

Abstract: Contraflow left-turn is one of the traffic organization ways at intersections. By analyzing the setting parameters of the contraflow left-turn, the length and the range of the contraflow left-turn lane, the constrained conditions of the contraflow left-turn are determined, and the applicable conditions are determined from the road, traffic and signal control. VISSIM software is used to analyze a road intersection, simulate and evaluate the indicators related to the intersection entrance, optimize the timing plan of contraflow left-turn lane, and validate the feasibility and advantages of contraflow left-turn lane. The results show that the intersection delays are reduced by …

Variety Recognition Based On Deep Learning And Double-Sided Characteristics Of Maize Kernel, Feng Xiao, Zhang Hui, Zhou Rui, Qiao Lu, Wei Dong, Dandan Li, Yuyao Zhang, Guoqing Zheng

Journal of System Simulation

Abstract: In order to construct a maize kernel variety recognition model with high recognition accuracy and suitable for mobile phone application, a mobile phone is used to obtain maize kernel double-sided (embryonic and non-embryonic) images. Based on the lightweight convolutional neural network MobileNetV2 and transfer learning, a maize kernel image variety recognition model is constructed. In view of the existing research methods are mainly for single-sided recognition of maize kernel variety, the performance of single-sided and double-sided characteristics modeling and recognition is compared. The results show that the double-sided recognition accuracy of maize kernel double-sided characteristics modeling is 99.83%, which …

Opening A Window To Evolution: David Angelini’S Research On Genetic Adaptation Gets Push From Mcvey Data Science Initiative, Christina Nunez

Colby Magazine

Most people think of soapberry bugs as little more than a nuisance, if they think of them at all. Found across much of the southeastern United States, the oblong insect is harmless to humans and likes to hang out on plants native to the soapberry family, hence its straightforward name.

Self-Relabeling For Noise-Tolerant Retina Vessel Segmentation Through Label Reliability Estimation, Jiacheng Li, Ruirui Li, Ruize Han, Song Wang

Faculty Publications

Background Retinal vessel segmentation benefits significantly from deep learning. Its performance relies on sufficient training images with accurate ground-truth segmentation, which are usually manually annotated in the form of binary pixel-wise label maps. Manually annotated ground-truth label maps, more or less, contain errors for part of the pixels. Due to the thin structure of retina vessels, such errors are more frequent and serious in manual annotations, which negatively affect deep learning performance.

Methods In this paper, we develop a new method to automatically and iteratively identify and correct such noisy segmentation labels in the process of network training. We consider …

The Impact Of Software Applications On Enhancing The Quality Of Educational Outputs (An Applied Study At Sharjah Police Academy), Medhat Aboubakar

Journal of Police and Legal Sciences

The study aims to introduce the concept of the role of interactive software applications, with application to the adopting of the Police Sciences Academy of specialized renewable educational methods, in addition to investing in software applications, multimedia and its smart applications that enable it to adapt to these modern and emerging data. The importance of using technology and modern applications in education as an effective and integrated approach to improving the quality and efficiency of educational outcomes through applying the best practices of software applications and integrating them into the educational learning environment, as a driving force to achieve a …

Security Strategy In Combating Online Extremist Ideologies In Uae, Khalid Al-Qasimi

Journal of Police and Legal Sciences

This study aims at identifying the role of security strategies; the foundations of correct belief and good upbringing in the face of extremist ideology in order to protect the intellectual security of society. The researcher adopted the descriptive analytical approach in order to describe and analyze the role of the security strategy, the foundations of the correct faith and good upbringing in spreading moderation and combating extremist ideology through electronic websites.

The study has arrived at a number of findings, the most important of which are: extremist ideology cannot be confronted by traditional methods, but must be combated through modern …

Combating Terrorism Financing In The Cyber Environment A Study In Light Of The Egyptian And Emirati Experiences, Amar Elbably

Journal of Police and Legal Sciences

The research deals with the study of combating the financing of terrorism on the dark Internet, where terrorism "cyber has become an international actor, engages in interactions with states, individuals and institutions, and creates an agreement between the intelligence services and some terrorist movements, that these movements obtain information, finance, arm or training, in exchange for cyber-attacks against a mutual adversary, and the Internet plays a prominent role in the manufacture of extremism, terrorism, incitement to violence, promotion of radical ideas and attracting elements qualified for deviation, exploited in That's the dark part of the Internet, with an expansion of …

Artificial Intelligence Framework Identifies Candidate Targets For Drug Repurposing In Alzheimer’S Disease, Jiansong Fang, Pengyue Zhang, Quan Wang, Chien Wei Chiang, Yadi Zhou, Yuan Hou, Jielin Xu, Rui Chen, Bin Zhang, Stephen J. Lewis, James B. Leverenz, Andrew A. Pieper, Bingshan Li, Lang Li, Jeffrey Cummings, Feixiong Cheng

Brain Health Faculty Research

Background: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer’s disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. Methods: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein–protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, …

Clinical Interactions In Electronic Medical Records Towards The Development Of A Token-Economy Model, Nicole Allison S. Co, Jason Limcaco, Hans Calvin L. Tan, Ma. Regina Justina E. Estuar, Christian E. Pulmano, Dennis Andrew Villamor, Quirino Sugon Jr, Maria Cristina G. Bautista, Paulyn Jean Acacio-Claro

Graduate School of Business Publications

The use of electronic medical records (EMRs) plays a crucial role in the successful implementation of the Universal Healthcare Law which promises quality and affordable healthcare to all Filipinos. Consequently, the current adoption of EMRs should be studied from the perspective of the healthcare provider. As most studies look into use of EMRs by doctors or patients, there are very few that extend studies to look at possible interaction of doctor and patient in the same EMR environment. Understanding this interaction paves the way for possible incentives that will increase the use and adoption of the EMR. This study uses …

Information Bottleneck In Deep Learning - A Semiotic Approach, Bogdan Musat, Razvan Andonie

Computer Science Faculty Scholarship

The information bottleneck principle was recently proposed as a theory meant to explain some of the training dynamics of deep neural architectures. Via information plane analysis, patterns start to emerge in this framework, where two phases can be distinguished: fitting and compression. We take a step further and study the behaviour of the spatial entropy characterizing the layers of convolutional neural networks (CNNs), in relation to the information bottleneck theory. We observe pattern formations which resemble the information bottleneck fitting and compression phases. From the perspective of semiotics, also known as the study of signs and sign-using behavior, the saliency …

Technical Behaviours Of Child Sexual Exploitation Material Offenders, Chad Steel, Emily Newman, Suzanne O'Rourke, Ethel Quayle

Journal of Digital Forensics, Security and Law

An exploration of the technological behaviours of previously convicted child sexual exploitation material (CSEM) offenders provides a foundation for future applied research into deterrence, investigation, and treatment efforts. This study evaluates the technology choices and transitions of individuals previously convicted of CSEM offenses. Based on their inclusion in two sex offender registries, anonymous survey results (n=78) were collected from English-speaking adults within the United States. CSEM offenders chose technologies based on both utility and perceived risk; peer-to-peer and web-browsers were the most common gateway technologies and showed substantial sustained usage; a substantial minority of users never stored CSEM and only …

Forensic Discoverability Of Ios Vault Applications, Alissa Gilbert, Kathryn C. Seigfried-Spellar

Journal of Digital Forensics, Security and Law

Vault Applications are used to store potentially sensitive information on a smartphone; and are available on Android and iOS. The purpose of using these applications could be used to hide potential evidence or illicit photos. After comparing five different iOS photo vaults, each vault left evidence and photos behind. However, of the three forensic toolkits used, each produced different results in their scans of the phone. The media left behind was due to the photo vaults not protecting their information as claimed, and using basic obfuscation techniques in place of security controls. Future research will look at how newer security …

Distributed Matrix Tiling Using A Hypergraph Labeling Formulation, Avah Banerjee, Maxwell Reeser, Guoli Ding

Computer Science Faculty Research & Creative Works

Partitioning large matrices is an important problem in distributed linear algebra computing, used in ML among others. Briefly, our goal is to perform a sequence of matrix algebra operations in a distributed manner on these large matrices. However, not all partitioning schemes work well with different matrix algebra operations and their implementations (algorithms). This is a type of data tiling problem. In this paper we consider a data tiling problem using hypergraphs. We prove some hardness results and give a theoretical characterization of its complexity on random instances. Additionally, we develop a greedy algorithm and experimentally show its efficacy.

Greedy Algorithms For Scheduling Package Delivery With Multiple Drones, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (or drones) can be used for a myriad of civil applications, such as search and rescue, precision agriculture, or last-mile package delivery. Interestingly, the cooperation between drones and ground vehicles (trucks) can even enhance the quality of service. In this paper, we investigate the symbiosis among a truck and multiple drones in a last-mile package delivery scenario, introducing the Multiple Drone-Delivery Scheduling Problem (MDSP). From the main depot, a truck takes care of transporting a team of drones that will be used to deliver packages to customers. Each delivery is associated with a drone's energy cost, a …

Eclipse, Osgi, And The Java Model, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

Abstract Syntax Trees (Asts) And The Visitor Pattern, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

Can Lethal Autonomous Weapons Be Just?, Noreen L. Herzfeld, Robert H. Latiff

Computer Science Faculty Publications

In 2018 the United States Department of Defense (DoD) created a new Joint Artificial Intelligence Center to study the adoption of AI by the military. Their strategy, outlined in a document entitled, “Harnessing AI to Advance Our Security and Prosperity,” proposes to accelerate the adoption of AI in the military by fostering a culture of experimentation and calculated risk taking, noting that AI will change the character of the future battlefield and, even more, the pace of battle. Is there any way to ensure that this future battlefield will be just? Can the age-old precepts of just warfare help guide …

Did They Really Tweet That?, Caleb Bradford, Michael L. Nelson (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

Algorithm-Based Fault Tolerance At Scale, Joshua Dennis Booth

Summer Community of Scholars (RCEU and HCR) Project Proposals

No abstract provided.

A Virtual Physics Laboratory For Remote/Online Learning, Themistoklis Chronis

Summer Community of Scholars (RCEU and HCR) Project Proposals

No abstract provided.

Synergistically Employing User Stories And Use Cases In The Practice And Teaching Of Systems Analysis And Design, Gary Spurrier, Heikki Topi

Computer Information Systems Faculty Publications

Over the past three decades, user stories and use cases have become increasingly dominant requirements techniques. Both support articulating functional requirements for software projects, although they evolved within different software development approaches—user stories from agile development and use cases from traditional software engineering—and differ significantly in the level of requirements detail they can capture. As such, user stories and use cases are neither synonyms nor mutually exclusive alternatives. Rather, they can and should be complementary in the systems requirements process. Unfortunately, this mix of similarities and differences—coupled with a lack of formal standards for either—make understanding and synergistically employing user …

Campus Mobile History Application, Drew Adan, Christine Sears

Summer Community of Scholars (RCEU and HCR) Project Proposals

No abstract provided.

Why Sine Membership Functions, Sofia Holguin, Javier Viaña, Kelly Cohen, Anca Ralescu, Vladik Kreinovich

Departmental Technical Reports (CS)

In applications of fuzzy techniques to several practical problems -- in particular, to the problem of predicting passenger flows in the airports -- the most efficient membership function is a sine function; to be precise, a portion of a sine function between the two zeros. In this paper, we provide a theoretical explanation for this empirical success.

Need To Combine Interval And Probabilistic Uncertainty: What Needs To Be Computed, What Can Be Computed, What Can Be Feasibly Computed, And How Physics Can Help, Julio Urenda, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

In many practical situations, the quantity of interest is difficult to measure directly. In such situations, to estimate this quantity, we measure easier-to-measure quantities which are related to the desired one by a known relation, and we use the results of these measurement to estimate the desired quantity. How accurate is this estimate?

Traditional engineering approach assumes that we know the probability distributions of measurement errors; however, in practice, we often only have partial information about these distributions. In some cases, we only know the upper bounds on the measurement errors; in such cases, the only thing we know about …

Computer-Based Scaffolding In Computer Science Education, Rebecca Trinh, Simone Levy

Summer REU Program

No abstract provided.

Explainabilityaudit: An Automated Evaluation Of Local Explainability In Rooftop Image Classification, Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, Girish Modgil

Published and Grey Literature from PhD Candidates

Explainable Artificial Intelligence (XAI) is a key concept in building trustworthy machine learning models. Local explainability methods seek to provide explanations for individual predictions. Usually, humans must check these explanations manually. When large numbers of predictions are being made, this approach does not scale. We address this deficiency for a rooftop classification problem specifically with ExplainabilityAudit, a method that automatically evaluates explanations generated by a local explainability toolkit and identifies rooftop images that require further auditing by a human expert. The proposed method utilizes explanations generated by the Local Interpretable Model-Agnostic Explanations (LIME) framework as the most important superpixels of …

An Attention-Based Resnet Architecture For Acute Hemorrhage Detection And Classification: Toward A Health 4.0 Digital Twin Study, Aftab Hussain, Muhammad Usman Yaseen, Muhammad Imran, Muhammad Waqar, Adnan Akhunzada, Mohammad Al-Ja'afreh, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

Due to the advancement of digital twin (DT) technology, Health 4.0 applications have become reality and starting to take roots. In this article, we focus on intracranial hemorrhage (ICH) which is a life-threatening emergency that needs immediate diagnosis and treatment. ICH is caused by bleeding inside the skull or brain. Radiologists typically examine computed tomography (CT) scans of the patients to determine the ICH and its subtype. But the manual assessment of the CT scan is a complex and time-consuming task. The existing pre-trained convolutional neural network (CNN) models are state-of-the-art for ICH classification. However, they employ poor feature extraction …