Computer Sciences Commons^™

Person-Level Action Recognition In Complex Events Via Tsd-Tsm Networks, Yanbin Hao, Zi-Niu Liu, Hao Zhang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

The task of person-level action recognition in complex events aims to densely detect pedestrians and individually predict their actions from surveillance videos. In this paper, we present a simple yet efficient pipeline for this task, referred to as TSD-TSM networks. Firstly, we adopt the TSD detector for the pedestrian localization on each single keyframe. Secondly, we generate the sequential ROIs for a person proposal by replicating the adjusted bounding box coordinates around the keyframe. Particularly, we propose to conduct straddling expansion and region squaring on the original bounding box of a person proposal to widen the potential space of motion …

What Is The Vocabulary Of Flaky Tests?, Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo D'Amorim, Christoph Treude, Antonia Bertolino

Research Collection School Of Computing and Information Systems

Flaky tests are tests whose outcomes are non-deterministic. Despite the recent research activity on this topic, no effort has been made on understanding the vocabulary of flaky tests. This work proposes to automatically classify tests as flaky or not based on their vocabulary. Static classification of flaky tests is important, for example, to detect the introduction of flaky tests and to search for flaky tests after they are introduced in regression test suites. We evaluated performance of various machine learning algorithms to solve this problem. We constructed a data set of flaky and non-flaky tests by running every test case, …

Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose …

Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn

Research Collection School Of Computing and Information Systems

Android platform has dominated the smart phone market for years now and, consequently, gained a lot of attention from attackers. Malicious apps (malware) pose a serious threat to the security and privacy of Android smart phone users. Available approaches to detect mobile malware based on machine learning rely on features extracted with static analysis or dynamic analysis techniques. Dif- ferent types of machine learning classi ers (such as support vector machine and random forest) deep learning classi ers (based on deep neural networks) are then trained on extracted features, to produce models that can be used to detect mobile malware. …

Attribute-Based Fine-Grained Access Control For Outscored Private Set Intersection Computation, Mohammad Ali, Mohajeri Javad, Mohammad-Reza Sadeghi, Ximeng Liu

Research Collection School Of Computing and Information Systems

Private set intersection (PSI) is a fundamental cryptographic protocol which has a wide range of applications. It enables two clients to compute the intersection of their private datasets without revealing non-matching elements. The advent of cloud computing drives the ambition to reduce computation and data management overhead by outsourcing such computations. However, since the cloud is not trustworthy, some cryptographic methods should be applied to maintain the confidentiality of datasets. But, in doing so, data owners may be excluded from access control on their outsourced datasets. Therefore, to control access rights and to interact with authorized users, they have to …

The Future Of Work Now: Automl At 84.51°And Kroger, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon.

Foodbot: A Goal-Oriented Just-In-Time Healthy Eating Interventions Chatbot, Philips Kokoh Prasetyo, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Recent research has identified a few design flaws in popular mobile health (mHealth) applications for promoting healthy eating lifestyle, such as mobile food journals. These include tediousness of manual food logging, inadequate food database coverage, and a lack of healthy dietary goal setting. To address these issues, we present Foodbot, a chatbot-based mHealth application for goal-oriented just-in-time (JIT) healthy eating interventions. Powered by a large-scale food knowledge graph, Foodbot utilizes automatic speech recognition and mobile messaging interface to record food intake. Moreover, Foodbot allows users to set goals and guides their behavior toward the goals via JIT notification prompts, interactive …

Efficient Sampling Algorithms For Approximate Temporal Motif Counting, Jingjing Wang, Yanhao Wang, Wenjun Jiang, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

A great variety of complex systems ranging from user interactions in communication networks to transactions in financial markets can be modeled as temporal graphs, which consist of a set of vertices and a series of timestamped and directed edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs which take into account edge orderings and durations in addition to structures. Counting the number of occurrences of temporal motifs is a fundamental problem for temporal network analysis. However, existing methods either cannot support temporal motifs or suffer from performance issues. In this paper, we focus on approximate …

Lis: Lightweight Signature Schemes For Continuous Message Authentication In Cyber-Physical Systems, Zheng Yang, Chenglu Jin, Yangguang Tian, Junyu Lai, Jianying Zhou

Research Collection School Of Computing and Information Systems

Cyber-Physical Systems (CPS) provide the foundation of our critical infrastructures, which form the basis of emerging and future smart services and improve our quality of life in many areas. In such CPS, sensor data is transmitted over the network to the controller, which will make real-time control decisions according to the received sensor data. Due to the existence of spoofing attacks (more specifically to CPS, false data injection attacks), one has to protect the authenticity and integrity of the transmitted data. For example, a digital signature can be used to solve this issue. However, the resource-constrained field devices like sensors …

Co2vec: Embeddings Of Co-Ordered Networks Based On Mutual Reinforcement, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

We study the problem of representation learning for multiple types of entities in a co-ordered network where order relations exist among entities of the same type, and association relations exist across entities of different types. The key challenge in learning co-ordered network embedding is to preserve order relations among entities of the same type while leveraging on the general consistency in order relations between different entity types. In this paper, we propose an embedding model, CO2Vec, that addresses this challenge using mutually reinforced order dependencies. Specifically, CO2Vec explores in-direct order dependencies as supplementary evidence to enhance order representation learning across …

Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh

Research Collection School Of Computing and Information Systems

Traffic congestion reduces productivity of individuals by increasing time spent in traffic and also increases pollution. To reduce traffic congestion by better handling dynamic traffic patterns, recent work has focused on online traffic signal control. Typically, the objective in traffic signal control is to minimize expected delay over all vehicles given the uncertainty associated with the vehicle turn movements at intersections. In order to ensure responsiveness in decision making, a typical approach is to compute a schedule that minimizes the delay for the expected scenario of vehicle movements instead of minimizing expected delay over the feasible vehicle movement scenarios. Such …

Topology-Guided Roadmap Construction With Dynamic Region Sampling, Read Sandström, Diane Uwacu, Jory Denny, Nancy M. Amato

Department of Math & Statistics Faculty Publications

Many types of planning problems require discovery of multiple pathways through the environment, such as multi-robot coordination or protein ligand binding. The Probabilistic Roadmap (PRM) algorithm is a powerful tool for this case, but often cannot efficiently connect the roadmap in the presence of narrow passages. In this letter, we present a guidance mechanism that encourages the rapid construction of well-connected roadmaps with PRM methods. We leverage a topological skeleton of the workspace to track the algorithm's progress in both covering and connecting distinct neighborhoods, and employ this information to focus computation on the uncovered and unconnected regions. We demonstrate …

Asymptotically-Optimal Topological Nearest-Neighbor Filtering, Read Sandström, Jory Denny, Nancy M. Amato

Department of Math & Statistics Faculty Publications

Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to a significant computational bottleneck for problems which require many configurations to find a solution. In this work, we develop a method of mapping configurations of a jointed robot to neighborhoods in the workspace that supports fast search for configurations in nearby neighborhoods. This expedites nearest-neighbor search by locating a small set of the most likely candidates for connecting to the query with a local plan. We show that this filtering technique can preserve asymptotically-optimal …

Cusz: An Efficient Gpu-Based Error-Bounded Lossy Compression Framework For Scientific Data, Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelityfor postanalysis. Because supercomputers and HPC applicationsare becoming heterogeneous using accelerator-based architectures,in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existingstate-of-the-art GPU-based lossy compressors suffer from eitherlow compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version,cuSZ, for one of the best error-bounded lossy compressors-SZ.To the best of our knowledge, cuSZ is the first error-boundedlossy compressor on GPUs for scientific data. …

Memory Foreshadow: Memory Forensics Of Hardware Cryptocurrency Wallets – A Tool And Visualization Framework, Tyler Thomas, Mathew Piscitelli, Ilya Shavrov, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

We present Memory FORESHADOW: Memory FOREnSics of HArDware cryptOcurrency Wallets. To the best of our knowledge, this is the primary account of cryptocurrency hardware wallet client memory forensics. Our exploratory analysis revealed forensically relevant data in memory including transaction history, extended public keys, passphrases, and unique device identifiers. Data extracted with FORESHADOW can be used to associate a hardware wallet with a computer and allow an observer to deanonymize all past and future transactions due to hierarchical deterministic wallet address derivation. Additionally, our novel visualization framework enabled us to measure both the persistence and integrity of artifacts produced by the …

Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA …

Exploring The Learning Efficacy Of Digital Forensics Concepts And Bagging & Tagging Of Digital Devices In Immersive Virtual Reality, Courtney Hassenfeldt, Jillian Jacques, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

This work presents the first account of evaluating learning inside a VR experience created to teach Digital Forensics (DF) concepts, and a hands-on laboratory exercise in Bagging & Tagging a crime scene with digital devices. First, we designed and developed an immersive VR experience which included a lecture and a lab. Next, we tested it with (n = 57) participants in a controlled experiment where they were randomly assigned to a VR group or a physical group. Both groups were subjected to the same lecture and lab, but one was in VR and the other was in the real world. …

Detecting Symptoms Of Chronic Obstructive Pulmonary Disease And Congestive Heart Failure Via Cough And Wheezing Sounds Using Smart-Phones And Machine Learning, Anthony Windmon

USF Tampa Graduate Theses and Dissertations

Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) are progressive disorders, and major health concerns among today’s aging population. COPD causes a large mucus buildup in the lungs, leading to chronic cough and difficulty to breathe. CHF causes fluid buildup in the lower lungs due to the failing heart, causing cough and difficulty to breath. People who are clinically diagnosed with COPD or CHF are expected to regularly monitor their symptoms and follow complex medical recommendations in an effort to prevent exacerbation. In this dissertation, we elaborate upon three different machine learning based techniques that we developed for …

Chess As A Testing Grounds For The Oracle Approach To Ai Safety, James D. Miller, Roman Yampolskiy, Olle Häggström, Stuart Armstrong

Faculty and Staff Scholarship

To reduce the danger of powerful super-intelligent AIs, we might make the first such AIs oracles that can only send and receive messages. This paper proposes a possibly practical means of using machine learning to create two classes of narrow AI oracles that would provide chess advice: those aligned with the player's interest, and those that want the player to lose and give deceptively bad advice. The player would be uncertain which type of oracle it was interacting with. As the oracles would be vastly more intelligent than the player in the domain of chess, experience with these oracles might …

Trainable Structure Tensors For Autonomous Baggage Threat Detection Under Extreme Occlusion, Taimur Hassan, Samet Akçay, Mohammed Bennamoun, Salman Khan, Naoufel Werghi

Computer Vision Faculty Publications

Detecting baggage threats is one of the most difficult tasks, even for expert officers. Many researchers have developed computer-aided screening systems to recognize these threats from the baggage X-ray scans. However, all of these frameworks are limited in identifying the contraband items under extreme occlusion. This paper presents a novel instance segmentation framework that utilizes trainable structure tensors to highlight the contours of the occluded and cluttered contraband items (by scanning multiple predominant orientations), while simultaneously suppressing the irrelevant baggage content. The proposed framework has been extensively tested on four publicly available X-ray datasets where it outperforms the state-of-the-art frameworks …

An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of …

Integrated Multiparametric Radiomics And Informatics System For Characterizing Breast Tumor Characteristics With The Oncotypedx Gene Assay, Michael A. Jacobs, Christopher B. Umbricht, Vishwa S. Parekh, Riham H. El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff

Radiology Faculty Publications

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained …

Feature Selection Via Random Subsets Of Uncorrelated Features, Long Kim Dang

USF Tampa Graduate Theses and Dissertations

The role of feature selection is crucial in many applications. A few of these include computational biology, image classification and risk management. In biology, gene expression micro array data sets have been used extensively in many areas of research. These data sets typically suffer from an important problem: the ratio between the number of features over the number of examples is very high. This problem mainly affects prediction accuracy because it is best to collect more labeled examples than features. A correlation based random subspace ensemble feature selector (CCC_RSM) was proposed to handle this problem [5]. In this approach, first …

Inheritance Details In Scala, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

A Comprehensive Survey Of The Tactile Internet: State-Of-The-Art And Research Directions, Nattakorn Promwongsa, Amin Ebrahimzadeh, Diala Naboulsi, Somayeh Kianpisheh, Fatna Belqasmi, Roch Glitho, Noel Crespi, Omar Alfandi

All Works

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communications and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, …

Implement Multi-Factor Authentication On All Federal Systems Now, Megan Walsh

Student Papers in Public Policy

The White House Office of Management and Budget recorded 31,107 information security incidents in fiscal year 2018. The most common attacks to gain access to a user’s login credentials were e-mail/phishing, web-based attack, and brute force entering of username/password combinations. Given this high number of incidents, strong reliance on computers for everyday business, and common attacks that target passwords, information security should be a priority for information technology administrators working in federal agencies.

Establishing Topological Data Analysis: A Comparison Of Visualization Techniques, Tanmay J. Kotha

USF Tampa Graduate Theses and Dissertations

When visualizing data, we would like to convey both the data and the uncertainty associated with it. There are many incentives to do this, ranging from hurricane path projection to geographical surveys. Important decision making tasks rely upon humans perceiving a clear picture of the data and having confidence in their decisions. Topological Data Analysis has the potential to visualize the data as features or hierarchies in ways that are familiar to human intuition, and thus could help us convey the variation associated with uncertainty.

In this thesis, we evaluate four visualization techniques: color maps, isocontours, Reeb graphs, and persistence …

Eeg Classification Based On Multi-Domain Features And Random Subspace Ensemble, Deng Xin, Can Long, Jianxun Mi, Boxian Zhang, Kaiwei Sun, Wang Jin

Journal of System Simulation

Abstract: Aiming at the preprocessing feature extraction and classification recognition in BCI system, a method for EEG classification of motion imagery based on random subspaces ensemble learning of multi-domain features is proposed. Based on the analysis on the ERD/ERS characteristics of motion imagery (MI) signals, the multi-domain features of best effective time and frequency bands are extracted as the feature vectors, and the scale of the random subspace ensemble with cross-validation is adaptively chosen, and the EEG classification is realized by using linear discriminant analysis (LDA) classifiers ensemble. The test results show that the accuracy of the multi-domain features and …

The Expansion From System Simulation To Domain Simulation, Xiaogang Qiu, Yazhou Chen, Zhang Peng

Journal of System Simulation

Abstract: With the generalization of simulation application and complex system simulation becoming the focus, domain simulation is becoming more and more important. In 1995 United States Department of Defense (DoD) presented the master plan of modeling and simulation, which gives birth to the basic idea of domain simulation. The advances in information technology such as network technology and cloud computing help domain simulation to access to use. Taking the change of simulation research object as the basis, the development of simulation research from the aspects of model researches, simulation tools and simulation applications are summarized. The goal of domain simulation …

Review On Agv Scheduling Optimization, Jianlin Fu, Hengzhi Zhang, Zhang Jian, Liangkui Jiang

Journal of System Simulation

Abstract: AGV scheduling plays an important role in improving the efficiency and reducing manufacturing cost, but it is also a very complex combinatorial optimization procedure. AGV scheduling optimization is divided into three types, AGV static scheduling, AGV dynamic scheduling and AGV simultaneous scheduling with other resources scheduling. Various methods are summarized and listed, including traditional analysis method, modeling and simulation method, intelligent optimization algorithm and hybrid optimization method, and the advantages and disadvantages of each method are also analyzed. The deficiencies of AGV scheduling research are pointed out and the research directions for future are presented.