Improving Text-To-Image Synthesis Using Contrastive Learning, Hui Ye, Xiulong Yang, Martin Takáč, Raj Sunderraman, Shihao Ji

Machine Learning Faculty Publications

The goal of text-to-image synthesis is to generate a visually realistic image that matches a given text description. In practice, the captions annotated by humans for the same image have large variance in terms of contents and the choice of words. The linguistic discrepancy between the captions of the identical image leads to the synthetic images deviating from the ground truth. To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images. In the pretraining stage, we utilize the contrastive learning approach to learn the consistent textual representations for …

Evaluation Of Deep Neural Network Prospr For Accurate Protein Distance Predictions On Casp14 Targets, Jacob A. Stern, Bryce Eric Hedelius, Olivia Fisher, Wendy M. Billings, Dennis Della Corte

Faculty Publications

The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference time for applications that require the folding of hundreds of sequences. The prediction of protein structure annotations, such as amino acid distances, can be achieved at a higher speed with existing tools, such as the ProSPr network. Here, we report on important updates to the ProSPr network, its performance in the recent Critical Assessment of Techniques for Protein Structure Prediction (CASP14) competition, and an evaluation of its …

Ggnb: Graph-Based Gaussian Naive Bayes Intrusion Detection System For Can Bus, Riadul Islam, Maloy K. Devnath, Manar D. Samad, Syed Md Jaffrey Al Kadry

Computer Science Faculty Research

The national highway traffic safety administration (NHTSA) identified cybersecurity of the automobile systems are more critical than the security of other information systems. Researchers already demonstrated remote attacks on critical vehicular electronic control units (ECUs) using controller area network (CAN). Besides, existing intrusion detection systems (IDSs) often propose to tackle a specific type of attack, which may leave a system vulnerable to numerous other types of attacks. A generalizable IDS that can identify a wide range of attacks within the shortest possible time has more practical value than attack-specific IDSs, which is not a trivial task to accomplish. In this …

Three-Dimensional Graph Matching To Identify Secondary Structure Correspondence Of Medium-Resolution Cryo-Em Density Maps, Bahareh Behkamal, Mahmoud Naghibzadeh, Mohammad Reza Saberi, Zeinab Amiri Tehranizadeh, Andrea Pagnani, Kamal Al Nasr

Computer Science Faculty Research

Cryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4–10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such …

Comparison Of Multiple Imputation Algorithms And Verification Using Whole-Genome Sequencing In The Cmuh Genetic Biobank, Ting-Yuan Liu, Chih-Fan Lin, Hsing-Tsung Wu, Ya-Lun Wu, Yu-Chia Chen, Chi-Chou Liao, Yu-Pao Chou, Dysan Chao, Hsing-Fang Lu, Ya-Sian Chang, Jan-Gowth Chang, Kai-Cheng Hsu, Fuu‑Jen Tsai

BioMedicine

A genome-wide association study (GWAS) can be conducted to systematically analyze the contributions of genetic factors to a wide variety of complex diseases. Nevertheless, existing GWASs have provided highly ethnic specific data. Accordingly, to provide data specific to Taiwan, we established a large-scale genetic database in a single medical institution at the China Medical University Hospital. With current technological limitations, microarray analysis can detect only a limited number of single-nucleotide polymorphisms (SNPs) with a minor allele frequency of >1%. Nevertheless, imputation represents a useful alternative means of expanding data. In this study, we compared four imputation algorithms in terms of …

A Novel Parabolic Model Of Instructional Efficiency Grounded On Ideal Mental Workload And Performance, Luca Longo, Murali Rajendran

Articles

Instructional efficiency within education is a measurable concept and models have been proposed to assess it. The main assumption behind these models is that efficiency is the capacity to achieve established goals at the minimal expense of resources. This article challenges this assumption by contributing to the body of Knowledge with a novel model that is grounded on ideal mental workload and performance, namely the parabolic model of instructional efficiency. A comparative empirical investigation has been constructed to demonstrate the potential of this model for instructional design evaluation. Evidence demonstrated that this model achieved a good concurrent validity with the …

The Development Of Qmms: A Case Study For Reliable Online Quiz Maker And Management System, Mohamed Abdelmoneim Elshafey Dr., Tarek Said Ghoniemy Dr.

Future Computing and Informatics Journal

The e-learning and assessment systems became a dominant technology nowadays and distribute across the globe. With severe consequences of COVID19-like crises, the key importance of such technology appeared in which courses, quizzes and questionnaires have to be conducted remotely. Moreover, the use of Learning Management Systems (LMSs), such as blackboard, eCollege, and Moodle, has been sanctioned in all respects of education. This paper presents an open-source interactive Quiz Maker and Management System (QMMS) that suits the research, education (under-grad, grad, or post-grad), and industrial organizations to perform distant quizzes, training and questionnaires with an integration facility with other LMS tools …

Multi-Modal Transformers Excel At Class-Agnostic Object Detection, Muhammad Maaz, Hanoona Bangalath Rasheed, Salman Hameed Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Ming-Hsuan Yang

Computer Vision Faculty Publications

What constitutes an object? This has been a longstanding question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and for unseen objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. To bridge this gap, we explore recent Multi-modal Vision Transformers (MViT) that have been trained with aligned image-text pairs. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on …

An Energy-Efficient Smart Space System Using Lora Network With Deadline And Security Constraints, Preti Kumari, Hari Prabhat Gupta, Rahul Mishra, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we develop techniques that create smart space in an efficient manner, wherein the efficiency is defined in terms of all-together: energy, security, delay, and cost. We design an energy-efficient smart space system using the Long-Range (LoRa) network. The system consists of various sensors that generate sensory data represented as Multi-dimensional Time Series (MTS). The sensors are connected with an Edge device and LoRa node for processing and transferring the MTS, respectively. The system first proposes a deep learning-based compression-decompression model for reducing the size of MTS at the Edge devices. Next, it uses game theory for finding …

Situate: An Agent-Based System For Situation Recognition, Max Henry Quinn

Dissertations and Theses

Computer vision and machine learning systems have improved significantly in recent years, largely based on the development of deep learning systems, leading to impressive performance on object detection tasks. Understanding the content of images is considerably more difficult. Even simple situations, such as "a handshake", "walking the dog", "a game of ping-pong", or "people waiting for a bus", present significant challenges. Each consists of common objects, but are not reliably detectable as a single entity nor through the simple co-occurrence of their parts.

In this dissertation, toward the goal of developing machine learning systems that demonstrate properties associated with understanding, …

Analysis And Strategy Of Ai Ethical Problems, Zhaoxiang Zhang, Jiyu Zhang, Tieniu Tan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is the core of the fourth industrial revolution, and it has brought challenges to ethics and social governance. On the basis of explaining the current ethical risks of artificial intelligence, the study furtherly analyzes the current consensus on ethics, governance principles, and governance approaches of artificial intelligence. Moreover, the study also proposes to take "co-construction, co-governance and sharing" as the guiding theory to gradually build a multi-dimensional ethical governance system, including education reform, ethical norms, technical supports, legal regulations, and international cooperation.

Teaching And Learning Under Covid-19 Public Health Edicts: The Role Of Household Lockdowns And Prior Technology Usage, Neil Guppy, David Boud, Tania Heap, Dominique Verpoorten, Uwe Matzat, Joanna Tai, Louise Lutze-Mann, Mary Roth, Patsie Polly, Jamie-Lee Burgess, Jenilyn L. Agapito, Silvia K. Bartolic

Department of Information Systems & Computer Science Faculty Publications

Public health edicts necessitated by COVID-19 prompted a rapid pivot to remote online teaching and learning. Two major consequences followed: households became students' main learning space, and technology became the sole medium of instructional delivery. We use the ideas of "digital disconnect" and "digital divide" to examine, for students and faculty, their prior experience with, and proficiency in using, learning technology. We also explore, for students, how household lockdowns and digital capacity impacted learning. Our findings are drawn from 3806 students and 283 faculty instructors from nine higher education institutions across Asia, Australia, Europe, and North America. For instructors, we …

Resilient Error-Bounded Lossy Compressor For Data Transfer, Sihuan Li, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Todays exa-scale scientific applications or advanced instruments are producing vast volumes of data, which need to be shared/transferred through the network/devices with relatively low bandwidth (e.g., data sharing on WAN or transferring from edge devices to supercomputers). Lossy compression is one of the candidate strategies to address the big data issue. However, little work was done to make it resilient against silent errors, which may happen during the stage of compression or data transferring. In this paper, we propose a resilient error-bounded lossy compressor based on the SZ compression framework. Specifically, we design a new independentblock-wise model that decomposes the …

Classifying Mosquito Presence And Genera Using Median And Interquartile Values From 26-Filter Wingbeat Acoustic Properties, Hernan S. Alar, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

Mosquitoes are known to be one of the deadliest creatures in the world. There have been several studies that aim to identify mosquito presence and species using various techniques. The most common ones involve automatic identification of mosquito species from the sounds produced by flapping its wings. The development of these important concepts and technologies can help reduce the spread of mosquito-borne diseases. This paper presents a simple model based on mean and interquartile values that aim to solve the mosquito classification. Despite its simplicity, the proposed model significantly outperforms a Convolutional Neural Network (CNN) model in identifying the mosquito …

The Spatial Effects Of Elderly Population Presence On Covid -19 Incidence In Dki Jakarta Before, During, And After Large-Scale Social Restriction, Chotib Chotib, I G A A Karishma Maharani Raijaya Mrs., Ahmad Aki Aki Muhaimin Mr., Novani Saputri

Smart City

ABSTRACT

Infected cases and suspect cases of covid-19 are increasing more and more daily. This increment happens either in whole regions of Indonesia and DKI Jakarta as a capital city. The purpose of this research is to seek the pattern in spatial of Covid-19 incidence with 3 different periods of before, during, and after large-scale social restriction, and to identify the influence of the presence of the elderly and other factors. One of the scopes of this study is the presence of the elderly because the elderly population is considered as influencing the increase of Covid-19 incidence. The analysis method …

From The Editors, Ahmad Gamal

Smart City

No abstract provided.

Crash Course: Student Team Uses Statistical Modeling And Bigelow Partnership To Map Moose-Car "Hot Zones", Gerry Boyle, Max Slomiak

Colby Magazine

The project began in 2004 when Alex Jospe ’06, a Nordic skier who traveled Maine roads to meets, decided to use skills learned in a GIS class taught by Associate Professor of Environmental Studies Philip Nyhus. Jospe used data supplied by state transportation officials to map moose-collision hot zones. On a trip to Vermont, the map came in handy. “She came back all excited and said, ‘I saw a moose right where my map said I would,’” Nyhus recalled.

Restormer: Efficient Transformer For High-Resolution Image Restoration, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang

Computer Vision Faculty Publications

Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks. Recently, another class of neural architectures, Transformers, have shown significant performance gains on natural language and high-level vision tasks. While the Transformer model mitigates the shortcomings of CNNs (i.e., limited receptive field and in-adaptability to input content), its computational complexity grows quadratically with the spatial resolution, therefore making it infeasible to apply to most image restoration tasks involving high-resolution images. In this work, we propose an efficient Transformer model by making several key …

Using Deep Learning To Predict The Path Of A Shuttlecock In Badminton, Sachleen Singh

Student Theses and Dissertations

With this thesis we intend to predict the movement of the shuttlecock given ten frames of a video for the each of the next ten frames. We also present a new regression model for the prediction of the frames called PIFR [Present Imputation and Future Regression] which consists of two models prepared for the same. Both the models try to predict the “future” position of the shuttlecock, the players and their rackets. There are two parts of the prediction network: An object detection stage based YOLO and the regression model. [40]

Memory Forensics Comparison Of Apple M1 And Intel Architecture Using Volatility Framework, Joshua Duke

LSU Master's Theses

Memory forensics allows an investigator to get a full picture of what is occurring on-device at the time that a memory sample is captured and is frequently used to detect and analyze malware. Malicious attacks have evolved from living on disk to having persistence mechanisms in the volatile memory (RAM) of a device and the information that is captured in memory samples contains crucial information for full forensic analysis by cybersecurity professionals. Recently, Apple unveiled computers containing a custom designed system on a chip (SoC) called the M1 that is based on ARM architecture. Our research focused on the differences …

Molecular Dynamics Simulations Of Vibrational Infrared And Raman Spectra Of H5o2+, Oluwaseun Omodemi, Ivonne Meares, Gabriella Garofalo, Martina Kaledin

Symposium of Student Scholars

We report infrared (IR) and Raman vibrational spectra of H₅O₂⁺ protonated water dimer using computational chemistry methods, the normal mode analysis (NMA), and molecular dynamics (MD) simulations. Various computational methods and basis sets were used. We also located the H₅O₂⁺ stationary points on the potential energy surface using the Gaussian 16 program. The H₅O₂⁺ Zundel complex serves as a benchmark system to study the proton transfer process. We also investigated IR and Raman intensities of other deuterated analogs, such as D₅O₂⁺, D_{4 …}

Fuzzy Information Granulation And Improved Rvm For Rolling Bearing Life Prediction, Xiaoman Hu, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the low accuracy in life prediction and unpredictable problems of degenerative performance trends and fluctuation ranges, etc. Of the bearing life prediction, an improved complete ensemble empirical mode decomposition with adaptive noise analysis and fuzzy information granulating method of improved relevance vector machine is proposed. Focusing on bearing data containing a lot of noise, through the improved complete ensemble empirical mode decomposition with adaptive noise analysis in combination with wavelet packet denoising, the principal component analysis is carride out by exitracing a variety of characeteristics of the signal, the effective information is extracted by granulating the fuzzy …

Research On Moffjsp Based On Multi-Strategy Fusion Quantum Particle Swarm Optimization, Cai Min, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: To improve the quality of the optimal scheduling solution set, a quantum particle swarm algorithm with multi-strategy fusion is proposed for the multi-objective fuzzy flexible job shop scheduling problem with fuzzy maximum completion time, fuzzy total machine load, and fuzzy bottleneck machine load as optimization objectives. Chaotic mapping is used to improve the initial population quality, and a Lévy flight strategy is introduced to enhance the algorithm's ability to jump out of the local optimum. The neighborhood search strategy based on machine mutation is designed for local search. Cross operation is used to maintain the diversity of elite individuals, …

Research On Stick-Slip Vibration Level Estimation Of Near-Bit Based On Optimized Xgboost, Hanwen Tang, Zhang Tao, Yumei Li, Li Lei, Jinghua Zhang, Dongliang Hu

Journal of System Simulation

Abstract: Stick-slip vibration is an important limiting factor affecting drilling speed, safety and cost. The establishment of a reliable stick-slip vibration classification model is very important for oil drilling decision-making. A new method based on Bayesian optimization and eXtreme Gradient Boosting (XGBoost) is proposed to evaluate the severity of stick-slip vibration near the bit. The classification processing of the near-bit stick-slip vibration data is carried out. The main feature vectors of the original data is extracted through time domain and frequency domain analysis. A stick-slip vibration level identification and prediction model based on XGBoost is established, and Bayesian algorithm is …

Simulation Of Pedestrian In Multifunctional Passageway Of Metro Station Area Based On Social Force Model, Wang Xi, Zhang Rui, Fei Shuo, Minghang Yang

Journal of System Simulation

Abstract: Transitional passageway connecting subway stations and commercial facilities is generally designed as the multifunctional passageway, in which the traffic function is the main and the service function is the auxiliary. The impact of the service facilities on both sides of the passage on pedestrian traffic is difficult to be quantitatively analyzed and simulated and modeled. Through measurement, it is found that the viscous effect of service facilities on pedestrian traffic is mainly slowing down the speed or changing the trajectory direction. Through analyzing the horizontal influence range of service facilities, dividing the passage into different areas, and introducing the …

Midcourse Guidance Method Based On Fading Memory Filter For Intercepting Near-Space Gliding Target, Yonghua Fan, Yilun Huangfu, Xiaowen Guo, Chenlu Li, Guofei Li

Journal of System Simulation

Abstract: Aiming at the interception of hypersonic gliding target in near space, a sliding mode guidance law based on fading memory filtering algorithm is proposed. The accurate motion parameters of the hypersonic target are obtained based on the current statistical model fading memory EKF(Extended Kalman Filter) algorithm. Based on the filtering information and sliding mode control theory, a sliding mode guidance law is designed to adjust the interception trajectory online according to the target maneuver law. The simulation results show that the proposed fading memory filtering algorithm can effectively track the gliding target with high filtering accuracy. For a …

Kernel Block Diagonal Representation Subspace Clustering And Its Convergence Analysis, Maoshan Liu, Zhicheng Ji, Wang Yan, Jianfeng Wang

Journal of System Simulation

Abstract: Focus on the problems that the linear block diagonal representation subspace clustering cannot effectively handle non-linear visual data, and the regular regularizers cannot directly pursue the k-block diagonal matrix, a kernel block diagonal representation subspace clustering is proposed. In the proposed algorithm, the original input space is mapped into the kernel Hilbert space which is linearly separable, and the spectral clustering is performed in the feature space. The convergence analysis is given, and the strong convex of variables and the boundedness of function is utilized to verify the monotonically decreasing of objective function and the boundedness and convergence of …

Simulation Of Zero-Speed Correction Algorithm For Underground Space Individual Positioning, Yijing Wang, Su Zhong, Li Qing, Li Lei

Journal of System Simulation

Abstract: In view of the complex and dangerous collapse environment of the tunnel, the related safety hazards of the positioning system of the tunnel rescuer are intensively analyzed, and the simulation of the inertial device worn on the chest, waist, calf, and foot surface shows that the correction on the foot surface is the best. Focus on the error accumulation of inertial devices, according to the fact that the speed is near zero when the sole of the foot fully touches the ground during walking, the algorithm of zero-speed correction for acceleration and angular velocity is compared, and a combination …

Research On Flexible Job-Shop Dynamic Scheduling Based On Game Theory, Yichen You, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: To quickly and effectively respond to the machine fault disturbance events in Flexible Job-shop Scheduling Problem (FJSP), a flexible job-shop dynamic scheduling based on game theory is established. A pre-scheduling scheme is generated under Non-Dominated Sort Genetic Algorithm-Ⅱ (NSGA-Ⅱ) algorithm which introduces self-adapted crossover operators to improve the population diversity. For FJSP dynamic scheduling with machine fault, a multi-stage complete information game model is built to better balance the stability and robustness indicators and respond quickly to the machine fault, in which the stability and robustness indicators are mapped to the game players, and a hybrid Nash Equilibrium which …

Combination Forecasting Model Of Photovoltaic Power Based On Empirical Wavelet Transform, Chen Tao, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: In order to improve the prediction accuracy of short-term photovoltaic power, a variable weight combined prediction model based on Empirical Wavelet Transform (EWT) and PSO-optimized random forest(RF) is proposed. Gray correlation analysis is used to select similar days, EWT is used to decompose the power time series into sub-modes of different frequencies, and three modes of high, medium, and low frequency are reconstructed according to the frequency, PSO-RF and PSO-BP and PSO-LSSVM prediction models are established to dynamically calculate their respective weights for reconstruction, and error correction is performed to output the prediction results. By predicting the output power …