Miracle-dz/UniFAR-FTUs huggingface dataset:
Using High-Order Prior Belief Predictions in Hierarchical Temporal Memory for Streaming Anomaly Detection

Autonomous streaming anomaly detection can have a significant impact in any domain where continuous, real-time data is common. Often in these domains, datasets are too large or complex to hand label. Algorithms that require expensive global training procedures and large training datasets impose strict demands on data and are accordingly not fit to scale to real-time applications that are noisy and dynamic. Unsupervised algorithms that learn continuously like humans therefore boast increased applicability to these real-world scenarios. Hierarchical Temporal Memory (HTM) is a biologically constrained theory of machine intelligence inspired by the structure, activity, organization and interaction of pyramidal neurons in the neocortex of the primate brain. At the core of HTM are spatio-temporal learning algorithms that store, learn, recall and predict temporal sequences in an unsupervised and continuous fashion to meet the demands of real-time tasks. Unlike traditional machine learning and deep learning encompassed by the act of complex functional approximation, HTM with the surrounding proposed framework does not require any offline training procedures, any massive stores of training data, any data labels, it does not catastrophically forget previously learned information and it need only make one pass through the temporal data. Proposed in this thesis is an algorithmic framework built upon HTM for intelligent streaming anomaly detection. Unseen in earlier streaming anomaly detection work, the proposed framework uses high-order prior belief predictions in time in the effort to increase the fault tolerance and complex temporal anomaly detection capabilities of the underlying time-series model. Experimental results suggest that the framework when built upon HTM redefines state-of-the-art performance in a popular streaming anomaly benchmark. Comparative results with and without the framework on several third-party datasets collected from real-world scenarios also show a clear performance benefit. In principle, the proposed framework can be applied to any time-series modeling algorithm capable of producing high-order predictions. THESIS RELEASE PERMISSION ROCHESTER INSTITUTE OF TECHNOLOGY GCCIS Ph.D. PROGRAM IN COMPUTING AND INFORMAITON SCIENCES
Online Decentralized and Cooperative Dispatch for Multi-Microgrids

Microgrid usually associated with distributed renewable energy sources plays a key role in reducing the requirement of traditional power sources and the emission of CO2. Connecting multiple microgrids to the electric power grid can facilitate a more powerful and reliable operation. Microgrids need decentralized dispatch with the requirement of security and privacy, and at the same time, they need to cooperate to satisfy the constraints from the power grid. We consider online decentralized and cooperative dispatch problem in this paper. In order to capture the randomness in the wind and solar power generation and the system dynamics, we formulate the problem as a decentralized Markov decision process (Dec-MDP). We propose a new method to calculate the approximate Q-value functions by constructing the initial feasible policy with reasonable space and time complexity. Bayesian games (BGs) is applied to model the decentralized dispatch with delayed communication in order not to slow down the online decision-making. Also, the alternating minimization method is used to solve this BGs based on the approximate Q-value functions. Numerical results demonstrate that the proposed algorithm is very effective in reducing the state space and computing time and achieving the requirement of real-time scheduling. Note to Practitioners—Motivated by the problem of decentralized dispatch of multi-microgrids in grid-connected mode, we consider the online decentralized operation and pay special attention to the grid tie-line power fluctuation. This problem is challenging due to the randomness of the renewable power generation, the global constraints about multiple microgrids, and the correlation among stages. We model the decentralized dispatch problem as a rolling horizon Dec-MDP and provide an approximate solution method, namely, alternately minimizing among different microgrids at the online stage and calculating approximate value functions in the offline preparation process. The approximate value functions make the online decision turn out to be a single-stage decentralized decision problem. Online decision calculation based on delayed communication is to guarantee all the microgrids to start conducting an action immediately while receiving the information related to them. We hope this paper brings insight onto the tie-line power fluctuation stabilization while multiple microgrids connecting to the same point of interconnection.
H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud/shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds, which better capture local atmospheric and surface effects. However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloud/shadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.
Robust and Fast Magnetic Dipole Localization With Singular Value Truncated SDM

The supervised descent method (SDM) avoids computing inverse of the Hessian matrix and is a potential tool to rapidly solve the nonlinear least squares problem of magnetic dipole localization. However, the magnetic measurements are often noisy, which will cause an error during the update of SDM. To address this issue, we proposed a singular value truncated SDM (TSDM) to seek the descent directions that have the greatest differences in magnetic intensities. The results of the simulations and the experiment show that: 1) TSDM is more robust than SDM and obtains localization errors comparable or lower than Levenberg-Marquardt (LM) and 2) TSDM is faster than LM when the number of sensors ≤ 25 and the signal-to-noise ratio (SNR) ≤ 30 dB. Thus, the proposed TSDM may help to build a robust and fast magnetic localization system.
Kinematic Object Track Stitcher for Post Tracking Fragmentation Detection and Correction

KINEMATIC OBJECT TRACK STITCHER FOR POST TRACKING FRAGMENTATION DETECTION AND CORRECTION Name: Beigh, Alex Wunderlin University of Dayton Advisor: Dr. Eric Balster A common goal of tracking objects over multiple image frames is to ensure that the object tracks are as accurate and error free as possible. One of the biggest impediments to accurate object tracking is an event called track fragmentation. Track fragmentation occurs when an object’s trajectory or track is reported by the tracker as two or more separate track instances. Fragmentation occurs because of features present in the imagery and the method at which the imagery is processed. Some causes of track fragmentation include: occlusions, object trajectory or velocity changes, proximity of objects to one another, parallax, image stitching, lighting changes, and image artifacts. Track fragmentation is tackled in multiple ways. The GATER tracker uses projected points to make predictions where tracks will be in future frames to compensate for events that cause fragmentation. Another solution is to use a process known as track stitching. A post-tracking kinematic track stitcher is proposed that is able to detect and correct track fragmentation, improving fragmentation from GATER’s projected points. The proposed track stitcher requires only a track’s centroid and frame, information any tracker can provide. The track stitcher will improve the accuracy iii of the tracks by reducing track fragmentation. The track stitcher consists of a series of filters, including a temporal filter and multiple kinematic filters, designed to determine the likelihood and feasibility of a track being fragmented. The filters are designed to be easily enabled, disabled, and integrated depending on the needs of a given data set. These filters include sets of parameters that can be manually altered by the user for different tracking scenarios. A selection algorithm, in this case Munkres, is used to evaluate the track scores and make stitches accordingly. The track stitcher is run on multiple track sets acquired from a Wide Area Motion Imagery image set. For the first experiment, one of the image sets is selected for tuning, and then the track stitcher is run with varying parameters for the kinematic filter and scoring filter. The best performing parameters for the first image set are then used to test the track stitcher on the remaining image sets in order to prove that the same parameters can be universally applied to similar imagery. In every test case the track stitcher reduces the amount of track fragmentation and error (spuriousness) present in the tracks. In a majority of the tests, the purity of the tracks also increases.When compared to the point projected tracks GATER produces, the proposed track stitcher improves spuriousness by 26.67% and fragmentation by 17.67% on average.
Estimation Method of Traffic Volume in Provincial City Using Big-Data

Traffic jams have recently become a significant problem in provincial cities in Japan, which tend to have poor railway services. Therefore, the main means of transportation are public buses, taxies, and private vehicles. Moreover, traffic accidents and road construction sites frequently block traffic. It is therefore difficult to estimate the travelling time from origin to destination in real-time. To estimate the travelling time, we must predict the behaviors of many vehicles that depend on an "origin to destination" (OD) traffic volume. In our previous study, we proposed an estimation method for OD traffic volume using two types of big-data, a road traffic census and mobile spatial statistics. In this study, we evaluated our proposed method on various situations through a traffic simulation.
Development of photoelastic fringe plotting scheme from 3D FE results

A novel approach to plot photoelastic fringe contours from three-dimensional finite element results is presented. Plotting of fringe contours from 3D FE models requires the calculation of secondary principal stresses along the light path. The definition of secondary principal stresses and the calculation of relevant photoelastic data for fringe plotting are discussed. The plotting scheme uses a scanning approach and the evaluation of total fringe order is achieved by combining a file structure and subsequent sorting of these data to enable integration along the light path. The methodology is validated for the problem of a cantilever beam with an edge load. The usefulness of the methodology to verify 3D FE modelling and for experimentalists to design their experiments is indicated. Copyright © 2006 John Wiley & Sons, Ltd.
DC-Link Voltage Disturbance Rejection Strategy of PWM Rectifiers Based on Reduced-Order LESO

In applications of pulse-width modulation (PWM) rectifiers, the high quality of the DC power is required. The fluctuation of grid voltage and the switching of equipment connected to the DC supply are likely to cause a sudden change in the DC-link voltage, resulting in equipment instability or even damage. Considering the bandwidth of the closed-loop current dynamic, this paper presents a novel DC-link voltage control strategy of PWM rectifiers based on the reduced-order linear extended state observer (RLESO). Using a first-order RLESO to observe and even compensate the model variations caused by grid voltage and DC-link voltage fluctuations or load transients, the proposed method can effectively improve the dynamic performance against grid and load disturbances, as well as for sudden changes in DC-link reference voltage. Combined with frequency domain analysis, this paper theoretically analyzes the sensitivity, stability, and tracking performance of the proposed method, providing a basis for the selection of controller parameters. And finally, simulation and experimental results are presented to demonstrate its validity and the superiority over the traditional PI control strategy.
Performance Comparison of a Hadoop DFS to a Centralized File System of a Single Machine

With the coming of a big data era, Hadoop, developed by Doug Cutting and Mike Cafarella, was presented in 2005 [1], which turned over a new page in the history of cloud computing. The Hadoop Distributed File System (HDFS) is one of the most fundamental layers in Hadoop. In the big data world, the performance of dealing with big data from HDFS cannot satisfy the need because the amount of big data is getting larger and larger, and simultaneously, the increasing rate of growth of big data is faster and faster. Nowadays various new distributed file systems (DFS) are published attempting to solve this issue. The core problem hindering the performance from becoming more effective is the metadata service layer in HDFS, and most of the new DFSs are focusing on improving the metadata service as well. Most of the above-mentioned cases are centering on the issue of solving the big data problem. However, for a small or medium-sized company, the data they may use is not so big. In this case, do they need to build a distributed system to deal with their data? Of course, the data in these companies will be getting larger and larger. When will be the best time for them to need a distributed system to manage their data? This paper attempts to address this problem by comparing the different performances between a distributed system computation and a serial computation.
Contributions to Head Pose Estimation methods

Head Pose Estimation (HPE) is currently a growing research field, mainly because of the proliferation of human-computer interfaces (HCI) in the last decade. It offers a wide variety of applications, including driver assistance systems, pose-invariant face recognition, human behavior analysis, or popular HCI applications such as gaze estimation systems. HCIs show an increasing tendency to integrate HPE as another bridge for interaction, since it is a rich form of communication. For instance, gaze tracking systems suffer in unconstrained environments because they are very sensitive to head motion, and HPE has become a key point for successful gaze estimation. This thesis thus aims to contribute to the development of robust and accurate HPE methods based on 2D tracking of the face in videos. With the idea of achieving a better understanding of every aspect of the HPE process, a complete framework has been created in the first part of the thesis as a pillar to sustain the rest of the work. This framework consists of both simulation and realistic environments for HPE algorithm analysis. It includes the recording of two head pose databases of videos, one with synthetically generated heads and the other one with real subjects. They have proven to be extremely useful tools for the purpose, and therefore we expect to make them available for the whole scientific community. The problem of 3D face reconstruction using only 2D images from the videos has received special attention. A whole chapter has been devoted to the study and comparison of different single-view and multi-view based reconstruction methods in a controlled simulation environment. This has allowed us to isolate the 3D model fitting problem, thus drawing several conclusions regarding the influence of this critical part in a HPE system. With the aim of achieving a wider impact with this thesis, the pose estimation problem is addressed from a general perspective in which techniques that are
Adaptive Transmission Control for Uplink/Downlink Fairness in Unsaturated CSMA Networks

In carrier sensing multiple access (CSMA)-based IEEE 802.11 systems, it is important for an access point (AP) and multiple stations to have access opportunity proportional to their traffic load, ensuring fairness between uplink (UL) and downlink (DL). This letter proposes an adaptive backoff algorithm based on estimation for the UL/DL backlog sizes in unsaturated CSMA networks. Based on the UL/DL backlog estimation, the optimal UL and DL transmission probabilities are derived as a function of the UL/DL backlog sizes. The proposed algorithm not only improves the overall system performance but also provides the near-perfect UL/DL fairness.
Analyzing Task Difficulty in a Bebras Contest Using Cuttle

Predicting the difficulty level of a task on the concepts of computer science or computational thinking, like in the Bebras Challenge, proves to be really hard. But the announced difficulty level is needed in the contest format used in many local challenges. The Dutch contest system Cuttle has a new module for analysis. This is applied to one specific contest in order to find parameters explaining task difficulty. Using quantitative methods we were able to confirm a relation between answer types and difficulty and a tendency that tasks on data, data structures and representation are better answered than tasks on algorithms and programming.
Maximizing accuracy of group peer assessment using item response theory and integer programming

With the wide spread of large-scale e-learning environments, peer assessment has been widely used to measure learner ability. When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups. However, in such cases, the peer assessment accuracy depends on the method of forming groups. To resolve that difficulty, this study proposes a group formation method to maximize peer assessment accuracy using item response theory and integer programming. Experimental results, however, have demonstrated that the method does not present sufficiently higher accuracy than a random group formation method does. Therefore, this study further proposes an external rater assignment method that assigns a few outside-group raters to each learner after groups are formed using the proposed group formation method. Through results of simulation and actual data experiments, this study demonstrates that the method can substantially improve peer assessment accuracy.
Improving the Detection of Sequential Anomalies Associated with a Loop

Workflow models are widely applied in business software design. A workflow model contains a set of systematic ordered tasks to achieve designated business goal(s) under the designed flow control. Analyzing artifact usage during design phase can prevent unexpected artifact result due to abnormal artifact operation(s). A sequential anomaly indicates a pair of activities operating on the same artifact that can result in redundant write or missing production. On the other hand, the iteration of a loop structure in a workflow cannot be statically analyzed, thus, detecting process of artifact anomalies in a loop is costly. In this paper, we present an effective method to detect all anomalies associated with a loop by removing the redundant computation due to the repeated structure of the body and control in the iterations. After the removing, the anomalies can be detected on a single iteration generated instead. Here, the process of anomaly detection is now simplified into two phases: First, a workflow model is transformed into a corresponding C-tree structure and next, the proposed anomaly detection methodology is applied to the C-tree. Compared with current approaches, our method can reduce the space complexity and decrease the execution times of anomaly detection as linear.
Building a SAR-Enabled Data Cube Capability in Australia Using SAR Analysis Ready Data

A research alliance between the Commonwealth Scientific and Industrial Research Organization and Geoscience Australia was established in relation to Digital Earth Australia, to develop a Synthetic Aperture Radar (SAR)-enabled Data Cube capability for Australia. This project has been developing SAR analysis ready data (ARD) products, including normalized radar backscatter (gamma nought, γ0), eigenvector-based dual-polarization decomposition and interferometric coherence, all generated from the European Space Agency (ESA) Sentinel-1 interferometric wide swath mode data available on the Copernicus Australasia Regional Data Hub. These are produced using the open source ESA SNAP toolbox. The processing workflows are described, along with a comparison of the γ0 backscatter and interferometric coherence ARD produced using SNAP and the proprietary software GAMMA. This comparison also evaluates the effects on γ0 backscatter due to variations related to: Near- and far-range look angles; SNAP’s default Shuttle Radar Topography Mission (SRTM) DEM and a refined Australia-wide DEM; as well as terrain. The agreement between SNAP and GAMMA is generally good, but also presents some systematic geometric and radiometric differences. The difference between SNAP’s default SRTM DEM and the refined DEM showed a small geometric shift along the radar view direction. The systematic geometric and radiometric issues detected can however be expected to have negligible effects on analysis, provided products from the two processors and two DEMs are used separately and not mixed within the same analysis. The results lead to the conclusion that the SNAP toolbox is suitable for producing the Sentinel-1 ARD products.
Performance analysis and formative assessment of visual trackers using PETS critical infrastructure surveillance datasets

Abstract. Surveillance of perimeter of critical infrastructures (CIs) has gained a particular attention worldwide due to rising threats of terrorist incidents. However, the generation and dissemination of real-world, challenging datasets concerning CIs for performance assessment of surveillance tasks, particularly tracking, have widely been overlooked in the community. Recent performance evaluation of tracking and surveillance (PETS) workshops have sought to address this issue by introducing ample CI surveillance datasets. We demonstrate the effectiveness of these publicly released real-world PETS CI visual datasets by providing a comprehensive statistically significant performance analysis as well as formative assessment of several state-of-the-art multitarget trackers using well-known and recent performance assessment criteria.