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arXiv 2022年

作者： Diamond, N'yoma Perkins, Grant Departments of Computer Science & Data Science Worcester Polytechnic Institute WorcesterMA01609 United States

In its semi-strong form, the Efficient Market Hypothesis (EMH) implies that technical analysis will not reveal any hidden statistical trends via intermarket data analysis. If technical analysis on intermarket data reveals trends which can be leveraged to significantly outperform the stock market, then the semi-strong EMH does not hold. In this work, we utilize a variety of machine learning techniques to empirically evaluate the EMH using stock market, foreign currency (Forex), international government bond, index future, and commodities future assets. We train five machine learning models on each dataset and analyze the average performance of these models for predicting the direction of future S&P 500 movement as approximated by the SPDR S&P 500 Trust ETF (SPY). From our analysis, the datasets containing bonds, index futures, and/or commodities futures data notably outperform baselines by substantial margins. Further, we find that the usage of intermarket data induce statistically significant positive impacts on the accuracy, macro F1 score, weighted F1 score, and area under receiver operating characteristic curve for a variety of models at the 95% confidence level. This provides strong empirical evidence contradicting the semi-strong EMH. © 2022, CC BY-NC-ND.

关键词： Financial markets

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Geographical Connectivity in the United States

SSRN

引用

SSRN 2023年

作者： Wills, Craig E. Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States

This work takes a comprehensive approach to compute, analyze and visualize multi-modal time travel maps from any location in the United States to any other. The comprehensive aspect of the work allows us to compute the relative connectivity, to either geographic area or population, for all locations, at the granularity of counties, in the United States. Centralized regions near major airports such as Denver, Minneapolis/St. Paul and Dallas/Fort-Worth have the best connectivity to the geographic area of the continental 48 states with locations around the edges of the U.S. generally having the worst connectivity. Extending this analysis to all 50 states, shows the regions of Denver, Minneapolis/St. Paul and Salt Lake City with the best connectivity. The best connectivity to the population of the continental 48 and all 50 states is in the Atlanta metropolitan region with the regions of Chicago, Charlotte and Washington, DC also having relatively shorter average travel times to the U.S. population. The work allows the mode of transportation to be determined with roughly 9% of the U.S. population reaching the remainder of the U.S. population in minimal travel time by driving. The remaining 91% of trips include an airline flight with ORD (Chicago) and LAX (Los Angeles) the most likely airports for origination of flights. © 2023, The Authors. All rights reserved.

关键词： Travel time

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Phoenix: IoT Trust Evaluation Using Game Theory with Second Chance Protocol

Phoenix: IoT Trust Evaluation Using Game Theory with Second ...

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International Conference on Internet of Things: Systems, Management and Security

作者： Himan Namdari Cihan Tunc Ram Dantu Dept. of Data Science Worcester Polytechnic Institute Worcester MA USA Dept. of Computer Science & Engineering University of North Texas Denton TX USA

Internet of Things (IoT) devices are a big part of our daily lives for their use in smart environments and open fields. However, IoT devices, characterized by limited computational capabilities and energy constraints, are prone to malfunctions and cyberattacks. This vulnerability raises substantial trust issues, particularly concerning the reliability of the data they provide. This paper proposes a hierarchical trust evaluation methodology by incorporating game theory and Bayesian inference designed for IoT devices to address the trust evaluation issue. We also explored the impact of historical behavior, such as nodes’ age and trust ratio improving our trust evaluation results. Furthermore, we proposed a technique named "second chance protocol" to identify a transitory malfunctioning node and introduce it back to the network once it regains trustworthiness, resulting in resource optimization and enhancing average trust in the model. We generated synthetic data to evaluate the proposed approaches with randomly distributed noise levels simulating failures or compromised IoT devices. The results of our experimental analysis demonstrate that our approach improves the overall trust ratio of the system concerning traditional approaches. The proposed second chance protocol further enhances the trust average and device utilization by accurately identifying malfunctioning nodes. Our result with different noise levels achieves five to ten times better than an alternative study. Finally, our model achieved a detection rate of 97.99%, 89%, and 79% for 10%, 20%, and 30% noise levels.

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Thermal Ablation Therapy Control with Tissue Necrosis-driven Temperature Feedback Enabled by Neural State Space Model with Extended Kalman Filter

Thermal Ablation Therapy Control with Tissue Necrosis-driven...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ryo Murakami Satoshi Mori Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA The United States Department of Biomedical Engineering Robotics Engineering and Computer Science Engineering Worcester Polytechnic Institute Worcester MA The United States

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Thermal ablation therapy is a major minimally invasive treatment. One of the challenges is that the targeted region and therapeutic progression are often invisible to clinicians, requiring feedback provided in numerical information or imaging. Several emerging imaging modalities offer visualization of the ablation-induced necrosis formation; however, relying solely on necrosis monitoring can result in tissue overheating and endangering patients. Some of the necrosis monitoring modalities are known for their capabilities in temperature sensing, but the principles on which they are based have several limitations, such as sensitivity to the tissue motion and their environment. In this study, we propose a necrosis progression-based temperature estimation technique as an added safety feature for avoiding overheating. This model-based method does not require additional sensing hardware. It is designed to work as an independent estimator or a complimentary estimation component with other thermometers for improved robustness. For this objective, the Neural State Space model is used to approximate the ablation therapy, whose theoretical models involve nonlinear partial differential equations. Then, the Extended Kalman Filter is designed based on the model. The simulation study shows the estimation module robustly estimates the tissue temperature under several types of noise. The maximum estimation error observed before terminating ablation was around 1 °C, and the desired safety feature was successfully demonstrated. The estimator is expected to be used in a variety of necrosis monitoring modalities to guarantee more precise and safer treatment. More ambitiously, the architecture with the Neural State Space model and Extended Kalman Filter is generalizable to other medical/biological procedures involving nonlinear and patient/environment-specific physics and even to procedures having no reliable theoretical models.

关键词： Thermometers Estimation Medical treatment Mathematical models Robustness Sensors Safety Temperature control Kalman filters Monitoring

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Quantifying and Improving Performance of Distributed Deep Learning with Cloud Storage 9

Quantifying and Improving Performance of Distributed Deep Le...

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9th IEEE International Conference on Cloud Engineering, IC2E 2021

作者： Krichevsky, Nicholas St Louis, Renee Guo, Tian Worcester Polytechnic Institute Computer Science Department WorcesterMA United States

ISBN: (纸本)9781665449700

Cloud computing provides a powerful yet low-cost environment for distributed deep learning workloads. However, training complex deep learning models often requires accessing large amounts of data, which can easily exceed the capacity of local disks. Prior research often overlooks this training data problem by implicitly assuming that data is available locally or via low latency network-based data storage. Such implicit assumptions often do not hold in a cloud-based training environment, where deep learning practitioners create and tear down dedicated GPU clusters on demand, or do not have the luxury of local storage, such as in serverless workloads. In this work, we investigate the performance of distributed training that leverages training data residing entirely inside cloud storage buckets. These buckets promise low storage costs, but come with inherent bandwidth limitations that make them seem unsuitable for an efficient training solution. To account for these bandwidth limitations, we propose the use of two classical techniques, namely caching and pre-fetching, to mitigate the training performance degradation. We implement a prototype, DELI, based on the popular deep learning framework PyTorch by building on its data loading abstractions. We then evaluate the training performance of two deep learning workloads using Google Cloud's NVIDIA K80 GPU servers and show that we can reduce the time that the training loop is waiting for data by 85.6%-93.5% compared to loading directly from a storage bucket-thus achieving comparable performance to loading data directly from disk-while only storing a fraction of the data locally at a time. In addition, DELI has the potential of lowering the cost of running a training workload, especially on models with long per-epoch training times. © 2021 IEEE.

关键词： Loading

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Adversarial Attacks Targeting Point-to-Point Wireless Networks

Adversarial Attacks Targeting Point-to-Point Wireless Networ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Ahmad Ghasemi Majid Moradikia Seyed Reza Zekavat Hossein Pishro-Nik ECE department University of Massachusetts Amherst Amherst USA Department of Data Science Worcester Polytechnic Institute (WPI) Worcester MA USA

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

This paper introduces a novel adversarial attack targeting Graph Neural Network (GNN)-based radio resource management in point-to-point networks. Our proposed attack, executed during the test phase, manipulates the system's input by exploiting specific constraints. Formulated as an optimization problem, the attack aims to maximize resource stealing, thereby degrading the quality of communication. We assess the attack's efficacy with respect to the number of users, signal-to-noise ratio, and the adversary's power budget. The results demonstrate that our proposed attack approaches the performance of an established upper-bound adversarial benchmark while maintaining lower complexity, highlighting its effectiveness and potential for real-world applicability.

关键词： Vehicular and wireless technologies Upper bound Wireless networks Perturbation methods Radio transmitters Receivers Security

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MAVR: Multi-Functional Point Cloud Annotations Using Virtual Reality

MAVR: Multi-Functional Point Cloud Annotations Using Virtual...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Xiao Zhang Zhanhong Huang Xinming Huang Department of Electrical & Computer Engineering and Department of Computer Science Worcester Polytechnic Institute Worcester MA USA

Learning-based point cloud perception methods rely on labeled data for training data-driven models, necessi-tating the development of precise and efficient tools for point cloud annotations. In this paper, we propose MAVR, a multi-functional annotation framework based on virtual reality (VR) technology, capable of accurately labeling point cloud data for diverse applications, including part segmentation and object detection. We begin by evaluating the user interface (UI) efficiency through interactive efficiency analysis. Subsequently, a comprehensive three-step process is introduced, which consists of pre-processing, point selection, and post-tagging. For 3D object part segmentation and scene perception, we propose two distinct tagging pipelines. Our experimental results on various datasets validate the effectiveness of MAVR in accurately annotating point clouds from different data sources within an immersive workspace.

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ǫ-Uniform Mixing in Discrete Quantum Walks

arXiv

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arXiv 2023年

作者： Zhan, Hanmeng Computer Science Department Worcester Polytechnic Institute MA United States

We study whether the probability distribution of a discrete quantum walk can get arbitrarily close to uniform, given that the walk starts with a uniform superposition of the outgoing arcs of some vertex. We establish a characterization of this phenomenon on regular non-bipartite graphs in terms of their adjacency eigenvalues and eigenprojections. Using theory from association schemes, we show this phenomenon happens on a strongly regular graph X if and only if X or X has parameters (4m2, 2m2 ± m, m2 ± m, m2 ± m) where m ≥ 2. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Exploring Scalable Bayesian Networks For Identification of Zero-day Attack Paths

Exploring Scalable Bayesian Networks For Identification of Z...

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Cybersecurity Conference (SVCC), Silicon Valley

作者： Ravi Nitinkumar Patel Xiaomei Zhang Xiaoyan Sun Jun Dai Department of Computer Science California State University Sacramento Dept. of Computer Science and Mathematics University of South Carolina Beaufort Department of Computer Science Worcester Polytechnic Institute

ISBN: (数字)9798350383140

ISBN: (纸本)9798350383157

Detecting zero-day attacks is still a challenging problem for large enterprise networks. Some previous research work captures the system-level activities and then builds Bayesian networks to collect the intrusion alerts as evidence for zero-day attack path detection. However, since such Bayesian networks are developed on top of the dependency graphs among system objects, scalability can easily become an issue due to the overwhelming amount of system level activities. As more system calls are collected over time, the size of the Bayesian network will increase drastically. Therefore, we propose an approach to address the scalability issue of the Bayesian network when using it to identify the zero-day attack *** paper focuses on three parts: the division of a single large Bayesian network, the virtual connection of multiple sub-Bayesian networks, and the Bayesian inference between sub-Bayesian networks to find the complete zero-day attack paths. The division of a large Bayesian network into smaller parts reduces the time needed to calculate probabilities and update the Bayesian networks. In the proposed approach, multiple Bayesian networks can virtually connect to work as an entire network for identification of zero-day attack paths. The experimental results demonstrate the effectiveness of this scalable Bayesian network approach.

关键词： Scalability Bayes methods computer security

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Cross-Modal Interaction between Temperature and Light Color Temperature in Reading Comprehension

SSRN

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SSRN 2025年

作者： Guo, Xingtong Luo, Wei Mangoubi, Oren Liu, Shichao Department of Civil Environmental and Architectural Engineering Worcester Polytechnic Institute WorcesterMA United States Department of the Built Environment Eindhoven University of Technology Eindhoven Netherlands Department of Data Science Worcester Polytechnic Institute WorcesterMA United States

To improve physical learning environments and explore the potential for energy savings through adjustments in lighting color, this study examines the cross-modal interactions between thermal and visual perceptions, along with their effects on reading comprehension. A within-subject experiment was conducted with 27 participants exposed to four scenarios combining two nominal room temperatures (24°C and 28°C) and two light correlated color temperatures (CCT) (2900K and 5700K) at 510 lux from 9 AM to 11 AM. Thermal perceptions, visual perceptions, physiological signals and reading comprehension were measured. Repeated measures two-way ANOVA results revealed that while CCT influenced participants' thermal sensations, it did not affect thermal comfort. The post-hoc analysis suggested that room temperature remained the dominant factor influencing both thermal sensation and comfort. Additionally, a significant cross-modal interaction between CCT and thermal sensations was observed. Furthermore, participants’ reading comprehension scores remained consistent across all test conditions, which might be caused by ceiling effects. Psychological attributes such as alertness and motivation, along with heart rate and heart rate variability showed no significant differences between the four conditions. Results of quiz score and time taken to finish the quizzes suggested that shifting the temperature and light color within a moderate range does not significantly impact reading comprehension, suggesting a potential of energy saving by relaxing environmental control setpoints. Additionally, decision tree models suggest that predicted mean vote (PMV), temperature, sample size, and clothing insulation influence whether the hue-heat hypothesis is supported or refuted in the literature. © 2025, The Authors. All rights reserved.

关键词： Heart

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