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arXiv 2024年

作者： Parekh, Sagar Bramblett, Lauren Bezzo, Nicola Losey, Dylan P. Dept. of Mechanical Engineering Virginia Tech BlacksburgVA24061 United States Dept. of Systems & Information Engineering Dept. of Electrical & Computer Engineering University of Virginia CharlottesvilleVA22903 United States

Humans interacting with robots often form predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might predict that the car is going to remain in the same lane. It is important for the robot to understand the human’s prediction for safe and seamless interaction: e.g., if the autonomous car knows the human thinks it is not merging — but the autonomous car actually intends to merge — then the car can adjust its behavior to prevent an accident. Prior works typically assume that humans make precise predictions of robot behavior. However, recent research on human-human prediction suggests the opposite: humans tend to approximate other agents by predicting their high-level behaviors. We apply this finding to develop a second-order theory of mind approach that enables robots to estimate how humans predict they will behave. To extract these high-level predictions directly from data, we embed the recent human and robot trajectories into a discrete latent space. Each element of this latent space captures a different type of behavior (e.g., merging in front of the human, remaining in the same lane) and decodes into a vector field across the state space that is consistent with the underlying behavior type. We hypothesize that our resulting high-level and course predictions of robot behavior will correspond to actual human predictions. We provide initial evidence in support of this hypothesis through proof-of-concept simulations, testing our method’s predictions against those of real users, and experiments on a real-world interactive driving dataset. © 2024, CC BY.

关键词： Prediction models

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Signal Processing Techniques from Accelerometers on Railway Transport Based on the Wavelet Transform

Signal Processing Techniques from Accelerometers on Railway ...

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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Alexander M. Boronakhin Alexandra V. Bolshakova Dmitry M. Klionskiy Daniil Yu. Larionov Roman V. Shalymov dept. of Laser Measurement and Navigational Systems faculty of Information Measurement and Biotechnical Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Laser Measurement and Navigational Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Information Systems dept. of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation

Railway transport safety is directly connected with the condition of railway tracks and also the condition of railway wheels’ surface. One of the main reasons for abnormal situations on railways are various defects such as irregularities of a railway track. Thus, we need to estimate the length of short and impulse irregularities. It is also important to carry out joint analysis of vibration acceleration signals recorded by accelerometers in order to study types and sizes of railway track irregularities.

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A Novel Technique to Parameterize Congestion Control in 6TiSCH IIoT Networks

A Novel Technique to Parameterize Congestion Control in 6TiS...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kushal Chakraborty Aritra Kumar Dutta Mohammad Avesh Hussain Syed Raafay Mohiuddin Nikumani Choudhury Rakesh Matam Mithun Mukherjee Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Hyderabad India Dept. of Computer Science & Engineering Indian Institute of Information Technology Guwahati India Nanjing University of Information Science and Technology Nanjing China

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the Time Slotted Channel Hopping mode of IEEE 802.15.4e) as an enabling technology facilitates low-power and low-latency communication between IoT devices in industrial environments. The Routing Protocol for Low power and lossy networks (RPL), which is used as the de-facto routing protocol for 6TiSCH networks is observed to suffer from several limitations, especially during congestion in the network. Therefore, there is an immediate need for some modifications to the RPL to deal with this problem. Under traffic load which keeps on changing continuously at different instants of time, the proposed mechanism aims at finding the appropriate parent for a node that can forward the packet to the destination through the least congested path with minimal packet loss. This facilitates congestion management under dynamic traffic loads. For this, a new metric for routing using the concept of exponential weighting has been proposed, which takes the number of packets present in the queue of the node into account when choosing the parent at a particular instance of time. Additionally, the paper proposes a parent selection and swapping mechanism for congested networks. Performance evaluations are carried out in order to validate the proposed work. The results show an improvement in the performance of RPL under heavy and dynamic traffic loads.

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TrustEvent: Cross-Platform IoT Trigger Event Verification Using Edge Computing

TrustEvent: Cross-Platform IoT Trigger Event Verification Us...

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IEEE International Conference on Communications (ICC)

作者： Trent Reichenbach Chenglong Fu Xiaojiang Du Jia Di Yuede Ji Dept. of Electrical and Computer Engr Stevens Institute of Technology Hoboken NJ USA Dept. of Software and Information Systems UNC Charlotte Charlotte NC USA Dept. of Computer Science and Engr University of Arkansas Fayetteville AR USA Dept. Computer Science and Engr University of North Texas Denton TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

As smart home IoT systems gain popularity, they inevitably become targets for security risks and concerns. Among various cyber-attacks targeting these systems, the fake event attack poses significant issues due to its ability to manipulate secure devices through automation rules. In response to this threat, we propose TrustEvent - a system designed to offer end-to-end event signature verification. By integrating TrustEvent with existing home automation platforms, event authenticity is verified against signatures generated from edge devices before these events trigger automation rule execution. Notably, we have developed a signature proxy module, enhancing our system's compatibility across various platform scenarios. We have implemented a TrustEvent prototype in conjunction with existing commercial smart home IoT platforms, evaluating its overhead in the process. Our experimentation demonstrates that our system only marginally increases the automation execution latency, by an average of 3.74 seconds, representing a acceptable compromise for enhanced security.

关键词： Handwriting recognition Automation System performance Instruments Prototypes Smart homes Static analysis

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Towards Adaptive AI Governance: Comparative Insights from the U.S., EU, and Asia

Towards Adaptive AI Governance: Comparative Insights from th...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Vikram Kulothungan Deepti Gupta Capitol Technology University Laurel Maryland USA Dept. of Computer Information Systems Texas A&M University - Central Texas Texas USA

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how different regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market-driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk-based framework emphasizing ethical safeguards, and Asia employs state-guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk-tiered oversight, innovation accelerators, and strategic alignment mechanisms. By bridging governance gaps, this study offers actionable insights for fostering responsible AI development while ensuring a balance between technological progress, ethical imperatives, and regulatory coherence.

关键词： Economics Ethics Technological innovation Biological system modeling Asia Coherence Market research Regulation Security Artificial intelligence

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Comparison of DenseNet201, InceptionV3, and Their Fusion Models in Breast Cancer Tissue Image Classification 16

Comparison of DenseNet201, InceptionV3, and Their Fusion Mod...

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16th International Conference on Digital Image Processing, ICDIP 2024

作者： Ge, Ruigang Chen, Guoyue Saruta, Kazuki Terata, Yuki Graduate School of Akita Prefectural University 84-4 Ebinokuchi Tsuchiya Yurihonjo city Akita015-0011 Japan Dept. of Information and Computer Science Faculty of Systems and Technology Akita Prefectural University 84-4 Ebinokuchi Tsuchiya Yurihonjo city Akita015-0011 Japan

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

In this study, we propose a fusion model based on DenseNet201 and InceptionV3 aimed at improving the accuracy of 8-class classification of the BreakHis breast cancer histopathological image dataset. The BreakHis dataset poses significant challenges for automatic classification tasks due to its high feature similarity and class imbalance. Our model leverages the deep connectivity of DenseNet201 and the broad exploratory capabilities of InceptionV3 to capture comprehensive features from local to global scales, enhancing adaptability to various feature scales. This fusion model outperforms individual DenseNet201 or InceptionV3 models in key performance metrics such as precision, recall, and F1 score. It shows marked performance improvements, particularly in categories with high feature similarity and fewer samples, demonstrating its effectiveness in addressing internal dataset imbalances. Additionally, the model exhibits stability across multiple training and testing iterations, further validating its reliability and effectiveness in classifying breast cancer histopathological images. The results indicate that the fusion of DenseNet201 and InceptionV3 models can enhance the accuracy of automatic classification of breast cancer histopathological images, especially in categories with high feature similarity. This finding provides valuable insights for the development and selection of classification models and contributes to future work in medical image analysis. © 2024 SPIE.

关键词： Lung cancer

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Architecture development of middleware and API for IOT-based Red Onion farming

Architecture development of middleware and API for IOT-based...

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International Seminar on Application for Technology of information and Communication (ISemantic)

作者： Alvin Steven Suharnawi Farrikh Alzami Filmada Ocky Saputra Rama Aria Megantara Pulung Nurtantio Andono Dept. of Information Systems Universitas Dian Nuswantoro Indonesia Dept. of Information Engineering Universitas Dian Nuswantoro Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Indonesia

IoT plays a crucial role in transforming the agricultural industry by offering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural community benefits immensely from IoT is due to its capacity to gather real-time data straight from their fields. Farming requires considerable attention, and wireless sensor networks enable farmers to deploy IoT devices throughout various parts of their farm so they can obtain information on soil moisture levels, temperature, humidity, among other essential parameters that are integral for effective agriculture management. This gathered data empowers farmers with accurate weather forecasts through machine learning techniques which assist them in making informed decisions related to planting or harvesting of crops - vital components critical for successful outcomes towards better yields. Moreover, middleware architectures/APIs developed specifically for IOT-based red onion farming utilize cloud computing services wherein these enlightening patterns generated get stored safely using analytics tools &automated systems. These intensify insights derived as it simplifies accessing important metrics aiding ease while analyzing historical trends advantageously helping make prudent future course corrective measures concerning planting/harvesting schedules based entirely on solid empirical evidence combined with artificial intelligence ensuring unprecedented efficiency gains and higher returns than previously achievable without technology guidance.

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

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(POSTER) Insights from Executing TinyML Models on Smartphones and Microcontrollers

(POSTER) Insights from Executing TinyML Models on Smartphone...

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International Conference on Distributed Computing in Sensor systems (DCOSS)

作者： Harman M. Singh Shrishailya Agashe Shreyans Jain Surjya Ghosh Aditya Challa Sravan Danda Sougata Sen Dept. of Computer Science & Information Systems BITS Pilani K K Birla Goa Campus India

In this paper, we empirically compare the system-level performances of executing a machine learning task on a resource-constrained IoT device and compare its performance to offloading the task to a more capable device, a smartphone. Our results indicate that although the resource-constrained device cannot run complex machine learning models, they can provide reasonable accuracy using similar models that can load on their memory. For running simpler models on the microcontroller, the best case accuracy for the machine learning task was 94.32% (SD: 1.7%). Furthermore, local computation resulted in almost 50% lesser current draw as compared to the offloading. These observations make a case for adopting an adaptive approach to ensure that applications meet the energy-accuracy-latency balance.

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Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board

Low power implementation of Geometric High-order Decorrelati...

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IEEE Cool Chips

作者： Ziquan Qin Kaijie Wei Hideharu Amano Kazuhiro Nakadai Dept. of Information and Computer Science Keio University Yokohama Japan Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

Open source software for robot audition called HARK aims to make “OpenCV” in audio signal processing, providing comprehensive functions from multichannel audio input to sound localization, sound source separation, and au-tomatic speech recognition. Since each of these HARK modules takes considerable energy when executed on PC, we propose to implement each module on an FPGA board called M-KUBOS connected. Here, we focus on the most computationally expensive function of HARK; the sound source separation, and implement it on a Zynq Ultrascale+ board. More than twice a performance improvement was achieved by using the sound frequency level parallelization in the HLS description compared to the software execution on the Ryzen 3990X64-core server. Power evaluation of the real board showed that the energy consumption is only 1/23.4 of the server.

关键词： Location awareness Energy consumption Source separation Power demand Speech recognition Parallel processing Servers

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