2023 57th Annual Conference on Information Sciences and Systems (CISS)最新文献_第5页

Differentially Private Community Detection over Stochastic Block Models with Graph Sketching 基于图绘制的随机块模型差分私有社区检测

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089679

Mohamed Seif, A. Goldsmith, H. Poor

引用次数: 0

On the determination of GRAND noise sequences by employing integer compositions 用整数组合法确定GRAND噪声序列

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089775

Steven N. Jones, A. Cooper

{"title":"On the determination of GRAND noise sequences by employing integer compositions","authors":"Steven N. Jones, A. Cooper","doi":"10.1109/CISS56502.2023.10089775","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089775","url":null,"abstract":"Guessing Random Additive Noise Decoding (GRAND) has been proposed as an efficient and universal decoding technique for linear block codes [1]. A necessary element of the GRAND decoder is a set of noise sequences of length $n$ equal to the code length. The decoded codeword is taken as the first valid codeword that results from the binary addition of the noise sequences, in order of likelihood, with the received word. For a binary code of length $boldsymbol{n}$ there are $2^{n}$ such noise sequences. When $boldsymbol{n}$ is large, it will typically be intractable to add every noise sequence during decoding. Moreover, these sequences exhibit a number of error bursts $(boldsymbol{m})$ and a total number of bit flips $(boldsymbol{l})$. In [1] the authors provide expressions to determine the probability of each error burst sequence for a two-state Markov channel model. The resulting decoder is called GRAND-Markov Order (GRAND-MO). The authors of [1] describe a GRAND-MO error pattern generator for the two-state Markov channel based upon sequential transition between $(boldsymbol{m},boldsymbol{l})$ pairs, incrementing $boldsymbol{m}$ first, and then treating each value of l. In this paper we address a method of constructing the n-length noise sequences based upon integer compositions on the required $l$ bit flips as well as the $boldsymbol{n}-boldsymbol{l}$ non-errors in the sequence. This method allows one to construct noise sequences in order of probability without the need to construct less likely noise sequences. A GRAND decoder may employ a limited number of noise sequences and abandon decoding upon finding a valid code word, or upon reaching an abandonment threshold without decoding success. In this way the most likely noise sequences can be prioritized in decoding.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127554691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Searching for the Most Probable Combination of Class Labels Using Etcetera Abduction 使用等等溯因法搜索类标签的最可能组合

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089729

A. Gordon, Andrew Feng

{"title":"Searching for the Most Probable Combination of Class Labels Using Etcetera Abduction","authors":"A. Gordon, Andrew Feng","doi":"10.1109/CISS56502.2023.10089729","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089729","url":null,"abstract":"Many machine perception tasks require a trained model to assign class labels to multiple entities in the same context, e.g., labeling multiple objects in a single photograph. In these tasks, different combinations of labels may be more likely than others, e.g., when co-occurrence biases are considered, such that the most-confident label assigned to an individual object is not always the best choice. In this paper, we propose a new method for combining evidence from multiple class probability distributions to identify the most probable combination of labels in multi-entity contexts. Our method encodes discrete class probability distributions as literals in first-order logic, and uses probability-ranked logical abduction to identify the most likely label combination, incorporating the prior and conditional probabilities of each label. We evaluate our method on two computer vision benchmarks, first for labeling common objects in photographs of everyday contexts, and second for labeling actions of athletes in sports videos. Results indicate significant gains in classifier accuracy over systems that merely select the model's most confident class label.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127581237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent Deep Reinforcement Learning for Multi-Cell Interference Mitigation 基于多智能体深度强化学习的多细胞干扰缓解

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089622

M. Dahal, M. Vaezi

引用次数: 0

When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features 神经形态传感器的性能何时优于相机?从动态特征中学习

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089678

Daniel Deniz, E. Ros, C. Fermüller, Francisco Barranco

{"title":"When Do Neuromorphic Sensors Outperform cameras? Learning from Dynamic Features","authors":"Daniel Deniz, E. Ros, C. Fermüller, Francisco Barranco","doi":"10.1109/CISS56502.2023.10089678","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089678","url":null,"abstract":"Visual event sensors only output data when changes in the scene happen at very high frequency. This allows for smartly compressing the scene and thus, enabling real-time operation. Despite these advantages, works in the literature have struggled to show a niche for these event-driven approaches compared to conventional sensors, especially when focusing on accuracy performance. In this work, we show a case that fully exploits event sensor advantages: for manipulation action recognition, learning events achieves superior accuracy and time performance. The recognition of manipulation actions requires extracting and learning features from the hand pose and trajectory and the interaction with the object. As shown in our work, approaches based on event sensors are the best fit for extracting these dynamic features contrarily to conventional approaches based on full frames, which mostly extract spatial features and need to reconstruct the dynamics from sequences of frames. Finally, we show how using a tracker to extract the features to be learned only around the hand, we obtain an approach that is scene- and almost object-agnostic and achieves good time performance with a very limited impact in accuracy.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114457055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FPGA Based Emulation Of B92 QKD Protocol 基于FPGA的B92 QKD协议仿真

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089628

U. Khokhar, Madiha Khalid, M. Najam-ul-Islam

引用次数: 0

A computational method for improving the data acquisition process in the Laser Metal Deposition 一种改进激光金属沉积中数据采集过程的计算方法

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089700

Muhammad Mu'az Imran, Gisun Jung, Young Kim, P. E. Abas, L. D. Silva, Y. Kim

{"title":"A computational method for improving the data acquisition process in the Laser Metal Deposition","authors":"Muhammad Mu'az Imran, Gisun Jung, Young Kim, P. E. Abas, L. D. Silva, Y. Kim","doi":"10.1109/CISS56502.2023.10089700","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089700","url":null,"abstract":"Laser metal deposition (LMD) has developed rapidly in recent years. Although the technology is gaining attention, the data obtained from in-situ sensors are noisy due to the brief processing window and must be analyzed automatically to ensure the reliability of the data acquisition process. Traditionally, researchers used a simple Moving Average (MA) to diminish the peaks of the signals that may inflate the estimation for further data analysis. Spatter is one of the indicators that can describe the process stability of LMD. The generation of spatters is linked to peaks of the signals and has concept drift characteristics. Therefore, this study aims to detect and distinguish between point anomaly and concept drift in data streams in order to remove the extreme values that can mask the actual performance of the deposition process. The proposed method comprises two main components: (1) differencing method to flag the potential point outlier and (2) the density-based method to verify whether the flagged observations are outliers or not. We evaluated and compared our proposed method with the DSPOT method. The results show that our proposed method outperforms the DSPOT method based on the evaluation metrics (Recall, Precision, and F1-score) and the computation time.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123117453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Particle Thompson Sampling through Regenerative Particles 通过再生粒子改进粒子汤普森采样

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089647

Zeyu Zhou, B. Hajek

引用次数: 0

Policy Poisoning in Batch Learning for Linear Quadratic Control Systems via State Manipulation 基于状态操纵的线性二次控制系统批量学习中的策略中毒

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089721

Courtney M. King, Son Tung Do, Juntao Chen

{"title":"Policy Poisoning in Batch Learning for Linear Quadratic Control Systems via State Manipulation","authors":"Courtney M. King, Son Tung Do, Juntao Chen","doi":"10.1109/CISS56502.2023.10089721","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089721","url":null,"abstract":"In this work, we study policy poisoning through state manipulation, also known as sensor spoofing, and focus specifically on the case of an agent forming a control policy through batch learning in a linear-quadratic (LQ) system. In this scenario, an attacker aims to trick the learner into implementing a targeted malicious policy by manipulating the batch data before the agent begins its learning process. An attack model is crafted to carry out the poisoning strategically, with the goal of modifying the batch data as little as possible to avoid detection by the learner. We establish an optimization framework to guide the design of such policy poisoning attacks. The presence of bi-linear constraints in the optimization problem requires the design of a computationally efficient algorithm to obtain a solution. Therefore, we develop an iterative scheme based on the Alternating Direction Method of Multipliers (ADMM) which is able to return solutions that are approximately optimal. Several case studies are used to demonstrate the effectiveness of the algorithm in carrying out the sensor-based attack on the batch-learning agent in LQ control systems.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129440776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Tools and Visualizations for Exploring Classification Landscapes 探索分类景观的工具和可视化

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089673

William Powers, Lin Shi, L. Liebovitch

引用次数: 0