Reviewerly/exHarmony huggingface dataset:
Bridging Software-Hardware for CXL Memory Disaggregation in Billion-Scale Nearest Neighbor Search

Authors:
raw_author_name: Junhyeok Jang
raw_affiliation_string: KAIST, Daejeon, Korea
raw_author_name: Hanyeoreum Bae
raw_affiliation_string: KAIST, Daejeon, Korea
raw_author_name: Seungjun Lee
raw_affiliation_string: KAIST, Daejeon, Korea
raw_author_name: Miryeong Kwon
raw_affiliation_string: KAIST and Panmnesia, Inc., Daejeon, Korea
raw_author_name: Myoungsoo Jung
raw_affiliation_string: KAIST and Panmnesia, Inc., Daejeon, Korea

Abstract: We propose CXL-ANNS , a software-hardware collaborative approach to enable scalable approximate nearest neighbor search (ANNS) services. To this end, we first disaggregate DRAM from the host via compute express link (CXL) and place all essential datasets into its memory pool. While this CXL memory pool allows ANNS to handle billion-point graphs without an accuracy loss, we observe that the search performance significantly degrades because of CXL’s far-memory-like characteristics. To address this, CXL-ANNS considers the node-level relationship and caches the neighbors in local memory, which are expected to visit most frequently. For the uncached nodes, CXL-ANNS prefetches a set of nodes most likely to visit soon by understanding the graph traversing behaviors of ANNS. CXL-ANNS is also aware of the architectural structures of the CXL interconnect network and lets different hardware components collaborate with each other for the search. Furthermore, it relaxes the execution dependency of neighbor search tasks and allows ANNS to utilize all hardware in the CXL network in parallel. Our evaluation shows that CXL-ANNS exhibits 93.3% lower query latency than state-of-the-art ANNS platforms that we tested. CXL-ANNS also outperforms an oracle ANNS system that has unlimited local DRAM capacity by 68.0%, in terms of latency.
Quantum speed limits of quantum system in colored environments

Authors:
raw_author_name: Yonggang Peng
raw_affiliation_string: School of Physics, Shandong University, Jinan, 250100, China

Abstract: In this paper, we investigate quantum speed limits (QSLs) of a two-level system in the colored environments which are described by stochastic processes. We present QSL can be affected by the correlation time, the partitions of the constituent elements of the two-level system in different colored environments: the Ornstein–Uhlenbeck environment, simple fluctuating bottleneck environment, and the Dichotomic (two-state) environment. The investigations provide us a potential way to manipulate the quantum speed limit time (QSLT) of the quantum system in the colored environments. Our results could provide different insights on quantum control technologies of QSL and decoherent dynamics for the quantum computation.
Assessing the Impact of Body Location on the Accuracy of Detecting Daily Activities through Accelerometer/Gyroscope Data Using Deep Learning

Authors:
raw_author_name: Xiangnan Dang
raw_affiliation_string: Eli Lilly and Company, 893 S Delaware St, Indianapolis, IN, USA.
raw_author_name: Wentao Li
raw_affiliation_string: Eli Lilly and Company, 893 S Delaware St, Indianapolis, IN, USA.
raw_author_name: Jasmine Zou
raw_affiliation_string: Eli Lilly and Company, 893 S Delaware St, Indianapolis, IN, USA.
raw_author_name: Yuanfang Guan
raw_affiliation_string: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48105, USA.

Abstract: Recent advancements in digital biomarkers have highlighted the importance of accelerometer and gyroscope data for monitoring activities, identifying motion-related diseases, and assessing disease severity. Prior studies predominantly limit sensor placement to one or two locations. Here, we conducted a trial focusing on the impact of sensor placement in predicting 21 common activities using convolutional neural networks (CNN) and long short-term memory networks (LSTM). Our research found that the optimal locations for activity detection are the right and left upper arms, right wrist, and lower back. These locations yielded an average AUC of 0.76-0.77 using both accelerometer and gyroscope data. Combining data from all locations improved AUC to 0.796 for accelerometer and 0.811 for gyroscope data. We also noted specific activity-body part sensitivity relationships. This study provides a valuable reference for selecting appropriate sensor locations in future digital biomarker studies focused on specific activities.
Chaotic neural network quantization and its robustness against adversarial attacks

Authors:
raw_author_name: Alaa Osama
raw_affiliation_string: Nanoelectronics Integrated Systems Center (NISC), Nile University, Egypt
raw_author_name: Samar I. Gadallah
raw_affiliation_string: Nanoelectronics Integrated Systems Center (NISC), Nile University, Egypt
raw_author_name: Lobna A. Said
raw_affiliation_string: Nanoelectronics Integrated Systems Center (NISC), Nile University, Egypt
raw_author_name: Ahmed G. Radwan
raw_affiliation_string: Engineering Mathematics and Physics Dept., Faculty of Engineering, Cairo University, Egypt; School of Engineering and Applied Sciences, Nile University, Giza, Egypt
raw_author_name: Mohammed E. Fouda
raw_affiliation_string: Rain Neuromorphics Inc. San Francisco, USA

Abstract: Achieving robustness against adversarial attacks while maintaining high accuracy remains a critical challenge in neural networks. Parameter quantization is one of the main approaches used to compress deep neural networks to have less inference time and less storage memory size. However, quantization causes severe degradation in accuracy and consequently in model robustness. This work investigates the efficacy of stochastic quantization to enhance robustness and accuracy. Noise injection during quantization is explored to understand the impact of noise types and magnitudes on model performance. A comprehensive comparison between different applying scenarios for stochastic quantization and different noise types and magnitudes was implemented in this paper. Compared to the baseline deterministic quantization, chaotic quantization achieves a comparable accuracy, however, it achieves up to a 43% increase in accuracy against various attack scenarios. This highlights stochastic quantization as a promising defense mechanism. In addition, there is a crucial role played by the choice of noise type and magnitude in stochastic quantization. Lorenz and Henon noise distributions in stochastic quantization outperform traditional uniform and Gaussian noise in defending against attacks. A transferability analysis was discussed to understand the generalizability and effectiveness of the proposed stochastic quantization techniques. A cross-validation definition was newly evaluated in this scope to analyse the model's stability and robustness against attacks. The study outperformed a quantization network technique and improved the model's robustness and stability against adversarial attacks using chaotic quantization instead of deterministic quantization or even instead of stochastic quantization using traditional noise.
A provably secure and practical end-to-end authentication scheme for tactile Industrial Internet of Things

Authors:
raw_author_name: Yimin Guo
raw_affiliation_string: Zhongnan University of Economics and Law, School of Information and Safety Engineering, China
raw_author_name: Ping Xiong
raw_affiliation_string: Zhongnan University of Economics and Law, School of Information and Safety Engineering, China
raw_author_name: Chengde Zhang
raw_affiliation_string: Zhongnan University of Economics and Law, School of Information and Safety Engineering, China

Abstract: In the Industrial Internet of Things (IIoT), haptic control of machines or robots can be managed remotely. However, with the emergence of Tactile Industrial Internet of Things (TIIoT), the transmission of haptic data over public channels has raised security and privacy concerns. In such an environment, mutual authentication between haptic users and remotely controlled entities is crucial to prevent illegal control by adversaries. Therefore, we propose an end-to-end authentication scheme, SecTIIoT, to establish secure communication between haptic users and remote IoT devices. The scheme addresses security issues by using lightweight hash cryptographic primitives and employs a useful piggyback strategy to improve authentication efficiency. We demonstrate that SecTIIoT is resilient to various known attacks with formal security proofs and informal security analysis. Furthermore, our detailed performance analysis shows that SecTIIoT outperforms existing lightweight authentication schemes as it provides more security features while reducing computation and communication costs.
AIPT: Adaptive information perception for online multi-object tracking

Authors:
raw_author_name: Yukuan Zhang
raw_affiliation_string: School of Physics and Electronic Information, Yunnan Normal University, Kunming, 650500, China
raw_author_name: Yunhua Jia
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
raw_author_name: Jingrui Meng
raw_affiliation_string: Pattern Recognition and Artificial Intelligence Lab, Yunnan Normal University, Kunming, 650504, China; School of Science, The Hong Kong University of Science and Technology, Hong Kong, China
raw_author_name: Shan Zhao
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
raw_author_name: Yang Yang
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China

Abstract: Information perception is crucial in MOT tasks. Recent approaches use positional, motion, and appearance information to model object states. However, in scenes involving camera motion, tracking tasks suffer from image distortion, trajectory loss, and mismatching issues. In this paper, we propose Adaptive Information Perception for Online Multi-Object Tracking, abbreviated as AIPT. AIPT consists of an Adaptive Motion Perception Module (AMPM) and an Asymmetric Information Suppression Module (AISM). In AMPM, we design an Adaptive Image Distortion Recovery Module (AIDRM) to perceive distortions in unknown scenes, allowing the tracker to autonomously recover distorted images as the scene changes. By designing the Information-Guided Trajectory Restoration Module (IGTRM), the tracker learns object motion states from prior information and constructs accurate reconstruction information during trajectory loss. Furthermore, our AISM module utilizes masking information to suppress potential relationships between asymmetric objects, thereby enhancing the ability of tracker to handle mismatches. Both AMPM and AISM exhibit excellent scalability, seamlessly integrating with most advanced tracking methods. Ultimately, our AIPT achieves leading performance on multiple benchmark platforms, including MOT17, MOT20, and KITTI.
A multi-aspect framework for explainable sentiment analysis

Authors:
raw_author_name: Jothi Prakash V.
raw_affiliation_string: Karpagam College of Engineering, Myleripalayam Village, Coimbatore, 641032, Tamil Nadu, India
raw_author_name: Arul Antran Vijay S.
raw_affiliation_string: Karpagam College of Engineering, Myleripalayam Village, Coimbatore, 641032, Tamil Nadu, India

Abstract: The demand for explainable sentiment analysis has intensified, emphasizing the need for models that are both accurate and interpretable. This research introduces the Multi-Aspect Framework for Explainable Sentiment Analysis (MAFESA), a groundbreaking model that seamlessly integrates aspect extraction, sentiment prediction, and explainability. By harnessing the power of Latent Dirichlet Allocation (LDA) for aspect extraction and leveraging hierarchical neural networks for sentiment prediction, MAFESA achieves remarkable performance metrics. Notably, our framework outperforms state-of-the-art baseline models across benchmark datasets such as IMDB Movie Reviews, Amazon Product Reviews, and Twitter Sentiment Analysis. The inclusion of an explainability module, built around techniques like LIME, offers unparalleled insights into the model's decision-making, ensuring predictions are transparent and trustworthy. Our performance evaluations, underscored by a thorough ablation study, cross-validation, and rigorous statistical tests, attest to MAFESA's robustness, generalizability, and superiority. A detailed qualitative analysis further showcases the model's adeptness at discerning aspect-level nuances and delivering clear explanations for sentiment predictions. This research not only sets a new benchmark in explainable sentiment analysis but also provides a holistic framework that balances prediction precision with interpretability.
Entanglement versus Bell non-locality via solving the fractional Schrödinger equation using the twisting model

Authors:
raw_author_name: A. El Allati
raw_affiliation_string: Laboratory of R&D in Engineering Sciences, Faculty of Sciences and Techniques Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco; Max Planck Institute for Physics of Complex Systems, Nöthnitzer Str. 38, D-01187 Dresden, Germany
raw_author_name: S. Bukbech
raw_affiliation_string: LaMAO laboratory, MSC team, FP, Mohammed First University, Nador, Morocco; Laboratory of R&D in Engineering Sciences, Faculty of Sciences and Techniques Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco
raw_author_name: K. El Anouz
raw_affiliation_string: Laboratory of R&D in Engineering Sciences, Faculty of Sciences and Techniques Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco
raw_author_name: Z. El Allali
raw_affiliation_string: LaMAO laboratory, MSC team, FP, Mohammed First University, Nador, Morocco

Abstract: The memory fractional effects of a one-axis twisting model on the dynamics of two-qubit entanglement and non-locality are discussed. It consists of solving the time-dependent fractional Schrödinger equation by extending any integration into non-integer orders using Riemann–Liouville integration. The obtained results present the possibility of controlling the fractional order of memory, varying the parameters to significantly generate concurrency and Bell's non-locality. Under the current investigation setup, it is noticeable that the behaviors of the proposed quantifiers are similar to each other, but with a small difference in the amplitude of non-locality with respect to entanglement. Importantly, we show that the most intriguing aspect of this paper is to detect that pair-qubit entanglement and non-locality can be preserved for an indefinite time, which still holds significance in quantum information processing.
Intent-Oriented Dynamic Interest Modeling for Personalized Web Search

Authors:
raw_author_name: Yutong Bai
raw_affiliation_string: School of Information, Renmin University of China, China
raw_author_name: Yujia Zhou
raw_affiliation_string: School of Information, Renmin University of China, China
raw_author_name: Zhicheng Dou
raw_affiliation_string: Gaoling School of Artificial Intelligence, Renmin University of China, China
raw_author_name: Ji-Rong Wen
raw_affiliation_string: Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education, China and Gaoling School of Artificial Intelligence, Renmin University of China, China

Abstract: Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two “dynamic” document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.
Data-Reuse Adaptive Algorithms for Graph Signal Estimation Over Sensor Network

Authors:
raw_author_name: Haiquan Zhao
raw_affiliation_string: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, and the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Chengjin Li
raw_affiliation_string: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, and the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Wang Xiang
raw_affiliation_string: Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, and the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China

Abstract: The least mean square (LMS) algorithm of the graph signal processing (GSP) can be used for sensor signal processing due to its simplicity and low computational complexity, and the recursive least squares (RLS) algorithm of GSP can be applied to sensor signal processing since it has fast convergence rate. However, the GSP LMS algorithm has poor convergence performance, the tracking performance of the GSP RLS algorithm turns weak after signal mutation. To solve the mentioned problems, we focus on the data-reuse strategy, aiming to improve the convergence/tracking performance of the related algorithms by reusing the same set of data several times, thus the graph signal processing data-reuse least mean square (GSP-DR-LMS) algorithm and the graph signal processing data-reuse recursive least squares (GSP-DR-RLS) algorithm are proposed. Moreover, to make the GSP-DR-LMS algorithm achieve a better coordination between steady state error and convergence rate, we propose the variable step size (VSS) strategy applicable to the GSP-DR-LMS algorithm, thus the GSP VSS-DR LMS algorithm is proposed. In addition, the performance analysis of the related algorithms is performed. Ultimately, we verify the superiority of the proposed algorithms in terms of convergence/tracking performance by performing computer simulations.
Discovering Predictive Relational Object Symbols With Symbolic Attentive Layers

Authors:
raw_author_name: Alper Ahmetoglu
raw_affiliation_string: Department of Computer Engineering, Bogazici University, Istanbul, T&#x00FC;rkiye
raw_author_name: Batuhan Celik
raw_affiliation_string: Department of Computer Engineering, Bogazici University, Istanbul, T&#x00FC;rkiye
raw_author_name: Erhan Oztop
raw_affiliation_string: Department of Computer Science, Ozyegin University, Istanbul, T&#x00FC;rkiye
raw_author_name: Emre Ugur
raw_affiliation_string: Department of Computer Engineering, Bogazici University, Istanbul, T&#x00FC;rkiye

Abstract: In this paper, we propose and realize a new deep learning architecture for discovering symbolic representations for objects and their relations based on the self-supervised continuous interaction of a manipulator robot with multiple objects in a tabletop environment. The key feature of the model is that it can take a changing number of objects as input and map the object-object relations into symbolic domain explicitly. In the model, we employ a self-attention layer that computes discrete attention weights from object features, which are treated as relational symbols between objects. These relational symbols are then used to aggregate the learned object symbols and predict the effects of executed actions on each object. The result is a pipeline that allows the formation of object symbols and relational symbols from a dataset of object features, actions, and effects in an end-to-end manner. We compare the performance of our proposed architecture with state-of-the-art symbol discovery methods in a simulated tabletop environment where the robot needs to discover symbols related to the relative positions of objects to predict the action's result. Our experiments show that the proposed architecture performs better than other baselines in effect prediction while forming not only object symbols but also relational symbols.
Bayesian mixed-effect higher-order hidden Markov models with applications to predictive healthcare using electronic health records

Authors:
raw_author_name: Ying Liao
raw_affiliation_string: Department of Management Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Yisha Xiang
raw_affiliation_string: Department of Industrial Engineering, University of Houston, Houston, TX, USA
raw_author_name: Zhigen Zhao
raw_affiliation_string: Department of Statistics, Operations, and Data Science, Fox School of Business, Temple University, Philadelphia, PA, USA

Abstract: The disease progression dynamics observed in electronic health records often reflect patients' health condition evolution, holding the promise of enabling the development of clinical predictive models. These dynamics, however, generally display significant variability among patients, due to some critical factors (e.g., gender and age) and patient-level heterogeneity. Moreover, future health state may not only depend on the current state, but also more distant history states due to the complicated disease progression. To capture this complex transition behavior and address mixed effects in clinical prediction problems, we propose a novel and flexible Bayesian Mixed-Effect Higher-Order Hidden Markov Model (MHOHMM), and develop a classifier based on MHOHMMs. A range of MHOHMMs are designed to capture different data structures and the optimal one is identified by using the k-fold cross-validation approach. An effective two-stage Markov chain Monte Carlo (MCMC) sampling algorithm is designed for model inference. A simulation study is conducted to evaluate the performance of the proposed sampling algorithm and the MHOHMM-based classification method. The practical utility of the proposed framework is demonstrated by a case study on the acute hypotensive episode prediction for intensive care unit patients. Our results show that the MHOHMM-based framework provides good prediction performance.