A Key Management Scheme for Wireless Sensor Networks Using Deployment Knowledge

To achieve security in wireless sensor networks, it is important to be able to encrypt messages sent among sensor nodes. Keys for encryption purposes must be agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is non-trivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. Recently, a random key predistribution scheme and its improvements have been proposed

Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification

Analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current statistical analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party multivariate statistical analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.Comment: Under Revie

Efficacy and safety of a combination of miglitol, metformin and insulin aspart in the treatment of type 2 diabetes

Purpose: To study the clinical effect of combining insulin aspart with different drugs in the treatment oftype 2 diabetes mellitus (T2DM).Methods: Two hundred and thirty-seven T2DM patients admitted to the Endocrinology Department of the Second Affiliated Hospital of Kunming Medical University from March to September 2018 were selected as subjects in this study. Miglitol and metformin were used in combination with insulin aspart in the treatment of T2DM. In addition, data on the effectiveness and safety of different treatment options,such as patient’s weight, waist circumference, blood glucose indicators, indices of heart, liver and kidney functions, and incidence of complications were recorded and compared between the two groups.Results: The use of a combination of miglitol and insulin aspart produced an excellent hypoglycaemic effect, and it significantly reduced the incidence of sensory neuropathy in the eyes and distal limbs (p < 0.05). The use of combination of metformin and insulin aspart effectively protected the heart and kidney, and prevented hypoglycaemia (p < 0.05).Conclusion: These results suggest that treatment with a combination of miglitol and insulin aspart is suitable for patients with T2DM whose blood sugar levels are out of control, while combined treatment with metformin and insulin aspart is more suited for patients who desire to reduce blood sugar and blood lipids through weight loss, and patients with cardiac and renal insufficiency

Electrochemical Impedance Spectroscopic Studies of the First Lithiation of Si/C Composite Electrode

The Si/C composite materials were prepared by ball milling method, and characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The result displayed that Si in the Si/C composite materials still maintained a good crystal structure and uniformly dispersed in carbon black matrix. The first discharge capacity was 3393 mAh/g, and 4 cycles later still retained 1000 mAh/g, showing better charge-discharge cycle performance. Electrochemical impedance spectroscopy test indicated that there appeare three semicircles respectively representing the impedance of contact problems, solid electrolyte interface film (SEI film), charge transfer and phase transformation in the first lithiation, and their evolutive principles were also investigated

Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification

analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current statistical analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party multivariate statistical analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems

Stateful DDoS attacks and targeted filtering

The goal of a DDoS (distributed denial of service) attack is to completely tie up certain resources so that legitimate users are not able to access a service. It has long been an open security problem of the Internet. In this paper, we identify a class of stateful DDoS attacks that defeat the existing cookie-based solutions. To counter these attacks, we propose a new defense mechanism, called targeted filtering, which establishes filters at a firewall and automatically converges the filters to the flooding sources while leaving the rest of the Internet unblocked. We prove the correctness of the proposed defense mechanism, evaluate its efficiency by analysis and simulations, and establish its worst-case performance bounds in response to stateful DDoS attacks. We have also implemented a Linux-based prototype with experimental results that demonstrate the effectiveness of targeted filtering

PrivacyPreserving Multivariate Statistical Analysis: Linear Regression and Classification

Multivariate statistical analysis is an important data analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current statistical analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party multivariate statistical analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems

Comparison of Recovery Effect for Sufentanil vs. Remifentanil Anesthesia in Elderly Patients Undergoing Curative Resection for Hepatocellular Carcinoma

Abstract Introduction The aim of this work is to evaluate the clinical efficacy and safety of sufentanil vs. remifentanil anesthesia in elderly patients undergoing curative resection for hepatocellular carcinoma (HCC). Methods Medical records of elderly patients aged ≥ 65 years who received curative resection for HCC between January 2017 and December 2020 were retrospectively reviewed. The patients were divided into either the sufentanil group or the remifentanil group according to the method of analgesia used. Vital signs including mean arterial pressure (MAP), heart rate (HR), and arterial oxygen saturation (SpO2), distribution of T-cell subsets (CD3, CD4, and CD8 lymphocytes), distribution of the stress response index [cortisol (COR), interleukin (IL)-6, C-reactive protein (CRP), and glucose (GLU)] were recorded prior to anesthesia (T0), after induction of anesthesia (T1), at the end of surgery (T2), 24 h after surgery (T3), and 72 h after surgery (T4). Postoperative adverse events were collected. Results Repeated measure analysis of variance (ANOVA) showed that after controlling for baseline patient demographic and treatment characteristics as covariates, both between- and within-group effects were significant (all P < 0.01), and the interaction between time and treatments was also significant (all P < 0.01) in the vital signs (MAP, HR, and SpO2), distribution of T-cell subsets (CD3, CD4, and CD8 lymphocytes), and distribution of the stress response index (COR, IL-6, CRP, and GLU), indicating that sufentanil led to stable hemodynamic and respiratory functions, lower reduction of T-lymphocyte subsets, and stable stress response indices compared to remifentanil. There is no significant difference in adverse reactions between the two groups (P = 0.72). Conclusions Sufentanil was associated with improved hemodynamic and respiratory function, less stress response, less inhibition of cellular immunity, and similar adverse reactions compared with remifentanil