Efficient Revocable ID-Based Signature With Cloud Revocation Server

Over the last few years, identity-based cryptosystem (IBC) has attracted widespread attention because it avoids the high overheads associated with public key certificate management. However, an unsolved but critical issue about IBC is how to revoke a misbehaving user. There are some revocable identity-based encryption schemes that have been proposed recently, but little work on the revocation problem of identity-based signature has been undertaken so far. One approach for revocation in identity-based settings is to update users\u27 private keys periodically, which is usually done by the key generation center (KGC). But with this approach, the load on the KGC will increase quickly when the number of users increases. In this paper, we propose an efficient revocable identity-based signature (RIBS) scheme in which the revocation functionality is outsourced to a cloud revocation server (CRS). In our proposed approach, most of the computations needed during key-updates are offloaded to the CRS. We describe the new framework and the security model for the RIBS scheme with CRS and we prove that the proposed scheme is existentially unforgeable against adaptively chosen messages and identity attacks in the random oracle model. Furthermore, we monstrate that our scheme outperforms previous IBS schemes in terms of lower computation and communication costs

A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing

As cloud computing offers services to lots of users worldwide, pervasive applications from customers are hosted by large-scale data centers. Upon such platforms, virtualization technology is employed to multiplex the underlying physical resources. Since the incoming loads of different application vary significantly, it is important and critical to manage the placement and resource allocation schemes of the virtual machines (VMs) in order to guarantee the quality of services. In this paper, we propose a decentralized virtual machine migration approach inside the data centers for cloud computing environments. The system models and power models are defined and described first. Then, we present the key steps of the decentralized mechanism, including the establishment of load vectors, load information collection, VM selection, and destination determination. A two-threshold decentralized migration algorithm is implemented to further save the energy consumption as well as keeping the quality of services. By examining the effect of our approach by performance evaluation experiments, the thresholds and other factors are analyzed and discussed. The results illustrate that the proposed approach can efficiently balance the loads across different physical nodes and also can lead to less power consumption of the entire system holistically

EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models

Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results

Improved breast lesion detection in mammogram images using a deep neural network

PURPOSEThis study aimed to investigate the effect of using a deep neural network (DNN) in breast cancer (BC) detection.METHODSIn this retrospective study, a DNN-based model was constructed from a total of 880 mammograms that 220 patients underwent between April and June 2020. The mammograms were reviewed by two senior and two junior radiologists with and without the aid of the DNN model. The performance of the network was assessed by comparing the area under the curve (AUC) and receiver operating characteristic curves for the detection of four features of malignancy (masses, calcifications, asymmetries, and architectural distortions), with and without the aid of the DNN model and by the senior and junior radiologists. Additionally, the effect of utilizing the DNN on diagnosis time for both the senior and junior radiologists was evaluated.RESULTSThe AUCs of the model for the detection of mass and calcification were 0.877 and 0.937, respectively. In the senior radiologist group, the AUC values for evaluation of mass, calcification, and asymmetric compaction were significantly higher with the DNN model than those obtained without the model. Similar effects were observed in the junior radiologist group, but the increase in the AUC values was even more dramatic. The median mammogram assessment time of the junior and senior radiologists was 572 (357–951) s, and 273.5 (129–469) s, respectively, with the DNN model, and the corresponding assessment time without the model, was 739 (445–1003) s and 321 (195–491) s, respectively.CONCLUSIONThe DNN model exhibited high accuracy in detecting the four named features of BC and effectively shortened the review time by both senior and junior radiologists

Acupuncture for treatment of erectile dysfunction : a systematic review and meta-analysis

Purpose: To assess the effectiveness and safety of acupuncture for erectile dysfunction (ED). Materials and Methods: We searched six major English and Chinese databases included randomized controlled trials (RCTs) testing acupuncture alone or in combination for ED. Dichotomous data were presented as risk ratio (RR) and continuous data were presented as mean difference (MD) both with 95% confidence interval (CI). The Revman (v.5.3) was used for data analyses. Quality of evidence across studies was assessed by the online GRADEpro tool. Results: We identified 22 RCTs, fourteen of them involving psychogenic ED. Most of the included RCTs had high or unclear risk of bias. There was no difference between electro-acupuncture and sham acupuncture with electrical stimulation on the rate of satisfaction and self-assessment (RR, 1.50; 95% CI, 0.71-3.16; 1 trial). Acupuncture combined with tadalafil appeared to have better effect on increasing cure rate (RR, 1.31; 95% CI, 1.00-1.71; 2 trials), and International Index of Erectile Function-5 scores (MD, 5.38; 95% CI, 4.46-6.29; 2 trials). When acupuncture plus herbal medicine compared with herbal medicine alone, the combination therapy showed significant better improvement in erectile function (RR, 1.68; 95% CI, 1.31-2.15; 7 trials). Only two trials reported facial red and dizziness cases, and needle sticking and pruritus cases in acupuncture group. Conclusions: Low quality evidence shows beneficial effect of acupuncture as adjunctive treatment for people mainly with psychogenic ED. Safety of acupuncture was insufficiently reported. The findings should be confirmed in large, rigorously designed and well-reported trials

Proteome changes of lungs artificially infected with H-PRRSV and N-PRRSV by two-dimensional fluorescence difference gel electrophoresis

<p>Abstract</p> <p>Background</p> <p>Porcine reproductive and respiratory syndrome with PRRS virus (PRRSV) infection, which causes significant economic losses annually, is one of the most economically important diseases affecting swine industry worldwide. In 2006 and 2007, a large-scale outbreak of highly pathogenic porcine reproductive and respiratory syndrome (PRRS) happened in China and Vietnam. However little data is available on global host response to PRRSV infection at the protein level, and similar approaches looking at mRNA is problematic since mRNA levels do not necessarily predict protein levels. In order to improve the knowledge of host response and viral pathogenesis of highly virulent Chinese-type PRRSV (H-PRRSV) and Non-high-pathogenic North American-type PRRSV strains (N-PRRSV), we analyzed the protein expression changes of H-PRRSV and N-PRRSV infected lungs compared with those of uninfected negative control, and identified a series of proteins related to host response and viral pathogenesis.</p> <p>Results</p> <p>According to differential proteomes of porcine lungs infected with H-PRRSV, N-PRRSV and uninfected negative control at different time points using two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) and mass spectrometry identification, 45 differentially expressed proteins (DEPs) were identified. These proteins were mostly related to cytoskeleton, stress response and oxidation reduction or metabolism. In the protein interaction network constructed based on DEPs from lungs infected with H-PRRSV, HSPA8, ARHGAP29 and NDUFS1 belonged to the most central proteins, whereas DDAH2, HSPB1 and FLNA corresponded to the most central proteins in those of N-PRRSV infected.</p> <p>Conclusions</p> <p>Our study is the first attempt to provide the complex picture of pulmonary protein expression during H-PRRSV and N-PRRSV infection under the in vivo environment using 2D-DIGE technology and bioinformatics tools, provides large scale valuable information for better understanding host proteins-virus interactions of these two PRRSV strains.</p