GSDME as an executioner of chemotherapy-induced cell death

Cell death is a crucial process required for development,tissue homeostasis,and pathological cell loss in multicellular organisms.Cell death mainly occurs in two alternative modes:apoptosis or necrosis.Apoptosis is a form of programmed cell death with typical morphological features,including cell shrinkage,chromatin condensation,and DNA fragmentation(Degterev and Yuan,2008).The dying cell is eventually fragmented into membrane-bound

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

Research on Multi-Core Processor Analysis for WCET Estimation

Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.

Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing

Emerging Internet of Things(IoT)applications require faster execution time and response time to achieve optimal performance.However,most IoT devices have limited or no computing capability to achieve such stringent application requirements.To this end,computation offloading in edge computing has been used for IoT systems to achieve the desired performance.Nevertheless,randomly offloading applications to any available edge without considering their resource demands,inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation.We introduce Edge-IoT,a machine learning-enabled orchestration framework in this paper,which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency.We further propose a variant bin-packing optimization model that co-locates applications firmly on edge resources to fully utilize available resources.Extensive experiments show the effectiveness and resource efficiency of the proposed approach.

Adaptive Emulation Framework for Multi-Architecture IoT Firmware Testing

Internet of things(IoT)devices are being increasingly used in numerous areas.However,the low priority on security and various IoT types have made these devices vulnerable to attacks.To prevent this,recent studies have analyzed firmware in an emulation environment that does not require actual devices and is efficient for repeated experiments.However,these studies focused only on major firmware architectures and rarely considered exotic firmware.In addition,because of the diversity of firmware,the emulation success rate is not high in terms of large-scale analyses.In this study,we propose the adaptive emulation framework for multi-architecture(AEMA).In the field of automated emulation frameworks for IoT firmware testing,AEMA considers the following issues:(1)limited compatibility for exotic firmware architectures,(2)emulation instability when configuring an automated environment,and(3)shallow testing range resulting from structured inputs.To tackle these problems,AEMAcan emulate not onlymajor firmware architectures but also exotic firmware architectures not previously considered,such as Xtensa,ColdFire,and reduced instruction set computer(RISC)version five,by implementing a minority emulator.Moreover,we applied the emulation arbitration technique and input keyword extraction technique for emulation stability and efficient test case generation.We compared AEMA with other existing frameworks in terms of emulation success rates and fuzz testing.As a result,AEMA succeeded in emulating 864 out of 1,083 overall experimental firmware and detected vulnerabilities at least twice as fast as the experimental group.Furthermore,AEMAfound a 0-day vulnerability in realworld IoT devices within 24 h.

The Trade-Off Between Performance and Security of Virtualized Trusted Execution Environment on Android

Nowadays,with the significant growth of the mobile market,security issues on the Android Operation System have also become an urgent matter.Trusted execution environment(TEE)technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security.However,for Android,TEE technologies still contain restrictions and limitations.The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE.Another issue of hardware-based TEE is the cross-platform problem.Since every mobile device supports different TEE vendors,it becomes an obstacle for developers to migrate their trusted applications to other Android devices.A software-based TEE solution is a potential approach that allows developers to customize,package and deliver the product efficiently.Motivated by that idea,this paper introduces a VTEE model,a software-based TEE solution,on Android devices.This research contributes to the analysis of the feasibility of using a virtualized TEE on Android devices by considering two metrics:computing performance and security.The experiment shows that the VTEE model can host other software-based TEE services and deliver various cryptography TEE functions on theAndroid environment.The security evaluation shows that adding the VTEE model to the existing Android does not addmore security issues to the traditional design.Overall,this paper shows applicable solutions to adjust the balance between computing performance and security.

Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.

Multi-User MmWave Beam Tracking via Multi-Agent Deep Q-Learning

Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems.In the meanwhile,the overhead cost of channel state information and beam training is considerable,especially in dynamic environments.To reduce the overhead cost,we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method.With online learning of users’moving trajectories,the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate.Considering practical implementation,we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity.Furthermore,we propose a scalable state-action-reward design for scenarios with different users and antenna numbers.Simulation results verify the effectiveness of the designed method.

Critical Success Factors Influencing the Implementation of Sustainable Energy System in Uganda: A Case of Inter-University Council of East Africa Energy Project at the Head Quarters in Kampala, Uganda

The widespread usage of clean and sustainable energy sources is leading to a significant transformation of the world’s energy systems. Over-reliance on only the national grid energy system has made institutions fail to sustain energy systems. The council is only connected to the national grid electricity supply system, with diesel generators as the only alternative, which is unhealthy and unsafe. Surprisingly, even with such alternatives, power shortages have persisted despite government efforts to provide a solution to the shortages by installing numerous off-grid systems. Due to such a situation, the council would construct a sustainable energy system as a remedy. Thus, the purpose of this study was to establish critical success factors influencing the implementation of a sustainable energy system at the Inter-University Council of East Africa (IUCEA) Head Quarters, Kampala-Uganda. A cross-sectional survey design was used;a sample size of 84 participants was selected. Questionnaire survey and interview methods were utilized. The study found that the most significant (p < 0.05) critical factors in the implementation of sustainable energy in institutions are;the use of innovative technologies and infrastructure, the use of efficient zero emissions for heating and cooling, integration of renewable energy use in the existing buildings, building and renovating in an energy-efficient way, integrating regional energy systems, improving energy efficiency in the buildings, enhanced zero emission power technologies, energy efficient equipment in place and stakeholder empowerment in energy management. This study concludes that institutions like;the Inter-University Council of East Africa (IUCEA) need to clearly state policies and actions of energy management. The roles and responsibilities of each member have to be clearly stated while capturing the activities involved in energy conservation, energy security and energy efficiency.

Review of Software Model-Checking Techniques for Dealing with Error Detection in Program Codes

Debugging software code has been a challenge for software developers since the early days of computer programming. A simple need, because the world is run by software. So perhaps the biggest engineering challenge is finding ways to make software more reliable. This review provides an overview of techniques developed over time in the field of software model checking to solve the problem of detecting errors in program code. In addition, the challenges posed by this technology are discussed and ways to mitigate them in future research and applications are proposed. A comprehensive examination of the various model verification methods used to detect program code errors is intended to lay the foundation for future research in this area.

面向WCET估计的Cache分析研究综述

实时系统时间分析的首要任务是估计程序的最坏情况执行时间(worst-case execution time,简称WCET).程序的WCET通常受到硬件体系结构的影响,Cache则是其中最为突出的因素之一.对面向WCET计算的Cache分析研究进行了综述,介绍了经典Cache分析框架与Cache分析核心技术,并从循环结构分析、数据Cache分析、多级Cache分析、多核共享Cache分析、非LRU替换策略分析等角度介绍了Cache分析在不同维度上的研究问题与主要挑战,总结了现有技术的优缺点,展望了Cache分析研究的未来发展方向.

一种基于CPN的BPEL异常处理逻辑的开发方法

针对WS-BPEL在面向服务软件异常处理方面不够完善的问题,提出了一种基于着色Petri网描述的BPEL异常处理逻辑开发方法。该方法利用着色Petri网(CPN)形式化地描述BPEL的异常处理机制,建立BPEL的异常处理CPN模型,指导对BPEL的异常处理逻辑开发,并依据此建模思想,提出了一个BPEL的异常处理CPN模型的转换工具,用以实现将异常处理的BPEL着色Petri网模型转换成对应的带异常处理的BPEL代码。该工具可在输入的原始的没有异常处理功能的BPEL代码基础上,通过动态地添加BPEL的异常处理语言成份,形成带有异常处理功能的BPEL流程。最后通过引入供应商流程案例,阐述了该方法的具体使用过程。

基于WCET分析的实时系统轨迹获取技术

时序约束是判断实时系统运行是否正确的重要规约.为了减小测试时由于对系统进行插装而产生的对实时系统行为的影响,提出了一种混合式监控方法.它对系统的时间干扰比纯软件方式小,并支持对系统的完全测试.此外,还提出一种基于WCET(worst-caseexecutiontime)分析技术的目标系统时间补偿方法,在精确地计算插入断言对目标系统的时间影响基础上,给出时间补偿.

ARCHITECTURE OF MANUFACTURING EXECUTION SYSTEM IN THE EXTENDED ENTERPRISE

The extended enterprise is formed according to the philosophy of dispersednetworked manufacturing. Manufacturing execution system (MES) can close the information gap whichexists between device control system and production information management system. The functions andthe web-based architecture of the MES in the extended enterprise are introduced. Using thecooperating system models of object-oriented and distributed agents and CORBA, all objects keep touniform interface standards and are easily inserted to object request broker. The utilization ofdistributed MES in extended enterprise can adapt fast change of manufacturing environment andresource. It also can improve the independent management capability of manufacturing cell and theenterprise response capability to global economic competition.

Task Offloading Decision in Fog Computing System

Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.

Distributed tasks-platforms scheduling method to holonic-C2 organization

To solve the problem of distributed tasks-platforms scheduling in holonic command and control(C2) organization,the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality,a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally,a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.

Event-driven process execution model for process virtual machine

Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.

Visual feedback therapy for restoration of upper limb function of stroke patients

Objective:To investigate the effects of mirror neuron theory-based visual feedback therapy(VFT)on restoration of upper limb function of stroke patients and motor-related cortical function using functional magnetic resonance imaging(fMRI).Methods:Hemiplegic stroke patients were randomly divided into two groups:a VFT group and a control(CTL)group.Sixteen patients in the VFT group received conventional rehabilitation(CR)and VFT for 8 weeks,while 15 patients in the CTL group received only CR.The Barthel Index(BI)was used to assess the activities of daily living at baseline and the 8th week of the recovery training period.The Fugl-Meyer assessment(FMA)scale,somatosensory evoked potential(SEP),and fMRI were used to evaluate the recovery effect of the training therapies.The latencies and amplitudes of N9 and N20 were measured.Before recovery training,fMRI was performed for all patients in the VFT and CTL groups.In addition,17 patients(9 in the VFT group and 8 in the CTL group)underwent fMRI for follow-up 2 months after treatment.Qualitative data were analyzed using the x2 test.The independent sample t-test was used to compare normally distributed data among different groups,the paired sample t-test was used to compare data between groups,and the non-parametric test was used to comparing data without normal distribution among groups.Results:There were no significant differences between the VFT and CTL group in all indexes.However,after 8 weeks of recovery training,these indexes were all significantly improved(P<0.05).As compared with the CTL group,the FMA scores,BI,and N9/N20 latencies and amplitudes of SEP in the VFT group were significantly improved(P<0.05).Two months after recovery training,fMRI showed that the degree of activation of the bilateral central anterior gyrus.parietal lobe,and auxiliary motor areas was significantly higher in the VFT group than the CTL group(P<0.05).Conclusions:VFT based on mirror neuron theory is an effective approach to improve upper extremity motor function and daily activity performance of stroke patients.The therapeutic mechanism promotes motor relearning by activating the mirror neuron system and motor cortex.SEP amplitudes increased only for patients who participated in visual feedback.VFT promotes sensory-motor plasticity and behavioral changes in both the motor and sensory domains.

A Testing Method for Web Services Composition Based on Data-Flow

This paper proposes a method of data-flow testing for Web services composition. Firstly, to facilitate data flow analysis and constraints collecting, the existing model representation of business process execution language （BPEL） is modified in company with the analysis of data dependency and an exact representation of dead path elimination （DPE） is proposed, which over-comes the difficulties brought to dataflow analysis. Then defining and using information based on data flow rules is collected by parsing BPEL and Web services description language （WSDL） documents and the def-use annotated control flow graph is created. Based on this model, data-flow anomalies which indicate potential errors can be discovered by traversing the paths of graph, and all-du-paths used in dynamic data flow testing for Web services composition are automatically generated, then testers can design the test cases according to the collected constraints for each path selected.