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Paper-based microfluidic devices have recently attracted attention for their ability to utilize the inherent capillary force, or absorptivity, of paper to generate flows instead of requiring an external force. This allows for a simple sensor system that can be readily manufactured at low costs; however, sophisticated flow control is still necessary to implement and analyze diverse functions. In this paper, we propose a facile flow rate control method by varying the permeability of chromatography paper using a wax printing method frequently used to make channel walls in paper-based microfluidic devices. We found that the flow rates in paper channels can be precisely controlled by varying either the brightness of wax patterns, which is inversely proportional to the amount of printed wax droplets, or the length of wax patterns printed on the paper channel. Based on these results, we developed a paper-based micromixer that minutely controls the mixing ratio of two dye flows as prescribed in printed wax patterns. We found experimental mixing ratio values that were in excellent agreement with predicted values. [ABSTRACT FROM AUTHOR]

When machine learning meets the IoT security, it can be divided into two parts: (i) use the machine learning to detect the malicious attacks; (ii) use the privacy-preserving methods to protect the machine learning model and data. Keywords: Internet-of-Things; Privacy; Security EN Internet-of-Things Privacy Security 1 4 4 05/10/21 20210501 NES 210501 INTRODUCTION With billions of Internet-of-Thing (IoT) devices collecting data interoperate within the existing Internet infrastructure technology, IoT techniques have been considered as a necessary part of our daily life, such as wearable IoT devices to monitor personal health status, banking and financial services, life sciences, retail and industrial, and home automation. The paper by Y. Jiang et al, entitled " I A verifiable and privacy-preserving multi-dimensional data aggregation scheme in mobile crowdsensing, i " proposed a verifiable privacy-preserving data aggregation scheme, which is based on data double-masking, Shamir's secret sharing, and bilinear mapping. [Extracted from the article]

Integrating a dissolvable membrane into a sensor allows the control of sample flow, location and duration in critical areas. These time-barrier films stop the flow of samples until the membrane has dissolved, thus, for example, allowing increased exposure time between immunoreagents for the formation of greater numbers of immuno-complexes, ensuring higher sensitivity, reactivity, and helping to reduce false-positive signals. In this study, dissolvable polyvinyl alcohol (PVA) films are used in a 3D-printed sensor holder, which enables film integration without the use of glue. PVA is a synthetic hydrophilic linear polymer, its solubility is dependent on its molecular weight and degree of hydrolysis. Three types of PVAs films were tested herein: (1) PVA 1-Mw: 30–70 K, 87–90% hydrolyzed; (2) PVA 2-Mw: 31–50 K, 98–99% hydrolyzed and (3) PVA 3-Mw: 89–98 K, >99% hydrolyzed. The films were exposed to water in (1) the novel 3D-printed holder and (2) directly immersed into a water droplet. After comparing the time taken to dissolve PVA 1–3 films, PVA 1 films of 5–20% (w/v) are found to be most suitable as time barrier films, due to their optimal dissolution times and physical properties for integration into the customized 3D-printed holder. [ABSTRACT FROM AUTHOR]

This document explores the concept of complexity in large complex projects and its management. It discusses the importance of transformational leadership and iterative project planning in adapting to changing circumstances and reducing complexity. The document introduces the concept of cyclomatic complexity and quantum complexity analysis, which have not been previously applied to project management. It provides a step-by-step guide on calculating cyclomatic complexity and compares alternative execution strategies based on complexity. The document also suggests strategies for reducing complexity, such as breaking down tasks and minimizing nested or repetitive activities. It emphasizes the significance of complexity assessment and reduction strategies in achieving success in project management. [Extracted from the article]

Fiber wireless (Fi-Wi) communication network is the amalgamation of optical and wireless access networks, which provides better bandwidth for achieving efficient data transmission. Medium access control (MAC) protocols are used in the wireless network for controlling the data flow from the transmitter to the receiver end. The delay produced by these protocols tells about the system efficiency. This paper shows a Fi-Wi system in the long-term evaluation-advanced (LTE-A) environment, which incorporates the dependency of delay generated by the specific MAC protocols during the transmission process. This paper aimed to scrutinize the effects of Carrier Sense Multiplexing Access with Collision Detection (CSMA/CD), Carrier Sense Multiplexing Access with Collision Avoidance (CSMA/CA) and Slotted ALOHA on the performance of the Fi-Wi system. Free space optical (FSO) channel is incorporated to forward the data to user end. In such system, the optical signal is multiplexed using the Orthogonal Frequency Division Multiplexing (OFDM) technique and finally the data are fetched at the receiver end and different criterions such eye-height, Q-factor and bit error rate are evaluated. Simulation results are performed using MATLAB software. The comparative analysis is also performed in terms of data transmission efficiency, delay and throughput of MAC protocols. This shows the effective results of the proposed system according to the delay produced by MAC protocols. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth research analysis on the evaluation of the development quality of regional economy through an improved convolutional neural network algorithm, and uses it to design a fuzzy comprehensive evaluation model for the practical process. Based on the measured indices of different variables, a spatial econometric model is constructed and provincial panel data are selected to empirically analyze the impact and spatial spillover effects of financial agglomeration and technological innovation on regional economic quality development from both static and dynamic aspects and to examine the spatial correlation of the factors. A new serial data flow model is adopted, which optimizes the control of data flow in convolutional computation, reduces the percentage of clock cycles used to read memory data, and increases the computational efficiency. At the same time, with dynamic data caching, a convolutional computation can be completed in one clock cycle, reducing the memory capacity required for caching intermediate data. The effectiveness of the evaluation system constructed in this paper is further tested. Most of the indicators have a significant positive or negative impact on the quality level of economic development, and the direction of the impact is consistent with the positive and negative attributes of the indicators in this study, which verifies the validity of the evaluation indicator system constructed in this paper. In summary of the study, effective suggestions are made in terms of human capital investment, reasonable allocation of fiscal expenditure, enhancing regional greening development and improving risk prevention measures. [ABSTRACT FROM AUTHOR]

With the increase development of Internet of Things devices, the data-intensive workflow has emerged as a new kinds of representation for IoT applications. Because most IoT systems are structured in multi-clouds environment and the data-intensive workflow has the characteristics of scattered data sources and distributed execution requirements at the cloud center and edge clouds, it brings many challenges to the scheduling of such workflow, such as data flow control management, data transmission scheduling, etc. Aiming at the execution constraints of business and technology and data transmission optimization of data-intensive workflow, a data-intensive workflow scheduling method based on deep reinforcement learning in multi-clouds is proposed. First, the execution constraints, edge node load and data transmission volume of IoT data workflow are modeled; then the data-intensive workflow is segmented with the consideration of business constraints and the first optimization goal of data transmission; besides, taking the workflow execution time and average load balancing as the secondary optimization goal, the improved DQN algorithm is used to schedule the workflow. Based on the DQN algorithm, the model reward function and action selection are redesigned and improved. The simulation results based on WorkflowSim show that, compared with MOPSO, NSGA-II, GTBGA and DQN, the algorithm proposed in this paper can effectively reduce the execution time of IoT data workflow under the condition of ensuring the execution constraints and load balancing of multi-clouds. [ABSTRACT FROM AUTHOR]

Predictive Business Process Monitoring (PBPM) is a significant research area in Business Process Management (BPM) aimed at accurately forecasting future behavioral events. At present, deep learning methods are widely cited in PBPM research, but no method has been effective in fusing data information into the control flow for multi-perspective process prediction. Therefore, this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion. Firstly, the first layer BERT network learns the correlations between different category attribute data. Then, the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events. Next, the multi-head attention mechanism within the framework is visualized for analysis, helping to understand the decision-making logic of the framework and providing visual predictions. Finally, experimental results show that the predictive accuracy of the framework surpasses the current state-ofthe- art research methods and significantly enhances the predictive performance of BPM. [ABSTRACT FROM AUTHOR]

In digital microfluidics, discrete liquid droplets move on a dielectric surface with the help of AC or DC voltage. Digital microfluidics system has extended the scope of microfluidics by providing automated, precise flow control for point-of-care devices capable of medical diagnostics, environmental, and chemical sensing platforms. In this paper, we introduce a digital microfluidics platform based on electrowetting on a dielectric using PCB substrate designed on Easy EDA software. The platform's performance was analyzed at various DC voltages between 200 V and 400 V for droplet actuation. PCB fabrication, the utilization of easily accessible and cheap materials such as cooking oil and grafting tape as a dielectric layer, allowed the fabrication to be affordable and simple. The results indicate that droplet actuation takes place at 220 V. The droplet velocity obtained was 2.6 mm/s and 1.51 mm/s using silicon oil and cooking oil, respectively, at 400 V. Our proposed digital microfluidics platform will play a vital role in droplet actuation and can be used for diagnostic applications such as DNA analysis and cell culture due to its simple fabrication and cost-effective technique. [ABSTRACT FROM AUTHOR]

Access control vulnerabilities that lead to elevated privileges are among the most dangerous vulnerabilities in Web applications. Most of the existing detection methods use dynamic or static analysis techniques alone, which suffer from high manual involvement, low automation, high leakage rate, low page coverage, and other deficiencies. To this end, this paper proposes a novel access control vulnerability detection method (DetAC) based on a sitemap model with global information representation. This method first constructs a static site-wide sitemap model based on the page link addresses in the Web application source code through static analysis techniques. After that, the application is logged in and executed dynamically with different role users. During this process, execution traces and request parameters are collected and converted into annotations to fill the corresponding edges of the static site-wide sitemap model. Then, the sitemap model with global information representation is obtained. This model can represent both the global control flow and data flow of the application. Then DetAC analyzes the role-based and user-based access control policies of the Web application based on the node reachability and annotated data features of the model. And according to the information such as role, user, and access resources, it generates attack vectors to achieve different roles and the same role of different users to access each other's resources. Finally, access control vulnerabilities are detected based on the equivalence of the results obtained using attack vector access and normal access to the Web application server. DetAC was validated on five real open-source Web applications, and the results showed that DetAC successfully detected up to 12 access control vulnerabilities, which are more than those of the traditional seven tools. The dynamic analysis page coverage rate was significantly improved during the detection process, reaching an average of 91.37%. [ABSTRACT FROM AUTHOR]

Biomedical research using human biological material and data is essential for improving human health, but it requires the active participation of many human volunteers in addition to the distribution of data. As a result, it has raised numerous vexing questions related to trust, privacy and consent. Trust is essential in biomedical research as it relates directly to the willingness of participants to continue participating in research. Privacy and the protection of personal information also influence trust. Informed consent has proven to be insufficient as it cannot overcome the informational deficit between primary and unknown future uses of material and data and is therefore not fully informed and invalid. Broad consent is also problematic as it takes full control of samples and data flow from the research participant and inherently requires that a participant must trust that the researcher will use their material or data in a manner that they would find acceptable. This paper attempts to offer some insight into how these related issues can be overcome. It introduces dynamic consent as a consent model in research involving human biological material and its associated data. Dynamic consent is explained, as well as its claims of superiority in instances where future research is possible. It is also shown how dynamic consent contributes to better control of the samples and data by the research participant, and how trust may be improved by using this consent model. Dynamic consent's co-existence with and support of the South African Protection of Personal Information Act of 2013 is also assessed. The limitations of dynamic consent are also discussed. [ABSTRACT FROM AUTHOR]

As technology evolves, embedded systems access more networks and devices, which means more security threats. Existing security-monitoring methods with a single parameter (data or control flow) are not effective in detecting attackers tampering with the data or control flow of an embedded system. However, simply overlaying multiple security methods will result in excessive performance overhead for embedded systems. In this paper, we propose a novel hardware security-monitoring architecture that extracts DI (data integrity) digests and CFI (control flow integrity) tags to generate reference information when the program is offline. To monitor the indirect jumping behavior, this paper maps the legal target addresses into the bitmap, thus saving the search time. When the program is loaded, the reference information and the bitmap are safely loaded into the on-chip memory. The hardware monitoring module designed in this paper will check the DI summary and CFI tags in real time while executing the program. The architecture proposed in this paper has been implemented on the Xilinx Virtex 5 FPGA platform. Experimental results show that, compared with existing protection methods, the proposed approach in this paper can effectively detect multiple tampering-type attacks on the data and control flow of the embedded system, with a performance overhead of about 6%. [ABSTRACT FROM AUTHOR]

Nowadays expectations against modern communication services involve not just Quality of Service (QoS) enhancement for real-time applications but also increased transmission rate between the storing and processing of Big Data nodes. Transmission Control Protocol (TCP) has strict flow control of the data stream providing automatic adaptation to the path load of the process-to-process communication. User Datagram Protocol (UDP) based solutions are proposed to settle the communication efficiency. In this paper, we analyse the effect of three independent communication parameters on the efficiency of looped UDP communication: the size of the Maximum Transfer Unit (MTU), the bandwidth of the end-to-end session, and the segment size of the UDP protocol data unit. The usage of nonstationary multi-resolution methods helps to identify three characteristic patterns offering identification of the objective qualitative features of the looped datagram communication services. [ABSTRACT FROM AUTHOR]

Contextual data are receiving increasing attention in Smart Cities as they enable the development and delivery of smart services for their citizens. The homogenization of contextual data flows has become an important topic for standardization bodies as they attempt to enable data flow control and vendor-independent solutions. Buildings are a critical component of cities, due to their role in several dimensions of Smart Cities (including the United Nations Sustainable Development Goals); these may include the monitoring of their operation, maintenance, energy consumption, ability to respond in emergencies, and people flows, all of which affect the sustainability of a Smart City's ecosystem. In this respect, Building Information Management Systems and Building Infrastructure Management Systems can benefit from this standardization. This paper presents how a novel solution named Smart-City-as-Hub can homogenize building contextual data and enable smart services' development and delivery based on these data. The analysis of the data from several IoT deployments in public school buildings is the method used to highlight the segmentation and fragmentation of the IoT landscape and to present the benefits that the Smart-City-as-Hub provides in this context. The ways in which the Smart-City-as-Hub concept can mitigate these challenges and enable Smart City stakeholders to concentrate their efforts on developing value-added services is presented in the discussion section. By providing real-life data of the IoT devices deployed in Smart City projects, this study aims to further advance research pursuing the homogenization and standardization of Smart City flows. [ABSTRACT FROM AUTHOR]

The paper analyses a networked control system consisting of a linear time-invariant system with a static feedback nonlinearity, subject to large delays in the feedback loop. This model is valid for example in wireless data flow control, where a saturation occurs since the flow is one-directional. The present paper extends previous results by proving necessity, in case the loop gain is uniformly less than one. The results are validated and illustrated in a simulation study. [ABSTRACT FROM AUTHOR]

The e-commerce business process net (EBPN) is a novel formal model for describing an e-commerce system and its interactive parts, such as shoppers, merchants, and the third-party payment platforms. Vulnerability analysis has a great impact on the trustworthiness of EBPN, which is an issue stemming from data inconsistency problems. Data inconsistency problems affect the consistency of the EBPN transaction analysis. The underlying causes of inconsistent data are closely related to concurrent operations, such as control flow and data flow. However, most of the existing detection methods have difficulties characterizing the vulnerabilities and interactions of control and data flows. In this paper, we propose a new method based on the dynamic data slice (DDS) that considers both transaction consistency and data state consistency. First, by analyzing control flow characteristics of EBPN, we obtain the dynamic slice. This dynamic slice is based on all paths of the EBPN reachability graph. Second, we perform the data inconsistency analysis by considering both transaction consistency and data-state consistency. Based on these, we construct a DDS to characterize the behavioral logic and the data-dependence information. The DDS acquires the dynamic data firing sequence. Based on that sequence and a given data marking, we can construct the DDSs for several types of EBPNs. Constructing the DDS can be completed in polynomial time. The DDS is designed to characterize the behavioral logic and data-dependence information. Based on these, we design a method to judge the data constraints. This method satisfies the EBPN need for transaction consistency by considering both the control and data states. In addition, according to the data-dependence information, we can lock the vulnerable regions caused by abnormal trading data in the system. Finally, we give a method to compute the vulnerability level. [ABSTRACT FROM AUTHOR]

Featured Application: The methodology proposed in this paper may benefit industrials to better bridge the gap between business requirements and implementation requirements by using a tool merging modeling and simulation in the same phase. The modeling and simulation techniques proposed are based on a resource-perspective approach. With the emergence of big data and cloud technologies, companies now evolve in complex IT environments. This situation requires good practices for data process automation to be adopted to ensure system interoperability. Visual computing helps companies to describe and organize the ways in which information systems, devices, and people must interact. It incorporates a number of fields including modeling and simulation (M&S). This paper focused on M&S of data workflow processes that are key steps to bridging the gap between business views and goals on the one side, and operational implementations on the other side. Simulation adds a dynamic view to static modeling; it increases understanding of the behavior of process mechanisms and the identification of weak areas in data flow. Several research projects have been focused on control flow and data flow, but less attention has been paid to resource characteristics. This work is based on the MDSEA approach and the eBPMN language, and proposes an approach that aims to distinguish the types of resources carrying out process tasks. Furthermore, it introduces a new composite resource made from the relationship between a user (human resource) and a task form (IT resource). Moreover, it proposes a resource aggregation based on process performance combination in order to run and display a global performance measurement of a process path. [ABSTRACT FROM AUTHOR]

The 'data revolution' marks a time of growing interest and investment in data – big, small, or otherwise. Critical attention to data is also proliferating, exposing the diverse ways that data produces inequality of opportunity and harm in society. This paper draws the nascent field of critical data studies into conversation with emerging narratives in data-for-development (D4D) to advance the conceptualization of data inequalities, explaining how they both align with and diverge from core tropes of digital inequalities research – and why this matters for development. The paper examines the causes, consequences, and potential solutions to three 'data divides' – access to data, representation of the world as data, and control over data flows – through examples of digital identity systems and national data infrastructures, user-generated data, and personal behavioral data produced through corporate platforms. This understanding provides a basis for future research, practice, and policymaking on data-related (in)equalities in development contexts and beyond. [ABSTRACT FROM AUTHOR]

Purpose: The principal purpose of this article is to address a critical issue emerging in the realm of interorganizational dependencies heavily impacted by digitalization, namely developing business models that would protect privacy in a sustainable way. On the one hand, companies have been jointly proposing, creating, delivering, and capturing value through an excessive, unethical exploitation of personal data and information. On the other, restricting and controlling flows of data and information hampers the processes that lead to social well-being. This article reflects on this paradox by building on the theories of business models for sustainability and contextual integrity, while offering a holistic conceptual narrative guiding the sustainable transition towards digital equity and inclusivity. Design/methodology/approach: This conceptual article can be classified as a theory synthesis paper with the ambition to achieve an outcome that enhances knowledge on concepts and a phenomenon by a conceptual integration across two different, previously unconnected literature streams and theories. Findings: This article suggests that businesses which play any role in transmission of data and information cannot be sustainable without protecting privacy as a social value. Furthermore, it argues that privacy cannot be protected without addressing the appropriateness of both flow and use of data and information with respect to all involved stakeholders. Ultimately, via linking two distinct yet interrelated and rigorously developed research streams, a heuristic framework for privacy and sustainability in business models is proposed as a system of key considerations for managers to apply in assessing and planning a business practice, so it protects privacy in a sustainable way. Originality/value: The key theoretical contribution of this article can be considered twofold. Firstly, it unfolds the relevance of privacy protection for the stream of business model research directed toward sustainable development in a way that is theoretically rigorous, complementary with the stakeholder theory, and reflecting the changing interorganizational dependencies affected by digitalization. Secondly, it contributes to the contemporary debate on privacy as a social value through identifying theoretically thorough avenue for adapting the theory of contextual integrity to a social domain where value proposition, creation, delivery, and capture with and for stakeholders involves transmission of data and information. [ABSTRACT FROM AUTHOR]

The communication security of unmanned aerial vehicles (UAVs) or drones based on flying ad hoc networks (FANETs) can be enhanced and maintained by assessing vulnerabilities, threats, and attacks. Thus, trust, high mobility, and complex communication interfaces are crucial for effective coordination and segregation of malicious drones from the genuine. A new efficient honesty-based detection scheme for malicious drones has been proposed to distinguish between intentional and unintentional misbehavior of the UAVs. The proposed scheme configures a drone system for packet transmission within the FANET, specifying buffer size and packet size variables to control data flow and prevent congestion. It then computes energy used for packet reception and transmission, optimizes energy consumption, and evaluates the drone's mobility through the link stability index (LSI) and honesty UAV. The honesty UAV parameter categorizes UAVs into unintentional malicious and intentional malicious categories. Fuzzy logic helps identify intentional and unintentional misbehaving drones and improves performance in the network. It continuously generates the malicious UAV detection mechanism by assessing the parameters i.e., energy, pattern of movements, and the honesty score about the UAVs as a whole. Thereby a dynamic rating system to adeptly identify and differentiate the cooperative and non-cooperative nature of drones from the network is assimilated in the paper. Simulation results show the proposed scheme approximately aligns with the actual cases for varying numbers of malicious drones. Finally, the observation of the simulation result reflects substantial enhancement in performance metrics, with a significant cutback in end to end delay and packet delivery ratio by 10–30% and 20–50% respectively, contrasting with the existing techniques. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a systematic Inter-Component Communications (ICCs) dynamic Analysis Technique (SIAT) for detecting privacy-sensitive data leak threats. SIAT's specific approach involves the identification of malicious ICC patterns by actively tracing both data flows and implicit control flows within ICC processes during runtime. This is achieved by utilizing the taint tagging methodology, a technique utilized by TaintDroid. As a result, it can discover the malicious intent usage pattern and further resolve the coincidental malicious ICCs and bypass cases without incurring performance degradation. SIAT comprises two key modules: Monitor and Analyzer. The Monitor makes the first attempt to revise the taint tag approach named TaintDroid by developing the built-in intent service primitives to help Android capture the intent-related taint propagation at multi-level for malicious ICC detection. Specifically, we enable the Monitor to perform systemwide tracking of intent with five abstraction functionalities embedded in the interactive workflow of components. By analyzing the taint logs offered by the Monitor, the Analyzer can build the accurate and integrated ICC patterns adopted to identify the specific leak threat patterns with the identification algorithms and predefined rules. Meanwhile, we employ the patterns' deflation technique to improve the efficiency of the Analyzer. We implement the SIAT with Android Open Source Project and evaluate its performance through extensive experiments on a particular dataset consisting of well-known datasets and real-world apps. The experimental results show that, compared to state-of-the-art approaches, the SIAT can achieve about 25% ∼200% accuracy improvements with 1.0 precision and 0.98 recall at negligible runtime overhead. Apart from that, the SIAT can identify two undisclosed cases of bypassing that prior technologies cannot detect and quite a few malicious ICC threats in real-world apps with lots of downloads on the Google Play market. [ABSTRACT FROM AUTHOR]

Recently, various applications and services are used in the Internet. Load balancing the increasing network traffic in real time can improve the network quality. The flow control technologies become much more important than before. Our research proposes an intelligent network flow identifying method, which is based on the neural network algorithm, GHSOM. In this paper, we suggest to utilize the structural classification of GHSOM for training the properties of packets, such as timestamp, source and destination. Based on our proposed normalization, IP network flows can be formed autonomously during the learning process. The combination use of the new normalization with the GHSOM can divide a flow to several sub-IP flows. This paper indicates that a flow shall consist of several sub-IP flows, and sub-IP flow shall consist of several IP packets. The experiments show that IP packets can be divided to flow and sub-IP flow classes properly. Furthermore, those repeated jumbo sub-IP flows can be used to discover communicating errors or abnormal attacks. [ABSTRACT FROM AUTHOR]

The growing prevalence of embedded systems in various applications has raised concerns about their vulnerability to malicious code reuse attacks. Current software-based and hardware-assisted security techniques struggle to detect or block these attacks with minor performance and implementation overhead. To address this issue, this paper presents a lightweight hardware-assisted scheme to enhance the security of embedded systems against code reuse attacks. We develop an on-chip lightweight hardware shadow stack to validate target addresses at runtime for backward-edge control flow integrity, which backs up valid return addresses during function calls and automatically verifies actual return addresses during the return phase. Additionally, we propose a lightweight stream cipher circuit that encrypts and decrypts critical stack data related to control flow manipulation, preventing attackers from analyzing or tampering with them. When designing and implementing the security mechanism for embedded systems, we fully consider the constraints of limited system resources and performance, optimizing both the architecture design and implementation of the proposed hardware. Finally, we integrate both the proposed lightweight hardware shadow stack and the runtime data encryption hardware into the OR1200 processor. We have verified the system security function on the Terasic DE1-SoC FPGA platform and evaluated the system performance as well as implementation overhead. The results show that the proposed lightweight hardware-assisted scheme can provide a dedicated defense capability against code reuse attacks for embedded systems, with an average system performance overhead of 0.39% and an area footprint of 0.316 mm2. [ABSTRACT FROM AUTHOR]

In recent years, underwater acoustic sensor networks (UASNs) have attracted widespread attention in academia. Prolonging the network lifetime is a crucial issue for UASNs. Compared with traditional wireless sensor networks (WSNs), stringent energy becomes more critical in UASNs because the battery equipped at sensor nodes has the limited amount of energy and it is much more difficult to replace or recharge in underwater circumstance. This fact motivates us to pursue the solutions to reduce power consumption by using relay nodes and flow allocation mechanism in order to extend the network lifetime. In this paper, the issues of relay node placement and the flow allocation have been considered as a joint problem and are formulated into an integer nonlinear programming problem, which is node placement-hard in general. To solve the problem efficiently, this paper proposes a novel heuristic scheme for UASNs, which works based on a 3-D architecture. The proposed scheme consists of three algorithms, named as Alternative Flow and Relay-node Adjustment as a whole. Extensive simulation experiments demonstrate that the proposed scheme offers a simple yet attractive solution to the problem. [ABSTRACT FROM PUBLISHER]

This paper proposes an enhanced transport layer protocol for precise flow control in data centers, referred to as PFDCT, combining explicit rate control (priority scheduling) and implicit rate control (marking algorithm with dynamic threshold) innovatively, while satisfying the transmission requirements of each data flow. It enables multiple data flows to share the bandwidth fairly, which is rarely achieved by previous data transmission protocols. Our design stems from the observation that different packets in a data center network need to be processed differentially and that the accuracy of the feedback of the indication information to the link state will directly affect the effectiveness of the congestion control mechanism. Our tests show that PFDCT has lower network jitter, better fairness, and significant performance gains over existing proposals (DCTCP, ICTCP, and L2DCT) in terms of Flow Completion Time (FCT) and throughput on average. Furthermore, we discussed the impact of the marking algorithm with dynamic thresholds on the performance of PFDCT, and the results of the experiments show that the addition of the marking algorithm will further improve the performance of PFDCT in terms of packet loss rate, latency, and FCT. [ABSTRACT FROM AUTHOR]

The paper describes the information base of the system, which is formed by algorithms of centralized control, automated analytical control system and control data of material flows. The data processing algorithm according to a special program generates additional information necessary to solve the control tasks of the system and includes algorithms for processing analytical data information, as well as data for monitoring material flows, calculations of additional electric furnace variables, loading of charge into furnace bunkers and technological variables by department. The simulation model of the functioning of the complex ”technological process – control system” is implemented in the form of a package of interconnected software modules. The package implementing the simulation model is divided into three complexes: a set of programs for ”Data collection and processing”; a complex for ”Optimal process control”; a set of programs for ”optimal energy regime management”. [ABSTRACT FROM AUTHOR]

The transmission of data flows in current networks is in a scattered and disordered state, which makes it difficult to effectively discover and defend against network attacks in a timely manner, while network managers lack the tools for the secure and orderly management of data flows. To solve this problem, a secure data flow forwarding method based on service ordering management is proposed in this paper. By defining the service header, the scheme realizes a fine-grained service-based division of data flows. The rules for services in the network are formulated, and orderly control over data flows based on the rules is implemented through the software-defined network architecture, such that only data flows meeting the rules are allowed to pass through the network. Meanwhile, to achieve secure data flow forwarding, data flow is signed, and the signature fields are sampled and verified on the forwarding device to ensure the correctness and tamperproof nature of the data flow forwarding process. The experimental results reveal that the proposed method based on service ordering management can achieve fine-grained and orderly secure data flow control forwarding, effectively defending against network attacks and improving network security. Furthermore, the additional forwarding delay introduced by the scheme is in the controllable range, making the approach practical. [ABSTRACT FROM AUTHOR]

A publish/subscribe (PS) model is an event-driven model of a distributed system. In traditional PS systems, each peer (process) can either publish or subscribe events. In this paper, we consider a peer-to-peer (P2P) type of topic-based PS model where each peer can both publish and subscribe events. In this paper, we newly propose a topic-based access control (TBAC) model for topic-based PS systems. Here, an access right is a pair ⟨ t , op ⟩ of a topic t and a publish or subscribe operation op. A peer is allowed to publish an event message with publication topics and subscribe interesting topics only if the publication and subscription access rights are granted to the peer, respectively. An event message e is delivered to a peer p i if the publication of e and subscription of p i include some common topic. If a peer p i publishes an event message e 2 after receiving an event message e 1 , the event message e 2 may bring some information of the event message e 1 . If a target peer p j is not allowed to subscribe at least one topic which is related with the event message e 1 , information in the peer p i illegally flows to the target peer p j . We newly propose a subscription-based synchronization (SBS) protocol to prevent illegal information flow. Here, an event message is banned by a target peer if the event message implies illegal information flow. However, event messages may be unnecessarily banned by a peer even if no illegal information flow to the peer occurs. In the evaluation, we show the number of event messages unnecessarily banned in the SBS protocol. [ABSTRACT FROM AUTHOR]

Code smell is an indicator of potential problems in a software design that have a negative impact on readability and maintainability. Hence, detecting code smells in a timely and effective manner can provide guides for developers in refactoring. Fortunately, many approaches like metric-based, heuristic-based, machine-learning-based and deep-learning-based have been proposed to detect code smells. However, existing methods, using the simple code representation to describe different code smells unilaterally, cannot efficiently extract enough rich information from source code. In addition, one code snippet often has several code smells at the same time and there is a lack of multi-label code smell detection based on deep learning. In this paper, we present a large-scale dataset for the multi-label code smell detection task since there is still no publicly sufficient dataset for this task. The release of this dataset would push forward the research in this field. Based on it, we propose a hybrid model with multi-level code representation to further optimize the code smell detection. First, we parse the code into the abstract syntax tree (AST) with control and data flow edges and the graph convolution network is applied to get the prediction at the syntactic and semantic level. Then we use the bidirectional long-short term memory network with attention mechanism to analyze the code tokens at the token-level in the meanwhile. Finally, we get the fusion prediction result of the models. Experimental results illustrate that our proposed model outperforms the state-of-the-art methods not only in single code smell detection but also in multi-label code smell detection. [ABSTRACT FROM AUTHOR]

A common requirement of embedded software in charge of safety tasks is to guarantee the identification of random hardware failures (RHFs) that can affect digital components. RHFs are unavoidable. For this reason, the functional safety standard devoted to automotive applications requires embedded software designs able to detect and eventually mitigate them. For this purpose, various software-based error detection techniques have been proposed over the years, focusing mainly on detecting control flow errors. Many control flow checking (CFC) algorithms have been proposed to accomplish this task. However, applying these approaches can be difficult because their respective literature gives little guidance on their practical implementation in high-level programming languages, and they have to be implemented in low-level code, e.g., assembly. Moreover, the current trend in the automotive industry is to adopt the so-called model-based software design approach, where an executable algorithm model is automatically translated into C or C++ source code. This paper presents two novelties: firstly, the compliance of the experimental data on the capabilities of control flow checking (CFC) algorithms with the ISO 26262 automotive functional safety standard; secondly, by implementing the CFC algorithm in the application behavioral model, the off-the-shelves code generator seamlessly produces the hardened source code of the application. The assessment was performed using a novel fault injection environment targeting a RISC-V (RV32I) microcontroller. [ABSTRACT FROM AUTHOR]

Rapid online adaptation to the new business requirements can improve innovation level and market competency of collaborative organizations. Complex and unstructured processes are provision e-services in collaborative networks through web service inter-connections, which unanticipated changes made it hard to manage them. In a cross-organizational domain, when partners deal with unexpected changes, the received requests are represented as complex theories, and business process adaptation will be more complicated in the occurrence of concept drifts. Rapid prediction and adaptation to new situations need concept drift detection and novel class prediction mechanism for the overall collaborative network processes in both data and control flows. In this article, a new approach to the online reflection of distributed rule concept drifts of collaborative network reference processes is introduced. The solution to the data-informed adaptation of unstructured process employed managing rule concept drifts and cross-organizational processes restructuring via the distributed model with minor updates. The method could react to the new events via an ensemble prediction mechanism. Its architecture has major components for monitoring, dynamic distributed rule reconfiguration, and partners side components. The paper suggests a service-oriented semantic network of distributed rules for e-services provision, substitution, and replacement via management of choreographed web-services. The approach has been validated and verified with real data belonging to the healthcare domain. The results prove the adaptation mechanism efficiency of the daily changes. [ABSTRACT FROM AUTHOR]

Vectorization of programs is crucial for achieving high performance on modern processors with SIMD (Single Instruction Multiple Data) extensions. Programs with IF-statements suffer from control flow divergence that seriously complicates automatic vectorization. Therefore, contemporary compilers employ the IF-conversion approach to convert control flow to data flow, which relies on using predicated execution techniques (i.e., masked or select SIMD instructions). In this paper, we enhance the compiler's capabilities to generate efficiently vectorized code for processors without masked instructions. We improve the state of the art in program vectorization by developing a novel approach—IF-select transformation—which is applicable to arbitrarily nested IF-statements. We implement our approach in the open-source Open64 compiler and evaluate its performance on the SW26010 processor used in the Sunway TaihuLight supercomputer (currently #3 in the TOP500 list) that does not support masked instructions. We extend our vectorization approach by providing an additional LLVM optimization pass to reduce the amount of masked memory accesses on processors without masked instructions, e.g., IBM Power8 and ARMCortex-A8. Experimental results demonstrate the performance advantages of the suggested vectorization techniques. [ABSTRACT FROM AUTHOR]

Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads, pointed tips, and thicknesses, object selection and assembly often lead to misjudgment. We have developed a practical robotic arm control system based on vision detection techniques for screwdriver bits' placement. In addition to effectiveness, easy deployment and high flexibility in the field are also taken into account. The vision-based system consists of four processing stages in the following order: world coordinate conversion from image pixel coordinates, object detection, edge detection, and object orientation. In the first stage, a manual two-point marking method is proposed to easily configure the coordinate conversion for robot operating system (ROS)-based manipulators. For the following stages, we focus on the fine integration of state-of-the-art methods for the technical feasibility of the screwdriver bit placement. Such integration includes the selection between object detection methods and the data flow control among system stages. The experimental results show that (1) in detecting screwdriver bits, You Only Look Once (YOLO) v4 outperforms YOLOv7 and Single Shot MultiBox Detector at an accuracy rate of 99.51%; (2) in the edge detection, the object detection output can better illustrate the object contour than a whole image, achieving a mean absolute error of 0.86% in estimating the object angle; and (3) a successful real-time replacement rate of 96% is achieved for 12 screwdriver bits randomly scattered on a conveyor belt. [ABSTRACT FROM AUTHOR]

In recent days, research in underwater fish farming has been seen as a sustainable method to land‐based fish farming due to the increasing global population and limitations in land‐based farming areas worldwide.The fish farming cage installations are usually operated in the depth area of seawater and are ideal circumstances for fish farming. Monitoring sensor‐based data collection from fish farming is highly efficient and more innovative. Due to the unique properties of seawater, the substantial challenge of an Underwater Wireless Sensor Network (UWSN) is guaranteeing reliable data communication between the underwater sensor nodes and surface nodes. To ensure reliable communication, the mathematical approach Stochastic Network Calculus (SNC) is considered for modeling the reliable data flow control in the logical link control layer to satisfy the requirements of the underwater fish farming application. This paper presents the SNC mathematical model construction of stochastic bounds for frame flow, window size, service element analysis, feedback loop analysis, delay, and backlog bounds derived in the proposed model. The proposed model produces the better frame delivery and throughput in an incremental order with respect to the simulation model. There is significant difference (4 to 12 percentage) between the stochastic and simulation model. Thus, the reliable frame flow can be adopted with the dynamic changes in the feedback loop to avoid the overflow by repeated requests. [ABSTRACT FROM AUTHOR]

As the large amount of video surveillance data floods into cloud data center, achieving load balancing in a cloud network has become a challenging problem. Meanwhile, we hope the cloud data center maintains low latency, low consumption, and high throughput performance when transmitting massive amounts of data. OpenFlow enables a software-defined solution through programing to control the scheduling of data flow in the cloud data center. However, the existing scheduling algorithm of the data center cannot cope with the congestion of the network center effectively. Even for some dynamic scheduling algorithms, adjustments can only be made after congestion occurs. Hence, we propose a proactive and dynamically adjusted mixed-flow load-balanced scheduling (MFLBS) algorithm, which not only takes into account the different sizes of flows in the network but also maintains maximum throughput while balancing the load. In this paper, the MFLBS problem was formulated, along with a set of heuristic algorithms for real-time feedback and adjustment. Experiments with mesh and tree network models show that our MFLBS is significantly better than other dynamic scheduling algorithms, including one-hop DLBS and static scheduling algorithm FCFS. The MFLBS algorithm can effectively reduce the delay of small flows and average delay while maintaining high throughput. [ABSTRACT FROM AUTHOR]

To meet the increasing network demands of enterprise environments and data centers, traditional network architectures have been replaced by software-enabled hardware devices for developing agile, dynamic, and programmable networks. Software Defined Networking (SDN) is a new paradigm shift that abstracts network design and infrastructure in the software and then implements it by using software across hardware devices. SDN is used to build and manage the network in a customized way. SDN architecture offers network virtualization, network programmability, and flexibility by decoupling control and data planes which further enriches the network performance. As such, the SDN controller is a tactical control point in SDN. An SDN normally allows data flow control to the switches and/or routers and the rationale of the application’s logic for deploying intelligent networks. SDN with Machine Learning (ML) and Artificial Intelligence (AI) techniques build network models, which essentially can take decisions based on self-learning and self-management capabilities. Accurate classification of topology is of prime importance to satisfy future network prerequisites like unpredictable traffic patterns, dynamic scaling, flexibility, and centralized control. The controller needs to have exact information about the topology of the network in order to configure and manage the network. Subsequently, topology classification is an important component of any Software Defined Network architecture. This paper presents the classification of topologies using different supervised ML algorithms. The accuracy obtained from Support Vector Machine (SVM) and Classification and Regression (CART) is 95% and 90% respectively. The experimental results show that according to the k Cross-Fold validation technique, SVM algorithm has been found to be the most accurate amongst the other ML algorithms with a mean accuracy value of 85%. [ABSTRACT FROM AUTHOR]

This paper focuses on the modelling and control of an interline current flow controller (CFC) for meshed HVDC grids. The operation states of the CFC are presented, and an average model is derived. The average model is used to perform steady-state analysis on a 3-terminal meshed grid, showing the current change capabilities and the benefits on the operation area. The converter control is designed using a linearized model. The system performance of the CFC is tested by means of simulation in a 3-terminal grid and in a 5-terminal grid. [ABSTRACT FROM AUTHOR]

This research paper deals with a system-on-chip (SoC) architecture design where multiple processors are inbuilt with other blocks of memory and control logic developed by nanomaterials. The multiprocessing-based SoC architecture is commonly used in the latest electronic devices such as smartphones, tablets, and smart wristwatches with large memory sizes. The data handing in these highly memory-dense devices is a critical task, and it needs special attention for the smooth operation of the device. This research proposed a smart controller to exchange data between various processors and input-output devices to tackle this challenge. A proposed controller block controls the data flow between memory and different SoC components and processors. A memory access controller (MAC) is presented in this research study to manage and accelerate data transmission speed and reduce the processors' activity for SoC-based devices. The proposed MAC will integrate into the SoC with multiprocessing units, including gaming processors, at minimum hardware overhead and low power consumption. It improves the memory accessing efficiency and reduces the processors' activity of a system. As a result, the system's performance and power consumption improve at an acceptable level compared with the other conventional methods. This research is aimed at enhancing the performance of any SoC-based device where multiprocessing engines are inbuilt and flexible enough to serve various SoCs. [ABSTRACT FROM AUTHOR]

Business process outcome prediction plays an essential role in business process monitoring. It continuously analyzes completed process events to predict the executing cases' outcome. Most of the current outcome prediction focuses only on the activity information in historical logs and less on the embedded and implicit knowledge that has not been explicitly represented. To address these issues, this paper proposes a Deep Latent Factor Model Predictor (DLFM Predictor) for uncovering the implicit factors affecting system operation and predicting the final results of continuous operation cases based on log behavior characteristics and resource information. First, the event logs are analyzed from the control flow and resource perspectives to construct composite data. Then, the stack autoencoder model is trained to extract the data's main feature components for improving the training data's reliability. Next, we capture the implicit factors at the control and data flow levels among events and construct a deep implicit factor model to optimize the parameter settings. After that, an expansive prefix sequence construction method is proposed to realize the outcome prediction of online event streams. Finally, the proposed algorithm is implemented based on the mainstream framework of neural networks and evaluated by real logs. The results show that the algorithm performs well under several evaluation metrics. [ABSTRACT FROM AUTHOR]

The turbine units are at the forefront of equipment in the process of pumping crude oil and exporting it to the oil ports, where there are many types of turbines used in different sites of Iraqi stations whether pumping oil stations or electrical power production stations. One of the most important types of turbines is the gas turbine, which is frequently used in oil depots. One of the remarkable depots in Iraq is Zubair-1 / Basra that exports around 850,000 barrels per day (B/D). Therefore, Zubair-1 should continue pumping the crude oil 24/7, which has five gas turbines (three Rolls-Royce AVON MK 1533 and two Siemens SGT 400). However, the three Rolls-Royce gas turbines in Zubair-1 have not worked in the summer season since 2016, when the ambient temperature goes high around 11:00 am to 3:30 pm. This paper proposes solution to solve a high exhaust temperature (EGT) shutdown signal (a preventing running turbine signal) without effect on the sequence of turbine running stages. The proposal is adding a fuzzy inference system (FIS) that controls the gas turbine in the first two running stages that demonstrates and controls of speed the turbine from 800 RPM to 3000 RPM. The inputs of FIS are the average temperature of eight combustion chambers (exhaust temperatures) and the speed of the gas turbine, while the output of FIS is the control signal to the flow control valve (FCV) with an amplifier to gain the signal. The FIS proposal has been applied in all three Rolls-Royce jet pumping turbines since April 7, 2021, and they work regularly at all times of the day. The FIS minimizes the maximum average of combustion chambers temperature at midafternoon in June 9, 2021 (48 ˚C ambience temperature) from 689 ˚C to 610 ˚C that means the improvement is around 45%. [ABSTRACT FROM AUTHOR]

Man-in-the-Middle (MitM), one of the best known attacks in the world of computer security, is among the greatest concerns for professionals in the field. Main goal of MitM is to compromise confidentiality, integrity and availability of data flowing between source and destination. However, most of its many variants involve difficulties that make it not always possible. The present paper aims at modelling and describing a new method of attack, named Browser-in-the-Middle (BitM) which, despite the similarities with MitM in the way it controls the data flow between a client and the service it accesses, bypasses some of MitM's typical shortcomings. It could be started by phishing techniques and in some cases coupled to the well-known Man-in-the-Browser (MitB) attack. It will be seen how BitM expands the range of the possible attacker's actions, at the same time making them easier to implement. Among its features, the absence of the need to install malware of any kind on the victim's machine and the total control it allows the attacker are to be emphasized. [ABSTRACT FROM AUTHOR]

Software plays an important role in modern society. New software features have to be completed and tested rapidly due to business and user needs and goals. To ensure the quality of modified software components, new test cases must be generated and included in the current test pool. Furthermore, test pool size often grows too large to test all cases. A considerably large test pool typically consumes an exorbitant quantity of time, leading to inefficient regression testing. Test suite reduction is often used to solve this problem by removing redundant test cases. Thus, time and resource constraints make it necessary to minimize the test suite. That is, test pool size should be efficiently reduced, while the remaining test cases should be adequate to provide the same coverage as the pool. The main purpose of this paper is to propose four weighted combinatorial algorithms and a three‐objective binary integer linear programming (ILP) model to improve fault detection effectiveness. We first consider both the control flow and data flow as our testing criteria and combine them by using a weighted means with four existing test suite reduction algorithms. A genetic algorithm is used to find the best combination of weighting factors to assign to the two testing criteria. Experiments based on real subject programs are presented. Experimental results demonstrate that 77.5% of our proposed weighted combinatorial algorithms have an even or better performance in suite size reduction, 83.75% of our approaches perform even or better in fault detection effectiveness, and 82.5% of our approaches have even or better effectiveness in fault‐to‐test ratio. For the three‐objective binary ILP model, test suite size is significantly reduced without any accompanying loss in fault detection ability. [ABSTRACT FROM AUTHOR]

"There and Back Again" (TABA) is a programming pattern where the recursive calls traverse one data structure and the subsequent returns traverse another. This article presents new TABA examples, refines existing ones, and formalizes both their control flow and their data flow using the Coq Proof Assistant. Each formalization mechanizes a pen-and-paper proof, thus making it easier to "get" TABA. In addition, this article identifies and illustrates a tail-recursive variant of TABA, There and Forth Again (TAFA) that does not come back but goes forth instead with more tail calls. [ABSTRACT FROM AUTHOR]

With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis. [ABSTRACT FROM AUTHOR]

Transient flow control in Water Transmission Systems (WTS) is one of the requirements of designing these systems. Hence, among control equipment, air chambers offer the best solution to control transient flow effects, i.e. both prevents water column separation and absorbs pressure increase. It is essential to carry out an accurate and optimized design of air chambers, not only due to high costs of their manufacturing but also their important protective role. Accordingly, hydraulic design parameters comprise tank volume, diameter of nozzle and coefficients of inflow and outflow of nozzle. In this paper, it is intended to optimize these parameters in order to minimize manufacturing costs. On the other hand, maximum and minimum pressures in main pipeline are considered as constraints which shall fall in allowed range. Therefore, a model has been developed which is a combination of a hydraulic simulation model of WTS and an optimization model based on genetic algorithm. This model is first applied to WTS of Dehgolan-Ghorveh plain as a case study. Results of this research demonstrate that based on suggested model, negative wave creation and pressure increase in pipeline is prevented as well as decrease in manufacturing costs of air chamber. [ABSTRACT FROM AUTHOR]

The paper derives a robust networked controller design method for systems with saturation where the delay is large and uncertain, as in one-directional data flow-control. A classical linear H ∞ criterion is first formulated in terms of the sensitivity and complementary sensitivity functions. A new asymptotic constraint is then derived, which specifies the minimum amount of low frequency gain that is needed in the sensitivity function to conclude on non-linear closed loop [ABSTRACT FROM AUTHOR]