Execution Characteristics Research Articles

Architectural Heritage Buildings and Tourism Sustainability in Karangasem Bali

Objective: This study aims to explore and understand the interrelationships between cultural tourism and architectural heritage sites through tourist image and perception. These interrelationships must balance community needs, available resources, executive efforts and regional planning while protecting the environment. Theoretical Framework: Historic buildings are considered important elements in cultural heritage, and architecture is further considered to be one of the most important aspects that attract tourists. As a significant part of cultural heritage and tourism resources, architectural heritage is the most important commemorative, secondary architectural buildings and its natural and artificial environments in ancient areas and characteristic villages. Method: This study's method is based on mixed-method research, which is known as triangulation. This approach entailed gathering initial data, conducting interviews and surveys, and creating house maps. Traditional architectural documentation tools, such as drawings, plans, sections, elevations, and photographs, are required in all three process stages. Results and Discussion: Bali has a diversity of forms and varieties of traditional architecture. East Bali, part of the Karangasem kingdom called puri during the Mindle era of Bali, has preserved several unique architectural masterpieces. In this context, architectural heritage is critical in increasing property values. Preserving architectural heritage is considered environmentally friendly for cultural tourism. Research Implications: This study explores to what extent heritage architecture can be utilized as cultural heritage ideally and efficiently. This can be achieved by building sustainable restoration of archaeological and heritage sites to achieve sustainable heritage tourism. Originality/Value: This research utilizes experiments in countries with cultural heritage to obtain lessons, values and concepts that contribute to achieving an efficient program for sustainable tourism development based on the preservation of heritage areas.

A Quasi-Cyclic LDPC Based Low Complexity and Area-Efficient Communication System for IEEE 802.11n

Low-density parity-check (LDPC) code, which has an excellent error-correcting performance that is close to the Shannon limit, is the most often used error correction code (ECC) for reliable and effective communication. Despite higher performance and lower decoding complexity, the main disadvantage of LDPC codes is their high encoding complexity. A significant problem is the VLSI implementation of the LDPC encoder and decoder. In this paper, structured LDPC codes—also known as quasi-cyclic low-density parity check codes—have been used since it is good bit error ratio (BER) performance and adaptable hardware execution characteristics. The Complete (Encoder-Channel-Decoder) communication system with a 1/2 code rate, 648 bits codeward length, and sub-block size of z = 27 has been constructed for the IEEE 802.11n wireless standard.

Exploration of Legal Issues in the Judicial Practice of Smart Contracts

The process of smart contracts, from conceptualization to actual landing applications, is accompanied by the continuous development of blockchain technology. China attaches great importance to the application of blockchain technology represented by smart contracts in judicial practice, with the background of the construction of smart courts, relying on the construction of judicial blockchain, according to the unique automated execution characteristics of smart contracts, the mediation and execution of civil disputes filed as an entry point, to explore the high-quality development of the judicial trial and execution business. In the current judicial practice, smart contracts face a lack of legal norms, technical construction loopholes, and information security risks. To guarantee the reasonable and compliant stable operation of smart contracts within the legal framework, it is necessary to clarify the legal positioning of smart contracts, strengthen the construction of the rule system for the judicial application of smart contracts, introduce the ODR to broaden the rights' relief channels, and at the same time, encourage the participation of diversified subjects in the construction of the judicial blockchain, and establish the legalized review mechanism of smart contracts.

Factors that affect handball execution in Australian Football

ABSTRACT A handball is one of two technical skills used to dispose of the ball in Australian Football. Previous research has only considered handball effectiveness in the analysis of team performance and there is a need to understand whether there are other more important characteristics of handball execution that explain effectiveness (i.e., performance). 1342 handballs from Australian Football League matches were analysed. Ten characteristic variables were created that represent the context and execution of each handball included in the analysis. A mixed effects generalised linear model was used to evaluate the effect of the (ten) characteristics on handball outcome. Three out of ten characteristics were associated with handball outcome, and these related to the posture of the handballer and the type of defensive pressure applied to the handballer and the receiver of the handball. These findings explain both how to increase handball effectiveness and how to reduce the handball effectiveness of an opposition team. Given the important role of handballing in passing sequences and maintaining ball possession, the practical application of these findings could enhance overall team performance.

Corporate digital transformation, governance shifts and executive pay-performance sensitivity

We utilized text mining techniques to gauge a company's level of digital transformation and found that the higher the degree of digitalization, the more apparent the decoupling of executive compensation from performance, indicating lower sensitivity of executive pay to performance outcomes. Within the framework of agency theory, digital technology empowers shifts in corporate governance models, enabling process-based behavioral monitoring and constraining governance mechanisms, thereby reducing reliance on outcome-based incentive mechanisms. Heterogeneous analysis reveals that the substitutive effect of digitization on the pay-performance sensitivity is more pronounced in companies with lower executive ownership. These companies often rely more on compensation incentives in the absence of digital technology. Moreover, the effectiveness of digital technology in enhancing governance depends on the user's wealth of knowledge and technical proficiency. We additionally confirmed that digital transformation has a positive impact on mitigating agency problems, as it enhances information quality, improves internal control standards, reduces excessive perk, and diminishes managerial short-termism. In essence, digital transformation indeed elevates governance regime. Furthermore, we discovered that digitalization increases the association between compensation and executive effort. Our research findings provide a fresh perspective on corporate governance and executive incentive mechanisms in emerging markets.

Strive for rewards or keep them accountable? Motivating employees’ effort and performance

Organizations often turn to extrinsic motivators to improve employee effort and performance, with mixed results. In this research, performance-based benefits and accountability are explored to assess whether they differentially affect where effort is exerted and how they influence overall performance. The study proposes that effort can be divided into two components—strategic effort and execution effort—and that each component will influence overall performance differently. The study uses a scenario-based experiment involving a complex and unfamiliar task. The results indicate that workers motivated by performance-based benefits increase the use of strategic effort, which improves performance through the identification of critical problems, and they develop a better overall strategy relative to workers who are held accountable. The study also indicates that while accountability increased the use of execution effort, the additional effort did not improve performance in the complex task setting. The study highlights the importance of aligning an extrinsic motivator to the type of effort desired for a particular task.

Estimation of timing characteristics in real-time computer systems using Petri nets

The work is devoted to the problem of determining the time characteristics of tasks in real-time systems, the success of which depends not only on their logical correctness, but also on the time for which they receive the result. Determining such time characteristics of the system at the design stage is a rather difficult problem. Its solution is currently based on the use of two main methods: theoretical calculations related to the calculation of the so-called feasibility criteria and modeling of the system's operation on models. Among the models, statistical models of mass service systems are the most widespread. However, in both the first and second options, it is impossible to obtain a guaranteed result, which significantly complicates the design process. Recently, it has been proposed to use the methods of researching models based on the use of the Petri net apparatus. The paper proposes a method for estimating the time characteristics of tasks in real-time systems by means of data analysis. Obtained by modeling the distribution of processor time between tasks according to the selected algorithms of the scheduler by using a model of the software system in the form of a Petri net. The paper proposes a method for estimating the time characteristics of tasks in real-time systems by means of data analysis. Obtained by modeling the distribution of processor time between tasks according to the selected algorithms of the scheduler by using a model of the software system in the form of a Petri net. The method guarantees obtaining the time characteristics of the tasks when choosing specific types of processor and scheduler, which is required to start the technical design of the real-time system. The use of the proposed mathematical model and a package of application programs allow, at the early stages of the development of a real-time system, to determine the real terms of tasks and choose the optimal option for the implementation of technical and lost support. The proposed mathematical models are based on determining the time characteristics of program execution by modeling the distribution of processor time between tasks, subject to the prior selection of planning algorithms and the characteristics of a complex of technical means. The research is based on the use of simulated statistical models and Petri net models, The use of the proposed tools allows you to significantly reduce the time of analysis of possible options for real-time system implementation, increases the quality of the project and significantly reduces the overall time and cost of the entire development. The paper examines in detail the process of modeling a multitasking computer system using a Petri net device. A concrete example shows the simulation of the operation of a complex of programs, including the development of simulation algorithms, the listing of the simulation program, and the analysis of the obtained results.

Perlindungan Hukum Terhadap Pemenang Lelang Atas Objek Eksekusi Hak Tanggungan

Carrying out an auction is the payment of the auction object from the buyer to the auction seller, with the aim of paying off the debtor's debt to the creditor. However, in its implementation obstacles often arise such as the auction object cannot be controlled by the winning party. This research aims to determine legal protection for auction winners, as well as other obstacles faced by auction winners. In this research, the approach methods applied include: case approach methods through court decisions and statutory regulations. The legal materials used in this research use secondary legal materials in the form of primary legal materials, secondary legal materials and tertiary legal materials. The results of this research are legal protection for the rights of auction winners in the form of repressive protection contained in HIR, Vendu Regulation, PMK No. 213/PMK.06./2020, and the Civil Code. To obtain their rights, they can make real execution efforts. Meanwhile, in this research, the obstacles that influence the auction winner are non-juridical obstacles.

The strategist’s view needs to extend beyond planning to execution

PurposeThe Oxford Strategy Insights Project was designed to assess the current approaches, aims, and focus of strategists across industries and geographies.Design/methodology/approachThe project received input from 167 executives and managers across twenty-six industries, spanning over thirty countries, regarding their firm’s strategy process.FindingsThe biggest gap highlighted by the Oxford Strategy Insights Project is effective strategy execution. An overwhelming majority of respondents indicated that their organization’s strategy execution efforts are ineffective and slow, with accelerated implementation being the exception rather than the norm. In line with these findings, few firms appear to be applying the best practice of establishing an implementation management infrastructure including a “Program Manager” tasked to manage and coordinate the firm’s strategy implementation effort. The one positive element related to strategy execution is that most firms seem to have an effective strategic measurement and reporting process.Practical implicationsOrganizations that can execute their strategies increase the likelihood of realizing the full potential of their plans. However, the study’s main finding is that effective strategy execution is severely lacking. Strategists need to become more foresighted, with a much greater focus on implementation.Originality/valueAlthough the value of effective strategy execution has been well documented for over five-decades in both academic and management practice literature, new research has found that most strategists are still short-sighted, viewing strategy as primarily planning with a limited focus on implementation.

Execution and emission characteristics of automotive compression ignition engine powered by cerium oxide nanoparticles doped water diesel emulsion fuel

The rising consumption of oil and air pollution in developing countries compelled us to seek suitable alternative fuels for the existing compression ignition engines used in power generation, transportation, and industrial sectors, all of which play a strong role in the development of the nation's economy. Moreover, rising demand and consumption also causes a huge impact on our economy as we are dependent on other nations to cater to our needs as domestic production fails to fulfill the present requirement and leads to huge import bills that affect the ordinary man. A water/diesel (W/D) emulsified formulation helps to diminish the engine exhaust discharge without compromising the engine’s performance. The current task experimentally evaluates the execution and discharge characteristics of cerium oxide nano-particles doped water diesel emulsion fuel viz E15, E15CeO2(40), E15CeO2(60), and E15CeO2(80) on multi-cylinder automotive compression ignition engine at steady 1650 rpm, under variable test load situation. Mechanical stirring was used to make E15 water diesel emulsion fuel, whereas ultrasonicator and mechanical stirrer were used to make E15CeO2(40), E15CeO2(60), and E15CeO2(80) blends. Surfactants span 80 and tween 80 were used. The concentrations of cerium oxide nanoparticles in emulsion fuel samples were 40, 60, and 80 ppm, respectively. The characteristics like thickness, flash point, and heating value of test fuels were evaluated and reported within the range as specified by governing standards. The execution and discharge characteristics of various emulsion fuel samples were compared to standard diesel. At maximum load, cerium oxide doped fuels greatly improve engine performance while lowering environmentally harmful engine pollutants such smoke opacity, NOx, carbon monoxide, and hydrocarbon emissions by 40.27%, 33.60%, 30%, and 18.75%, respectively.

Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics

This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big data execution characteristics. Specifically optimized for graph-processing applications, which exhibit complex and irregular memory access patterns, a dual prefetching scheme is proposed. This scheme comprises a fast-response model with low-cost algorithms for regular memory access patterns and an intelligent model based on an adaptive Gaussian-kernel-based machine-learning prefetch engine. The intelligent model can acquire knowledge from real-time data samples, capturing distinct memory access patterns via an adaptive Gaussian-kernel-based regression algorithm. These methods allow the model to self-adjust its hyperparameters at runtime, facilitating the implementation of locally weighted regression (LWR) for the Gaussian process of irregular access patterns. In addition, we introduced an efficient hybrid main memory architecture that integrates two different kinds of memory technologies, including DRAM and PCM, providing cost and energy efficiency over a DRAM-only memory structure. Based on the simulation-based experimental results, our proposed model achieved performance enhancement of 57% compared to the conventional DRAM model and of approximately 12% compared to existing prefetcher-based models.

Scheduling Framework for Accelerating Multiple Detection-Free Object Trackers

In detection-free tracking, after users freely designate the location of the object to be tracked in the first frame of the video sequence, the location of the object is continuously found in the following video frame sequence. Recently, technologies using a Siamese network and transformer based on DNN modules have been evaluated as very excellent in terms of tracking accuracy. The high computational complexity due to the usage of the DNN module is not a preferred feature in terms of execution speed, and when tracking two or more objects, a bottleneck effect occurs in the DNN accelerator such as the GPU, which inevitably results in a larger delay. To address this problem, we propose a tracker scheduling framework. First, the computation structures of representative trackers are analyzed, and the scheduling unit suitable for the execution characteristics of each tracker is derived. Based on this analysis, the decomposed workloads of trackers are multi-threaded under the control of the scheduling framework. CPU-side multi-threading leads the GPU to a work-conserving state while enabling parallel processing as much as possible even within a single GPU depending on the resource availability of the internal hardware. The proposed framework is a general-purpose system-level software solution that can be applied not only to GPUs but also to other hardware accelerators. As a result of confirmation through various experiments, when tracking two objects, the execution speed was improved by up to 55% while maintaining almost the same accuracy as the existing method.

Legal Problems The Implementation Of Credit Agreements With Fiduciary Guarantees

This look at targets to analyze the legal problems with the implementation of Credit Agreements, especially in relation to the credit using fiduciary guarantees as collateral. Financing institutions use Credit Agreements that are included in the Standard Agreement. Because credit is a business activity of financial institutions, in this case it is a bank, in order to achieve a national balance in distributing funds to the public. In carrying out its function, banks as financial institutions provide credit, therefore the provisions of the Guarantee Law are intended to help business activities both macro and micro, as well as create legal certainty for stakeholders. To achieve the goal of obtaining legal certainty regarding Credit Agreements with Fiduciary Guarantees, This study uses an empirical legal approach method, and the research specifications are descriptive analytical.This study produced a data, namely it was concluded that Fiduciary Guarantee is a form of guarantor by the debtor at his request, in order to guarantee the repayment of credit debts, movable objects become the object of guarantee in this study. The Supplementary Agreement or what is often referred to as the Accesoir of the Principal Credit Agreement can be guaranteed by the Fiduciary Guarantee, while the charge is made in the form of a notarized, by guaranteeing legal certainty so that it needs to be Register with the Trustee Registration Authority and apply for issuance of a Trustee Certificate to gain enforcement powers. The obstacle arises when defaulting by the debtor, with various causes and factors. Sometimes execution efforts are made to save credit that has been channeled by financing institutions, especially banks, but in practice there are many obstacles, including in executing the object of the guarantee that is not found, has changed hands or is controlled by others, so that execution in the form of auctioning fiduciary objects is difficult to do.

SpecTerminator: Blocking Speculative Side Channels Based on Instruction Classes on RISC-V

In modern processors, speculative execution has significantly improved the performance of processors, but it has also introduced speculative execution vulnerabilities. Recent defenses are based on the delayed execution to block various speculative side channels, but we show that several of the current state-of-the-art defenses fail to block some of the available speculative side channels, and the current most secure defense introduces a performance overhead of up to 24.5%. We propose SpecTerminator, the first defense framework based on instruction classes that can comprehensively and precisely block all existing speculative side channels. In SpecTerminator, a novel speculative side channel classification scheme based on the features of secret transmission is proposed, and the sensitive instructions in the speculative window are classified and identified using optimized hardware taint tracking and instruction masking techniques to accurately determine the scope of leakage. Then, according to the execution characteristics of these instructions, dedicated delayed execution strategies, such as TLB request ignoring, selective issue, and extended delay-on-miss, are designed for each type of sensitive instruction to precisely control that these instructions are delayed only in pipeline stages that are at risk of leakage. In contrast to previous defenses based on the Gem5 simulator, we have innovatively implemented defenses against Spectre attacks based on the open-source instruction set RISC-V on an FPGA-accelerated simulation platform that is more similar to real hardware. To evaluate the security of SpecTerminator, we have replicated various existing x86-based Spectre variants on RISC-V. On SPEC 2006, SpecTerminator defends against Spectre attacks based on memory hierarchy side channels with a performance overhead of 2.6% and against all existing Spectre attacks with a performance overhead of 6.0%.

EVMTracer: Dynamic Analysis of the Parallelization and Redundancy Potential in the Ethereum Virtual Machine

Ethereum is one of the first blockchains executing smart contracts (financial applications directly executed on the ledger using a virtual machine). The performance of the Ethereum virtual machine has become a bottleneck caused by high volumes of transactions due to blockchain applications, including decentralized finance and non-fungible tokens. Hence, there is a need to detail the execution characteristics of the Ethereum virtual machine before its performance can be improved. This work introduces an off-line Ethereum virtual machine tracer called EVMTracer that produces runtime data dependence graphs from executed transactions as an alternative program representation. From the runtime dependence graphs, we can deduce valuable metrics about program execution characteristics, including the degree of parallelization and computational redundancies in smart contracts. Our experiments encompass all blocks up to 12M on the Ethereum mainnet. We found a geometric mean of 1.90× theoretical maximum speedup when executing the smart contracts in parallel and identified 34.97% of SLOAD instructions as redundant.

Planning Method for Future-Proof Factory Buildings

Due to competitive pressure and varying customer demands, product and technology life cycles are constantly shortening. Thus, manufacturing companies must handle the increasing discrepancy between long building life cycles and shortening product and technology life cycles. For economic and sustainability reasons, shortening the building life cycles is no viable option. Instead, factory buildings should be able to meet the changing production requirements and thus lay the foundation for a resilient manufacturing system. As it is not economically reasonable to build production buildings that simply maximize flexibility and changeability, an estimation of consistent future requirements is necessary. This estimation should focus on the requirements of the most rigid parts of the building. Traditional, statistic-based forecasting methods are not suitable because of the long planning horizons in building planning. However, methods used in corporate foresight promise good results. This paper gives a short overview of the research status and the relevant terminologies. After that, a method to cope with the described challenge is presented. The method identifies possible future developments of the production system. It then translates these developments into requirements on the building system and evaluates different degrees of requirement fulfillment. On this basis, the method helps to decide what specific developments the building should cover. Unlike existing approaches, this method focuses not only on the factory building itself as the system of highest relevance for long-term changeability but also reduces the execution effort and improves the integration of production planning and building planning.

Incremental Testing in Software Product Lines—An Event Based Approach

One way of developing fast, effective, and high-quality software products is to reuse previously developed software components and products. In the case of a product family, the software product line (SPL) approach can make reuse more effective. The goal of SPLs is faster development of low-cost and high-quality software products. This paper proposes an incremental model-based approach to test products in SPLs. The proposed approach utilizes event-based behavioral models of the SPL features. It reuses existing event-based feature models and event-based product models along with their test cases to generate test cases for each new product developed by adding a new feature to an existing product. Newly introduced featured event sequence graphs (FESGs) are used for behavioral feature and product modeling; thus, generated test cases are event sequences. The paper presents evaluations with three software product lines to validate the approach and analyze its characteristics by comparing it to the state-of-the-art ESG-based testing approach. Results show that the proposed incremental testing approach highly reuses the existing test sets as intended. Also, it is superior to the state-of-the-art approach in terms of fault detection effectiveness and test generation effort but inferior in terms of test set size and test execution effort.

NDTAEP: Design of a novel deadline-aware task scheduling model using augmented ensemble pattern analysis

A wide variety of scheduling models have been proposed by researchers over the years, and each of them has varying performance in terms of deadline hit ratio, scheduling effort, efficiency of task mapping, etc. However, these models are highly context-sensitive and cannot be scaled to heterogeneous task types due to their internal mapping characteristics. To improve task scalability, this work proposes a design of a novel deadline-aware task scheduling model that uses augmented ensemble pattern analysis for task clustering. The pattern analysis module uses a combination of K-means, hierarchical, and Fuzzy C Means (FCM) clustering to effectively segregate tasks depending on their completion and deadline parameters. These tasks are given to a modified deadline-aware League Championship Algorithm (LCA) optimizer, which assists in mapping the clustered tasks with worker threads. The modified LCA model uses a combination of task priority, task deadline, and worker capacity for scheduling. The model maps tasks that require higher execution effort with moderately performing worker nodes, while tasks with nearer deadlines are allotted to higher-performance workers. Due to the use of an ensemble augmented pattern analyzer with a modified LCA optimizer, the proposed model can improve execution speed by 8%, deadline hit ratio by 1.5%, and scheduling efficiency by 6.5% when compared with various state-of-the-art scheduling approaches. The proposed model was evaluated and showcased a deadline hit ratio of 99.95%, computational efficiency of 96.26%, and average task-scheduling delay of less than 0.1 ms, which makes it highly useful for a wide variety of task scheduling application scenarios.

A Learning-based Methodology for Scenario-aware Mapping of Soft Real-time Applications onto Heterogeneous MPSoCs

Soft real-time streaming applications often process input data that evoke varying workloads for their tasks. This may lead to high energy consumption or deadline misses in case their mapping onto a heterogeneous MPSoC target architecture is not adapted, e.g., when tasks with high execution times for the current input are assigned to resources of low computational power. To handle the vast variety of different input data, we propose to cluster data with similar execution characteristics into so-called data scenarios for which we determine specialized mappings by performing a scenario-aware design space exploration (DSE). A runtime manager (RTM) uses these mappings to adapt the execution of the running applications to their upcoming input by first identifying their best-suited scenarios. Subsequently, the RTM selects mappings considering their identified scenarios, which minimize the total number of deadline misses and the consumed energy. We embed the RTM into hybrid application mapping (HAM); ergo, performing time-consuming optimizations offline. In this article, we propose a novel data-scenario-aware HAM methodology that can cope with multiple applications and comprises two novel scenario-based mapping selection algorithms: Inter-Application Resource Mediation Mapping introduces barely any runtime overhead. Adaptive multi-app mapping selection is highly adaptive to changes in the application workload but imposes a small runtime overhead. Our HAM approach is fully automated and uses machine-learning techniques to learn the selection of suitable mappings from training data sequences at design time. Experiments on three differently complex target architectures show that our proposed approach consistently outperforms existing state-of-the-art solutions regarding the number of deadline misses and consumed energy.

On the Characteristics of Symbolic Execution in the Problem of Assessing the Quality of Obfuscating Transformations

On the Characteristics of Symbolic Execution in the Problem of Assessing the Quality of Obfuscating Transformations