This study aims to identify the moderating construct of trust in the performance of agricultural cooperatives in North-Western Nigeria. Business management cannot be overseen by the owner alone; it may require the services of other professionals. However, the high incidence of corrupt practices in the public and private sectors in developing African countries cripples many businesses. It makes it difficult to entrust the management of organisations to a third party. Trust is essential, especially in an environment with a loose execution of legal charges. Although a direct relationship between corporate governance and performance has been established across many disciplines, the influence of trust as an interactive construct has yet to be established. Therefore, this study addresses this gap. This study used concurrent triangulation design with a significant quantitative approach complemented by the qualitative segment involving seven open-ended questions. Data were collected from 384 cooperative for rice farmer’s by used of a survey design. Structural Equation Modelling was used to assess the measurement model to test the hypotheses. An Excel spreadsheet was used to pre-code the data derived from open-ended questions, and later exported to ATLAS.ti software for qualitative analysis through coding, group coding and network. The findings revealed that corporate governance and trust significantly influenced agricultural cooperative performance. The moderating effect of trust on corporate governance was supported. The findings illustrate how social capital theory explains the processes of African trust, especially in corrupt environments with weak legal penalties. This study examines corporate governance within the internal control mechanisms of an agricultural cooperative society. Further studies should understand corporate governance within an external tie. To our knowledge this is the first study to examine the moderating effect of trust on the interacting variables in the African social capital theory model.

The importance of the internet across the globe cannot be over-emphasized as such network security is essential to curb future attack occurrences. Cyber-attacks like DDoS and Ransomware yielded a lot of damage to connected devices by endangering and accessing them, notwithstanding these damages are air marked to be on the rise. To overcome these issues, machine learning has been used in different computing aspects such as cyber–Intrusion Detection. Recently, deep learning, extreme learning, and deep extreme learning networks have superseded machine learning in this context due to their iterative hidden layers that can manipulate complex features of cyber intrusion data. Hence, this research surveys the application of data-driven intelligent algorithms for cyber security attack detection in comparison to conventional machine learning techniques. The review focuses on the performance evaluation of several state-of-the-art intelligent algorithms and provides research gaps and future 2trends in the context of Data Security Attacks and Cyber Intrusion Detection.

The speedy growth in urbanization has resulted to considerable growth in electricity consumption with about 40% consumed by building sectors. To reduce the gap for future demands, there is need for efficient load forecasting using novel paradigms. Attention-LSTM focuses on input features that are crucial to a prediction model and have theoretically proven to be better prediction approach with better accuracy. This strategy can extract the critical feature sequences efficiently and lower the impact of the unused feature sequences that may results from the attention weight. Additionally. Occupancy behavior have huge impact to energy consumption in buildings. However, most existing study ignores the occupancy profile uncertainty which can led to a substantial performance gap between the actual and predicted energy. Therefore, this research work focuses on improving the prediction performance using attention base LSTM transfer learning mechanism on a novel Jos electricity distribution company (JEDC) dataset. Upon evaluation with different baseline models, the proposed model outperforms the baseline models with an average MAPE of 0.0116 and CV-RMSE of 0.0113. These results opine that transfer learning base on Attention-LSTM method can efficiently select the critical predictive features and can helps track uncertainties to building electricity consumption with accuracy improvement to some extent.

In this study, a multispecies gadget model (GadCap) simulating the interactions among the Flemish Cap cod (Gadus morhua), redfish (Sebastes spp.), and shrimp (Pandalus borealis) has been incorporated as the operating model in a management strategy evaluation (MSE) framework (a4a-FLR) to test the performance of multiple combinations of harvest control rules (HCRs) for the three stocks when recruitment uncertainty and assessment error are accounted for. The results indicate that due to the strong trophic interactions, it is not possible to achieve the precautionary exploitation of all the stocks at the same time. Maintaining shrimp biomass above the limit reference point (B lim ) would require unsustainable fishing pressure on cod and redfish to reduce predation mortality. In contrast, maintaining cod biomass above B lim would involve high predation on and high risk of collapse of the shrimp and redfish stocks. The implementation of alternative two-stage HCRs would reduce predation, resulting in higher productivity and lower probability of collapse for cod and redfish. The results of this study support the need of accounting for species interactions when designing management strategies for a group of interdependent commercial stocks.

PurposeAgricultural accounting is gaining ground across different disciplines, rendering it a significant research area. This study aims to assess agricultural accounting research for the past 93 years in terms of publication frequency, subject areas, topics that received the most attention among researchers, as well as the institutions that contribute to this subject area.Design/methodology/approachThis study employs a bibliometric analysis collected through the Scopus database. The sample included 3,612 documents. The analyzed variables include the number of publications per year, documents published, country, author affiliation, keywords and active institutions. Analyses include graphical network maps.FindingsThe findings of this study reveal the importance of supportive institutions, human capabilities and international collaboration in aiding research and development. It provides an overview of agricultural accounting literature over the years and aid researchers in this research domain to explore more studies and develop better arguments. The results also indicate the continuing growth in the number of publications in recent years by authorship; country include the USA, China, the UK, Australia and Germany; institutes include Chinese Academy of Sciences, Wageningen University and Research Centre; and the subject areas include Environmental Science; Agriculture and Biology sciences; and Social Sciences. The most frequent keywords connecting to author’s area of research, as highlighted in Figure 5, include agriculture, accounting, water accounting, environmental accounting and cost analysis.Research limitations/implicationsThe study is based on the Scopus database, which has limited coverage. The keywords of the literature search were restricted to “agriculture and accounting” or “agricultural and accounting” and the research approach limited to quantitative perspective.Practical implicationsThe findings may benefit policymakers as well as academicians toward understanding the areas of interest in agricultural accounting.Originality/valueThis study provides the potential areas within agricultural accounting literature in a broader scope that deserve multiple accounting practices to cover diverse agricultural activities such as cost accounting, financial reporting, managerial accounting, auditing, taxation and financial information systems. The study suggests developing countries promote innovative research on agricultural practice to meet global scientific and technological developments.

Recently, human activity recognition (HAR) and classification have driven interest in the industry and academic studies due to a huge application in the area. However, monitoring of activity from video and images is challenging due to the nature of video data, different weather conditions, the uncertain movement of human being, and availability of suitable datasets. There is very few research on deep learning-based activity recognition as the area is in its early stages. However, existing study was able to detect single individual known activities but unable to detect unknown and complex activities. This can be extended from single person to multi-person classification. Additionally, in the existing 1Dimentional Convolutional Neural Network (1D CNN), the core goes in a single way, thus, the input and output data of 1D CNN is 2-dimensional. Therefore, this technique is more suitable for time series data than images or videos. However, optical flow is a best way to abstract and define shadows for an active video background. Hence, this research further explores activity recognition using multiple activities frames, optical flow, I3D CNN, LSTM and SoftMax Classifier. In this work, LSTM is employed to aid in detecting information based on HAR recognition. The i3D-CNN model, optical flow approach and LSTM algorithm are employed in the feature extraction step. Experimental results on MATLAB 2021a shows that, improving the number of hidden layers more than the number of layers can decrease the accuracy in the test set, thereby making the network to fit to the training set. It was further observed that the proposed model achieved best and stable recognition accuracy when the configuration settings were 10 LSTM layers and 256 hidden units. In this case the proposed model achieved the best-case scenario for the recognition accuracy with 97.8% for draw sword, 94.8% for shoot bow, 9.81% for shoot gun, 94.85% for Hit and 95.9% for punch respectively. On comparison with other base line classifiers, the SoftMax classifier attained the best recognition accuracy of 97.8% as against the SVM with 94.35% and KNN with 92.91%.

Biased estimates of population status are a pervasive conservation problem. This problem has plagued assessments of commercial exploitation of marine species and can threaten the sustainability of both populations and fisheries. We develop a computer-intensive approach to minimize adverse effects of persistent estimation bias in assessments by optimizing operational harvest measures (harvest control rules) with closed-loop simulation of resource-management feedback systems: management strategy evaluation. Using saithe (Pollachius virens), a bottom water, apex predator in the North Sea, as a real-world case study, we illustrate the approach by first diagnosing robustness of the existing harvest control rule and then optimizing it through propagation of biases (overestimated stock abundance and underestimated fishing pressure) along with select process and observation uncertainties. Analyses showed that severe biases lead to overly optimistic catch limits and then progressively magnify the amplitude of catch fluctuation, thereby posing unacceptably high overharvest risks. Consistent performance of management strategies to conserve the resource can be achieved by developing more robust control rules. These rules explicitly account for estimation bias through a computational grid search for a set of control parameters (threshold abundance that triggers management action, B-trigger, and target exploitation rate, F-target) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters-for saithe, raising B-trigger and lowering F-target-would safeguard against a overharvest risk (<3.5% probability of stock depletion) and provide short-term stability in catch limit (<20% year-to-year variation), thereby minimizing disruption to fishing communities. The precautionary approach to fine-tuning adaptive risk management through management strategy evaluation offers a powerful tool to better shape sustainable harvest boundaries for exploited resource populations when estimation bias persists. By explicitly accounting for emergent sources of uncertainty, our proposed approach ensures effective conservation and sustainable exploitation of living marine resources even under profound uncertainty.

As cloud resource demand grows, supply chain management (SCM), which is the core function of cloud computing, faces serious challenges. Quite a number of techniques have been proposed by many researchers for such a challenge. As such, numerous proposed strategies are still under reckoning and modification so as to enhance its potential. An optimized dynamic scheme that combined several algorithms' characteristics was proposed to map out such a challenge. The hybridized proposed scheme involved the meta-heuristic swarm mechanism of ant colony optimization (ACO) and deterministic spanning tree (SPT) algorithm as it obtained faster convergence chain, ensured resource utilization in least time and cost. Extensive experiments conducted in cloudsim simulator provided an efficient result in terms of minimized makespan time and throughput as compared to SPT, round robin (RR), and pre-emptive fair scheduling algorithm (PFSA) as it significantly improves performance.

Erik Olsen (HI), Sondre Aanes Norwegian Computing Center, Magne Aldrin Norwegian Computing Center, Olav Nikolai Breivik Norwegian Computing Center, Edvin Fuglebakk, Daisuke Goto, Nils Olav Handegard, Cecilie Hansen, Arne Johannes Holmin, Daniel Howell, Espen Johnsen, Natoya Jourdain, Knut Korsbrekke, Ono Kotaro, Håkon Otterå, Holly Ann Perryman, Samuel Subbey, Guldborg Søvik, Ibrahim Umar, Sindre Vatnehol og Jon Helge Vølstad (HI)

A handover decision algorithm in a hybrid Light Fidelity (Li-Fi) and Wireless Fidelity (Wi-Fi) network is investigated in this paper. Li-Fi, a wireless network uses visible light spectrum to provide high-speed indoor data transmission along with illumination. As there is no interference between optical and Radio Frequency (RF) spectrum operating devices, a hybrid Li-Fi/Wi-Fi network (HLWNet) can be explored in order to improve the user Quality of Service (QoS). However, in a HLWNet system setup, user mobility may often prompt frequent handover which as a result, degrades the system throughput. In this paper, we proposed a Fuzzy Logic (FL) and fuzzy rule-based Artificial Neural Network (ANN) handover decision algorithms. The FL based handover algorithm uses input parameters namely the instantaneous Signal to Interference Noise Ratio (SINR), Received Signal Strength (RSS), average SINR and user velocity to decide whether handover needs to be prompted. However, because of the increase in the number of the input parameters which, in turn, increases the number of fuzzy rules, the computational complexity greatly affects the FL system. Thus, it is envisioned that using the learning power of ANN, limited fuzzy rules are generated, which it can be made to learn from the limited rules and be able generalize to make handover decision. Based on the accuracy test conducted, the FL based handover decision algorithm is 1.66 times more accurate than the ANN fuzzy rule-based handover decision algorithm in terms of successfully assigning access points (AP) to users.

Concurrent search trees are crucial data abstractions widely used in many important systems such as databases, file systems and data storage. Like other fundamental abstractions for energy-efficient computing, concurrent search trees should support both high concurrency and fine-grained data locality in a platform-independent manner. However, existing portable fine-grained locality-aware search trees such as ones based on the van Emde Boas layout (vEB-based trees) poorly support concurrent update operations while existing highly-concurrent search trees such as non-blocking search trees do not consider fine-grained data locality. In this paper, we first present a novel methodology to achieve both portable fine-grained data locality and high concurrency for search trees. Based on the methodology, we devise a novel locality-aware concurrent search tree called GreenBST. To the best of our knowledge, GreenBST is the first practical search tree that achieves both portable fine-grained data locality and high concurrency. We analyze and compare GreenBST energy efficiency (in operations/Joule) and performance (in operations/second) with seven prominent concurrent search trees on a high performance computing (HPC) platform (Intel Xeon), an embedded platform (ARM), and an accelerator platform (Intel Xeon Phi) using parallel micro-benchmarks (Synchrobench). Our experimental results show that GreenBST achieves the best energy efficiency and performance on all the different platforms. GreenBST achieves up to 50 percent more energy efficiency and 60 percent higher throughput than the best competitor in the parallel benchmarks. These results confirm the viability of our new methodology to achieve both portable fine-grained data locality and high concurrency for search trees.

This deliverable reports the results of the power models, energy models and libraries for energy-efficient concurrent data structures and algorithms as available by project month 30 of Work Package 2 (WP2). It reports i) the latest results of Task 2.2-2.4 on providing programming abstractions and libraries for developing energy-efficient data structures and algorithms and ii) the improved results of Task 2.1 on investigating and modeling the trade-off between energy and performance of concurrent data structures and algorithms. The work has been conducted on two main EXCESS platforms: Intel platforms with recent Intel multicore CPUs and Movidius Myriad platforms.

This deliverable reports our early energy models for data structures and algorithms based on both micro-benchmarks and concurrent algorithms. It reports the early results of Task 2.1 on investigating and modeling the trade-off between energy and performance in concurrent data structures and algorithms, which forms the basis for the whole work package 2 (WP2). The work has been conducted on the two main EXCESS platforms: (1) Intel platform with recent Intel multi-core CPUs and (2) Movidius embedded platform.

The advent of exascale computing, with the unparalleled rise in the scale of data in Internet of Things (IoT), high performance computing (HPC), and big data domains, both at the center and the edge of the system, requires optimal exploitation of energy-efficient computing hardware dedicated for edge processing. Emerging hardware for data processing at the edge must take advantage of advanced concurrent data locality-aware algorithms and data structures in order to provide better throughput and energy efficiency. Their design must be performance portable for their implementation to perform equally well on the edge hardware as well as other high performance computing, embedded and accelerator platforms. Concurrent search trees are one such widely used back-end for many important big data systems, databases, and file systems. We analyze DeltaTree, a concurrent energy-efficient and locality-aware data structure based on relaxed cache-oblivious model and van Emde Boas trees, on Intel's specialized computing platform Movidius Myriad 2, designed for machine vision and computing capabilities at the edge. We compare the throughput and energy efficiency of DeltaTree with B-link tree, a highly concurrent B+tree, on Movidius Myriad 2, along with a high performance computing platform (Intel Xeon), an ARM embedded platform, and an accelerator platform (Intel Xeon Phi). The results show that DeltaTree is performance portable, providing better energy-efficiency and throughput than B-link tree on these platforms for most workloads. For Movidius Myriad 2 in particular, DeltaTree performs really well with its throughput and efficiency up to 4× better than B-link tree.

This deliverable reports the results of white-box methodologies and early results of the first prototype of libraries and programming abstractions as available by project month 18 by Work Package 2 (WP2). It reports i) the latest results of Task 2.2 on white-box methodologies, programming abstractions and libraries for developing energy-efficient data structures and algorithms and ii) the improved results of Task 2.1 on investigating and modeling the trade-off between energy and performance of concurrent data structures and algorithms. The work has been conducted on two main EXCESS platforms: Intel platforms with recent Intel multicore CPUs and Movidius Myriad1 platform. Regarding white-box methodologies, we have devised new relaxed cache-oblivious models and proposed a new power model for Myriad1 platform and an energy model for lock-free queues on CPU platforms. For Myriad1 platform, the im- proved model now considers both computation and data movement cost as well as architecture and application properties. The model has been evaluated with a set of micro-benchmarks and application benchmarks. For Intel platforms, we have generalized the model for concurrent queues on CPU platforms to offer more flexibility according to the workers calling the data structure (parallel section sizes of enqueuers and dequeuers are decoupled). Regarding programming abstractions and libraries, we have continued investigat- ing the trade-offs between energy consumption and performance of data structures such as concurrent queues and concurrent search trees based on the early results of Task 2.1.The preliminary results show that our concurrent trees are faster and more energy efficient than the state-of-the-art on commodity HPC and embedded platforms.

Work package 2 (WP2) aims to develop libraries for energy-efficient inter-process communication and data sharing on the EXCESS platforms. The Deliverable D2.4 reports on the final prototype of programming abstractions for energy-efficient inter- process communication. Section 1 is the updated overview of the prototype of programming abstraction and devised power/energy models. The Section 2-6 contain the latest results of the four studies: i) GreenBST, a energy-efficient and concurrent search tree (cf. Section 2) ii) Customization methodology for implementation of streaming aggregation in embedded systems (cf. Section 3) iii) Energy Model on CPU for Lock-free Data-structures in Dynamic Environments (cf. Section 4.10) iv) A General and Validated Energy Complexity Model for Multithreaded Algorithms (cf. Section 5)

Like other fundamental abstractions for energy-efficient computing, search trees need to support both high concurrency and fine-grained data locality. However, existing locality-aware search trees such as ones based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations while existing highly-concurrent search trees such as the non-blocking binary search trees do not consider data locality.We present GreenBST, a practical energy-efficient concurrent search tree that supports fine-grained data locality as vEB-based trees do, but unlike vEB-based trees, GreenBST supports high concurrency. GreenBST is a k-ary leaf-oriented tree of GNodes where each GNode is a fixed size tree-container with the van Emde Boas layout. As a result, GreenBST minimizes data transfer between memory levels while supporting highly concurrent (update) operations. Our experimental evaluation using the recent implementation of non-blocking binary search trees, highly concurrent B-trees, conventional vEB trees, as well as the portably scalable concurrent trees shows that GreenBST is efficient: its energy efficiency (in operations/Joule) and throughput (in operations/second) are up to 65% and 69% higher, respectively, than the other trees on a high performance computing (HPC) platform (Intel Xeon), an embedded platform (ARM), and an accelerator platform (Intel Xeon Phi). The results also provide insights into how to develop energy-efficient data structures in general.

Recent research has suggested that improving fine-grained data-locality is one of the main approaches to improving energy efficiency and performance. However, no previous research has investigated the effect of the approach on these metrices in the case of concurrent data structures. This paper investigates how fine-grained data locality influences energy efficiency and performance in concurrent search trees, a crucial data structure that is widely used in several important systems. We conduct a set of experiments on three lock-based concurrent search trees: DeltaTree, a portable fine-grained locality-aware concurrent search tree; CBTree, a coarse-grained locality-aware B+tree; and BST-TK, a locality-oblivious concurrent search tree. We run the experiments on a commodity x86 platform and an embedded ARM platform. The experimental results show that DeltaTree has 13--25% better energy efficiency and 10--22% more operations/second on the x86 and ARM platforms, respectively. The results confirm that portable fine-grained locality can improve energy efficiency and performance in concurrent search trees.

Like other fundamental abstractions for high-performance computing, search trees need to support both high concurrency and data locality. However, existing locality-aware search trees based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations. We present DeltaTree, a practical locality-aware concurrent search tree that integrates both locality-optimization techniques from vEB-based trees, and concurrency optimization techniques from highly-concurrent search trees. As a result, DeltaTree minimizes data transfer from memory to CPU and supports high concurrency. Our experimental evaluation shows that DeltaTree is up to 50% faster than highly concurrent B-trees on a commodity Intel high performance computing (HPC) platform and up to 65% faster on a commodity ARM embedded platform.