Strict Resource Research Articles

Перспективы использования дериватов промысловых животных

Game animals are a source of biologically active substances that requires a strict resource and biodiversity control. The research objective was to analyze three years of supply, demand, export, and import for brown bear, deer, and musk deer by-products.
 The review featured Russian and foreign articles on game animals published in 2016–2020, as well as customs information on imports and exports for this raw material. The research methods included systematization, analysis, and description.
 The population of brown bears in the Russian Federation is constantly growing. In 2018, it increased by 4% compared to 2017. Bear derivatives are in high demand in China, Italy, France, etc. The total number of importers in 2019 exceeded 25. In 2017, deer products were the most popular game derivatives exported from Russia to 35 countries. The biggest importer is China. In 2018, the volume of exports of deer and musk deer derivatives maintained the same value, but the list of importers changed. In 2019, the export volume increased by 1.4 times, and the number of importing countries reached 50. Russia is the leading exporter and importer of raw materials and derivatives from brown bears, deer, and musk deer.
 The market for game derivatives is actively developing. An increase in the number of predatory game animals, e.g., the brown bear, may adversely affect the local ecosystems, which can be prevented by licensed hunting. The high content of biologically active substances makes it possible to use game raw materials for new functional products.

PROMPT: Learning dynamic resource allocation policies for network applications

A growing number of service providers are exploring methods to improve server utilization and reduce power consumption by co-scheduling high-priority latency-critical workloads with best-effort workloads. This practice requires strict resource allocation between workloads to reduce contention and maintain Quality-of-Service (QoS) guarantees. Prior work demonstrated promising opportunities to dynamically allocate resources based on workload demand, but may fail to meet QoS objectives in more stringent operating environments due to the presence of resource allocation cliffs, transient fluctuations in workload performance, and rapidly changing resource demand. We therefore propose PROMPT, a novel resource allocation framework using proactive QoS prediction to guide a reinforcement learning controller. PROMPT enables more precise resource optimization, more consistent handling of transient behaviors, and more robust generalization when co-scheduling new best-effort workloads not encountered during policy training. Evaluation shows that the proposed method incurs 4.2x fewer QoS violations, reduces severity of QoS violations by 12.7x, improves best-effort workload performance, and improves overall power efficiency over prior work.

Area-Optimized Constant-Time Hardware Implementation for Polynomial Multiplication

This work presents a lightweight, FPGA-based hardware implementation for polynomial multiplication, which is the major bottleneck in the NTRU public-key cryptographic scheme. NTRU is a quantum-resilient, lattice-based key exchange cryptosystem, and is currently a finalist in the ongoing National Institute of Standards and Technology post-quantum cryptography standardization. It is challenging to fit these quantum-resilient schemes into Internet of Things (IoT) sensor nodes due to the strict resource constraints (smaller area, less memory, and lower energy budgets) and the limited computational capabilities in embedded devices. We undertake this compact implementation for polynomial multiplication with two motivations: 1) constant-time implementation ensuring inherent security against timing side-channel attacks and 2) optimized hardware consumption to make it suitable for IoT applications. A single-step multiplexer-based iterative architecture is proposed to achieve both goals simultaneously. Compared to the architectures presented in the literature, our proposed work eliminates the utilization of a modular arithmetic unit and replaces it with the correct selection of input followed by an accumulator, which can help to save substantial device resources. Experimental results with an FPGA show that our proposed architecture achieves an area reduction of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.86 \times $ </tex-math></inline-formula> and the throughput increase up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.23 \times $ </tex-math></inline-formula> compared to the state-of-the-art implementation strategies, providing comparable latency along with an inherent-timing attack resilience that is absent in several NTRU hardware implementation schemes.

A study on green technology efficiency of China’s marine ship industry chain based on meta-frontier framework and three-stage EBM model

Under strict resource and environmental constraints, improving the overall green efficiency of the industrial chain is crucial to the sustainable development of the marine ship industry. Based on the data of 40 listed companies in the industry chain from 2015 to 2019, the meta-frontier framework and three-stage epsilon based measure (EBM) model are employed to study the green technical efficiency (GTE) in each link of the industry chain and the technical gap between each link. The impact of the external environment on GTE, the main reasons hindering the development of GTE, and the ways to improve GTE are also discussed for policy reference. The results show that: (1) under the meta-frontier, the GTEs of the whole industry chain and the composed links are all high and rising year by year. (2) There are obvious green technological development gaps among the links of the industry chain. The GTEs are ranked as the upstream&amp;gt;the downstream&amp;gt;the midstream. (3) The inefficiencies of green technology (GTEI) in the upstream and downstream of the industry chain come from endogenous hindrance, while the GTEI of the midstream is due to exogenous hindrance. (4) The external environment has a significant influence on the development of GTE in the industrial chain. Highly open and innovative external environment can effectively reduce the input redundancy. (5) After eliminating the external influencing factors and random interferences, the actual GTE of the industrial chain is only 0.4 or so, with more serious imbalance among the three links. Therefore, it is important for the marine ship industry chain to optimize the allocation of innovation resources and cultivate an open and shared innovation environment.

Balancing memory-accessing and computing over sparse DNN accelerator via efficient data packaging

Embedded devices are common carriers for deploying inference networks, which leverage the customized accelerator to achieve the promised performance with strict resource constraints. In the inference of Deep Neural Network(DNN), the sparsity existing in the activations and weights of every layer contributes massive non-effective memory accesses and computing operations. The data compression is adopted as a data pruning method for accelerator design, which eliminates the zero-valued data with a specific data packaging method. However, the data compression, in varying degrees, breaks the data regularity of the processing array DNN accelerators calculates with. The complexity of data access caused by irregular data organization will add extra control logic and decoding logic to compensate. The accelerator architecture that supports sparsity can use the sophisticated memory access scheming and parallel on-chip decoder structure via an efficient data packaging method to balance memory-accessing and computing for acceleration. In this paper, we propose a flexible and highly parallel accelerator architecture that uses a quantitative data packaging method which is efficient and stable for different degrees of sparsity and parallel optimization to explore the sparsity in DNN to achieve high performance with low energy consumption. The total DRAM accesses, performance and energy consumption of the proposed sparse architecture are evaluated with different inference networks. Experiments show that the DRAM accesses of the proposed efficient data packaging method is significantly lower than other commonly used sparse data compression storage methods, the improved performance and saved energy of the sparse accelerator architecture after adopting the optimization method proposed in this paper are up to 1.2x and 1.6x, respectively, over a comparably provisioned do not support sparsity accelerator. In addition, the accelerator architecture proposed has achieved energy efficiency and performance improvements of up to 1.70x and 1.56x, compared with the state-of-the-art architectures.

Defending Against Stealthy Attacks on Multiple Nodes With Limited Resources: A Game-Theoretic Analysis

Stealthy attacks have become a major threat to cybersecurity. Previous works in this direction fail to capture the practical resource constraints and mainly focus on one-node settings. In this article, we propose a two-player game-theoretic model, including a system of multiple independent nodes, a stealthy attacker, and an observable defender. In our model, the attacker can fully observe the defender's behavior and the system state, whereas the defender has zero feedback information. Furthermore, a strict resource constraint is introduced to limit the frequency of the attacks/defenses for both players. We characterize the best responses for both attacker and defender under both nonadaptive and adaptive strategies. We then study the sequential game where the defender first announces its strategy and the attacker then responds accordingly. We have designed an algorithm that finds a nearly optimal strategy for the defender and provides a full analysis of its complexity and performance guarantee.

A ZigBee Intrusion Detection System for IoT using Secure and Efficient Data Collection

The market for Internet of Things (IoT) products and services has grown rapidly. It has been predicted that the deployment of these IoT applications will grow exponentially in the near future. However, the rapid growth of the IoT brings new security risks and potentially opens systems and networks to new attacks. This paper outlines various techniques to detect known attacks and new types of attacks particularly on ZigBee-based IoT systems. We introduce a novel hybrid Intrusion Detection System (IDS) by merging rule-based intrusion detection and machine learning-based anomaly detection. The rule-based attack detection technique is used to provide an accurate detection method for known attacks. However, specifying accurate and precise detection rules require significant human effort. It is tedious and error prone and may lead to false alarms if done incorrectly. Hence, to mitigate this potential problem, the system is enhanced by combining it with machine learning-based anomaly detection. This paper discusses our IDS implementation that covers various types of detection techniques both to detect known attacks, as well as potential new types of attack in ZigBee-based IoT systems. Furthermore, this paper introduces a secure and efficient method for large-scale IDS data collection. Thus, it provides a trusted reporting mechanism that can operate under the strict resource requirements imposed by current IoT systems.

Predator avoidance leads to separate emergence cycles in the protoperiodical Okanagana magnifica Davis, 1919 (Hemiptera: Cicadidae)

Predator avoidance is often a key factor in the behavior of North American cicadas. In the protoperiodical genus OkanaganaDistant, 1905, several species synchronize emergences based on a cumulative rainfall emergence threshold, avoiding predators via a selfish herd. One of these species, O. magnificaDavis, 1919, extends its range beyond that of its synchronizing cohorts. The hypothesis is tested that outside of sympatry with these species, O. magnifica would lose a rainfall-based emergence pattern. Using multiple databases, locations of consecutive emergences of O. magnifica are identified. The cumulative rainfall between emergences is calculated and compared to a known emergence threshold of 1181 millimeters. The results show that non-sympatric populations have a protoperiodical lifecycle that is independent of rainfall yet does not follow a strict resource accumulation phenology. These two strategies form allochronically isolated populations with a boundary reinforced by the presence of co-synchronizing species.

GMAI

Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by their nature closed source, forcing system engineers to consider them as black boxes, complicating resource provisioning. In this work, we reverse engineer the internal operations of the GPU system software to increase the understanding of their observed behaviour and how resources are internally managed. We present our methodology that is incorporated in GMAI (GPU Memory Allocation Inspector), a tool that allows system engineers to accurately determine the exact amount of resources required by their critical systems, avoiding underprovisioning. We first apply our methodology on a wide range of GPU hardware from different vendors showing its generality in obtaining the properties of the GPU memory allocators. Next, we demonstrate the benefits of such knowledge in resource provisioning of two case studies from the automotive domain, where the actual memory consumption is up to 5.6× more than the memory requested by the application.

Abstract No. 637 Implementation of American College of Surgeons Committee on Trauma guidelines for interventional radiology: a retrospective review of prospective data compared with historic cohort

The American College of Surgeons Committee on Trauma (ACS-COT) recommends that an interventional radiologist (IR) be available within 30 minutes to perform procedures in all designated Level 1 and Level 2 trauma centers but does not require an in-house IR service. Several studies have observed that delays in IR response times are linked to poor patient outcomes. The purpose of this study was to evaluate the overall feasability and patient outcomes associated with a prospective continuous quality improvement (QI) process that was enacted to meet the ACS-COT criterion for angioembolization. A QI process was established at our institution to increase compliance with the ACS-COT criterion by establishing clearly defined indications for IR trauma activation. In addition, a simplified, reproducible and measurable workflow for electronic recording of response times was adopted. We reviewed 110 acute trauma patients for whom Level 1 activation for angioembolization was initiated between April 2015 and March 2019. The prospective cohort (n = 58) was collected after QI changes were enacted in April 2017. The prospective and retrospective cohorts were comparable with no statistical difference in demographic data, ISS scores and anatomical location of bleeding (pelvic vs. non-pelvic vascular). Median IR response time in the prospective group was 28.5 minutes (IQR 15,41), and 40/58 (69%) were compliant with the ACS-COT criteria of being available to perform procedure within 30 minutes. Median activation to procedure start time was 72 minutes in the prospective group and 183 minutes in the retrospective (P <0.001). In the prospective group, 40/58 patients (69%) underwent an embolization procedure, while in the retrospective group, 23/52 (44.2%) underwent an embolization procedure (P = 0.007). No significant difference was observed in the mortality rates or the hospital length of stay between the two groups. It is possible to meet the ACS-COT criteria for IR responses by establishing strict resource utilization parameters.

DVFS and Its Architectural Simulation Models for Improving Energy Efficiency of Complex Embedded Systems in Early Design Phase

Dealing with resource constraints is an inevitable feature of embedded systems. Power and performance are the main concerns beside others. Pre-silicon analysis of power and performance in today’s complex embedded designs is a big challenge. Although RTL (Register-Transfer Level) models are more precise and reliable, system-level modeling enables the power and performance analysis of complex and dense designs in the early design phase. Virtual prototypes of systems prepared through architectural simulation provide a means of evaluating non-existing systems with more flexibility and minimum cost. Efficient interplay between power and performance is a key feature within virtual platforms. This article focuses on dynamic voltage and frequency scaling (DVFS), which is a well-known system-level low-power design technique together with its more efficient implementations modeled through architectural simulation. With advent of new computing paradigms and modern application domains with strict resource demands, DVFS and its efficient hardware-managed solutions get even more highlighted. This is mainly because they can react faster to resource demands and thus reduce induced overhead. To that end, they entail an effective collaboration between software and hardware. A case review in the end wraps up the discussed topics.

Simulated annealing and genetic algorithm-based hybrid approach for energy-aware clustered routing in large-range multi-sink wireless sensor networks

In recent years, the researchers have got progressively more interest in wireless sensor networks (WSNs) as it plays a vital role in numerous applications. WSNs have strict resource constraints such as limited bandwidth, limited energy, limited processing capability, and limited memory. Among them, the most critical one is limited energy. So, energy efficiency has got significant importance in the WSNs. Energy consumption is mainly due to computation and communication between sensor nodes and the base station. Energy consumption for communication is more when compared to computation. Hence it is indispensable to focus on designing an energy-efficient routing for WSN. In this paper, a cluster-based routing using simulated annealing and genetic algorithm-based hybrid (SAGA-H) approach has been proposed. The proposed approach is explained and simulated using MATLAB. Further, the results observed have been compared with existing GA-based approach with respect to network lifetime, the number of packets sent to BS and sink, and average residual energy. From the simulation results, it is observed that the proposed approach outperforms the existing GA-based approach.

Decentralized gene regulatory networks: An approach to energy efficient node coordination with delay constraints for wireless sensor systems

Wireless sensor networks (WSNs) systems have been used for several applications from target tracking to environment monitoring. Recently, there has been increased interest in the design of WSN protocols for delay-sensitive applications, such as military surveillance, health monitoring, and infrastructure security. However, due to the strict resource constraints of the sensor nodes, WSNs pose critical challenges for network protocol design for reliable communications with delay constraints. Biologically inspired modeling techniques have received considerable attention for their robustness, scalability, and adaptability with simple local interactions and limited information. In this paper, we apply gene regulatory networks (GRNs) principles to the WSN system and propose a new GRN model for decentralized node coordination to achieve energy saving while meeting delay requirements. Using control theory, we provide conditions of system parameters to ensure system convergence and stability. Simulation results indicate that the proposed scheme achieves superior performance with energy savings as well as desirable delay guarantees compared with other wellknown schemes.

Reducing ED visits by “closing the loop” for symptomatic patients.

100 Background: Improving the value provided to patients and payers in our system of cancer care relies on reduction of avoidable hospital and emergency department (ED) utilization. Furthermore, recent payer pressures on community oncology practices (COPs) have resulted in an increased focus on improving care coordination (CC) under strict resource constraints. As part of the ASCO Quality Training Program, we tested several low-cost CC interventions, leveraging workflow redesign, already employed care team members, and technology already implemented, to reduce ED visits (EDV) in a single large COP clinic where more than 3,200 cancer patients were treated during 2016. Methods: Baseline EDV rates were obtained through nurse chart review during Jan.-Jun. 2016. The following CC interventions were implemented: Initiated after hours call process with access to EMR and patient access to bidirectional real-time messaging with care team members; Implemented new in-office process to “close the loop” on patient evaluations by creating follow up guidelines for symptomatic telephone triage and in-clinic patient evaluations; Implemented a standard 48 hour follow up process for all EDV and hospital admissions; Increased patient awareness of telephone triage services during and after clinic hours by: augmenting new patient education by staff, developing a magnetic reminder to call the office for non-emergent and emergency situations, and instructions for use of afterhours call system. Nurse chart reviews were conducted throughout implementation to observe effects of new CC processes on EDV. Results: We observed a 30% reduction in EDV from baseline measurement. No new FTEs added and no new technology licenses acquired for this initiative. Conclusions: Low-cost CC interventions can be implemented in COPs to avoid ED utilization. Limitations of this analysis included manual chart abstraction that could not account for EDV outside the partnering health system, illustrating data access for hospital utilization remains a major challenge for quality improvement efforts for COPs. Additional challenges have been experienced in expanding these process improvements from a single large clinic to the broader Tennessee Oncology network of more than 30 clinics.

Low-Cost and Secure Firmware Obfuscation Method for Protecting Electronic Systems From Cloning

The continuous growth of the cloning of electronic devices poses a severe threat to our critical infrastructure that uses the Internet, as cloned devices can transmit secret information and cause security concerns. Cloned devices can also be unreliable as they may be manufactured with inferior quality materials, and they may have many defects as they may not be tested properly. It is thus extremely important to protect these electronic devices from cloning. An efficient way to prevent a device being cloned is to prevent the firmware from being copied because, without the proper firmware, the device will not function like the original. In this paper, we present a novel firmware obfuscation method without encrypting the entire memory. The firmware is obfuscated by swapping a subset of instructions. The instructions to be swapped are specifically chosen so that an attacker cannot discover their location. During operation, the hardware reconstructs the original program using a physically unclonable function-generated identifier and a small memory that stores the swapped instructions. An adversary cannot make a program work completely without knowing which instructions have been swapped, as the program will execute in the wrong sequence and produce the incorrect result. Our proposed solution requires only a small overhead to reconstruct the firmware, making it practical for devices with strict resource constraints. This solution also allows remote updates of new obfuscated firmware without any modification and is practical for the rising trend of ubiquitous computing.

Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks

Recently, mobility support has become an important requirement in various Wireless Sensor Networks (WSNs). Low-power and Lossy Networks (LLNs) are a special type of WSNs that tolerate a certain degree of packet loss. However, due to the strict resource constraints in the computation, energy, and memory of LLNs, most routing protocols only support static network topologies. Data collection and data dissemination are two basic traffic modes in LLNs. Unlike data collection, data dissemination is less investigated in LLNs. There are two sorts of data-dissemination methods: point-to-multipoint and point-to-point. In this paper, we focus on the point-to-point method, which requires the source node to build routes to reach the destination node. We propose an adaptive routing protocol that integrates together point-to-point traffic and data-collection traffic, and supports highly mobile scenarios. This protocol quickly reacts to the movement of nodes to make faster decisions for the next-hop selection in data collection and dynamically build routes for point-to-point traffic. Results obtained through simulation show that our work outperforms two generic ad hoc routing protocols AODV and flooding on different performance metrics. Results also show the efficiency of our work in highly mobile scenarios with multiple traffic patterns.

Mobility support enhancement for RPL with multiple sinks

In recent years, low power and lossy networks (LLNs) have attracted the attention of researchers. They typically operate with strict resource constraints in communication, computation, memory, and energy. Routing protocol for LLNs (RPL) is an ad-hoc distance vector routing protocol for LLNs that adapts IPv6 and runs on top of IEEE 802.15.4 standard. Although RPL was originally designed for static topologies, for many applications mobility support has been a major requirement in LLNs. In addition, part of these applications requires multiple sink nodes rather than one sink node. This is due to the fact that in a data collection wireless sensor network (WSN), only one sink easily leads to a faster energy depletion, more packet loss, higher latency, and smaller network range. Deploying multiple sinks in the network can help solve these problems. This paper focuses on addressing mobility support in RPL with multiple sinks. We propose an enhancement based on signal strength monitoring and rank updating in order to improve the network performance in mobility scenarios where all nodes except sink nodes are moving. This enhancement helps RPL to better cope with mobility scenarios and to make faster decisions on updating next-hop neighbors. Moreover, we propose a dynamic management of control messages in order to reduce the overhead in the network. Simulation results show that our technique outperforms the standard RPL in different network configurations.

A Distributed Hierarchical Heavy Hitter Detection Method in Software-Defined Networking

Software-defined networking (SDN) enables fast innovation in networks. The management and measurement features that we require can be easily implemented in SDN. However, when simplicity is introduced, SDN also faces some problems, of which the most challenging one is the scalability problem. As the foundation of the networking management, scalability of the traffic measurement is also important. Recently, many works have focused on TCAM-based measurement, which is considered to be scalable and efficient enough. In this paper, we propose a distributed hierarchical heavy hitter (HHH) detection method, which is also a TCAM-based method. Unlike previous works, this method focuses on optimizing the detection speed by dynamically controlling the resource allocated to each measurement task. We propose an efficient solution to solve the optimization problem. The simulation with network-wide tasks and diverse traffic has shown that this method can improve the detection speed when compared with other resource allocation methods, and it can work better under strict resource limitation.

New multi-objective methods to solve resource-constraint multi-project scheduling problems in integrated ERP-APS systems

This paper presents a new scheduling model and methods to solve multi-project scheduling problems considering strict resource constraints and multiple objective functions simultaneously. The solution approach is based on an integration of enterprise resource planning system (ERP) and an advanced planning and scheduling application (APS). An ERP system software component defines a set of projects with their details (activities, due dates, processing times, precedence constraints, etc.) and the available resources with their capabilities and real constraints. The user can specify many objective functions and their priorities. The goal of this scheduling problem type is to create a detailed execution plan that meets all the hard constraints and gives a near optimal compromised solution from objective functions point of view. In real execution environments, more than one projects may run in parallel, and they may require the same resources. For example, projects may produce the standard type of products, and they use the same manufacturing resources. In different decision-making situations, dynamic priorities may be defined for projects or activities to express the relative importance of items. The paper focuses on modeling and solving of multi-objective multi-project scheduling problems, and we present the integration of ERP and APS systems.

Taming the IoT data deluge: An innovative information-centric service model for fog computing applications

Fog Computing is a new computation paradigm, recently emerged from the convergence of IoT, WSN, mobile computing, edge computing, and Cloud Computing, which is particularly well suited for Smart City environments. Fog Computing aims at supporting the development of time-sensitive, location-, social-, and context-aware applications by using computational resources in close proximity of information producers and consumers, such as increasingly common cheap and powerful modern hardware platforms. However, realizing Fog Computing solutions for Smart Cities represents a very challenging task, because of the massive amount of data to process, the strict resource and time constraints, and the significant dynamicity and heterogeneity of computation and network resources. These formidable challenges suggest taking into consideration new information and service model solutions that explore several trade-offs between processing speed and accuracy. Along these guidelines, we designed the SPF Fog-as-a-Service platform, which proposes a new information-centric and utility-based service model and allows the definition of self-adaptive and composition-friendly services, which can execute either on edge devices or in the Cloud. In numerous evaluations, SPF proved to be a very effective platform for running Fog services on heterogeneous devices with significantly different computational capabilities while also demonstrating remarkable ease of development and management characteristics.