A Sybil attack occurs when an adversary controls multiple system identifiers (IDs). Limiting the number of Sybil (bad) IDs to a minority is critical for tolerating malicious behavior. A popular tool for enforcing a ba...
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A Sybil attack occurs when an adversary controls multiple system identifiers (IDs). Limiting the number of Sybil (bad) IDs to a minority is critical for tolerating malicious behavior. A popular tool for enforcing a bad minority is resource burning (RB): the verifiable consumption of a network resource. Unfortunately, typical RB defenses require non-Sybil (good) IDs to consume at least as many resources as the adversary. We present a new defense, ERGO, that guarantees (1) there is always a bad minority;and (2) during a significant attack, the good IDs consume asymptotically less resources than the bad. Specifically, despite high churn, the good-ID RB rate is O (root T J + J), where T is the adversary's RB rate, and J is the good-ID join rate. We show this RB rate is asymptotically optimal for a large class of algorithms, and we empirically demonstrate the benefits of ERGO. (c) 2023 Elsevier Inc. All rights reserved.
Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because it promises to bring vast benefits for consumers and businesses, with consi...
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Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because it promises to bring vast benefits for consumers and businesses, with considerable benefits promised in productivity growth and innovation. To date, significant accomplishments have been reported in many areas that have been deemed challenging for machines, ranging from computer vision, natural language processing, audio analysis to smart sensing and many others. The technology trend in realizing success has developed towards increasingly complex and large-size AI models to solve more complex problems at superior performance and robustness. This rapid progress, however, has taken place at the expense of substantial environmental costs and resources. In addition, debates on the societal impacts of AI, such as fairness, safety, and privacy, have continued to grow in intensity. These issues have reflected major concerns pertaining to the sustainable development of AI. In this work, major trends in machine learning approaches that can address the sustainability problem of AI have been reviewed. Specifically, the emerging AI methodologies and algorithms are examined for addressing the sustainability issue of AI in two major aspects, i.e., environmental sustainability and social sustainability of AI. Then, the major limitations of the existing studies are highlighted, and potential research challenges and directions are proposed for the development of the next generation of sustainable AI techniques. It is believed that this technical review can help promote a sustainable development of AI R&D activities for the research community.
The pattern matching problem remains in survival since past decades and becomes more sophisticated due to exponential increase in size of text databases. An effective deterministic classical algorithm is always expect...
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The pattern matching problem remains in survival since past decades and becomes more sophisticated due to exponential increase in size of text databases. An effective deterministic classical algorithm is always expected to be at least O (N) time. Quantum computations are enough capable of performing exponential operations in single step of execution, so the quantum algorithms are effective. In general, the quantum pattern matching solution is possible in O (root N) time as its design is based on Grover's quantum search algorithm. To our knowledge, quantum algorithms for single pattern matching are available with limitations, and no algorithm has designed for multiple pattern matching. The main objective is to design quantum algorithm for both single and multiple patterns on a processing architecture of quantum random access memory (QuRAM). This gives a significant advantage to process large text databases in an efficient manner. Our complexity analysis justifies that the quantum algorithmic solutions achieve computational speedup over classical methods. We summarize the emergence of quantum-based pattern matching algorithms to process biological applications. The simulation is additionally done to validate and analyze the performance of proposed quantum algorithms. Lastly, we justify that our algorithms outperform the classical and quantum solutions and they are competent for implementing over quantum computer.
A multiprocessor system should be able to identify and eliminate faults in time to avoid the paralysis of a whole system. This paper proposes an improved binary bat algorithm to identify faulty processors in a multipr...
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A multiprocessor system should be able to identify and eliminate faults in time to avoid the paralysis of a whole system. This paper proposes an improved binary bat algorithm to identify faulty processors in a multiprocessor system. Compared with most existing works based on metaheuristic algorithms, the proposed algorithm employs a random initial population and does not require transfer functions. The exclusive-OR operation in the velocity equation is used to measure the distance between two individuals in binary space. To improve population diversity and avoid local optima, the mutation operator is integrated into the position update equation. A new local search strategy is proposed to strengthen the ability of local search in binary space. Experimental results show that the proposed algorithm based on the Malek model can maintain approximately 100% diagnostic accuracy in a small random initial population with fewer iterations and less CPU running time.
Since the inception in 1995, Differential Evolution (DE) has gained significant attention from researchers worldwide, and many DE variants proposed in the last decades obtained excellent performance in many scientific...
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Since the inception in 1995, Differential Evolution (DE) has gained significant attention from researchers worldwide, and many DE variants proposed in the last decades obtained excellent performance in many scientific and engineering applications. However, the vast majority of these well-performed DE variants employ binomial crossover rather than exponential crossover in generating trial vector candidates though both of the two schemes were proposed simultaneously. That may be also the reason why there still doesn't exist such a thorough analysis of DE with exponential crossover. Different from the majority of DE researchers believing that DE variants with binomial crossover usually exhibit superior performance than the ones employing exponential crossover and DE variants with exponential crossover are good at tackling optimization problems with linkages among neighboring variables, we found that DE variants with exponential crossover can achieve competitive performance with the ones employing binomial crossover regardless of whether there are linkages among the variables or not after discovering the proper crossover rate $CR$ and its corresponding parameter control. In order to enrich research of DE on exponential crossover, this paper presents an thorough experimental analysis of DE algorithm with exponential crossover in numerical optimization, presenting the basic concepts of exponential crossover, giving the mathematical analysis why they can actually achieve similar optimization between exponential crossover and binomial crossover, experimental validation under the 100 benchmark functions from CEC2013, CEC2014, CEC2017, CEC2022 test suites as well as the tension/compression spring design problem, and summarizing and classifying various engineering applications that exponential crossover DEs are used to solve. Furthermore, we also look into the future challenges and potential directions for further development of DE with exponential crossover.
To reform the electricity selling trading and standardize the electricity retail market, the optimal participating strategies of power generation companies and electricity customers in an electricity retail market und...
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To reform the electricity selling trading and standardize the electricity retail market, the optimal participating strategies of power generation companies and electricity customers in an electricity retail market under the spot electricity market mode are investigated. First, the influence of the external environment on power generation companies is considered, the dispatching sequence of power generation companies is optimized, and the profit models of power generation companies and power retailers, as well as the utility model of electricity customers are built. Second, an improved genetic algorithm (IGA) is applied to solve the formulated optimal participating strategies model for power generation companies and electricity customers, and the effect of IGA is compared with that of traditional genetic algorithm (GA), simulated annealing (SA) algorithm and particle swarm optimization (PSO) algorithm. The simulation results show that the IGA algorithm has the advantages of fast convergence and saving electricity consumption in this paper. Finally, two examples are employed to demonstrate the feasibility and efficiency of the developed strategies. Both example 1 for presented method in this paper and example 2 for multiple retailers competing, the simulation results show that the interests of market competing entities (participants) can be well balanced. Furthermore, the advantages of power retailers acted as a guider in electricity retail market are revealed, and the credibility and security of the electricity market management system are maintained.
Fast extraction of top- k distances from graph data is a primitive of paramount importance in the fields of data mining, network analytics and machine learning, where ranked distances are exploited for several purpose...
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Fast extraction of top- k distances from graph data is a primitive of paramount importance in the fields of data mining, network analytics and machine learning, where ranked distances are exploited for several purposes (e.g., link prediction or network classification). While investigation on computational methods to address this retrieval task for regularly sized, static inputs has been extensive, much less is known when managed graphs are massive, i.e., having millions of vertices/edges, and time-evolving, i.e., when their structure can grow over time, a scenario that introduces a number of scalability and effectiveness issues otherwise not arising. Since, nowadays, most real-world applications exploiting top- k distances have to handle inherently dynamic and rapidly growing graphs, in this paper we present the first dynamic indexing scheme that supports very fast queries on top- k distances when graphs are massive and incrementally time-evolving. We assess the scalability and effectiveness of our method through extensive experimentation on both real-world and artificial graph datasets.
Some electrical parameters of the SIS-type hysteretic underdamped Josephson junction(JJ)can be measured by its current-voltage characteristics(IVCs).Currents and voltages at JJ are commensurate with the intrinsic nois...
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Some electrical parameters of the SIS-type hysteretic underdamped Josephson junction(JJ)can be measured by its current-voltage characteristics(IVCs).Currents and voltages at JJ are commensurate with the intrinsic noise level of measuring *** leads to the need for multiple measurements with subsequent statistical *** this paper,the digital algorithms are proposed for the automatic measurement of the JJ parameters by *** algorithms make it possible to implement multiple measurements and check these JJ parameters in an automatic mode with the required *** complete sufficient statistics are used to minimize the root-mean-square error of parameter measurement.A sequence of current pulses with slow rising and falling edges is used to drive JJ,and synchronous current and voltage readings at JJ are used to realize measurement *** algorithm performance is estimated through computer *** significant advantage of the proposed algorithms is the independence from current source noise and intrinsic noise of current and voltage meters,as well as the simple implementation in automatic digital measuring *** proposed algorithms can be used to control JJ parameters during mass production of superconducting integrated circuits,which will improve the production efficiency and product quality.
The problem of multi-robot target tracking asks for actively planning the joint motion of robots to track targets. In this article, we focus on such target tracking problems in adversarial environments, where attacks ...
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The problem of multi-robot target tracking asks for actively planning the joint motion of robots to track targets. In this article, we focus on such target tracking problems in adversarial environments, where attacks or failures may deactivate robots' sensors and communications. In contrast to the previous works that consider no attacks or sensing attacks only, we formalize the first robust multi-robot tracking framework that accounts for any fixed numbers of worst-case sensing and communication attacks. To secure against such attacks, we design the first robust planning algorithm, named Robust Active Target Tracking (RATT), which approximates the communication attacks to equivalent sensing attacks and then optimizes against the approximated and original sensing attacks. We show that RATT provides provable suboptimality bounds on the tracking quality for any non-decreasing objective function. Our analysis utilizes the notations of curvature for set functions introduced in combinatorial optimization. In addition, RATT runs in polynomial time and terminates with the same running time as state-of-the-art algorithms for (non-robust) target tracking. Finally, we evaluate RATT with both the qualitative and quantitative simulations across various scenarios. In the evaluations, RATT exhibits a tracking quality that is near-optimal and superior to varying non-robust heuristics. We also demonstrate RATT's superiority and robustness against varying attack models (e.g., worst-case and bounded rational attacks) and with over- and under-estimated numbers of attacks.
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