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关键词： Sensors   Autonomous aerial vehicles   Approximation algorithms   Disasters   Wireless sensor networks   Capacitive sensors   Water pollution   Social networking (online)   Contamination   Computer science   UAV deployment   sensor placement   submodular function maximization   approximation algorithms  

摘要： In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K , the problem is to find a subset S with K nodes from a graph G , so that a given submodular function f(S) on S is maximized and the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in S , the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel 1-1/e/2h+2 -approximation algorithm for the problem, improving the best approximation ratio 1-1/e/2h+3 for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter that depends on the problem and its typical value is 2. In addition, when h=2 , the algorithm approximation ratio is at least 1-1/e/5 and may be as large as 1 in some special cases when K <= 23 , and is no less than 1-1/e/6 when K >= 24 , compared with the current best approximation ratio 1-1/e/7(=1-1/e/2h+3) for the problem. Finally, experimental results in the application of deploying a UAV network demonstrate that, the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and the throughput of the deployed UAV network by the proposed algorithm is up to 9.7% larger than those by the algorithms. Furthermore, the empirical approximation ratio of the proposed algorithm is between 0.7 and 0.99, which is close to the theoretical maximum value one.

关键词： Streams   Windows   Out of order   Binary trees   Throughput   Aggregates   Partitioning algorithms   Time complexity   Indexes   Computer science   Bulk eviction   incremental computation   out-of-order data streams   sliding window aggregation   stream algorithm  

摘要： Sliding window aggregation, which extracts summaries from data streams, is a core operation in streaming analysis. Though existing sliding window algorithms that perform single eviction and insertion operations can achieve a worst-case time complexity of O(1) for in-order streams, real-world data streams often involve out-of-order data and exhibit burst data characteristics, which pose performance challenges to these sliding window algorithms. To address this challenging issue, we propose Gecko - a novel sliding window aggregation algorithm that supports bulk eviction. Gecko leverages a granular-based eviction strategy for various bulk sizes, enabling efficient bulk eviction while maintaining the performance close to that of in-order stream algorithms for single evictions. For large data bulks, Gecko performs coarse-grained eviction at the chunk level, followed by fine-grained eviction using leftward binary tree aggregation (LTA) as a complementary method. Moreover, Gecko partitions data based on chunks to prevent the impacts of out-of-order data on other chunks, thereby enabling efficient handling of out-of-order data streams. We conduct extensive experiments to evaluate the performance of Gecko. Experimental results demonstrate that Gecko exhibits superior performance over other solutions, which is consistent with theoretical expectations. In real-world data scenarios, Gecko improves the average throughput of the state-of-the-art algorithm b_FiBA by 1.7 times, with a maximum improvement of up to 3.5 times. Gecko also demonstrates the best latency performance among all compared schemes.

关键词： Consumer electronics   Internet of Things   Security   Computational modeling   Blockchains   Accuracy   Logic gates   Computer science   Training   Semantic Web   Artificial intelligence   block chain   Komodo Mlipir optimization   multi-threat detection   Web 3.0  

摘要： Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0- based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models.

关键词： Pedestrians   Surveys   Trajectory   Target tracking   Cameras   Reviews   Object tracking   Circuits and systems   Computer science   Computational modeling   Multiple object tracking   multiple pedestrian tracking   occlusion handling   trajectory prediction   trajectory recovery  

摘要： As an intermediate task in computer vision, multiple pedestrian tracking (MPT) aiming at tracking the pedestrians from a given video, has attracted attention due to its potential academic and commercial value. However, pedestrians commonly suffer from occlusion due to diverse and complex scenarios, which increases the challenge of this task. This survey provides comprehensive review in terms of occlusion scenarios encountered during MPT, and investigates the model robustness of the existing methods in this scenarios. Firstly, this survey introduces the various and states of occlusion. Secondly, the related occlusion datasets are introduced. Subsequently, we categorize existing occlusion handling methods according to the tracking process and detail their pros and cons. In addition, occlusion handling precision (OHP) metric is proposed to evaluate the ability of a tracker in handling occlusion in this survey. Moreover, comprehensive analyzes and discussions in several public datasets are provided to verify the effectiveness of these methods. Finally, the existing issues and future directions for occlusion handling methods are discussed. In doing so, this work serves as a foundation for future research by providing researchers with information about the occlusion handling method of MPT.

关键词： Buildings   Optimization   Collaboration   Global communication   Cultural differences   Videos   Mathematical models   History   Computer science   Chaos   Collaborative recommendation   environment-classes   agents   roles   groups   and objects (E-CARGO)   national image resource   targeted international communication  

摘要： With the acceleration of globalization, the targeted international communication of national images contributes to enhancing a nation's soft power and international recognition. It is challenging to select appropriate resources from the mass candidates for creating promotional works of national image. Existing research only focuses on the methodologies and lacks the systematic modeling and solving of national image resources recommendation. A collaborative recommendation approach to national image resources is proposed for targeted international communication. In it, the multidimensional features of national image resources and characteristics of communication audiences are modeled, and an evaluation mechanism is proposed to measure the comprehensive compatibility between national image resources and communication audiences. By innovatively introducing the role-based collaboration (RBC) theory and the environment-classes, agents, roles, groups, and objects (E-CARGO) model, the national image resources recommendation is formalized as a collaborative optimization problem. The mathematical model is built and solved via an optimization package. Finally, the case study and experiments show that the approach is efficient, feasible, and conducive to enhancing the efficiency of selecting national image resources. It offers a novel research paradigm for targeted international communication.

关键词： Testing   Complexity theory   Probability distribution   Computer science   Upper bound   Surveys   Polynomials   Machine learning algorithms   Machine learning   Limiting   Distribution testing   tolerant testing   non-tolerant testing   sample complexity  

摘要： The framework of distribution testing is currently ubiquitous in the field of property testing. In this model, the input is a probability distribution accessible via independently drawn samples from an oracle. The testing task is to distinguish a distribution that satisfies some property from a distribution that is far in some distance measure from satisfying it. The task of tolerant testing imposes a further restriction, that distributions close to satisfying the property are also accepted. This work focuses on the connection between the sample complexities of non-tolerant testing of distributions and their tolerant testing counterparts. When limiting our scope to label-invariant (symmetric) properties of distributions, we prove that the gap is at most quadratic, ignoring poly-logarithmic factors. Conversely, the property of being the uniform distribution is indeed known to have an almost-quadratic gap. When moving to general, not necessarily label-invariant properties, the situation is more complicated, and we show some partial results. We show that if a property requires the distributions to be non-concentrated, that is, the probability mass of the distribution is sufficiently spread out, then it cannot be non-tolerantly tested with o(root n) many samples, where n denotes the universe size. Clearly, this implies at most a quadratic gap, because a distribution can be learned (and hence tolerantly tested against any property) using O(n) many samples. Being non-concentrated is a strong requirement on properties, as we also prove a close to linear lower bound against their tolerant tests. Apart from the case where the distribution is non-concentrated, we also show if an input distribution is very concentrated, in the sense that it is mostly supported on a subset of size s of the universe, then it can be learned using only O(s) many samples. The learning procedure adapts to the input, and works without knowing s in advance.

关键词： Death penalty   Capital punishment   Education   Politics   Race  

摘要： We argue that the decrease in support for the death penalty associated with college education should differ across political groups given the salience of political identities in the United States. We expect the negative association between education and views of capital punishment is minimized for Republicans and conservatives, especially among White Americans. We use 2002-2018 General Social Survey data and present marginal effects at the mean from the logistic regression models and tests of first and second differences from race-specific interaction models. Moderation analyses demonstrate that the association between education and reduced support for capital punishment is robust among White Democrats and liberals, and relatively weak among White Republicans and especially conservatives. Among non-Whites, the association between higher education and support for capital punishment does not vary by party but it is in the opposite directions for liberals and conservatives. We discuss the implication of the results for party and orientation and for White and non-White respondents, highlighting the rigidity of capital punishment support among political conservatives even as support decreases among other groups.

关键词： Neurons   Visualization   Computational modeling   Artificial neural networks   Backpropagation   Perturbation methods   Heating systems   Decision making   Computer science   Artificial intelligence   Attribution method   deep neural networks   explainable AI   visual explanation  

摘要： With the remarkable success of deep neural networks, there is a growing interest in research aimed at providing clear interpretations of their decision-making processes. In this paper, we introduce Attribution Equilibrium, a novel method to decompose output predictions into fine-grained attributions, balancing positive and negative relevance for clearer visualization of the evidence behind a network decision. We carefully analyze conventional approaches to decision explanation and present a different perspective on the conservation of evidence. We define the evidence as a gap between positive and negative influences among gradient-derived initial contribution maps. Then, we incorporate antagonistic elements and a user-defined criterion for the degree of positive attribution during propagation. Additionally, we consider the role of inactivated neurons in the propagation rule, thereby enhancing the discernment of less relevant elements such as the background. We conduct various assessments in a verified experimental environment with PASCAL VOC 2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our method outperforms existing attribution methods both qualitatively and quantitatively in identifying the key input features that influence model decisions.

关键词： Black sea bream   Muscle cells   Scaffolds   Three-dimensional culture  

摘要： Culturing fish myogenic cells in vitro holds significant potential to revolutionize aquaculture practices and support sustainable food production. However, advancement in in vitro culture technologies for skeletal muscle-derived myogenic cells have predominantly focused on mammals, with limited studies on fish. Scaffold-based three-dimensional (3D) culture systems for fish myogenic cells remain underexplored, highlighting a critical research gap compared to mammalian systems. This study evaluated the effects of scaffold composition and manufacturing methods on cellular growth in the 3D culture of black sea bream (Acanthopagrus schlegelii) myogenic cells. Scaffolds were manufactured using three natural polymers: black sea bream-derived extracellular matrix (ECM), sodium alginate, and gelatin. Two scaffold types were tested: "cell-laden scaffolds" prepared by mixing cells into the pre-scaffold solution followed by gelation, and "cell-seeding scaffolds" produced by freezing, gelation, and lyophilization before cell inoculation. Scaffold characteristics, including pore size, porosity, swelling ratio, and degradation rate, were assessed. Cell-seeding scaffolds exhibited relatively larger pore size, higher porosity, and higher degradation rate, while cell-laden scaffolds had higher swelling ratios. When black sea bream myogenic cells were cultured in these scaffolds, cell-seeding scaffolds supported cellular growth, particularly when composed of 3% sodium alginate and 4% gelatin with any concentration of ECM. In contrast, cell-laden scaffolds did not support cellular growth regardless of their composition. These findings provide fundamental insights for optimizing scaffold properties to develop more optimized conditions for 3D culture of fish muscle lineage cells.

关键词： automatic question generation   computer science   generative AI   GPT-4   large language models   retrieval-augmented generation  

摘要： BackgroundOver the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational *** paper explores the potential of large language models (LLMs) for generating computer science questions that are sufficiently annotated for automatic learner model updates, are fully situated in the context of a particular course and address the cognitive dimension *** previous attempts that might use basic methods such as ChatGPT, our approach involves more targeted strategies such as retrieval-augmented generation (RAG) to produce contextually relevant and pedagogically meaningful learning *** and ConclusionsOur results show that generating structural, semantic annotations works well. However, this success was not reflected in the case of relational annotations. The quality of the generated questions often did not meet educational standards, highlighting that although LLMs can contribute to the pool of learning materials, their current level of performance requires significant human intervention to refine and validate the generated content.