Collective Task Research Articles

Inventing Latin America Under the Good Neighborhood Policy: The Case of the MoMA Collection, 1943

This article describes the strategies of the North American government to help establish a Latin American Collection in the Museum of Modern Art (MoMA) and the role that Lincoln Kirstein had as a collector of the works that made up the collection. The dialogue that Kirstein had with various personalities of the culture in the tasks of exhibition and collection is analyzed. We emphasize how the fine arts were spaces of political weighting, and areas usable by Good Neighbor politics. Finally, it is explained what kind of Latin American art was collected to make up the collection in 1943, and what idea of Latin America was represented through that selection. The research uses primary sources collected from MoMA Archives, Rockefeller Personal Archives, New York Public Library and Lincoln Kirstein Archives. The comparative method in history was used to review the different cases analyzed.

THEORY OF BUILDING AUTOMATED CONTROL SYSTEMS

The task of collecting, processing and disseminating (exchanging) information stood at all stages of human development. For a long time, the main tools for solving it were the human brain, hearing and language. Storing information in computer memory gives a fundamentally new quality of dynamics, that is, the ability to quickly recover and use it directly in problems solved on a computer. Modern printing equipment, equipped with modern computers, allows, if necessary, to quickly present any selected information on paper. In the process of development of the administrative data management system, the need arose for an automated facility management system (ACS).

When being flexible matters: Ecological underpinnings for the evolution of collective flexibility and task allocation

Task allocation is a central feature of collective organization. Living collective systems, such as multicellular organisms or social insect colonies, have evolved diverse ways to allocate individuals to different tasks, ranging from rigid, inflexible task allocation that is not adjusted to changing circumstances to more fluid, flexible task allocation that is rapidly adjusted to the external environment. While the mechanisms underlying task allocation have been intensely studied, it remains poorly understood whether differences in the flexibility of task allocation can be viewed as adaptive responses to different ecological contexts—for example, different degrees of temporal variability. Motivated by this question, we develop an analytically tractable mathematical framework to explore the evolution of task allocation in dynamic environments. We find that collective flexibility is not necessarily always adaptive, and fails to evolve in environments that change too slowly (relative to how long tasks can be left unattended) or too quickly (relative to how rapidly task allocation can be adjusted). We further employ the framework to investigate how environmental variability impacts the internal organization of task allocation, which allows us to propose adaptive explanations for some puzzling empirical observations, such as seemingly unnecessary task switching under constant environmental conditions, apparent task specialization without efficiency benefits, and high levels of individual inactivity. Altogether, this work provides a general framework for probing the evolved diversity of task allocation strategies in nature and reinforces the idea that considering a system’s ecology is crucial to explaining its collective organization.

STRATEGI PENGELOLAAN ZIS PADA MUSIM PANDEMI COVID-19 (STUDI PADA BAZNAS SIDOARJO)

ZIS stands for Zakat, Infaq, and Shadaqah, for its management is officially carried out by BAZNAS or the National Amil Zakat Agency because this institution is the official and only body established by the government based on Presidential Decree No. 8 of 2001 which has the task and function of collecting and distributing zakat, infaq, and alms (ZIS) at the national level. However, at the beginning of 2020 ZIS management activities underwent a slight change due to the Covid-19 pandemic which caused social activities to be limited and the economic decline in Indonesia. Because of this in this article, we will focus on discussing how to manage ZIS strategies during the COVID-19 pandemic study season. at BAZNAS Sidoarjo. The research method used is descriptive qualitative, namely the research method used to investigate, find, describe and explain the quality or features of social influences that cannot be described, measured or described through a quantitative approach. The results of this study include making it easier for people to pay zakat/infaq and shadaqah at BAZNAS Sidoarjo using barcodes that can be paid through online payment applications such as gopay, ovo, link aja, funds, or other merchants/banks. As for the distribution, BAZNAS gave an invitation to Mustahiq to come to the BAZNAS office in stages, so as not to cause crowds and required to always implement health protocols.

A Photoelectric Spiking Neuron for Visual Depth Perception.

The biological visual system encodes optical information into spikes and processes them by the neural network, which enables the perception with high throughput of visual processing with ultralow energy budget. This has inspired a wide spectrum of devices to imitate such neural process, while precise mimicking such procedure is still highly required. Here, a highly bio-realistic photoelectric spiking neuron for visual depth perception is presented. The firing spikes generated by the TaOX memristive spiking encoders have a biologically similar frequency range of 1-200Hz and sub-micro watts power. Such spiking encoder is integrated with a photodetector and a network of neuromorphic transistors, for information collection and recognition tasks, respectively. The distance-dependent response and eye fatigue of biological visual systems have been mimicked based on such photoelectric spiking neuron. The simulated depth perception shows a recognition improvement by adapting to sights at different distances. The results can advance the technologies in bioinspired or robotic systems that may be endowed with depth perception and power efficiency at the same time.

SmartValidator: A framework for automatic identification and classification of cyber threat data

A wide variety of Cyber Threat Information (CTI) is used by Security Operation Centres (SOCs) to perform validation of security incidents and alerts. Security experts manually define different types of rules and scripts based on CTI to perform validation tasks. These rules and scripts need to be updated continuously due to evolving threats, changing SOCs’ requirements and dynamic nature of CTI. The manual process of updating rules and scripts delays the response to attacks. To reduce the burden of human experts and accelerate response, we propose a novel Artificial Intelligence (AI) based framework, SmartValidator. SmartValidator leverages Machine Learning (ML) techniques to enable automated validation of alerts. It consists of three layers to perform the tasks of data collection, model building and alert validation. It projects the validation task as a classification problem. Instead of building and saving models for all possible requirements, we propose to automatically construct the validation models based on SOC’s requirements and CTI. We built a Proof of Concept (PoC) system with eight ML algorithms, two feature engineering techniques and 18 requirements to investigate the effectiveness and efficiency of SmartValidator. The evaluation results showed that when prediction models were built automatically for classifying cyber threat data, the F1-score of 75% of the models were above 0.8, which indicates adequate performance of the PoC for use in a real-world organization. The results further showed that dynamic construction of prediction models required 99% less models to be built than pre-building models for all possible requirements. Thus, SmartValidator is much more efficient to use when SOCs’ requirements and threat behaviour are constantly evolving. The framework can be followed by various industries to accelerate and automate the validation of alerts and incidents based on their CTI and SOC’s preferences.

SISFOSPEM (Information System of Supervision, Reporting, Evaluation and Monitoring) East Java Regional Work Council

In collecting data on the work program reports of the East Java Branch Work Councils, the East Java Regional Work Councils obtain data from forms sent via email to each branch. In 2020, supervision was carried out by sending SPEM instruments via google forms, in which data collection and the data have been recording manually via excel. That data collection and report task are very ineffective because it takes a long time to collect and sort the data. In order to overcome the problems, a SPEM information system is needed as a medium for delivering information as well as supervising, reporting, evaluating, and monitoring the Branch Work Councils throughout East Java. In making this information system, we use the waterfall research methodology, where there are six stages of the system design process: problem identification, system requirements analysis, system design, system creation, system testing, and research analysis results. The result of this research is a SPEM information system. Furthermore, the test results using black-box testing state that the information system of SPEM that has been created can run well according to the expected criteria. The testing results using user acceptance testing with 18 respondents from the East Java Regional Work Council and 20 respondents from the Branch Work Council throughout East Java, stated that the SPEM information system is easy to use. This system is beneficial for users and organizations.

Completion time minimization for UAV enabled data collection with communication link constrained

This paper studies unmanned aerial vehicles (UAV) enabled industrial Internet of Things while a UAV dispatched to collect data of low-power ground sensor nodes (SNs) in multi-obstacle environment. The authors aim to minimize the completion time while satisfying the communication link constraints of each SN and obstacle avoidance, data collection requirements etc. To this end, the authors first formulate the completion time minimization problem by jointly optimizing the UAV trajectory and collection sequence of SNs. The problem is difficult to be optimally solved, as it is non-convex. To tackle this problem, the authors first transform the original problem to a Traveling Salesman Problem-like (TSP-like) problem based on a hover point that can naturally satisfy the communication link constraints of data collection. The dynamic programming (DP) algorithm to figure out the order in which the UAV collects each SN, which gives the initial path of the UAV traversing each SN from the beginning point to the end point. Next, the authors consider the general scenarios of data collection tasks where the UAV also communicates while flying. The authors construct an equivalent problem with integer variable constraints for the original problem with indicative function constraints. The authors rewrite the non-convex constraints of the equal problem by introducing slack variables and leveraging the SCA, and add the discrete region threat constraints for the traditional path discretization method. Finally, the simulation results verify the effectiveness of the proposed algorithm under different parameter configurations.

Computational Simulation of an Agricultural Robotic Rover for Weed Control and Fallen Fruit Collection—Algorithms for Image Detection and Recognition and Systems Control, Regulation, and Command

The continuous rise in the world’s population has increased the need for food, resulting in a rise of agricultural holdings to ensure the supply of these goods directly to the populations and indirectly to all processing industries in the food business. This situation has led agriculture to reinvent itself and introduce new technics and tools to ensure tighter control of the crops and increase yields in food production. However, the lack of labor coupled with the evolution of weeds resistant to herbicides created a crisis in agricultural food production. However, with the growing evolution in electronics, automation, and robotics, new paths are emerging to solve these problems. A robotic rover was designed to optimize the tasks of weed control and collection of fallen fruits of an orchard. In weed control, a localized spraying system is proposed, therefore reducing the amount of applied herbicides. With fruit collection, it is possible to direct fallen fruits for animal feeding and possible to reduce microbial activity on the next campaign crops, therefore avoiding damage. This study proposes the simulation of this robotic rover on robotic simulation software. It also proposes the replication of a similar environment of an orchard to generate an algorithm that controls the rover on the tasks of localized spraying and fallen fruit collection. Creating and testing these algorithms by using a robotic simulator speed up and ease the evaluation of different scenarios and hypotheses, with the added benefit of being able to test two tasks simultaneously. This method also allows greater freedom and creativity because there are no concerns about hardware damage. It should also be noted that development costs are very low.

Joint Optimization of AoI, SINR, Completeness, and Energy in UAV-Aided SDCNs: Coalition Formation Game and Cooperative Order

As one of the most important representatives for the edge intelligence, Unmanned Aerial Vehicle (UAV) can be used to provide the intelligent edge service, such as performing data collection, computation and transmission tasks in the air-to-ground networks. In the considered Sensed Data Collection Networks (SDCNs), the UAV is in charge of collecting and computing the sensed data from mobile ground nodes, and multiple performance metrics need to be jointly optimized, including the collected data’s Age of Information (AoI) and the Signal to Interference plus Noise Ratio (SINR) at the UAV from mobile sensor nodes, the data collection areas’ completeness, and the mobile sensor nodes’ energy consumption. Due to the absence of such a joint optimization framework, this paper formulates the mobile sensing nodes deployment problem in the UAV-aided SDCNs. Afterwards, the formulated problem is transformed into a utility maximization problem from the perspective of Coalition Formation Game (CFG), where the orders, actions, rule, and stability in the coalitions are analyzed and proved. Then, two algorithms are put forward to reach the NE (Nash Equilibrium) of the CFG by determining the mobile sensor nodes’ coalitions and positions. Finally, extensive simulations are conducted with different parameters settings to evaluate the correctness and effectiveness of the proposed algorithms. Results have shown that the mobile sensor nodes, which adopt the proposed cooperative order to formulate the coalitions, could reach lower AoI, larger SINR, higher data completeness, and less energy consumption than the typical Pareto order.

The UAV Trajectory Optimization for Data Collection from Time-Constrained IoT Devices: A Hierarchical Deep Q-Network Approach

Recently, using unmanned aerial vehicles (UAVs) to collect information from distributed sensors has become one of the hotspots in the Internet of Things (IoT) research. However, previous studies on the UAV-assisted data acquisition systems focused mainly on shortening the acquisition time, reducing the energy consumption, and increasing the amount of collected data, but it lacked the optimization of data freshness. Moreover, we hope that UAVs can perform long-term data collection tasks in dynamic scenarios within a constantly changing age of information (AoI) and within their own power levels. Therefore, we aim to maximize the quality of service (QoS) based on the freshness of data, while considering the endurance of the UAVs. Since our scenario is not an inertial order decision process with uniform time slots, we first transform the optimization problem into a semi-Markov decision process (SMDP) through modeling, and then we propose a hierarchical deep Q-network (DQN)-based path-planning algorithm to learn the optimal strategy. The simulation results show that the algorithm is better than the benchmark algorithm, and the tradeoff between the system QoS and the safe power state can be achieved by adjusting the parameter βe.

Splitting–merging-based automatic scheduling scheme for balancing energy consumption and task allocation in WSANs

In wireless sensor and actuator networks (WSANs), sensing region partition, task allocation, and collector schedule greatly affect the performance of WSANs. Specifically, nonuniform data aggregation causes unbalanced energy consumption, thereby reducing the network lifetime. Additionally, unbalanced allocation of data collection tasks of collectors increases the gathering delay. Thus, it is critical to balance the energy consumption of sensor nodes, as well as task allocation and scheduling with the limited number of collectors. This study proposes a novel splitting–merging-based automatic scheduling scheme to balance energy consumption and task allocation for WSANs. First, a sensing region splitting–merging scheme is proposed to make the data load uniform, in which the sensing region is self-organized into many subregions taking into account the maximum number of sensor nodes and the maximum distance between sensor nodes in each subregion. Second, a task allocation strategy based on genetic algorithm is proposed to optimize the number of collectors and uniformly assign tasks to collectors. Finally, the trajectory of each collector is optimized by applying ant colony optimization algorithm. Numerical experiments show that the proposed method outperforms well in terms of prolonging the network lifetime and reducing the gathering delay when compared with several related methods.

Bayesian Multi-Targets Strategy to Track Apis mellifera Movements at Colony Level.

Simple SummaryThe number of honey bee, Apis mellifera L., colonies has reduced around the globe, and one potential cause is their unintended exposure to sublethal stressors such as agricultural pesticides. The quantification of such effects at colony level is a very complex task due to the innumerable collective activities done by the individual within colonies. Here, we present a Bayesian and computational approach capable of tracking the movements of bees within colonies, which allows the comparison of the collective activities of colonies that received bees previously exposed to uncontaminated diets or to diets containing sublethal concentrations of an agricultural pesticide (a commercial formulation containing the synthetic fungicides thiophanate-methyl and chlorothalonil). Our Bayesian tracking technique proved successful and superior to comparable algorithms, allowing the estimation of dynamical parameters such as entropy and kinetic energy. Our efforts demonstrated that fungicide-contaminated colonies behaved differently from uncontaminated colonies, as the former exhibited anticipated collective activities in peripheral hive areas and had reduced swarm entropy and kinetic energies. Such findings may facilitate the electronic monitoring of potential unintended effects in social pollinators, at colony level, mediated by environmental stressors (e.g., pesticides, electromagnetic fields, noise, and light intensities) alone or in combination.Interactive movements of bees facilitate the division and organization of collective tasks, notably when they need to face internal or external environmental challenges. Here, we present a Bayesian and computational approach to track the movement of several honey bee, Apis mellifera, workers at colony level. We applied algorithms that combined tracking and Kernel Density Estimation (KDE), allowing measurements of entropy and Probability Distribution Function (PDF) of the motion of tracked organisms. We placed approximately 200 recently emerged and labeled bees inside an experimental colony, which consists of a mated queen, approximately 1000 bees, and a naturally occurring beehive background. Before release, labeled bees were fed for one hour with uncontaminated diets or diets containing a commercial mixture of synthetic fungicides (thiophanate-methyl and chlorothalonil). The colonies were filmed (12 min) at the 1st hour, 5th and 10th days after the bees’ release. Our results revealed that the algorithm tracked the labeled bees with great accuracy. Pesticide-contaminated colonies showed anticipated collective activities in peripheral hive areas, far from the brood area, and exhibited reduced swarm entropy and energy values when compared to uncontaminated colonies. Collectively, our approach opens novel possibilities to quantify and predict potential alterations mediated by pollutants (e.g., pesticides) at the bee colony-level.

Collective cognitive tasks as a method for studying political reasoning.

Collective cognitive tasks as a method for studying political reasoning.

Characterization of geo-material parameters: Gene concept and big data approach in geotechnical engineering

Due to their inherent natural properties, the basic physio-mechanical properties of geo-materials generally exhibit varying degrees of spatial variability. Therefore, describing and characterizing the global traits of geo-materials based on very limited analytical data pose a great challenge. Furthermore, the differences of their characteristics significantly impact the determination of geotechnical engineering stability as well as the relevant engineering design results. With a view to resolve this issue, in this study, we apply the concept of “Gene” from biology, and propose a novel approach to describe the “genetic” characteristics of geo-materials based on general attributes of the specific rock (soil) materials belongings to a certain region or zone. We propose the definition of genetic characteristics as well as the basic attributes in this paper. Our study provides the methodology of utilizing the big data theory to statistically analyze the geo-material parametric data identified in the previous projects and the framework for search of the genetic characteristics. We also developed a software system for big data management and analysis of genetic characteristics of geo-materials, which can fulfill the tasks of collecting, transmitting, classifying, screening, managing and analyzing the big data. Based on more than 80,000 sets of standard geo-material physio-mechanical data from a representative region, the genetic features of typical geo-materials are analyzed to obtain the corresponding gene maps. The findings from this study offer a reliable guidance for the determination of geo-material parameters, data support for engineering construction, and potential application in the mitigation of natural disasters.

Worker task organization in incipient bumble bee nests

Bumble bees (Bombus: Apidae) are a long-standing model system for understanding animal behaviour, ecology and evolution. However, how workers in this system are organized to perform fundamental tasks related to brood feeding and food collection remains unclear. Bumble bees undergo dramatically different life stages, across which the social environment, and therefore task organization, changes over time. Queens initiate nests solitarily, and when the first cohort of workers emerge, they help the queen carry out brood-feeding and food collection tasks, whereas the queen transitions to primarily egg laying. Although task organization has been studied in mature colonies, few studies to date have explored how these tasks are organized in young, incipient nests. Here, we explored how food-related tasks, including brood feeding and food collection, are organized by workers in incipient colonies. We found that food-related tasks were nested, in that the majority of workers fed brood, a subset also collected nectar and a smaller subset also collected pollen. These patterns suggest that brood feeding is a task shared by most workers, and that the distinction between pollen collecting versus nonpollen collecting might be the most important axis of division of labour in bumble bee nests, at least at the early nesting stage.

Sensor-aware frontier exploration and mapping with application to thermal mapping of building interiors

The combination of simultaneous localization and mapping (SLAM) and frontier exploration enables a robot to traverse and map an unknown area autonomously. Most prior autonomous SLAM solutions utilize information only from depth sensing devices. However, in situations where the main goal is to collect data from auxiliary sensors such as thermal camera, existing approaches require two passes: one pass to create a map of the environment and another to collect the auxiliary data, which is time consuming and energy inefficient. We propose a sensor-aware frontier exploration algorithm that enables the robot to perform map construction and auxiliary data collection in one pass. Specifically, our method uses a real-time ray tracing technique to construct a map that encodes unvisited locations from the perspective of auxiliary sensors rather than depth sensors; this encourages the robot to fully explore those areas to complete the data collection task and map making in one pass. Our proposed exploration framework is deployed on a LoCoBot with the task to collect thermal images from building envelopes. We validate with experiments in both multi-room commercial buildings and cluttered residential buildings. Using a metric that evaluates the coverage of sensor data, our method significantly outperforms the baseline method with a naive SLAM algorithm.

EVENT-ORIENTED ARCHITECTURE SYSTEM FOR COLLECTING AND PROCESSING BIBLIOGRAPHIC INFORMATION

Today, one of the most important indicators of the level of international activity of a scientific institution is the number and quality of scientific publications placed in the most prestigious international bibliographic databases. The collection and processing of such information requires significant time and resources, given their size and daily replenishment, the variety of storage formats and access to information repositories. Therefore, such a task of collecting, processing, consolidating and analyzing bibliographic information and assessing the level of international activity of researchers of state scientific organizations and teachers of higher educational institutions is a task of processing big data and requires special approaches in the construction of specialized software systems for its solution. The article considers the construction of the structure of the software system and the main elements of the event-oriented microservice architecture of the system of collecting and analyzing information from bibliographic sources. The basis for the implementation was the microservice architecture with the distribution of services according to the functional purpose. The proposed structure of the system makes it possible to adaptively change the selection of information sources based on the evaluation of the effectiveness of obtaining relevant information from relevant sources, in accordance with the events that occur during the collection of information. The choice of directions for data collection and processing is based on the use of typical scenarios, which allows to significantly increase the efficiency of the analyst's work. The event-oriented microservice architecture of the system makes it possible to adapt the operation of the system to the loads on individual microservices by analyzing the created relevant events. The developed system provides an opportunity to collect information about the international activities of individual people or institutions from sources that are heterogeneous in terms of storage form and composition. The composition of information sources in this system can be expanded without interfering with the work of other system components. Each individual processor of information from the sources can provide only partial data, which is supplemented at the expense of others. The system adapts to the user's request in such a way that data is taken only from those sources that meet the request's requirements and can satisfy them. The developed approach is built on the basics of event-oriented microservice architecture and can be used in the development of various information and analytical systems.

Efficient Adaptive Matching for Real-Time City Express Delivery

City express delivery services (a.k.a.~last-mile delivery) have become more prominent in recent years. Many logistics giants, such as Amazon, JD, and Cainiao, have deployed intelligent express delivery systems to deal with the growing demand for parcel delivery. Existing works adopt queuing or batch processing approaches to assign parcels to couriers. However, these approaches do not fully consider the distribution of parcels and couriers, leading to poor quality of task assignment. In this paper, we investigate a problem of delivery matching based on revenue maximization in real-time city express delivery services. Given a set of couriers and a stream of parcel collection tasks, our problem aims to assign each collection task to a suitable courier to maximize the overall revenue of the platform. The problem is shown to be NP-hard. To tackle the problem efficiently, we present a time-aware batch matching algorithm to offer high-quality courier-task matching in each sliding window. We further theoretically analyze the matching approximation bound. In addition, we propose an efficient deep reinforcement learning-based approach to adaptively determine the sliding window size for better matching results. Finally, extensive experiments demonstrate that our proposed algorithms can achieve desirable effectiveness and efficiency under a wide range of parameter settings.

Unit Collective Medical Training in the 75th Ranger Regiment.

The 75th Ranger Regiment's success with eliminating preventable death on the battlefield is innate to the execution of a continuous operational readiness training cycle that integrates individual and unit collective medical training. This is a tactical solution to a tactical problem that is solved by the entire unit, not just by medics. When a casualty occurs, the unit must immediately respond as a team to extract, treat, and evacuate the casualty while simultaneously completing the tactical mission. All in the unit must maintain first responder medical skills and medics must be highly proficient. Leaders must be prepared to integrate casualty management into any phase of the mission. Leaders must understand that (1) the first casualty can be anyone; (2) the first responder to a casualty can be anyone; (3) medical personnel manage casualty care; and (4) leaders have ownership and responsibility for all aspects of the mission. Foundational to training is a command-directed casualty response system which serves as a forcing function to ensure proficiency and mastery of the basics. Four programs have been developed to train individual and collective tasks that sustain the Ranger casualty response system: (1) Ranger First Responder, (2) Advanced Ranger First Responder, (3) Ranger Medic Assessment and Validation, and (4) Casualty Response Training for Ranger Leaders. Unit collective medical training incorporates tactical leader actions to facilitate the principles of casualty care. Tactical leader actions are paramount to execute a casualty response battle drill efficiently and effectively. Successful execution of this battle drill relies on a command-directed casualty response system and mastery of the basics through rehearsals, repetition, and conditioning.