Collective Task Research Articles

ANALYSIS OF DESIGN AND IMPLEMENTATION OF INFORMATION SYSTEM OF LIBRARY OF UIN SUNAN KALIJAGA JOGYAKARTA

<p>The purpose of writing this article is to find out how to design and implement the Information System of the Sunan Kalijaga State Islamic University Yogyakarta Library which is supported by the System Development Life Cycle (SDLC). The method used in this study is the type of field research that is "field research" which has the nature of skin research and data collection techniques with direct observation and information obtained through documentation in the field. The results and discussion in this article show that CYPRUS (Library Information System) has the task of collecting, processing, preserving, reinventing information, borrowing and returning books in the circulation section, already using information technology to store data and library collections, in the design process This library information system also uses the MySQL database in streamlining the library information system, and from the plan, it can be used by the admin and librarian and the results are shown in the library information system design show that there is data collection on the members, login columns, member data types, display reports free library, and display collection returns and borrowing columns.</p><p>Design and Implementation, Library Information System.</p>

An Information-Centric Semantic Data Collection Tree for Wireless Sensor Networks.

Data collection is an important application of wireless sensor networks (WSNs) and Internet of Things (IoT). Current routing and addressing operations in WSNs are based on IP addresses, while data collection and data queries are normally information-centric. The current IP-based approach incurs significant management overheads and is inefficient for semantic data collection and queries. To address the above issue, this paper proposes a semantic data collection tree (sDCT) construction scheme to build up a semantic data collection tree for wireless sensor networks. The semantic tree is rooted at the edge/sink and supports data collection tasks, queries, and configurations efficiently. We implement the sDCT in Contiki and evaluate the performance of the sDCT in comparison with the state-of-the-art scheme, 6LoWPAN/RPL and L2RMR, using telosb sensors under various scenarios. The obtained results show that the sDCT achieves a significant improvement in terms of the energy efficiency and the packet transmissions required for data collection or a query task compared to 6LoWPAN/RPL and L2RMR.

A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility

Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms.

Task unit bid- spatial coverage and post input density (TUBSC_PID) based crowd sourcing network

A huge number of items are associated with the Internet of Things (IoT) which is fixed with software, electronics and sensors. It has a wide variety of applications, namely smart homes, smart grids and smart cities. The sensor devices combine with Internet of Things (IoT) operates as robot system to execute data collection task. The IoT control objects, sense devices and gathers data. In crowd sourcing network there are two main issues, namely to guarantee the Quality of Service (QoS) of tasks and to reduce the data collection cost. There is also some problems arise between the task circulator and the data reporter in terms of profit. Since, IoT sensing devices have increased a lot, the relationship for finishing the task is very much important. In this paper, a novel framework called Task Unit Bit-based Spatial Coverage and Post Input density (TUBSC_PID) has been proposed. The input density is applied to estimate the contribution of a single data collector to a particular sensing task. A Task Unit Bid-based task selection strategy is proposed to choose the task which provides more contribution density and higher profit to the system. A novel spatial coverage technique is also applied to cover all the information obtained from the data collector. The present and post input density is applied to estimate the contribution of a single data collector to a particular sensing task as well as future sensing tasks. This method reduces the cost of data selection and maximizes the system profit. Experimental results predict that compared to the traditional techniques, namely Random Task selection with Input Density Reporter selection (RTCDR) and Collaborative Multi-Tasks Data Collection Scheme (CMDCS), the profit of the system is improved by 96.1%.

Sentiment Analysis of Speech with Application to Various Languages

In this paper we aim to explore and implement a modern speech recognition system using Natural Language Processing (NLP) and sentiment analysis that can be applied in the area of audio and text archive investigation from various languages. To that end we developed a project that can be used to convert speech to text and perform various analysis on a converted text. Furthermore, we focused on recognizing different information such as names , emotions and also determine the overall sentiment of a script. Furthermore, we perform web scraping for names and organizations of importance used in speech. To achieve this, we used Python with various specialized modules. In order to simplify the task of collecting and storing audio inputs for processing we have developed an Android app with connection to a cloud database. This methodology can easily be applied for the purposes of digital presentation and preservation of cultural and scientific heritage.

Coarse-to-Fine Visual Object Catching Strategy Applied in Autonomous Airport Baggage Trolley Collection

In this study, a systematic solution for mobile manipulation with the application of autonomous trolley collection in the airport is presented. The developed system features a novel integrated hardware and software system, incorporating a specially designed manipulator capable of tightly catching the trolley. Particularly, we innovatively present a supervisory control guided coarse-to-fine visual trolley catching strategy in large-scale airport environments. At the coarsest level, an image-based visual servoing method is utilized to approach the trolley from far to near, where a learning-based object detection method is adopted to identify trolleys in the environment in real-time. At the finest level, we present an accurate point cloud registration based object pose estimation method to promote the object manipulation with a position-based visual servoing controller. The tightly integrated system and the subsystems are evaluated through conducting experiments in the trolley collection task and comparing the key module with the state of the art. Extensive experiments demonstrate that the proposed framework can find and fetch the trolley robustly. Notably, the proposed object detection and pose estimation methods exhibit higher accuracy, providing credible perceptual approaches for mobile manipulation. Moreover, the proposed framework can be easily extended to other mobile manipulation tasks for object search and retrieval.

Improving utility and security of the shuffler-based differential privacy

When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the central aggregator. LDP imposes large amount of noise as each user executes the randomization independently. To address this issue, recent work introduced an intermediate server with the assumption that this intermediate server does not collude with the aggregator. Under this assumption, less noise can be added to achieve the same privacy guarantee as LDP, thus improving utility for the data collection task. This paper investigates this multiple-party setting of LDP. We analyze the system model and identify potential adversaries. We then make two improvements: a new algorithm that achieves a better privacy-utility tradeoff; and a novel protocol that provides better protection against various attacks. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.

A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences.

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

A MULTI-PURPOSE CULTURAL HERITAGE DATA PLATFORM FOR 4D VISUALIZATION AND INTERACTIVE INFORMATION SERVICES

Abstract. The already arduous task of collecting, processing and managing heterogeneous cultural heritage data is getting more intense in terms of indexing, interaction and dissemination. This paper presents the creation of a 4D web-based platform as a centralized data hub, moving beyond advanced photogrammetric techniques for 3D capture and multi-dimensional documentation. Precise metric data, generated by a combination of image-based, range and surveying techniques, are spatially, logically and temporally correlated with cultural and historical resources, in order to form a critical knowledge base for multiple purposes and user types. Unlike conventional information systems, the presented platform, which adopts a relational database model, has the following front-end functionalities: (i) visualization of high-resolution 3D models based on distance dependent Level of Detail (LoD) techniques; (ii) web Augmented Reality; and (iii) interactive access and retrieval services. Information deduced from the developed services is tailored to different target audiences: scientific community, private sector, public sector and general public. The case study site is the UNESCO world heritage site of Meteora, Greece, and particularly, two inaccessible huge rocks, the rock of St. Modestos, known as Modi, and the Alyssos rock.

Anatomical classification of upper gastrointestinal organs under various image capture conditions using AlexNet

BackgroundMachine learning has led to several endoscopic studies about the automated localization of digestive lesions and prediction of cancer invasion depth. Training and validation dataset collection are required for a disease in each digestive organ under a similar image capture condition; this is the first step in system development. This data cleansing task in data collection causes a great burden among experienced endoscopists. Thus, this study classified upper gastrointestinal (GI) organ images obtained via routine esophagogastroduodenoscopy (EGD) into precise anatomical categories using AlexNet. MethodIn total, 85,246 raw upper GI endoscopic images from 441 patients with gastric cancer were collected retrospectively. The images were manually classified into 14 categories: 0) white-light (WL) stomach with indigo carmine (IC); 1) WL esophagus with iodine; 2) narrow-band (NB) esophagus; 3) NB stomach with IC; 4) NB stomach; 5) WL duodenum; 6) WL esophagus; 7) WL stomach; 8) NB oral–pharynx–larynx; 9) WL oral–pharynx–larynx; 10) WL scaling paper; 11) specimens; 12) WL muscle fibers during endoscopic submucosal dissection (ESD); and 13) others. AlexNet is a deep learning framework and was trained using 49,174 datasets and validated using 36,072 independent datasets. ResultsThe accuracy rates of the training and validation dataset were 0.993 and 0.965, respectively. ConclusionsA simple anatomical organ classifier using AlexNet was developed and found to be effective in data cleansing task for collection of EGD images. Moreover, it could be useful to both expert and non-expert endoscopists as well as engineers in retrospectively assessing upper GI images.

Updating the algorithm for processing laser scanning data using linear objects as an example

An increased specification degree concerning the received information about the object as one of the three-dimensional laser scanning technology’s main advantages is determined in the article according to the results analyzes of the Russian and foreign sources. Information redundancy of data at the stage of their processing can be considered as a disadvantage. This is illustrated in the article by the example of processing laser scanning data in the Credo 3D Scan program for the purpose of monitoring the air routes status. In the case under consideration, it was necessary to determine the deflection of the air routes wires over a point cloud. Before directly measuring the deflection, it is necessary to follow the procedure for processing the raw data described in the article. One of the algorithm stages is the automated search for supports and wires provided for the Credo 3D Scan program. It was found that the team accelerates the search for wires among an array of other objects. However, there is no command to calculate the numerical value of the wire deflection, that is why it is necessary to measure the distance from the points corresponding to the Earth’s surface to the lower point of each wire manually. It has been determined that the geoinformation data operative collection task has been generally resolved, while the process of their processing requires optimization. To do this, it is necessary to improve the “Power Transmission Lines Recognition” function by adding a function for automated deflection detection with visualization of each wire given value in the inscription form both in a three-dimensional model and in plan using a conventional symbol.

Partisipasi Masyarakat Sebagai Agen Perubahan Kurikulum Pendidikan Di Indonesia

Global life is something that cannot be avoided, and indeed it is not necessary to avoid it. But the problem is how to empower Muslims to color the global culture. For this reason, the role of Islamic education is very significant in providing provisions for humanity to be able to move swiftly in the area of globalization. In short, the quality of our education is still very concern. Its rank is far below other countries. In addition, the educational problems experienced by Indonesia also cause a decline in the academic moral quality of our education which causes the ultimate purpose of education is never seen and influential in our lives. Although it is recognized that it is still very difficult to see signs that allow our education get a resurgence and have an increasingly large role in the future, we are always required to "scavenge" the signs of its development. The handling of various educational problems cannot be done by one component only, especially if it is imposed to the government itself. Because educational activities are collective activities and education development is the collective task of all components of the nation. For this reason, raising public participation is something that needs to be pursued.

Fighting Hatred and Teaching Love in a ‘World that is Common to us all’: Recontextualizing Stefan Zweig’s Pedagogy of Humanism

Abstract In late 1933, Stefan Zweig stood before a committee to aid German Jews, and pleaded for immediate action to help German-Jewish children find new homes abroad. This article examines Zweig’s call to accept refugees within his larger quest to promote a humanist and universalist Europe. His goal was not merely intended to help individual Jewish children enjoy happier childhoods in other parts of the world, but a collective task to combat hatred. Stefan Zweig’s humanism and cosmopolitanism expressed themselves in a pedagogic and educational mission that was based on a particularly Jewish commitment to Bildung and took two major forms—a literary one (as seen in his historical-biographical writings) and an activist one (in his speeches, interviews and newspaper writings). The two expressions worked in tandem and reflected the same message and concerns. Moreover, both reflected his general aim to promote and help realise an alternative, humanist Europe; one in which the healthy and happy future of Jewish youth (and others) would be ensured.

Dual Sink-Based Optimized Sensing for Intelligent Transportation Systems

Wireless sensor networks (WSNs) as one of the non-negligible components of the Internet of Things (IoT) have proven to be a pillar of the Intelligent Transportation Systems (ITS). The tasks of collecting, processing and fusing the information related to traffic, accidents, congestion and also the detection of pavement distress on roads, are efficiently handled and monitored by WSN-based IoT. However, the energy constraints of the sensor nodes deployed along the roadside, create a perturbing concern for their realization in architecture. Therefore, to address this concern, in this paper, we have proposed an optimized sensing technique that employs two sinks. We term it as Dual sink-based Optimized Clustering Architecture employing Tunicate Swarm Algorithm (TSA), i.e., DOCAT in short. The fitness function of DOCAT integrates the novel fitness parameters for Cluster Head (CH) selection. The parameters are: 1) Residual and initial Energy, 2) Distance of the node from sink, 3) Intra-Cluster Average Distance (ICAD), 4) Network's average energy, and 5) Energy threshold. DOCAT is anticipated to be employed for accident prone roads, from where the critical accidental information is transmitted to healthcare venues through the IoT platform. The simulation results reveal that DOCAT acquires the proliferated performance compared to several similar algorithms in terms of the network reliability, network lifetime, throughput, and energy efficiency.

Collective information processing in human phase separation.

In our digital societies, individuals massively interact through digital interfaces whose impact on collective dynamics can be important. In particular, the combination of social media filters and recommender systems can lead to the emergence of polarized and fragmented groups. In some social contexts, such segregation processes of human groups have been shown to share similarities with phase separation phenomena in physics. Here, we study the impact of information filtering on collective segregation behaviour of human groups. We report a series of experiments where groups of 22 subjects have to perform a collective segregation task that mimics the tendency of individuals to bond with other similar individuals. More precisely, the participants are each assigned a colour (red or blue) unknown to them, and have to regroup with other subjects sharing the same colour. To assist them, they are equipped with an artificial sensory device capable of detecting the majority colour in their 'environment' (defined as their k nearest neighbours, unbeknownst to them), for which we control the perception range, k = 1, 3, 5, 7, 9, 11, 13. We study the separation dynamics (emergence of unicolour groups) and the properties of the final state, and show that the value of k controls the quality of the segregation, although the subjects are totally unaware of the precise definition of the 'environment'. We also find that there is a perception range k = 7 above which the ability of the group to segregate does not improve. We introduce a model that precisely describes the random motion of a group of pedestrians in a confined space, and which faithfully reproduces and allows interpretation of the results of the segregation experiments. Finally, we discuss the strong and precise analogy between our experiment and the phase separation of two immiscible materials at very low temperature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

Individual learning phenotypes drive collective behavior

Individual differences in learning can influence how animals respond to and communicate about their environment, which may nonlinearly shape how a social group accomplishes a collective task. There are few empirical examples of how differences in collective dynamics emerge from variation among individuals in cognition. Here, we use a naturally variable and heritable learning behavior called latent inhibition (LI) to show that interactions among individuals that differ in this cognitive ability drive collective foraging behavior in honey bee colonies. We artificially selected two distinct phenotypes: high-LI bees that ignore previously familiar stimuli in favor of novel ones and low-LI bees that learn familiar and novel stimuli equally well. We then provided colonies differentially composed of different ratios of these phenotypes with a choice between familiar and novel feeders. Colonies of predominantly high-LI individuals preferred to visit familiar food locations, while low-LI colonies visited novel and familiar food locations equally. Interestingly, in colonies of mixed learning phenotypes, the low-LI individuals showed a preference to visiting familiar feeders, which contrasts with their behavior when in a uniform low-LI group. We show that the shift in feeder preference of low-LI bees is driven by foragers of the high-LI phenotype dancing more intensely and attracting more followers. Our results reveal that cognitive abilities of individuals and their social interactions, which we argue relate to differences in attention, drive emergent collective outcomes.

Building referring expression corpora with and without feedback

The design of data collection experiments involving human participants is a common task in Referring Expression Generation (REG) and related fields. Many (or most) REG data collection tasks are implemented by making use of a human–computer (e.g., web-based) communicative setting, in which participants do not have any particular addressee in mind and do not receive any feedback regarding the appropriateness (e.g., uniqueness) of the descriptions that they produce. Others, at a possibly higher cost, make use of participant pairs engaged in some form of dialogue in which hearers may provide feedback allowing speakers to rephrase ambiguous or otherwise ill-formed descriptions. Leaving the issue of cost aside, however, it remains unclear whether the two methods elicit similar referring expressions for the purpose of REG research. To shed light on this issue, this paper presents a REG corpus built under three experimental conditions: a standard human–computer (or web-based) setting in which no feedback is available to the speaker, and two settings in which feedback regarding the appropriateness of the description may be provided either by an automated parsing tool or by a second participant at the receiving end of the communication. The corpus contains fully annotated descriptions in two domains—simple geometric objects and realistic human face images—and it is provided as a resource for the training and testing of REG algorithms in these communicative settings.

Collective olfactory search in a turbulent environment.

Finding the source of an odor dispersed by a turbulent flow is a vital task for many organisms. When many individuals concurrently perform the same olfactory search task, sharing information about other members' decisions can potentially boost the performance. But how much of this information is actually exploitable for the collective task? Here we show, in a model of a swarm of agents inspired by moth behavior, that there is an optimal way to blend the private information about odor and wind detections with the public information about other agents' heading direction. Our results suggest an efficient multiagent olfactory search algorithm that could prove useful in robotics, e.g., in the identification of sources of harmful volatile compounds.

ALGORITHM COLLECTION OF TEXT DATA IN THE KAZAKH LANGUAGE

This work is devoted to the creation of a system for the automatic collection and processing of open data in Kazakh from Internet resources, and bears practical significance in the tasks of collecting and analyzing text. The introduction substantiates the relevance of the chosen topic, a review of existing approaches, formulates the objectives of the study. We consider such a problem as the collection and primary processing of text data with subsequent analysis. Data collection is a priority, since open data from Internet resources is not structured and needs to be processed. The authors provide a system for processing web pages of Kazakh-language portals, and also gives practical application of this approach to real data of open resources using the created system. The approach of indexing documents using features is presented. The system will help structure open data from Internet resources, as well as analyze collected data. Practical results are presented

A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing

Tool condition monitoring (TCM) during the manufacturing process is of great significance for ensuring product quality and plays an important role in intelligent manufacturing. Current TCM systems deployed in the local device or cloud computing environment unable meet the requirements of low response latency and high accuracy at the same time. The emerging fog computing provides new solutions for the above problem. This paper presents a tool wear monitoring and prediction (TWMP) system based on deep learning models and fog computing. In order to improve monitoring and prediction accuracy, we propose a multiscale convolutional long short-term memory model (MCLSTM) to complete the tool wear monitoring task and a bi-directional LSTM model (BiLSTM) to complete the tool wear prediction task. To reduce the response latency of the TWMP system, we deploy the MCLSTM model and the BiLSTM model in a fog computing architecture. The fog computing architecture consists of an edge computing layer, a fog computing layer, and a cloud computing layer. The edge computing layer undertakes real-time signal collection task. The fog computing layer undertakes real-time tool wear monitoring task. The cloud computing layer with powerful computing resources undertakes intensive computing and latency-insensitive tasks such as data storage, tool wear prediction, and model training. A twist drill wear monitoring and prediction experiment is conducted to test the performance of the proposed system in terms of accuracy, response time, and network bandwidth consumption.