Inefficient Task Research Articles

An auction based task dispatching and pricing mechanism in bike-sharing

As an economical, low-carbon and convenient travel model, bike-sharing has become common in many cities around the world. However, the daily usage of shared bikes results in the dispatching problem, i.e., dispatching bikes to the specific destinations to satisfy riding demands. The bike-sharing platform can hire riders as workers and pay to incentivize them to accomplish the dispatching tasks. However, there exist multiple workers competing for the dispatching tasks, and they may strategically report their task accomplishing costs (which are usually private information only known by themselves) in order to make more profits, which may result in inefficient task dispatching results. In this paper, we first design a dispatching algorithm named GDY-MAX to allocate tasks to workers, which can achieve good performance. However, it cannot prevent workers strategically misreporting their task accomplishing costs. Regarding this issue, we further design a strategy proof mechanism under the budget constraint, which consists of a task dispatching algorithm and a worker pricing algorithm. We theoretically prove that our mechanism can satisfy incentive compatibility, individual rationality, budget constraint and a constant approximation ratio. Furthermore, we run extensive experiments to evaluate our mechanism based on a Mobike dataset. The results show that the performance of the proposed strategy proof mechanism and GDY-MAX is similar to the optimal algorithm in terms of the coverage ratio of accomplished task regions and the sum of task region value, and our mechanism has better performance than the uniform algorithm in terms of the total payment and the unit cost value.

ONLINE FIXED TANK VOLATILE ORGANIC CONTENT QUANTIFICATION USING MACHINE LEARNING

Tank emission calculation is typically performed offline, and manual data gathering from various tanks could be a timely and inefficient task. This paper presents iCON’s first principal process simulation software to perform tank emission quantification by leveraging its extensive database of all process simulation models and chemical components’ physical properties. Hence, a lot of operation cost is saved by eliminating the need for inventory sampling activity for each tank. This paper also will discuss online tank emission work processes to make them sustainable. iCON Tank Emission complies with the United States Environmental Protection Agency (USEPA) Compilation of Air Pollutant Emission Factors (AP-42) Fifth Edition Chapter 7 Liquid Storage Tanks that covers fixed roof tanks (vertical and horizontal) and floating roof tanks (internal, external, and domed) with a respective combination of the roof, shell, rim seal, fittings and breather vent settings. It is also linked to the monthly Malaysian Climate Condition covering domestic municipals’ temperature (minimum and maximum), Solar Irradiance Factor, and wind speed that improve the tank emission calculation accuracy. iCON Tank Emission lets users calculate thousands of tank emissions simultaneously by using iCON Machine Learning feature and displays the monthly and yearly summary reports for report standardisation among multiple business units. The simulation convergence time is instantaneous, making it time efficient for HSE engineers to perform strict monthly calculations and reporting online. Case studies and whatif scenarios could also be run to reduce tank emission via the introduction of vapour recovery and optimising tank mechanical parameters, i.e., type of rim seal, colour, and fittings selection. iCON Online Tank Emission links the iCON model with real-time plant information data for real-time tank emission calculation, transparent to management and regulators. The findings from this compositional first principle thermodynamic base tank emission simulation study are considered more efficient, faster, and cheaper than the conventional method.

A dense connection encoding–decoding convolutional neural network structure for semantic segmentation of thymoma

Accurately positioning and segmenting thymoma from computed tomography (CT) images is of great importance for an image-driven thymoma analysis. In clinical practice, the diagnosis and segmentation of thymomas for radiologists are time-consuming and inefficient tasks. Thus, it is necessary to develop a method to accurately and efficiently realize automatic segmentation of thymoma. Here, a dense skip connection encoding–decoding model (DSC-Net), which is a deep convolutional neural network, was proposed to perform automatic segmentation of thymoma with the ability to fuse feature maps under receptive fields of different scales. An image preprocessing method was also proposed to provide much more texture information and enhance the contrast between thymoma and its surrounding tissues. A total of 310 subjects who underwent contrast-enhanced CT scanning were included in this ethically-approved retrospective study. All of the CT slices were manually labeled by four experienced radiologists, and 80% of images were included in the training set and the rest were included in the testing set. The performance of segmentation was evaluated by calculating the accuracy, intersection over union (IoU), and Boundary F1 contour matching score (BFScore) between the predicted segmentation and the manual labels. For segmentation of thymoma in the testing set, the accuracy, IoU and BFScore were 92.96%, 87.86% and 0.9087 respectively. Compared to the U-Net method, the DSC-Net model improved IoU by 3.94%. In addition, the efficacy and robustness of DSC-Net in segmentation of different patients and different types of thymoma classified by the WHO histological classification criteria were verified. The proposed preprocessing method and DSC-Net demonstrated improved performance in segmentation of thymomas, suggesting the ability to provide consistent delineation and assist radiologists in their clinical applications.

GEUINF: Real-Time Visualization of Indoor Facilities Using Mixed Reality.

Mixed reality (MR) enables a novel way to visualize virtual objects on real scenarios considering physical constraints. This technology arises with other significant advances in the field of sensors fusion for human-centric 3D capturing. Recent advances for scanning the user environment, real-time visualization and 3D vision using ubiquitous systems like smartphones allow us to capture 3D data from the real world. In this paper, a disruptive application for assessing the status of indoor infrastructures is proposed. The installation and maintenance of hidden facilities such as water pipes, electrical lines and air conditioning tubes, which are usually occluded behind the wall, supposes tedious and inefficient tasks. Most of these infrastructures are digitized but they cannot be visualized onsite. In this research, we focused on the development of a new application (GEUINF) to be launched on smartphones that are capable of capturing 3D data of the real world by depth sensing. This information is relevant to determine the user position and orientation. Although previous approaches used fixed markers for this purpose, our application enables the estimation of both parameters with a centimeter accuracy without them. This novelty is possible since our method is based on a matching process between reconstructed walls of the real world and 3D planes of the replicated world in a virtual environment. Our markerless approach is based on scanning planar surfaces of the user environment and then, these are geometrically aligned with their corresponding virtual 3D entities. In a preprocessing phase, the 2D CAD geometry available from an architectural project is used to generate 3D models of an indoor building structure. In real time, these virtual elements are tracked with the real ones modeled by using ARCore library. Once the alignment between virtual and real worlds is done, the application enables the visualization, navigation and interaction with the virtual facility networks in real-time. Thus, our method may be used by private companies and public institutions responsible of the indoor facilities management and also may be integrated with other applications focused on indoor navigation.

Logo Detection With No Priors

In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors.

No effect of a dopaminergic modulation fMRI task by amisulpride and L-DOPA on reward anticipation in healthy volunteers

RationaleDysregulation of dopaminergic neurotransmission, specifically altered reward processing assessed via the reward anticipation in the MID task, plays a central role in the etiopathogenesis of neuropsychiatric disorders.ObjectivesWe hypothesized to find a difference in the activity level of the reward system (measured by the proxy reward anticipation) under drug administration versus placebo, in that amisulpride reduces, and L-DOPA enhances, its activity.MethodsWe studied the influence of dopamine agonist L-DOPA and the antagonist amisulpride on the reward system using functional magnetic resonance imaging (fMRI) during a monetary incentive delay (MID) task in n = 45 healthy volunteers in a randomized, blinded, cross-over study.ResultsThe MID paradigm elicits strong activation in reward-dependent structures (such as ventral striatum, putamen, caudate, anterior insula) during reward anticipation. The placebo effect demonstrated the expected significant blood oxygen level–dependent activity in reward-dependent brain regions. Neither amisulpride nor L-DOPA led to significant changes in comparison with the placebo condition. This was true for whole-brain analysis as well as analysis of a pre-defined nucleus accumbens region-of-interest mask.ConclusionThe present results cast doubt on the sensitivity of reward anticipation contrast in the MID task for assessing dopamine-specific changes in healthy volunteers by pharmaco-fMRI. While our task was not well-suited for detailed analysis of the outcome phase, we provide reasonable arguments that the lack of effect in the anticipation phase is not due to an inefficient task but points to unexpected behavior of the reward system during pharmacological challenge. Group differences of reward anticipation should therefore not be seen as simple representatives of dopaminergic states.

Techniques and Issues in Text Mining

Digital data in huge amount is being persistently generated at an unparalleled and exponential rate. In this digital era where internet stands the prime source for generating incredible information, it is vital to develop better means to mine the available information rapidly and most capably. Manual extraction of the salient information from the large input text documents is a time consuming and inefficient task. In this fast-moving world, it is difficult to read all the text-content and derive insights from it. Automatic methods are required. The task of probing for relevant documents from the large number of sources available, and consuming apt information from it is a challenging task and is need of the hour. Automatic text summarization technique can be used to generate relevant and quality information in less time. Text Summarization is used to condense the source text into a brief summary maintaining its salient information and readability. Generating summaries automatically is in great demand to attend to the growing and increasing amount of text data that is obtainable online in order to mark out the significant information and to consume it faster. Text summarization is becoming extremely popular with the advancement in Natural Language Processing (NLP) and deep learning methods. The most important gain of automatic text summarization is, it reduces the analysis time. In this paper we focus on key approaches to automatic text summarization and also about their efficiency and limitations.

Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks

Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. This paper proposes the use of a deep convolutional neural network (CNN) trained on a large urban sound dataset capable of efficiently predicting psycho-acoustic annoyance from raw audio signals continuously. We evaluate the proposed regression model and compare the resulting computation times with the ones obtained by the conventional direct calculation approach. The results confirm that the proposed model based on CNN achieves high precision in predicting psycho-acoustic annoyance, predicting annoyance values with an average quadratic error of around 3%. It also achieves a very significant reduction in processing time, which is up to 300 times faster than direct calculation, making CNN designed a clear exponent to work in IoT devices.

Altered relationship between prefrontal glutamate and activation during cognitive control in people with high trait anxiety

Trait anxiety can affect cognitive control resulting in ineffective and/or inefficient task performance. Moreover, previous functional Magnetic Resonance Imaging (fMRI) studies have reported altered dorsolateral prefrontal cortex (DLPFC) activity in anxious cohorts, particularly when executive control is required. Recently, it has been demonstrated that cortical glutamate levels can predict both functional activation during cognitive control, and anxiety levels. In the present study we sought to investigate the relationship between trait anxiety, prefrontal glutamate levels and functional activation in DLPFC during a cognitive control task. Thirty-nine participants assigned to either low trait anxiety (LTA) or high trait anxiety (HTA) groups underwent 1H-Magnetic Resonance Spectroscopy (1H-MRS) to measure levels of resting glutamate in the prefrontal cortex (PFC). Participants also completed fMRI during a Stroop task comprising congruent and incongruent colour word trials. The HTA group showed reduced task performance relative to the LTA group. In the LTA group, there was a positive association between PFC Glu levels and DLPFC activation during incongruent trials. This association was absent in the HTA group. Individual differences in trait anxiety affect the relationship between PFC glutamate levels and DLPFC activation, possibly contributing to ineffective task performance when cognitive control is required.

Clustering University Programs in Accounting to Enhance Selection Productivity: Precursor to Recommendation System Development

ABSTRACT Selecting prospective programs in higher education can be a problematic and inefficient task for applicants. In particular, one of the most significant challenges entails locating a specific subset of programs likely to be a good fit. In this paper, clustering techniques are employed in evaluating a specifically created data set of AACSB-accredited doctoral programs in accounting so as to aggregate them by type. In so doing, one is then better positioned to identify which schools best align with his/her relevant characteristics and objectives, thus gaining insight concerning the most appropriate subset of schools to initially consider for application purposes. This approach provides meaningful differentiation between the various program types, offers a means for improving productivity relative to the university application process, and demonstrates promise as an initial foundation for eventual construction of a program recommendation system for use in ostensibly any program application initiative within the higher education domain.

Discrimination of Camellia japonica cultivars and chemometric models: An interlaboratory study

Camellia japonica is a valued plant since ancient times throughout the world mostly due to their ornamental flowers. It has a high number of cultivars, with very similar phenotypic and genotypic characteristics which are difficult to discriminate, being some of them often rare and with a high price at the market. Their discrimination is mostly done through visual inspection of the morphologic characteristics which is a hard and inefficient task. Spectroscopic techniques had already been used for taxonomic purposes at species and sub-species level with success and could be an alternative for accurate C. japonica cultivars discrimination. Despite the already recognized success of such techniques, most of the studies arises from a single laboratory and little is known about the robustness of these techniques regarding interlaboratory data transferability. In this context, the work developed herein presents a double aim: (I) to explore the ability of near infrared (NIR) spectroscopy and partial least square – discriminant analysis (PLS-DA) to discriminate C. japonica cultivars and (II) to evaluate data transferability between two independent laboratories (Lab A and Lab B). Air-dried leaves NIR spectra of 43 C. japonica plants (15 distinct cultivars) were acquired in both laboratories using two similar NIR instruments (same manufacturer and model). Spectra were further modelled by PLS-DA after exploratory analysis using principal component analysis (PCA): (I) individually for Lab A and Lab B; (II) using Lab A as calibration and Lab B as validation set and vice-versa and (III) with using Lab A and Lab B data together. The percentage of C. japonica cultivars discrimination for both laboratories was nearly the same (around 83%) indicating no significant differences between Lab A and Lab B analysis. However, the results were quite poor when spectra were modelled with data from a single laboratory and validated with the other (65.5 and 63.8%). When data were merged, 85.9% of correct cultivars assignments were obtained. The results herein obtained could benefit of including additional cultivars and plants; but demonstrated the ability of NIR spectroscopy for C. japonica cultivars discrimination. Regarding data transferability, even when dealing with a similar instrument, some issues arisen preventing easy and efficient spectral library transfers.

Rework Quantification and Influence of Rework on Duration and Cost of Equipment Development Task

Rework is a sub-task within equipment development tasks that is revised after initial completion to meet task requirements. Some sub-tasks require multiple rework iterations due to their uncertainty and complexity, or the technology and process needs of the overall task, resulting in inefficient task implementation and resource wastage. Therefore, studying the impact of rework iterations on the duration and cost of development tasks is worthwhile. This study divides rework into foreseeable and hidden types and uses several methods to express and quantify their parameters. The main influencing factors in rework iterations—the uncertainty and complexity of the development task—are quantitatively analyzed. Then, mathematical and mapping models of the dependence between sub-tasks, uncertainty, complexity, and rework parameters are established. The impacts of rework type and rework parameters on the duration and cost of equipment development tasks are analyzed via simulation based on the design structure matrix (DSM). Finally, an example is used to illustrate the influence of different rework types and rework parameters on development tasks’ duration and cost. The results show that the duration and cost of development tasks are greater, their volatility range is wider, and the distribution is more dispersed when both foreseeable and hidden rework are considered.

Laparoscopic Liver Resection with Augmented Reality: A Preclinical Experience.

Intraoperative imaging, such as ultrasound, provides subsurface anatomical information not seen by standard laparoscopy. Currently, information from the two modalities may only be integrated in the surgeon's mind, an often distracting and inefficient task. The desire to improve intraoperative efficiency has guided the development of a novel, augmented reality (AR) laparoscopic system that integrates, in real time, laparoscopic ultrasound (LUS) images with the laparoscopic video. This study shows the initial application of this system for laparoscopic hepatic wedge resection in a porcine model. The AR system consists of a standard laparoscopy setup, LUS scanner, electromagnetic tracking system, and a laptop computer for image fusion. Two liver lesions created in a 40-kg swine by radiofrequency ablation (RFA) were resected using the novel AR system and under standard laparoscopy. Anatomical details from the LUS were successfully fused with the laparoscopic video in real time and presented on a single screen for the surgeons. The RFA lesions created were 2.5 and 1 cm in diameter. The 2.5 cm lesion was resected under AR guidance, taking about 7 minutes until completion, while the 1 cm lesion required 3 minutes using standard laparoscopy and ultrasound. Resection margins of both lesions grossly showed noncoagulated liver parenchyma, indicating a negative-margin resection. The use of our AR system in laparoscopic hepatic wedge resection in a swine provided real-time integration of ultrasound image with standard laparoscopy. With more experience and testing, this system can be used for other laparoscopic procedures.

Passive Location Resource Scheduling Based on an Improved Genetic Algorithm.

With the development of science and technology, modern communication scenarios have put forward higher requirements for passive location technology. However, current location systems still use manual scheduling methods and cannot meet the current mission-intensive and widely-distributed scenarios, resulting in inefficient task completion. To address this issue, this paper proposes a method called multi-objective, multi-constraint and improved genetic algorithm-based scheduling (MMIGAS), contributing a centralized combinatorial optimization model with multiple objectives and multiple constraints and conceiving an improved genetic algorithm. First, we establish a basic mathematical framework based on the structure of a passive location system. Furthermore, to balance performance with respect to multiple measures and avoid low efficiency, we propose a multi-objective optimal function including location accuracy, completion rate and resource utilization. Moreover, to enhance its practicability, we formulate multiple constraints for frequency, resource capability and task cooperation. For model solving, we propose an improved genetic algorithm with better convergence speed and global optimization ability, by introducing constraint-proof initialization, a penalty function and a modified genetic operator. Simulations indicate the good astringency, steady time complexity and satisfactory location accuracy of MMIGAS. Moreover, compared with manual scheduling, MMIGAS can improve the efficiency while maintaining high location precision.

CRIMINALITY SPATIAL DYNAMIC IN MANAUS CITY, AM

Thefts and robberies are common crimes in urban centers. Criminality control is a state responsibility and the complex urban context turns the police tactics into a hard and, frequently, inefficient task. This paper presents a method to identify, characterize and diagnose areas with highest incidence of thefts and robberies. The method has as a goal to increase security perception in citizens and improve police efficiency. The results are presented through density maps, photographic records, field reports and descriptive statistics, producing geo-intelligence results dealing with geographic and temporal dynamics of theft and robbery crimes in three neighborhoods of Manaus city, in Amazonas state, Brazil.

An automatic performance model-based scheduling tool for coupled climate system models

The prediction ability of the climate system is highly depended on the efficient integration of observations and simulations of the Earth, which is regarded as a canonical example of the cyber–physical system. The climate system model, the simulation engine in this cyber–physical system, is one of most challenging applications in scientific computing. It utilizes the multi-physics simulation that couples multiple components, conducts decadal to millennium simulations, and has long been an important application on supercomputers. However, current climate system models suffer from the inefficient task scheduling methods resulting in an intolerable simulation time. Take the Community Earth System Model (CESM), the most widely used climate system model, as an example, one major reason that CESM suffers from bad performances is the huge overhead to rationally distribute processes among the coupled heterogeneous components. According to the report of NCAR, every percent improvement in CESM performance frees up to the equivalent of $250,000 in computing resources in their scientific experiments. To address such challenge, our paper first constructs a lightweight and accurate performance model for effectively capturing and predicting the heterogeneous time-to-solution performance of end-to-end CESM components with a given simulation configuration. Then, based on the performance model, we further propose an efficient scheduling strategy based on rectangular packing method to determine the best process layout among different components, and the process numbers assigned to each component. Our evaluations show that we can achieve 58% average run time reductions on CESM comparing to the widely used sequential process layout for a scale of 144–480 cores on typical CPU clusters. And we can save 4 million CPU hours when we conduct one standard scientific experiment (a 2870-year simulation), which equals to save $40,089 with a charge of $0.01 per CPU hour. Meanwhile, 26% extra performance improvements also could be gained in our methods comparing to the heuristic branch and bound algorithm with the guidance of the known curve-fitting performance model.

QoS enhanced distributed load balancing and task scheduling framework for wireless networks using hybrid optimisation algorithm

Quality of service (QoS) is the main challenging issue in load balancing, task scheduling process in dynamic systems, such as grid system, peer-to-peer system, ad hoc networks, cloud computing system, pervasive computing system, and online social network system. The inefficient task scheduling process reduces QoS parameters, such as energy consumption, throughput, network lifetime, deadline missing ratio and schedule delay time. In this paper, we proposed QoS enhanced distributed load balancing, task scheduling frameworks for wireless networks (WNs) using hybrid optimisation algorithm (EDFHOA). We improve QoS of distributed frameworks by two phase system through an efficient clustering methodology using particle swarm optimisation (PSO) algorithm for load balancing in the first phase. A cuckoo search (CS) algorithm with load balanced for task scheduling in the second phase. The proposed two-phase hybrid algorithms enhance task scheduling, such as latency experienced by the balanced clustering, energy consumption, throughput, network lifetime, efficient resource utilisation and less processing time requirement and highly accurate than fault tolerant task allocation algorithm (FTAOA).

Principles for designing mathematical tasks that enhance imitative and creative reasoning

The design research programme learning by imitative and creative reasoning (LICR) studies whether, how and why tasks and teaching that enhance creative reasoning lead to a more productive struggle and more efficient learning than the common but inefficient task designs based on imitating given solution procedures. The purpose of this paper is to synthesise the research outcomes determined to date by providing the following: a conceptual framework for key concepts and relationships among teaching, tasks, student activities and learning; a theoretical basis for analyses of causal effects between task/teaching design and learning outcomes; a design research methodology for transforming initial design ideas, through cycles of evaluation and revision, into firmer design principles; and an application of this theory and methodology to empirical studies carried out to date, in order to propose task-design principles related to imitative and creative reasoning.

Fingerprint indexing based on minutiae pairs and convex core point

Fingerprint identification is an important issue for identifying fingerprints and plays a key role in the fingerprint recognition systems. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms. Fingerprint indexing is a key strategy in automatic fingerprint identification systems (AFISs) which allows us to reduce the number of candidates, the search space, and the occurrences of false acceptance in large databases. In this paper, an efficient indexing algorithm is proposed using minutiae pairs and convex core point which employs k-means clustering and candidate list reduction criteria to improve the identification performance. Our proposal can effectively reduce the search space and number of candidates for fingerprint matching, and thus achieves higher efficiency and significantly improves the system retrieval performance. Experimental results over some of the fingerprint verification competition (FVC) and the national institute of standards and technology (NIST) databases prove the superiority of the proposed approach against some of the well known indexing algorithms.

A Secure and Practical Authentication Scheme Using Personal Devices

Authentication plays a critical role in securing any online banking system, and many banks and various services have long relied on username/password combos to verify users. Memorizing usernames and passwords for a lot of accounts becomes a cumbersome and inefficient task. Furthermore, legacy authentication methods have failed over and over, and they are not immune against a wide variety of attacks that can be launched against users, networks, or authentication servers. Over the years, data breach reports emphasize that attackers have created numerous high-tech techniques to steal users’ credentials, which can pose a serious threat. In this paper, we propose an efficient and practical user authentication scheme using personal devices that utilize different cryptographic primitives, such as encryption, digital signature, and hashing. The technique benefits from the widespread usage of ubiquitous computing and various intelligent portable and wearable devices that can enable users to execute a secure authentication protocol. Our proposed scheme does not require an authentication server to maintain static username and password tables for identifying and verifying the legitimacy of the login users. It not only is secure against password-related attacks, but also can resist replay attacks, shoulder-surfing attacks, phishing attacks, and data breach incidents.