Valuable Tasks Research Articles

Handwritten Multi-Scale Chinese Character Detector with Blended Region Attention Features and Light-Weighted Learning.

Character-level detection in historical manuscripts is one of the challenging and valuable tasks in the computer vision field, related directly and effectively to the recognition task. Most of the existing techniques, though promising, seem not powerful and insufficiently accurate to locate characters precisely. In this paper, we present a novel algorithm called free-candidate multiscale Chinese character detection FC-MSCCD, which is based on lateral and fusion connections between multiple feature layers, to successfully predict Chinese characters of different sizes more accurately in old documents. Moreover, cheap training is exploited using cheaper parameters by incorporating a free-candidate detection technique. A bottom-up architecture with connections and concatenations between various dimension feature maps is employed to attain high-quality information that satisfies the positioning criteria of characters, and the implementation of a proposal-free algorithm presents a computation-friendly model. Owing to a lack of handwritten Chinese character datasets from old documents, experiments on newly collected benchmark train and validate FC-MSCCD to show that the proposed detection approach outperforms roughly all other SOTA detection algorithms.

A method for selecting processes for automation with AHP and TOPSIS

Organizations are more frequently turning towards robotic process automation (RPA) as a solution for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. These software robots can handle various rule-based, digital, repetitive tasks. However, currently available process identification methods must be qualified to select suitable automation processes accurately. Wrong process selection and failed attempts are often the origin of process automation's bad reputation within organizations and often result in the avoidance of this technology. As a result, in this research, a method for selecting processes for automation combining two multi-criteria decision-making techniques, 'Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), will be proposed, demonstrated, and evaluated. This study follows the Design Science Research Methodology (DSRM) and applies the proposed method for selecting processes for automation to a real-life scenario. The result will be a method to support the proper selection of business processes for automation, increasing the success of implementing RPA tools in an organization.

Unlocking the Benefits of Mobile Manipulators for Small and Medium-Sized Enterprises: A Comprehensive Study

The mobile manipulator is a relatively recent development in the robotics domain. It is a type of robot system that extends the manipulation capabilities of a robotic arm by the mobility capabilities of a mobile platform. As a result, mobile manipulators promise to provide a wider usage of manipulators (for example, by extending their reach) and to enable automation of use cases that are still manually performed. They can, for instance, automate repetitive tasks, freeing human workers to focus on more valuable tasks. They can also be deployed to perform dangerous or difficult tasks, reducing the risk of workplace injuries. Additionally, using mobile manipulators can help meet the growing demand for fast and flexible production processes, enabling them to better compete in a rapidly changing market. However, integrating these systems bears complexity, high acquisition costs, and unsolved safety and security challenges. Therefore, this new approach is only beginning to be adopted in the manufacturing industry. Especially small and medium-sized companies (SMEs) suffer from these challenges, as they usually have fewer resources. However, mobile manipulators are a valuable tool to enhance their competitiveness and profitability in various industries. In order to unleash these benefits, this paper provides a comprehensive review of application cases of mobile manipulators within smart factory setups. It focuses on specific requirements and advantages for SMEs.

Recent advances in the application of nanocatalysts in C‐N coupling reactions

The development of various methodologies for the formation of carbon‐nitrogen bonds is one of the valuable tasks in the field of organic synthesis because the final products are extensively utilized in pharmaceuticals, biologically active compounds, and natural products. The utilization of nanosized metal catalysts with high surface energy, large surface‐to‐volume ratio, excellent thermal stability, and reactive morphology in C‐N cross‐coupling reactions has received considerable attention, in recent years. The excellent catalytic performance, the high yield of products, carrying out the reactions under relatively mild and ligand‐free conditions, and less toxicity to the environment, recoverability, and reusability of catalysts for several times without a remarkable degradation in activity are advantages of these nanocatalysts.This review intends to summarize the latest progress in the fabrication of nanocatalysts and their applications in the construction of carbon‐nitrogen bonds via cross‐coupling reactions. Abundant nanocatalysts based on metal and metal oxide nanoparticles/complexes including copper, palladium, nickel, cobalt, silver, gold, zirconium, and zinc have successfully been applied in these reactions. Various aspects of the reactions, different strategies of fabrication of nanocatalysts, and their recyclability have been surveyed. Literature has been investigated from 2015 to 2021.

Setting course for a translational pharmacology and a predictive toxicology based on the numerical probability of clinical relevance

For a significant share of the chemicals, current bioassays mispredicted the outcomes in the reference methods they simulate. For any drug or chemical, and depending on the regulatory or corporate situation, three different approaches calculate the numerical probability by which agreement (or discrepancy) can be statistically expected between (1) the result of a predictive bioassay, and (2) the outcome on its reference method. If such concordance is expected with enough confidence based on a sufficient percentage probability, then specific results from that bioassay can be considered as correctly predictive. The statistical approaches analyzed in this article assist in valuable tasks, including (1) a better translation of the clinical relevance (or insignificance) of specific preclinical findings; (2) waiving unnecessary animal testing (or any other unpredictive testing; e.g., a given in vitro bioassay), and (3) in advancing only the most promising candidates in the pharmaceutical, pesticide, or chemical development process.

Job Upgrading and Earnings Growth for Non-college Workers

Workers without a college degree have faced slow wage growth for nearly four decades. Some employers organize work in ways associated with higher pay for these workers, by implementing job enrichment, high-performance work systems, or structured management practices. However, these job upgrading strategies can be biased toward higher-skill workers and in some cases actually reduce employment opportunities for workers without a college degree. We clarify this trade-off by specifying several pathways through which job upgrading can affect pay, some of which are more or less skill-biased. We then test these ideas by matching restricted-use linked employer-employee data to the near-universe of online job postings. This allows us to assess shifting worker selection and to compare among different types of job upgrading. We show that jobs listing more valuable tasks pay more, but that around half of this premium is due to more positive worker selection. We then show that jobs that specifically involve higher discretion and more tasks involving on-the-job learning have higher starting pay.

Robotic Process Automation (RPA) Adoption: A Systematic Literature Review

Abstract Robotic process automation (RPA) is a recent technology that has recently become increasingly adopted by companies as a solution for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. The increased interest, reflected in the increasing number of articles regarding approaches and test cases, has triggered the necessity for a summary that could extract the more generalisable ideas and concepts about these software robots. This paper used a Systematic Literature Review (SLR) approach to find and synthesise information from articles obtained on this subject. This research identified the most general implementation approaches of successful RPA adoption cases, observed benefits, challenges commonly faced by organisations, characteristics that make processes more suitable for RPA, and research gaps in the current literature. The findings presented in this paper have two purposes. The first is to provide a way for companies and organisations to become more familiar with good practices regarding the adoption of robotic process automation. The second is to foster further research on the subject by complementing the current knowledge and proposing new paths for research.

Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation.

Background: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies. Methods and procedures: This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), to many thousands of synthetic cases. Results: Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model’s capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities. Conclusion: We can confirm the ability of deep learning methods and the proposed Znet framework to detect and segment tumors in MR images. Furthermore, pixel accuracy evaluation may not be a suitable evaluation measure for semantic segmentation in case of class imbalance in MR images segmentation. This is because the dominant class in ground truth images is the background. Therefore, a high value of pixel accuracy can be misleading in some computer vision applications. On the other hand, alternative evaluation metrics, such as dice and IoU (Intersection over Union), are more factual for semantic segmentation. Clinical impact: Artificial intelligence (AI) applications in medicine are advancing swiftly, however, there is a lack of deployed techniques in clinical practice. This research demonstrates a practical example of AI applications in medical imaging, which can be deployed as a tool for auto-segmentation of tumors in MR images.

Online Resource Allocation With Machine Variability: A Bandit Perspective

Approximation jobs that allow partial execution of their many tasks to achieve valuable results have played an important role in today's large-scale data analytics. This fact can be utilized to maximize the system utility of a big data computing cluster by choosing proper tasks in scheduling for each approximation job. A fundamental challenge herein, however, is that the machine service capacity may fluctuate substantially during a job's lifetime, which makes it difficult to assign valuable tasks to well-performing machines. In addition, the cluster scheduler needs to make online scheduling decisions without knowing future job arrivals according to machine availabilities. In this paper, we tackle this online resource allocation problem for approximation jobs in parallel computing clusters. In particular, we model a cluster with heterogeneous machines as a multi-armed bandit where each machine is treated as an arm. By making estimations on machine service rates while balancing the exploration-exploitation trade-off, we design an efficient online resource allocation algorithm from a bandit perspective. The proposed algorithm extends existing online convex optimization techniques and yields a sublinear regret bound. Moreover, we also examine the performance of the proposed algorithm via extensive trace-driven simulations and demonstrate that it outperforms the baselines substantially.

Battery charge scheduling in long-life autonomous mobile robots via multi-objective decision making under uncertainty

The daily working hours of mobile robots are limited primarily by battery life. Most systems use a combination of thresholds and fixed periods to decide when to charge. This produces charging behaviour that ignores high-value tasks that must be performed within time-windows or by deadlines. Instead the robot should schedule charging adaptively, taking into account the times of day when it is expected to be given more valuable tasks to perform. This paper proposes an approach that exploits the fact that, during long-term deployments, the robot can learn when it is most probable that valuable tasks are added to the system, enabling it to schedule charging at times that are expected to be less busy. We pose the problem of scheduling battery charging as a multi-objective sequential decision making problem over a time-dependent Markov decision process model of expected task rewards and battery dynamics. We evaluate the scalability and solution quality of our multi-objective scheduler, and compare it with a typical rule-based approach. Empirical results show that our approach enables more flexible and efficient robot behaviour, which takes into account both the value of current available tasks and the predicted value of future tasks to decide whether to charge at a given time.

Adaptive Parallel Fuzzing with Multi-candidate Task Scheduling

Parallel fuzzing is a widely used technique for bug detection. It improves fuzzing performance by taking full advantage of the computing resource. Nevertheless, existing parallel fuzzing approaches fail to extend fuzzing optimizations of single mode to parallel mode, due to the lack of efficient information synchronization and task division. To address the challenge, researchers propose a parallel fuzzing framework that combines guiding information synchronization with task division based on branch bitmap to improve the performance of the fuzzers in parallel mode. However, when augmenting existing fuzzers with the parallel framework, we find two types of limitations caused by some mechanisms of the fuzzers, such as task scheduling mechanism. In this paper, we present an optimized parallel fuzzing approach to improve fuzzing efficiency by making valuable tasks executed as much as possible. On the one hand, we introduce a multi-candidate task scheduling mechanism, which takes multiple tasks corresponding to an input as candidates, rather than one to obtain the chances of executing tasks that are valuable but can be missed. On the other hand, we apply a synchronization information-centric design solution to some mechanisms of the fuzzers and take synchronization information as a good indicator to decide whether the parallel instances ought to shift their running mode, which can improve the adaptive ability of parallel instances and facilitate them to timely execute valuable tasks. We implement a prototype system OPAFL on top of Fairfuzz and PAFL, and evaluate its performance on several real-world software. The experimental results show that OPAFL can execute more tasks valuable and achieve higher branch coverage, compared with original parallel mode of Fairfuzz and Fairfuzz augmented with PAFL.

Near-Infrared and Visible Photoactivation to Uncage Carbon Monoxide from an Aqueous-Soluble PhotoCORM.

Multiphoton excitation allows one to access high energy excited states and perform valuable tasks in biological systems using tissue penetrating near-infrared (NIR) light. Here, we describe new photoactive manganese tricarbonyl complexes incorporating the ligand 4'-p-N,N-bis(2-hydroxyethyl)amino-benzyl-2,2':6',2″-terpyridine (TPYOH), which can serve as an antenna for two photon NIR excitation. Solutions of Mn(CO)3(TPYOH)X (X = Br- or CF3SO3-) complexes are very photoactive toward CO release under visible light excitation (405 nm, 451 nm). The same responses were also triggered by multiphoton excitation at 750 and 800 nm. In this context, we discuss the potential applications of these complexes as visible/NIR light photoactivated carbon monoxide releasing moieties (photoCORMs). We also report the isolation and crystal structures of the TPYOH complexes Mn(TPYOH)Cl2 and [Mn(TPYOH)2](CF3SO3)2, to illustrate a possible photolysis product(s).

Using the Lean Six Sigma Methodology to Reduce Mouse Cage Sanitation Time for Animal Care and Use Programs.

Although efficient sanitization is a high priority for Animal Care and Use Programs (ACUP), the cage sanitation process for animals used in biomedical research can be labor-intensive. To increase the efficiency of sanitation services provided by ACUP, the current project used the Lean Six Sigma methodology to reduce the time to sanitize mouse cages by implementing countermeasures to reduce causes of waste within this process for the Animal Care Operations (ACO) group at a leading public research university. Lean Six Sigma was selected for use in this project given its goal to make organizations' routine operations flow as smoothly and efficiently as possible and its applicability to biomedical and research settings as discussed in the literature. Through observation of the ACO's existing mouse cage sanitation process and brainstorming sessions with the ACO staff, the existing process was mapped, a baseline measurement of the current process performance was established, the process was analyzed to identify potential causes of waste, and appropriate countermeasures were implemented to improve the process. These countermeasures involved actions specifically targeted at reducing process delays, which included implementing new procedures, clarified schedules, and visual controls. As a result of this project, ACO's average mouse cage sanitation process cycle time was reduced by 35 min, and the time saved was reinvested within the ACO to provide technicians additional time to complete other valuable tasks and thus support animal research endeavors at this university.

Position-Information-Indexed Classifier for Improved Through-Wall Detection and Classification of Human Activities Using UWB Bio-Radar

Noncontact penetrating detection and classification of human activities based on micro-Doppler signatures (MDs) using ultrawideband (UWB) bio-radars are valuable tasks in various practical applications such as post-disaster search-and-rescue operations and urban military operations. However, for all classifiers, MD features of different-magnitude activities at different positions are likely to result in classification errors due to MD attenuation and confusions. This letter proposes a classifier improving method called position-information-indexed classifier (PIIC). It aims at enhancing the performance of various classifiers in terms of recognition and classification. This method fully exploits the position information acquired by UWB bio-radar to create a position-labeled modularized database of MD features. It also guides searching adaptively for optimal predict submodel of PIICs for activity classification at a random position. We report through-wall detection and classification experimental results related to five activities within a range of 6 m. These results, based on four typical classifiers, demonstrate that PIIC-based classifiers can avoid those classification errors in an effective manner. Moreover, all PIIC-based classifiers present a better classification performance with an average accuracy rise of 8.16% compared with those of overall-model-based classifiers. These performance evaluation experiments suggest that this method is strongly robust and stable, presenting wide applicability to various classifiers.

Factor 10 Expedience of Monthly Linac Quality Assurance via an Ion Chamber Array and Automation Scripts.

Purpose:While critical for safe and accurate radiotherapy, monthly quality assurance of medical linear accelerators is time-consuming and takes physics resources away from other valuable tasks. The previous methods at our institution required 5 hours to perform the mechanical and dosimetric monthly linear accelerator quality assurance tests. An improved workflow was developed to perform these tests with higher accuracy, with fewer error pathways, in significantly less time.Methods:A commercial ion chamber array (IC profiler, Sun Nuclear, Melbourne, Florida) is combined with automation scripts to consolidate monthly linear accelerator QA. The array was used to measure output, flatness, symmetry, jaw positions, gated dose constancy, energy constancy, collimator walkout, crosshair centering, and dosimetric leaf gap constancy. Treatment plans were combined with automation scripts that interface with Sun Nuclear’s graphical user interface. This workflow was implemented on a standard Varian clinac, with no special adaptations, and can be easily applied to other C-arm linear accelerators.Results:These methods enable, in 30 minutes, measurement and analysis of 20 of the 26 dosimetric and mechanical monthly tests recommended by TG-142. This method also reduces uncertainties in the measured beam profile constancy, beam energy constancy, field size, and jaw position tests, compared to our previous methods. One drawback is the increased uncertainty associated with output constancy. Output differences between IC profiler and farmer chamber in plastic water measurements over a 6-month period, across 4 machines, were found to have a 0.3% standard deviation for photons and a 0.5% standard deviation for electrons, which is sufficient for verifying output accuracy according to TG-142 guidelines. To minimize error pathways, automation scripts which apply the required settings, as well as check the exported data file integrity were employed.Conclusions:The equipment, procedure, and scripts used here reduce the time burden of routine quality assurance tests and in most instances improve precision over our previous methods.

Discovering Foreign Keys on Web Tables with the Crowd

Foreign-key relationship is one of the most important constraints between two tables. Previous works focused on detecting inclusion dependencies (INDs) or foreign keys in relational database. To discover foreign-key relationship is obviously helpful for analyzing and integrating data in web tables. However, because of poor quality of web tables, it is difficult to discover foreign keys by existing techniques based on checking basic integrity constraints. In this paper, we propose a hybrid human-machine framework to detect foreign keys on web tables. After discovering candidates and evaluating their confidence of being true foreign keys by machine algorithm, we verify those candidates leveraging the power of the crowd. To reduce the monetary cost, a dynamical task selection technique based on conflict detection and inclusion dependency is proposed, which could eliminate redundant tasks and assign the most valuable tasks to workers. Additionally, to make workers complete tasks more effectively and efficiently, sampling strategy is applied to minimize the number of tuples posed to the crowd. We conducted extensive experiments on real-world datasets and results show that our framework can obviously improve foreign key detection accuracy on web tables with lower monetary cost and time cost.

The Relationship Between Motivation and Online Self-Regulated Learning

manage their own learning. The self-regulated learning practices of goal setting, environment structuring, task strategies, self-evaluation, time management, and help seeking are developed through experience and motivation. This study sought to determine the levels of self-regulated learning and identify the motivation constructs that correlated to the levels of self-regulated learning of students in an online agriculture dual enrollment course. Students had the highest self-regulation in the areas of goal setting and environment structuring. The lowest online learning self-regulation was in help seeking. Task value was the motivation construct receiving the highest mean score, while test anxiety received the lowest score. Relationships between online self-regulated learning and the motivation constructs of task value, self-efficacy, intrinsic motivation, extrinsic motivation, control beliefs, and test anxiety were statistically significant. Faculty in online courses are encouraged to aid in the development of help seeking, time management, and meta-analysis strategies. Faculty are also encouraged to incorporate valuable tasks within the online curriculum to increase students’ motivation to learn. Course developers are encouraged to incorporate problem-based learning, authentic assessments, and team-based learning approaches to better engage students. Research should continue to investigate these practices as they relate to increasing student motivation.

Order Entry Protocols Are an Amenable Target for Workflow Automation

PurposeOrder entry protocol selection of advanced imaging studies is labor-intensive, can disrupt workflow, and may displace staff from more valuable tasks. The aim of this study was to explore and compare the behaviors of radiologic technologists and radiologists when determining protocol to identify opportunities for workflow automation. MethodsA data set of over 273,000 cross-sectional examination orders from four hospitals within our health system was created. From this data set, we isolated the 12 most frequently requested examinations, which represent almost 50% of the entirety of advanced imaging volume. Intergroup comparisons were made between behavior of radiologic technologists and radiologists or residents when determining protocol. Frequencies of changes were calculated. Common parameters of changed examinations were identified. ResultsThe overall change rate for both radiologists and residents (4%) is very low and comparable to the overall change rate of radiologic technologists (1%). The change rates for the 12 most ordered examinations were calculated and compared individually. Most examinations that underwent change involved a patient with a low estimated glomerular filtration rate, a patient with a contrast allergy, or a provider ordering a general examination but in fact wanting an organ-specific protocol or an angiographic study. ConclusionOrder entry protocol selection of the most frequently ordered advanced imaging examinations was rarely a value-added activity because these examinations are rarely changed. Changes follow predictable patterns that make order entry protocol selection of most radiology orders for advanced imaging amenable to workflow automation.

Spanish Initiative for the Automation in Urban Transport: AutoMOST

The progressive automation of transport will imply a new paradigm in mobility, which will profoundly affect people, logistics of goods, as well as other sectors dependent on transport. It is precise within this automation where the development of new driving technologies is going to cause a great impact on the mobility of the near future, and that will have an effect on the economic, natural and social environment. It is therefore a primary issue at the global level, as it is reflected in the work programs of the European Commission in relation to the road transport [1] [2]. Thus, the size impact is caused by the following novelties and advantages: 1) Safety: Accidents reduction caused by human error; 2) Efficiency increase in transportation, both in energy consumption and time; 3) Comfort for users and professionals who will increase their operational availability to execute other more valuable tasks, both for them and enterprises; 4) Social Inclusion: enabling mobility easily for everybody during more time; 5) Accessibility, to get to city centers and other difficult reach places. It should be noted that the economic impact projected for automated driving for the years to come ranges up to €71 bn in 2030, when estimated global market for automated vehicles is 44 million vehicles, as is reflected in document Automated Driving Roadmap by ERTRAC [3], European Road Transport Research Advisory Council (http://www.ertrac.org/uploads/documentsearch/id38/ERTRAC_Automated-Driving-2015.pdf). As background that already anticipates these im-provements, the Advance Driver Assistance System (ADAs) have already showed the safety increase in the last ten years, but always maintain a leading role for the driver. Related to the efficiency increase, automated driving offers great opportunities for those companies where mobility is a key factor in operating costs, and affects the whole value chain. The project opportunity is consistent with ERTRAC vision, especially in applications focused on the urban environment [4], where it is expected a deployment of the technology of high level automation in an immediate future. This is possible by the potential to incorporate smart infrastructure to improve guidance and positioning, as well as lower speed, which eases its progressive deployment. The objective of AutoMOST is developing technologies for the automation of vehicles in urban transport and industrial applications, to increase significantly the efficiency, safety and environmental sustainability. More specifically, AutoMOST will allow the implementation of shared control systems (Dual-Mode) [5] for future automated vehicles that allow the services operate more efficiently and flexibly, in a context of intelligent and connected infrastructures.

Toward Efficient Mechanisms for Mobile Crowdsensing

Mobile crowdsensing systems aim to provide various novel applications by employing pervasive smartphones. A key factor to enable such systems is substantial participation of normal smartphone users, which requires effective incentive mechanisms. In this paper, we investigate incentive mechanisms for online scenarios, where users arrive and interact with a task requester in a random order, and they have preferences (e.g., photographing) or limits (e.g., travel distance) over the sensing tasks. In existing online mechanisms, the task requester has limited power in assigning tasks to the selected users, i.e., it has to pay for all of the tasks specified by the selected users, although some of these tasks are of little value. To accommodate this, we investigate a more flexible setting, where the requester can actively assign most valuable tasks to the selected users. We design two online incentive mechanisms motivated by a sampling-accepting process and weighted maximum matching. We prove that the designed mechanisms achieve computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, and constant competitiveness. By carrying out extensive experiments on two real-world geographical datasets, we demonstrate the practical applicability of the proposed mechanisms.