Incomplete Tasks Research Articles

Automating TODO-missed Methods Detection and Patching

TODO comments are widely used by developers to remind themselves or others about incomplete tasks. In other words, TODO comments are usually associated with temporary or suboptimal solutions. In practice, all the equivalent suboptimal implementations should be updated (e.g., adding TODOs) simultaneously. However, due to various reasons (e.g., time constraints or carelessness), developers may forget or even are unaware of adding TODO comments to all necessary places, which results in the TODO-missed methods . These “hidden” suboptimal implementations in TODO-missed methods may hurt the software quality and maintainability in the long-term. Therefore, in this paper, we propose the novel task of TODO-missed methods detection and patching, and develop a novel model, namely TDPatcher ( T O D O-comment Patcher ), to automatically patch TODO comments to the TODO-missed methods in software projects. Our model has two main stages: offline learning and online inference. During the offline learning stage, TDPatcher employs the GraphCodeBERT and contrastive learning for encoding the TODO comment (natural language) and its suboptimal implementation (code fragment) into vector representations. For the online inference stage, we can identify the TODO-missed methods and further determine their patching position by leveraging the offline trained model. We built our dataset by collecting TODO-introduced methods from the top-10,000 Python GitHub repositories and evaluated TDPatcher on them. Extensive experimental results show the promising performance of our model over a set of benchmarks. We further conduct an in-the-wild evaluation which successfully detects 26 TODO-missed methods from 50 GitHub repositories.

Montreal Cognitive Assessment (MoCA) XpressO: Validation of a digital self-administered cognitive prescreening tool.

The need for cognitive testing is increasing with the aging population and the advent of new Alzheimer disease therapies. To respond to the increased demand, the XpressO was developed as a self-administered digital cognitive prescreening tool that will help distinguish between populations of subjective and objective cognitive impairment according to the Montreal Cognitive Assessment (MoCA). This is a prospective validation study. XpressO is composed of tasks that assess memory and executive functions. It is validated compared to the digital MoCA as a gold standard. Out of 118 participants screened from the MoCA Clinic and a family practice clinic, 88 met inclusion criteria, two participants had missing data due to incomplete tasks, 86 participants were included in the analysis; the mean age was 70.34 years. A logistic regression model was built, and its accuracy was evaluated by the sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic. Analysis showed strong correlation between (1) XpressO memory tasks scores and the MoCA Memory Index Score (p-values < 0.001), and between (2) XpressO sub-test scores and MoCA total score (p-values < 0.005). The AUC for predicting MoCA performance is 0.845. To classify individuals with normal and abnormal MoCA scores, two threshold values were introduced for the total XpressO scores: sensitivity of 91% at a cutoff of 72, specificity of 90% at a cutoff of 42, and an undetermined range in between. XpressO demonstrated high AUC, high sensitivity and specificity to predict cognitive performance compared to the digital MoCA. It may provide efficient cognitive prescreening by identifying individuals who would benefit from further clinical assessments, potentially reducing waiting times and high burden on healthcare clinics.

Optimal termination strategies considering phase redundancy

Ensuring the survival of safety-critical systems, such as airplanes and submarines, is of utmost importance, even if it means incomplete task completion. System failures during task execution can have catastrophic consequences. Therefore, prioritizing system survival and temporarily suspending tasks are necessary measures to mitigate the risk of failure. Phase redundancy is a widely employed strategy to enhance the reliability of tasks. By incorporating task redundancy, it becomes possible to successfully accomplish the task even in the event of individual task failures. This study presents a novel condition-based task termination approach that leverages phase redundancy to strike a balance between task success and system survival. Specifically, the proposed approach dynamically terminates tasks based on specific condition information and task progress during each phase. To determine optimal termination thresholds, the study conducts iterative evaluations of task reliability and system survivability. The results demonstrate the effectiveness of the proposed approach in enhancing the probability of system survival by maximizing overall success while prioritizing system safety through active consideration of the system condition and task progress when terminating tasks.

Generating Task Reallocation Suggestions to Handle Contingencies in Human-Supervised Multi-Robot Missions

In a mission with significant uncertainty due to intermittent communications, delayed information flow, and robotic failures, the role of human supervisors is extremely challenging. As and when any new information arrives, humans must infer both the existing and predicted future states, identify potential contingencies, and update task assignments to robots rapidly. We propose methodologies for automated generation of task reallocation suggestions to humans to assist in the decision-making process. Our generated robot retasking plan minimizes a modified makespan of the mission, which incorporates task criticality and penalty for incomplete tasks. The plan considers the effects of potential mission contingencies on the tasks, the robots, and the future performance of the robots operating based on the previous task plans. Our method includes the incorporation of two optional tasks, i.e., relay and robot rescue, for performance improvement. The rescue task has probabilistic outcomes affecting the team size. One or more rescues are incorporated in a way that can minimize the expected value of the modified makespan overall possibilities of rescue outcomes. We have conducted performance evaluation using simulation, demonstrating the value of the optional tasks and performance enhancement using our method of incorporating them. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The work reported in this paper will be useful in applications where a team of agents is deployed to carry on a large-scale mission with communication constraints where the number of functional agents can change probabilistically. Typically, such uncertainty is encountered in applications that are challenging or dangerous in nature. Agents can have non-zero probabilities to fail while doing certain risky tasks and to get recovered by other agents. The proposed centralized multi-agent task reallocation method can help in proactively addressing potential contingencies in surveillance, search and rescue, or disaster relief to support resilient operations while having a supervisor in the higher chain of command.

An efficient weighted partial MaxSAT encoding for scheduling in overloaded real-time systems

Scheduling tasks in overloaded real-time systems is a challenging problem that has received a significant amount of attention in recent years. The processor is overloaded with more tasks than its capacity, resulting in missed deadlines and degraded system performance. Therefore, scheduling algorithms play a critical role in ensuring that high-priority tasks are completed on time while minimizing the impact of incomplete lower-priority tasks. This paper proposes an efficient Weighted Partial MaxSAT(WPMS) encoding that returns an optimal solution in which the total weight of incomplete tasks is minimized in a single machine environment. To assess the efficiency of our proposed formulation, a comparative analysis is conducted alongside the state-of-the-art encoding. By examining the solving 75 distinct problems, it becomes evident that the WPMS encoding proposed herein exhibits a considerable advantage in terms of both time and memory efficiency.

Task Migration and Resource Allocation Scheme in IoV With Roadside Unit

Mobile Edge Computing (MEC) has attracted attention for its short-range and low-latency computing services for the Internet of Vehicles (IoV). However, in the Vehicle-RSU-MEC environment, incomplete task migration is a problem when Vehicle Users(VUs) are moving at high speeds around the Road Side Units (RSUs). Additionally, during peak traffic periods or road congestion, completed tasks compete for MEC server computing resources, leading to load imbalance. Therefore, making reasonable migration and offloading decisions is an important challenge. To address this challenge, the paper proposes a Cooperative Offloading strategy to jointly optimize offloading Decisions and Allocation of computing resources (CODA) step by step. First, to solve the problem of incomplete task migration, CODA proposed a Greedy-Based Task Completion Migration (GBTCM) algorithm. The algorithm calculates the required RSU set for each task to achieve complete migration, and greedily searches for the optimal migration target in the corresponding set to reduce task transmission latency. Second, after completing the task migration, CODA proposed a Distance-Based Computing Resource Allocation (DBCRA) algorithm to achieve load balancing for MEC servers. The algorithm prioritizes distance and finds MEC servers with sufficient computing resources to achieve better load balancing performance. Experimental results have shown that CODA is a low-complexity algorithm applicable to IoV, which can make reasonable and rapid decisions for task migration and offloading. Compared to three other benchmarks, CODA exhibits higher effectiveness and superiority.

Turkey's Nuclear Security Regime: An Assessment

AbstractThe 9/11 attacks prompted the development of an international nuclear security regime. States are expected to adopt legislation and institutionalize measures to ensure cooperation among stakeholders and create their own national nuclear security regimes. This article evaluates the steps taken by Turkey, a newcomer in nuclear energy. It argues that while Ankara has acted in line with its traditional conception of security, this is not enough. The inadequate cooperation and coordination among stakeholders, and the insufficient development of a security culture specifically oriented to nuclear energy, leave the country vulnerable. The analysis explains the vulnerabilities and incomplete tasks, and proposes actions necessary for Turkey to create a sound national nuclear security regime.

The evolution of cooperation in the unidirectional division of labour on a tree network.

Division of labour on complex networks is rarely investigated using evolutionary game theory. We investigate a division of labour where divided roles are assigned to groups on the nodes of a general unidirectional finite tree graph network. From the network's original node, a task flows and is divided along the branches. A player is randomly selected in each group of cooperators and defectors, who receives a benefit from a cooperator in the upstream group and a part of the task. A cooperator completes their part by paying a cost and then passing it downstream until the entire task is completed. Defectors do not do anything and the division of labour stops, causing all groups to suffer losses due to the incomplete task. We develop a novel method to analyse the local stability in this general tree. We discover that not the benefits but the costs of the cooperation influence the evolution of cooperation, and defections in groups that are directly related to that group's task cause damage to players in that group. We introduce two sanction systems, one of which induces the evolution of cooperation more than the system without sanctions, and promote the coexistence of cooperator and defector groups.

0678 Consequences of following the herd: Perceptions drive a vicious cycle of poor student sleep habits at an elite university

Abstract Introduction Poor sleep health is widespread among undergraduate university students. This study sought to understand the contributors to poor sleep quality at an elite undergraduate university in the United States. In particular, we were interested in uncovering the social influences on sleep regularity, which is increasingly regarded as a critical determinant of overall sleep quality. Methods An exploratory, mixed-methods study was designed combining a week-long diary study (n=5) and in-depth contextual interviews (n=5) to better understand undergraduate students’ college experience and sleep behaviors, followed by a student survey (n=181) to uncover primary macro trends and factors related to those sleep behaviors. Results A significant irregular sleep schedule emerged as highly common among students. Sleep irregularity was strongly influenced by students’ perceptions about their peers’ sleep and working habits. These perceptions combined with a personal desire to keep pace with their peers and the prioritization of academic work implicitly stated by the university culture influenced when and how students went about their lives, even if those behaviors meant acting against their knowledge and judgment. The convergence of perceptions and behaviors generates a vicious cycle of sleep deprivation and low productivity. The cycle stems from students’ desire to match their peers, which leads to late nights and sleep deprivation. The resulting impaired executive functioning contributes to subsequent inefficient scheduling and lack of concentration. As concentration drops, students multi-tasked more often and incomplete tasks piled up, necessitating further sleep deprivation to get it all done. Conclusion Poor sleep regularity in college is influenced by a set of interconnected factors derived at both the level of the individual student and at the student-body level. Interventions should consider a dual approach to break the vicious cycle, targeting the harmful beliefs among students about the sleep and working habits of their peers, and the prioritization of academic work. Support (if any)

Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas.The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage.

In Vivo Uranium Decorporation by a Tailor-Made Hexadentate Ligand.

The sequestration of uranium, particularly from the deposited bones, has been an incomplete task in chelation therapy for actinide decorporation. Part of the reason is that all previous decorporation ligands are not delicately designed to meet the coordination requirement of uranyl cations. Herein, guided by DFT calculation, we elaborately design a hexadentate ligand (TAM-2LI-MAM2), whose preorganized planar oxo-donor configuration perfectly matches the typical coordination geometry of the uranyl cation. This leads to an ultrahigh binding affinity to uranyl supported by an in vitro desorption experiment of uranyl phosphate. Administration of this ligand by prompt intraperitoneal injection demonstrates its uranyl removal efficiencies from the kidneys and bones are up to 95.4% and 81.2%, respectively, which notably exceeds all the tested chelating agents as well as the clinical drug ZnNa3-DTPA, setting a new record in uranyl decorporation efficacy.

Interfacial Reinitiation of Free Radicals Enables the Regeneration of Broken Polymeric Hydrogel Actuators

Interfacial Reinitiation of Free Radicals Enables the Regeneration of Broken Polymeric Hydrogel Actuators

EXPERIMENTAL TOOLS FOR DIAGNOSTICS OF UNITS OF THE MACHINE AND TRACTOR FLEET BY THERMAL CHARACTERISTICS

Scientific works, claiming about the positive impact of the operational assessment of thermal processes in nodes and aggregates during operation on the reliability of equipment, are known and methodically worked out quite well. The wide practical application of the proposed thermal control methods without specially developed measuring instruments is an incomplete task (Research purpose) The research purpose is developing experimental meters-indicators for test diagnostics of thermal parameters of components and aggregates to increase the controllability and operational reliability of the machine and tractor fleet. (Materials and methods) Thermodynamic and research methods based on the use of proven methods for determining the technical condition were used; the object of the study was a control system of assessing compliance with the operating rules and compliance by the operator with the temperature regimes recommended by the manufacturer. In order to carry out maintenance and repair according to the methodology "Reliability-oriented maintenance", it is necessary to conduct a continuous assessment of the threshold thermodynamic characteristics of controlled objects during their operation for further forecasting of the technical condition. (Results and discussion) The possibility of using experimental counters-indicators of technical condition was described. Only the operator's control of the operating rules and compliance with the temperature modes recommended by the manufacturer does not minimize the risks. It is necessary to develop monitoring systems that provide independent monitoring and measurement of the current temperature, the rate of its change, determination of the time and ranges of thermal conditions of controlled objects during operation. (Conclusions) When focusing maintenance on reliability according to the specified methodology, it is necessary to provide temperature control during the operation of the machine and tractor fleet, which will increase the level of controllability and adaptability to the diagnosis of units.

Automatic car service recommendation system using machine learning techniques

The automobile industry has been growing at a high rate in the past few decades, contributing about 7.5% to India's total Gross Domestic Product (GDP). As the number of vehicle owners are increasing the demand and need for automobile service is also high, but people are busy with their routines, hence failing to perform proper maintenance on their vehicles. This paper uses machine learning algorithms and object detection to come up with the idea to develop a web application that suggests users some offers and timing for their car maintenance by analyzing a car using computer vision without the owner's involvement. This project aims at both the owner's convenience and the growth of the service provider's business. Generally, we do not realize that multiple tasks can be done at a time, which results in incomplete tasks. This paper presents a machine learning-based automated car maintenance system with effective time utilization, by using the Internet of Things (IoT) device that could be installed at the parking's main gate in places where people tend to spend many hours, like offices or malls. This device consists of a camera that is responsible for detecting a car image from the live video. These images are then sent to the device, which uses pre-trained models to detect any damages or dirtiness in the vehicle.

Identification of trusted IoT devices for secure delegation

In the era of ubiquitous computing, Internet of Things (IoT) devices have a remarkable impact on human lives. In addition to numerous services provided by IoT devices, there are some constraints and limitations in their service provision. For instance, due to their resource constraint nature, sometimes these devices have insufficient resources to perform assigned tasks. Therefore, a delegation mechanism is usually required to handle task requests assigned to devices with limited resources. However, at the same time, the data possessed by these devices can be sensitive, so a trust mechanism is needed to identify trusted devices for delegating data sensitive services. Ontologies provide a robust method to integrate models with their corresponding real-world scenarios. This paper proposes a model for the identification of trusted IoT devices for delegation, designed through ontologies to resolve the problems of incomplete task requests owned by resource-constrained IoT devices. The proposed ontology provides a unique view of domain knowledge from the operational knowledge of pervasive and ubiquitous systems, like IoT devices. The paper also proposes a semantic matching algorithm to rank highly favorable and trusted devices to handle resource and trust priorities. The proposed ontology is developed in protégé editor and evaluated through a web query language. The experimental results based on simulation and parametric evaluations reflect that the proposed system performs better than existing approaches.

Quality comparison of remote vs. in‐person digital speech assessment for dementia

AbstractBackgroundThe COVID‐19 pandemic has brought the need for reliable, remote assessments for clinical trials into sharp focus. Remote, home‐based assessment reduces patient and caregiver burden, enabling more frequent monitoring. There are concerns, however, regarding the quality of in‐clinic vs. remote assessments. In the present study, we compared the quality of verbal responses to a tablet‐based speech assessment in patients with dementia across two settings: in‐person at a clinical research site, and remotely, at home.MethodThe in‐person sample consisted of individuals with Alzheimer’s disease, participating in a clinical trial. The remote sample consisted of individuals with variants of Frontotemporal dementia, participating in a longitudinal observational study. In‐person assessments were conducted at a clinical research site and administered by a trained rater. Remote assessments were conducted in the participant’s home, by a caregiver who had received assessment training and a tablet by mail. 575 in‐person speech samples and 574 remote speech samples were compared. All samples were manually transcribed and tagged for recording anomalies or task compliance issues by trained transcriptionists.ResultOverall incidence of recording anomalies was low and did not differ significantly (p = 0.63) between in‐person (10.1%; 58/574) and remote (11.1%, 64/575) recordings. Less than 1% of samples in either study were marked as “incomplete task”, “low audio quality” or “noisy background”. Incidences of clinician/caregiver interference were more frequent for remote recordings (1.2% in‐person, 4.5% remote, p = 0.001) and incidences of quiet participants were more frequent for in‐person recordings (4.5% in‐person, 1.0% remote, p &lt; 0.001), though both were relatively rare overall. The mean duration of in‐person samples was significantly longer than remote samples, but mean speech rate did not differ.ConclusionThis study suggests that remote speech assessments yield recordings of comparable quality to in‐person assessments. We found higher, though still low, rates of caregiver interference for remote assessment, which should be monitored and mitigated in future remote assessment. Remote assessments yielded shorter recordings, but this may be due to the different dementia diagnoses across groups. Future work should compare the same participants across both assessment settings.

Spatio-temporal deep learning models of 3D turbulence with physics informed diagnostics

Direct Numerical Simulations (DNSs) of high Reynolds number turbulent flows, encountered in engineering, earth sciences, and astrophysics, are not tractable because of the curse of dimensionality associated with the number of degrees of freedom required to resolve all the dynamically significant spatio-temporal scales. Designing efficient and accurate Machine Learning (ML)-based reduced models of fluid turbulence has emerged recently as a promising approach to overcoming the curse of dimensionality challenge. However, to make the ML approaches reliable one needs to test their efficiency and accuracy, which is recognised as important but so far incomplete task. Aiming to improve this missing component of the promising approach, we design and evaluate two reduced models of 3D homogeneous isotropic turbulence and scalar turbulence based on state-of-the-art ML algorithms of the Deep Learning (DL) type: Convolutional Generative Adversarial Network (C-GAN) and Compressed Convolutional Long-Short-Term-Memory (CC-LSTM) Network. Quality and computational efficiency of the emulated velocity and scalar distributions is juxtaposed to the ground-truth DNS via physics-rich statistical tests. The reported results allow to uncover and classify weak and strong aspects of C-GAN and CC-LSTM. The reported results, as well as the physics-informed methodology developed to test the ML-based solutions, are expected to play a significant role in the future for making the DL schemes trustworthy through injecting and controlling missing physical information in computationally tractable ways.

Recovery, Interrupted: The Zeigarnik Effect in EMDR Therapy and the Adaptive Information Processing Model

This theoretical article proposes that the Zeigarnik effect (ZE) (i.e., better memory for interrupted rather than completed tasks) explains the formation of traumatic memories as incomplete tasks. These tasks are thought to foster pathological rumination toward their completion while simultaneously stoking avoidance. After looking at the role of the ZE in the development and maintenance of posttraumatic stress disorder (PTSD), the article examines the physiological substrates of the ZE, including brain network imbalance, excessive autonomic nervous system activation, and prospective memory acuity for incomplete events. Eye movement desensitization and reprocessing (EMDR) therapy is proposed as uniquely capable of providing closure to interrupted facets of traumatic recollection, as this treatment may facilitate greater memory reconsolidation and inhibition of Zeigarnik reminders than extinction-based models. The ZE may be implicated in the overall EMDR treatment effect, as it is activated in several EMDR phases. Specifically, the use of brief interrupted exposures during EMDR reprocessing may benefit from the ZE in resolving traumatic events. Eye movements themselves are also considered interruptions to rumination upon traumatic memory. Recommendations for the further use of the ZE are described, and suggestions are made for future research. Protocol modifications and interweaves which mobilize the preceding insights are also provided.

Checklist Design Reconsidered: Understanding Checklist Compliance and Timing of Interactions.

We examine the association between user interactions with a checklist and task performance in a time-critical medical setting. By comparing 98 logs from a digital checklist for trauma resuscitation with activity logs generated by video review, we identified three non-compliant checklist use behaviors: failure to check items for completed tasks, falsely checking items when tasks were not performed, and inaccurately checking items for incomplete tasks. Using video review, we found that user perceptions of task completion were often misaligned with clinical practices that guided activity coding, thereby contributing to non-compliant check-offs. Our analysis of associations between different contexts and the timing of check-offs showed longer delays when (1) checklist users were absent during patient arrival, (2) patients had penetrating injuries, and (3) resuscitations were assigned to the highest acuity. We discuss opportunities for reconsidering checklist designs to reduce non-compliant checklist use.

Correlation of Gandhi with cleanliness drive and Swachh Bharat Mission: Study on methodology of spreading awareness and motivating rural section of Chandigarh to maintain hygienic conditions

The cleanliness drive most popularly known as “Swacch Bharat Mission” started by Prime Minister Narendra Modi from 2nd October, 2014 to 2nd October, 2019 as a tribute to Mahatma Gandhi on his 150th Anniversary lays its foundation from Gandhi’s effort to eradicate the Untouchability and humiliation of Indians on account of their unhygienic lifestyle and poor sanitation habits. Receiving a negative feedback from white Europeans about Indians lifestyle, he observed, analyzed and accepted the views received by European’s, instead of defending them. Mahatma Gandhi motivated Indians to self-groom themselves to become capable to compete with white European’s, instead of fighting for Independence. Modi took over the drive in order to complete the incomplete task planned and furnished by Mahatma Gandhi. This drive is supported not only by each Indian citizen, but also by each educational institute, academic institutes, research institutes and many more. As a part of this program we also conducted a cleanliness drive in Daddu Majra colony, Chandigarh. The program begun while delivering talks to group of people of the colony highlighting the significance of cleanliness in day-today life of an individual as well as community. The role of community and collective effort of people in maintaining clean environment while practicing appropriate sanitation and hygienic conditions was discussed. The community was well motivated and inspired to work collectively for up liftment of their village area while maintaining proper surroundings, cleaning them regularly to prevent themselves from harmful diseases such as malaria and dengue. A special session was also conducted for Women in order to guide them to ensure proper disposal of their wastes during menstrual cycles and taking care of other hygienic practices during their periods. People were also motivated to spread awareness in their nearby developing villages and other rural areas and encouraging people to use proper toilets during disposal of their wastes. A well supportive behavior was shown by the community indicating the interest of community in amending their lifestyles by practicing appropriate hygienic practices.