Search

Decades after being replaced with digital platforms, analogue computing has experienced a surging interest following developments in metamaterials and intricate fabrication techniques. Specifically, wave-based analogue computers which impart spatial transformations on an incident wavefront, commensurate with a desired mathematical operation, have gained traction owing to their ability to directly encode the input in its unprocessed form, bypassing analogue-to-digital conversion. While promising, these systems are inherently limited to single-task configurations. Their inability to concurrently perform multiple tasks, or compute in parallel, represents a major hindrance to advancing conceptual mechanical devices with broader computational capabilities. In here, we present a pathway to simultaneously process independent computational tasks within the same architected structure. By breaking time invariance in a set of metasurface building blocks, multiple frequency-shifted beams are self-generated which absorb notable energy amounts from the fundamental signal. The onset of these tunable harmonics enables distinct computational tasks to be assigned to different independent "channels," effectively allowing an analogue mechanical computer to multitask. [ABSTRACT FROM AUTHOR]

Cognitive fatigue occurs in various situations and is an essential condition to detect. In this study, how single and multi-tasking tests affect cognitive workload was examined, and multi-tasking was detected using electroencephalography (EEG) signals. In the cognitive workload paradigm, single-task tests with blocks 1 and 2 and multi-tasking tests with block 3 were created. EEG signals obtained from these blocks were treated as different frequency bands and lengths, and binary classification was performed. Two binary classifications were made: block 1–block 3 and block 2–block 3. According to the results, the highest classification accuracy for block 1–block 3 was obtained as 97.11% using the gamma frequency band and 5-s EEG length. For block 2–block 3, the highest classification accuracy was obtained as 90.88% using the gamma frequency band and 5-s EEG length. As a result, this study distinguished multi-tasking and single task with high classification accuracy. The developed model can be used to detect attention deficit and inability to focus. In addressing the prevalent challenges of distinguishing cognitive fatigue in single—task versus multitasking scenarios, our study offers a new method, which achieve a remarkable accuracy rate, thereby illuminating a new path in the research of cognitive fatigue. [ABSTRACT FROM AUTHOR]

A zombie process is a type of process that has completed its execution but still has an entry in the process table. Zombie processes are typically created when a child process terminates, but its parent process fails to collect its termination status. Now that the fork () has created a new process, it duplicates the calling process. The new process is referred to as the child process. The calling process is referred to as the parent process. The child and parent processes are to run in separate memory spaces. Both memory spaces have the same content at the time of fork (). Whenever in Linux, a process is an instance of executing a program or command. While these processes exist, they will be in one of the five possible states, such as Running or Runnable (R), Uninterruptible Sleep (D), or Interruptable Sleep (S). Another process (the parent process) creates a child computing process. This technique pertains to multitasking operating systems and is sometimes called a subprocess or, traditionally, a subtask. Hence, there are five Linux process states. Such as running & runnable, interruptable sleep, uninterruptable sleep, stopped, and zombie. Each of these processes exists for a very well-defined reason. After all, a zombie process in Linux is a terminated child process that remains in the system's process table while waiting for its parent process to collect its exit status. A zombie process is a type of process that has completed its execution but still has an entry in the process table. As a result, if too many zombie processes are generated, the process table will be full. That is, the system will not be able to generate any new process, then the system will come to a standstill. [ABSTRACT FROM AUTHOR]

People in modern society are media multitaskers due to portable devices and omnipresent wireless networks, and college students are no exception. Previous studies have indicated that students' media multitasking behaviours in class harm their academic performances, and understanding the reasons for college students' engagement in such behaviour is meaningful. However, the literature takes media multitasking behaviour as an audience behaviour, which ignores the interaction between students and their surroundings. This study fills this gap by emphasizing the role of the social learning process. A survey was conducted in a public and comprehensive university in western China, and a total of 457 valid respondents were obtained. The results from PLS‐SEM revealed that college students' in‐class media multitasking behaviours were influenced by both observational learning (ie, imitating others) and reinforcement learning (ie, in‐class interventions), and this learning process was shaped by students' media multitasking self‐efficacy and self‐management of learning. These findings contribute to the current literature by providing a relatively new perspective for understanding college students' in‐class media multitasking behaviours, and suggestions about how to deal with such behaviours are also provided. Practitioner notesWhat is already known about this topic In‐class media multitasking behaviours (ICMMBs) damage college students' academic performance.College students' ICMMBs are affected by individual factors, such as internet addiction and sensation seeking.Social learning theory is widely adopted as a general theoretical background to explain those behaviours of which possible consequences are easily observed no matter from others or from individuals themselves.What this paper adds This work enriches the media multitasking literature by investigating the predictors of ICMMBs from a relatively new perspective, that is, the social learning perspective.This work also contributes to the literature on media multitasking by examining how the social learning process of college students' ICMMBs differs across students.This work extends the application scope of social learning theory to the context of college students' ICMMBs.Implications for practice and/or policy. The role of imitating others proves the importance of the learning atmosphere in classrooms, so teachers or lecturers should pay special attention to build a learning atmosphere away from ICMMBs.Some in‐class interventions should be established, especially the formal policy of schools to require students to hand in their smartphones or other media before class and students' own mindfulness of such behaviour.Teachers or lecturers should take actions to lower the academic performance expectations of students with higher media multitasking self‐efficacy (MMSE) because students with higher MMSE often overestimate their capabilities to have good performance when conducting other tasks simultaneously. [ABSTRACT FROM AUTHOR]

Background: The number of accidents due to distracted pedestrian is on the rise and many governments and institutions are enacting public policies which restrict texting while walking. However, pedestrians do more than just texting when they use their mobile devices on the go. Objective: Exploring pedestrian multitasking, this paper aims to examine the effects of mobile device task type on pedestrian performance outcomes. Method: We performed two studies in lab simulations where 78 participants were asked to perform different tasks on a mobile device (playing a game, reading, writing an email, texting one person, group texting) while performing a pedestrian visual discrimination task while either standing or walking on a treadmill. Behavioral performance as well as neurophysiological data are collected. Results: Results show that compared to a no-phone control, multitasking with any of the tasks on a mobile device leads to poor performance on a pedestrian visual discrimination task. Playing a game is the most cognitively demanding task and leads to the greatest performance degradation. Conclusion: Our studies show that multitasking with a mobile device has the potential to negatively impact pedestrian safety, regardless of task type. However, the impacts of different mobile device tasks are not all equivalent. More research is needed to tease out the different effects of these various tasks and to design mobile applications which effectively and safely capture pedestrians' attention. Application: Public policy, infrastructure, and smart technologies can be used to mitigate the negative effects of mobile multitasking. A more thorough understanding of mobile device task-specific factors at play can help tailor these counter-measures to better aid distracted pedestrians. [ABSTRACT FROM AUTHOR]

In recent years, the coverage path planning (CPP) of unmanned aerial vehicles (UAVs) has attracted attention in reconnaissance, patrol, and search and rescue efforts, aiming to plan the paths for UAVs to cover a specified area as efficiently as possible. This paper proposes a UAV path fast coverage model to prioritize important targets with domain composition based on the starting point and location of the targets, combined with the domain decomposition strategy of important targets. Considering the constraints of the number of UAVs, the number of operators, and the flight time, the parallel search strategy can plan the coverage scheme with the shortest search time for the search range, and further obtain the coordinate points and path coordinates of the UAV turning. Finally, through multiple simulation experiments in four maps of various islands, the proposed method is verified to have an improved performance compare to the two track path coverage algorithms methods in terms of the coverage efficiency and the time complexity, thus providing a more scientific basis for the path coverage research of multi-target searches. [ABSTRACT FROM AUTHOR]

The paper presents a practical methodology for the prediction of power consumption of a multitask real-time system. The authors briefly recall the structure of the original multitasking time-predictable system. The system has a regular and scalable architecture. It consists of many cores based on pipeline processing with threads' interleaving mechanism. In previous works, the authors have presented a number of issues related to the methodology of designing a time-predictable system and the scheduling of hardware threads with different design goals and constraints. The current article presents an accurate method for measuring the dynamic power dissipated in a system for various scenarios and the system configuration. The proposed approach is based on continuous monitoring of the power consumed by the FPGA, transmitting the measurements to the computer from the measuring device, and online analysis of the obtained results. The authors based their power estimation method on the original task frequency factor (TF). A theoretical analysis was made, and a series of practical experiments were carried out. The results obtained enable accurate estimation of the power requirements for the tasks performed in the real-time system. The presented technique can be useful during the design stage of multitasking real-time systems. [ABSTRACT FROM AUTHOR]

Graph-based cross-domain recommendations (CDRs) are useful for suggesting appropriate items because of their promising ability to extract features from user–item interactions and transfer knowledge across domains. Thus, the model can effectively alleviate cold start and data sparsity issues. Although the graph-based CDRs can capture valuable information, they still have some limitations. First, embeddings are highly vulnerable to noisy interactions, because the message aggregation in the graph convolutional network can further enlarge the impact. Second, because of the property of graph-structured data, the influence of high-degree nodes on representation learning is more than that of the long-tail items, and this can cause a poor recommendation performance. In this study, we devised a novel Adaptive Adversarial Contrastive Learning framework for graph-based Cross-Domain Recommendation (ACLCDR). The ACLCDR introduces reinforcement learning to generate adaptive augmented samples for contrastive learning tasks. Then, we leveraged a multitask training strategy to jointly optimize the model with auxiliary tasks. Finally, we verified the effectiveness of the ACLCDR through nine real-world cross-domain tasks adopted from Amazon and Douban. We observed that ACLCDR exceeded the best state-of-the-art baseline by 25%, 42.5%, 16.3%, and 23.8% in terms of HR@10 and NDCG@10 for the Music & Movie task from Amazon. [ABSTRACT FROM AUTHOR]

Catherine Volpilhac-Auger's biography of Montesquieu offers a comprehensive and revisionist account of the Enlightenment thinker's life. The book explores Montesquieu's upbringing, education, career, and contributions as an author, challenging previous interpretations and providing insight into the institutional contexts that shaped his life. The biography also delves into Montesquieu's extensive library and his methods of learning and research. Overall, this nuanced and detailed portrait highlights Montesquieu's multifaceted life and his significant contributions to philosophy and political thought. The text also discusses Montesquieu's published works and argues that they should not be seen as definitive representations of his intellectual process. It explores the relationship between his personal life and intellectual production. The English version of the book includes expanded chronology and some changes for an English-speaking audience, although there are minor errors and inconsistencies. The text concludes by praising the book as a valuable contribution to Montesquieu scholarship. [Extracted from the article]

In recent times, there has been tremendous growth in the number of multi-lingual users on social media platforms. Consequently, the code-mixing phenomenon, i.e., mixing of more than one language, has become ubiquitous in Internet content. In this paper, we present a shared-private, multi-lingual, multi-task model coupled with a transformer-based pre-trained encoder for sentiment analysis of code-mixed and English languages. Our model is tailored for multitasking that transfers the knowledge between code-mixed and English sentiment tasks. We consider code-mixed sentiment analysis as the primary task and enhance its performance by English sentiment analysis (auxiliary task) by sharing knowledge between them. We fine-tune the Bidirectional Encoder Representation using Transformer (BERT) encoder in a shared-private fashion to obtain the shared and task-specific features using the multi-task objective function. We evaluate our proposed framework using three benchmark datasets for the Hindi-English (Hinglish), Punjabi-English (Punglish) code-mixed and English sentiment tasks. Experiment results justify that our proposed multi-task framework improves the performance of our primary task in comparison to the state-of-art single-task systems. [ABSTRACT FROM AUTHOR]

With the pervasiveness of laptops in the classroom setting, the effectiveness of laptop-assisted note-taking has not been comprehensively investigated. Many inconsistencies in this area still existed with intense debate towards academic performance, learning strategies, and student satisfaction. To fill this missing gap, this study probed the effect of laptop notetaking on the above constructs. The present study applied the comprehensive review by objectively selecting all relative literature from online database, with a main focus on learning areas and conducting the objective procedure. This study covered the positive, negative, as well as neutral effects of laptop note-taking on learning performance. Reasons behind the negative impact and worries were investigated in caution. Tackling the major concerns of distraction and multitasking, this study argued that these concerns might not be the main cause of low performance, individual's characteristics and preference for the teaching styles shall be taken into consideration. Based on the above arguments, this study provided educators with multiple suggestions on alternative pedagogical approaches to improve teaching practice and student learning experience. The satisfaction of courses was probed together with the reasons for low satisfaction which promoted relative teaching instruction and teacher training. In this vein, this study contributed to the laptop note-taking areas by comprehensively analyzing the effect of laptop note-taking on learning strategies and satisfaction, which were unfortunately ignored by previous studies. Moreover, the present study enriches the e-learning knowledge and supports its practice by proving the side effects of simply banning laptops in class and suggests educators to integrate laptops into their pedagogical designs as well as learn more technology-based teaching strategies. Future research should reinvestigate the effect of laptop note-taking in class with more caution and endeavor to enhance the effectiveness of laptop note-taking in the class by capturing all possible variables of student learning, especially technology-relative variables. [ABSTRACT FROM AUTHOR]

Weakly supervised object detection (WSOD) is becoming increasingly important for computer vision tasks, as it alleviates the burden of manual annotation. Most WSOD techniques rely on multiple instance learning (MIL), which tends to localise the discriminative parts of salient objects instead of the whole object. In addition, network training is often supervised using simple image‐level annotations, without including object quantities or location information. However, this can lead to ambiguous differentiation of object instances, both in terms of location and semantics. To address these issues, propose an end‐to‐end triple critical feature capture network (TCFCNet) for WSOD is proposed. Specifically, a multi‐task branch, which can perform fully supervised classification and regression task, was integrated with a PCL in an end‐to‐end network for refining object locations in an online method. A cyclic parametric dropblock module (CPDM) was then designed to help the detector focus on the contextual information by using cyclic masking techniques to maximise the removal of the discriminative components of an object instance to alleviate the part domination problem. Finally, a feature decoupling module (FDM) is proposed to further reduce the ambiguous distinction of object instances by adaptively constructing robust critical features that adapt to multi‐task branch for classification and regression tasks, which contains a feature enhancement module and task‐specific polarisation functions. Comprehensive experiments are carried out on the challenging Pascal VOC 2007 and VOC 2012 datasets. The proposed method achieves a 54.6% mAP and a 44.3% mAP on the Pascal VOC 2007 and VOC 2012 datasets respectively, showed that our method outperformed existing mainstream techniques by a considerable margin. [ABSTRACT FROM AUTHOR]

Summary: This study proposes a lifelong deep learning control scheme for robotic manipulators with bounded disturbances. This scheme involves the use of an online tunable deep neural network (DNN) to approximate the unknown nonlinear dynamics of the robot. The control scheme is developed by using a singular value decomposition‐based direct tracking error‐driven approach, which is utilized to derive the weight update laws for the DNN. To avoid catastrophic forgetting in multi‐task scenarios and to ensure lifelong learning (LL), a novel online LL scheme based on elastic weight consolidation is included in the DNN weight‐tuning laws. Our results demonstrate that the resulting closed‐loop system is uniformly ultimately bounded while the forgetting is reduced. To demonstrate the effectiveness of our approach, we provide simulation results comparing it with the conventional single‐layer NN approach and confirm its theoretical claims. The cumulative effect of the error and control input in the multitasking system shows a 43% improvement in performance by using the proposed LL‐based DNN control over recent literature. [ABSTRACT FROM AUTHOR]

Evolutionary multitasking optimization, which concentrates on solving multiple tasks simultaneously, has been a core area of interest for researchers in recent years. Existing Multifactorial Evolutionary Algorithms (MFEA) are quite dependent on the synergy among the tasks. Consequently, solving multiple tasks is prone to fall into local traps when the optimization enters a certain stage. The objective of this paper is to investigate these problems in more detail and provide corresponding solutions. Specifically, we propose a three-stage analysis method that divides the multitasking optimization problem into three stages and explain the MFEA features according to the individual distribution in each stage, based on which we further develop a Parametric Segmented Multifactorial Evolutionary Algorithm (PS-MFEA) and apply a precise search strategy in the algorithm. Additionally, we propose both a reinitialization mechanism and a backtracking mechanism to avoid local optima. We conduct a comprehensive experiment on two test sets of benchmark problems with different similarity levels and the discrete combinatorial optimization tasks of the traveling salesman problem. The results demonstrate that using PS-MFEA can obtain better performances in both test sets and solve practical problems. [ABSTRACT FROM AUTHOR]

Learning environments are undergoing a dynamic alteration with the robust impact of technological transformation. Therefore, adapting to dynamic learning settings has now become a key criterion for academic performance. The factors that we know have a detrimental effect on learners' academic performance and cognitive capacity are related to today's traditional learning settings, but the circumstances in interactive learning environments such as virtual reality learning environments remains uncertain. To address this gap, we aimed to investigate the circumstances in virtual reality learning environments by considering both task characteristics and multitasking, both of which have been shown to have controversial effects on cognitive load and academic performance. The research was carried out with a total of 218 participants assigned to one of four treatment groups: (1) Multitasking & Regular Task (f = 59), (2) Multitasking & Hard Task (f = 56), (3) Monotasking & Regular Task (f = 52), and (4) Monotasking & Hard Task (f = 51), all with previous experience in virtual reality learning environments. Learners' working memory capacity and prior knowledge levels were measured as covariate variables. MANCOVA results showed significant differences across the treatment groups, in terms of achievement and retention scores. Findings not only reveal the more need for multifaceted empirical research in the field of education but also indicate that the outcomes of interactive learning environments may differ from those of traditional learning settings. [ABSTRACT FROM AUTHOR]

This study addresses dynamic task allocation challenges in coordinated surveillance involving multiple unmanned aerial vehicles (UAVs). A significant concern is the increased UAV flight distance resulting from the assignment of new missions, leading to decreased reconnaissance efficiency. To tackle this issue, we introduce a collaborative multi-target and multi-UAV reconnaissance scheme. Initially, the multitasking constrained multi-objective optimization framework (MTCOM) is employed to optimize task allocation and reconnaissance time in static scenarios. Subsequently, in case of emergency, we iteratively refine the outcomes of static task allocation through an enhanced auction-based distributed algorithm, effectively reducing UAV flight costs in response to new missions, UAV withdrawal, or damage. Simulation results demonstrate the efficacy of our proposed multi-UAV and multi-target cooperative reconnaissance scheme in resolving dynamic task allocation issues. Additionally, our approach achieves a 5.4% reduction in UAV flight distance compared to traditional allocation methods. The main contribution of this paper is to consider a dynamic scenario model involving UAV damage and the emergence of new reconnaissance areas. Then we propose an innovative collaborative multi-target and multi-UAV reconnaissance scheme to address this issue and, finally, conduct experimental simulations to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Smartphones play a crucial role in daily activities, however, situationally-induced impairments and disabilities(SIIDs) can easily be experienced depending on the context. Previousstudies explored the effect of context but mainly done in controlled environments with limited research done in the wild. In this article, we present an in-situ remote user study with 48 participants’ keyboard interaction on smartphones including the performance and context details. We first propose an automated approach for error detection by combining approaches introduced in the literature and with a follow-up study, show that the accuracy of error detection is improved. We then investigate the effect of context on the typing performance based on five dimensions: environment, mobility, social, multitasking, and distraction, and reveal that the context affects participants’ error rate significantly but with individual differences. Our main contribution is providing empirical evidence with an in-situ study showing the effect of context on error rate. [ABSTRACT FROM AUTHOR]

Recent developments in neural networks have enabled them to achieve comparable or better than human accuracy on different computer vision tasks like image classification, object detection, segmentation, keypoint estimation etc. Large scale datasets have explicitly been curated for each of these tasks, and researchers from around the world compete to get state of the art results on these benchmarks. Outside these datasets, these models may not always perform equally better. Real-world applications often require multiple models to be used together to generate meaningful results. The processing time primarily relies on the slowest model in the pipeline. The allocated resources for other models remain idle in the meantime, causing a sub-optimal processing pipeline. Each of these models comes with its own input data pre-processing, feature extraction, output post-processing etc., causing a significant unnecessary overhead. To overcome these issues, we explored multitasking architectures to do multiple closely related tasks together. In this work, we developed a single multitasking model to perform object detection, instance segmentation and keypoint estimation tasks. We presume such models will be more robust to data specific noises as it finds a better representation of the trained data by learning to predict multiple closely related tasks. Our most accurate model gave 41.2 AP on object detection, 38.2 AP on instance segmentation and 53.0 AP on keypoint estimation tasks when evaluated on COCO validation dataset. We optimised the models through layer fusion and float 16 quantisation. We achieved 107 frames per second (fps), while a lighter version achieved 131fps on RTX 3090 GPU. We also benchmarked the models on Nvidia Jetson Tx2 and got 4.2 fps and 6.3 fps for the two respective models. The models were successfully deployed in a server-client system architecture / cloud computing with future possibilities for on-premise deployment using edge devices. [ABSTRACT FROM AUTHOR]

The focus of miniaturization is the production of small and portable devices that can be carried in the pocket anywhere and anytime. Small and portable devices that perform multitask such the smartphone requires a portable and efficient antenna that operates in many frequency bands. A single planar miniaturized Archimedean spiral antenna, which operates in a frequency range that is determined by its inner and outer radii of its arc, has been adjourned to be a better candidate for these multiple tasks. This study examined the geometry of a miniaturized Archimedean spiral antenna of varying turns. An inner radius of 4.90 mm and a thickness of 0.0356 mm suitable for the printed antenna were previously chosen for the study. The length of the arc and the outer radius were determined for spiral turns ranging from 0.5 to 100 with an incremental step of 0.5. Results revealed the radial distance generating the spiral, the length of its arc, the outer radius, and the surface area were 4.97 mm, 15.51 mm, 4.97 mm and 77.16 mm² for 0.5 spiral turns, and 19.12 mm, 7,546.80 mm, 2,849.33 mm and 1,148.64 mm² for 100 spiral turns. Based on the outer radii, the frequency range of operation will be between 16.76 MHz and 9.60 GHz. The mathematical functions formulated through curve fitting described the relationship between the outer radius and arc length with a power function and the number of turns and frequency with an exponential function, while arc length and radial distance, area and number of turns, and area and pitch angle are described by polynomial functions. It is recommended that further analysis on the geometry of the minimized Archimedean spiral antenna be conducted. [ABSTRACT FROM AUTHOR]

Age-related decline in cognitive-motor multitasking performance has been attributed to declines in executive functions and physical fitness (motor coordinative fitness and cardiovascular fitness). It has been suggested that those cognitive and physical resources strongly depend on lifestyle factors such as long-term regular physical activity and cognitive engagement. Although research suggests that there is covariation between components of executive functions and physical fitness, the interdependence between these components for cognitive-motor multitasking performance is not yet clear. The aim of the study was to examine the contribution and interrelationship between executive functions, motor coordinative fitness, and cardiovascular fitness on street crossing while multitasking. We used the more ecologically valid scenario to obtain results that might be directly transferable to daily life situation. Data from 50 healthy older adults (65–75 years, 17 females, recruited in two different cities in Germany) were analyzed. Participants' executive functions (composite score including six tests), motor coordinative fitness (composite score including five tests), and cardiovascular fitness (spiroergometry), as well as their street crossing performance while multitasking were assessed. Street crossing was tested under single-task (crossing a two-line road), and multitask conditions (crossing a two-line road while typing numbers on a keypad as simulation of mobile phone use). Street crossing performance was assessed by use of cognitive outcomes (typing, crossing failures) and motor outcomes (stay time, crossing speed). Linear mixed-effects models showed beneficial main effects of executive functions for typing (p = 0.004) and crossing failures (p = 0.023), and a beneficial main effect of motor coordinative fitness for stay time (p = 0.043). Commonality analysis revealed that the proportion of variance commonly explained by executive functions, motor coordinative fitness, and cardiovascular fitness was small for all street crossing outcomes. For typing and crossing failures (cognitive outcomes), the results further showed a higher relative contribution of executive functions compared to motor coordinative fitness and cardiovascular fitness. For stay time (motor outcome), the results correspondingly revealed a higher relative contribution of motor coordinative fitness compared to executive functions and cardiovascular fitness. The findings suggest that during cognitive-motor multitasking in everyday life, task performance is determined by the components of executive functions and physical fitness related to the specific task demands. Since multitasking in everyday life includes cognitive and motor tasks, it seems to be important to maintain both executive functions and physical fitness for independent living up to old age. [ABSTRACT FROM AUTHOR]

Capitalism and the development of technology are inextricably interlinked, from capitalism's origins. In the name of the "reality principle", psychoanalysts may throw up their hands in the face of the juggernauts of technology and capitalism, avoiding examination of the exploitative and extractive elements of technology as much as they avoid examining those of capitalism. However, as a piece of technology, the smartphone and other internet-connected devices become extensions not only of the self outward, but extensions of technology and the interests of the businesses that develop them inward into the self. Yet most of us, at least most adult Americans, including most adult American psychoanalysts, are so resigned to technological advance, as well as to the acceptance of ubiquitous use of technology, in the name of a prosperous economy ("well, that's just the way it is, that's how capitalism works"), that they are rendered spectators to the infiltration of technology into our existences. [Extracted from the article]

In multi-domain networks, the efficiency of path computation becomes more and more important. The Inter-Domain Path Computation under Nodedefined Domain Uniqueness Constraint (IDPC-NDU) is a recently investigated problem where its objective is to determine the effective routing path between two nodes that traverses every domain at most once. IDPC-NDU is NP-Hard, so the approximation approaches are suitable to deal with this problem for large instances. Multifactorial Evolutionary Algorithm (MFEA) is an emerging research topic in the field of evolutionary computation that can efficiently tackle multiple optimization problems at the same time. This study proposed an approach based on the combination of the Adaptive Multifactorial Evolutionary Algorithm (dMFEA-II) and Dijkstra algorithm for solving IDPC-NDU. The encoding and evaluating methods based on the permutation representation are also introduced, and the new individual representation is always to produce valid solutions. The proposed algorithm is evaluated on two types of instances. Simulation results demonstrate the superior performance of the proposed algorithm in comparison with the existing algorithms in terms of the quality of the solution. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Task generalization has been a long-standing challenge in Natural Language Processing (NLP). Recent research attempts to improve the task generalization ability of pre-trained language models by mapping NLP tasks into human-readable prompted forms. However, these approaches require laborious and inflexible manual collection of prompts, and different prompts on the same downstream task may receive unstable performance. We propose Unified Schema Prompt, a flexible and extensible prompting method, which automatically customizes the learnable prompts for each task according to the task input schema. It models the shared knowledge between tasks, while keeping the characteristics of different task schema, and thus enhances task generalization ability. The schema prompt takes the explicit data structure of each task to formulate prompts so that little human effort is involved. To test the task generalization ability of schema prompt at scale, we conduct schema promptbased multitask pre-training on a wide variety of general NLP tasks. The framework achieves strong zero-shot and few-shot generalization performance on 16 unseen downstream tasks from 8 task types (e.g., QA, NLI, etc.). Furthermore, comprehensive analyses demonstrate the effectiveness of each component in the schema prompt, its flexibility in task compositionality, and its ability to improve performance under a full-data fine-tuning setting. [ABSTRACT FROM AUTHOR]

The multitasking behavior is burgeoning in today's work environment which reflects capabilities of individual to manage multiple things simultaneously to attain the efficient work performance. Study examines effect of multitasking on human behavior in workplace and its impact upon work performance. A search strategy was formulated to conduct this literature review-based study, and different databases containing scholarly research material were explored to find related literature. Inclusion-exclusion criteria were defined & included studies fulfilled selection criteria. PRISMA-P (2015) guidelines were followed to complete study. After reviewing literature pertaining to subject, finding established the fact, internal and external interruption triggers the individuals' working behavior towards multitasking at the workplace. Multitask working behavior exerts an impact on overall work performance particularly, on work quality, productivity, and working memory. In this linking, no prior literaturebased evidence is available in this domain in particular context. The scope of study is confined to published literature on human multitasking and human performance, literature on system multitasking and media multitasking is not included. [ABSTRACT FROM AUTHOR]

Multiple deep neural networks (DNNs) are increasingly used in real-world intelligent applications, such as intelligent robotics and autonomous vehicles to collectively complete complicated tasks running on edge devices. Because each layer of the subtasks prefers a distinct dataflow due to the heterogeneity in shape and scale of the network layers, a variable dataflow approach on the DNN accelerators is urgently required. On DNN accelerators that enable multiple dataflows, however, we detect a dimension mismatch between parallel processing under the dataflow approach and linear data memory arrangement. When multiple DNN tasks share partial features or weights, the issue is further exacerbated. During processing, this mismatch causes a sluggish data supply from both off-chip and on-chip memory. Consequently, the overall throughput, performance, and energy efficiency suffer since DNN models are sensitive to data density. In this work, we reveal the mechanism behind this data dimension mismatch and present a series of metrics that quantify the influence on system performance. On this foundation, we offer a framework that tracks the data tensor dimension conversion and employs a flexible data arrangement over multi-DNN computation to adapt to dataflow variability. An accelerator architecture named data arrangement multi-DNN accelerator (DARMA) that features a data arrangement and distribution circuit and hierarchical memory for data dimension conversion is also presented. Since the mismatch is mitigated, the suggested accelerator outperforms current accelerators in terms of bandwidth and processing unit utilization. Through tests on VR/AR, MLperf, and other multitask applications, the evaluation results show that the proposed architecture provides both energy-efficiency and throughput improvements. [ABSTRACT FROM AUTHOR]

An RF integrated circuit design heavily relies upon experienced experts to iteratively tune the circuit parameters. A Bayesian optimization (BO) method is explored in existing works for automated analog and RF circuit synthesis. The overall performance can be further improved by constructing a model to exploit the correlation among different circuit specifications. In this article, we propose a BO approach for RF circuit synthesis via a multitask neural network enhanced Gaussian process (MTNN-GP). We present a novel multioutput GP model, in which the kernel functions of multiple outputs are constructed from a multitask neural network with shared hidden layers and task-specific layers. Therefore, the correlation between the outputs can be captured by the shared hidden layers. Our proposed MTNN-GP-based BO method is compared with several state-of-the-art BO methods on three real word RF circuits and achieves best performance. The experimental results demonstrate the effectiveness and efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. The specific professions of aviation personnel include the professions of the pilot and air traffic controller. These occupations are specific in that while performing their work, they must be able to simultaneously operate the devices in the handling area and in the pedipulation area, supplemented by acoustic sensations in the form of correspondence between flying and ground stations. The performance requirements of pilots and air traffic controllers place high demands on their health, psychological condition, attention, and concentration, due to being in constant pursuit of minimization of erroneous decisions, otherwise defined as the human factor in aviation. This article is focused on the development and testing of a technical device for measuring the relative error rate of students in multitasking tasks in preparation for employment. The main result is a designed technical device with hardware (HW) and software (SW) parts. An experimental method was used to measure the qualitative and quantitative performance indicators of the test subjects. The results of the experiment were observed and evaluated based on the analytical-synthetic method based on critical thinking. By comparing and abstracting the measured data, the reference values of the performance indicators of the tested subjects were determined. The selection of the final sample of subjects consisted of two phases. In the first phase, questionnaires were evaluated, and in the second phase, reaction time measurements during multitasking tasks using technical equipment were evaluated. Based on the measurements, an error ratio was defined, which could be graphically represented. The testing proved the full functionality of the designed technical equipment for these purposes in aviation education. [ABSTRACT FROM AUTHOR]

Recently, the problem of security distributed estimation in adversarial multitask wireless sensor networks has attracted extensive attention. For example, malicious attackers always affect signal processing and reduce network estimation performance by destroying data information. To address this issue, by using task similarity, this article proposes a diffusion least-mean square algorithm based on Bayes (BDLMS) in multitask adversarial network environment. The BDLMS algorithm can divide the wireless sensor system into two subsystems: Noncooperative LMS (NCLMS) subsystem and distributed diffusion LMS (DLMS) subsystem. Among them, the NCLMS subsystem ensures the state consistency of neighboring nodes by task similarity and calculates the posterior probability of node being attacked by Bayes rule to achieve attack detection. The DLMS subsystem performs parameter estimation by combining safe and reliable information. In addition, the mean and mean-square stability of the BDLMS algorithm are analyzed. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments in different network environments. [ABSTRACT FROM AUTHOR]

In their previous studies, the authors have shown that the integration of geophysical methods allows improving the quality of the solution of an inverse problem of exploration geophysics in comparison with the individual use of each of them. However, in practice, it is possible that for some measurement points, data from one of the geophysical methods used is missing. In this study, we investigate an approach associated with neural network recovery of the missing data of one geophysical method from the known data of another, and their further joint application to solve the inverse problem. In addition, we explore the effectiveness of applying multitask learning approach at the data recovery stage for the subsequent solution of the inverse problem. [ABSTRACT FROM AUTHOR]

We assess the role of monetary incentives in a mission-oriented organization by randomly assigning workers to one of two bonus schemes, incentivizing either the performance of a microcredit program (bottom line) or the empowerment of clients (mission). We find that the credit bonus improved credit-related outcomes but undermined the social mission, while the social bonus did not harm the bottom line. These results are consistent with a multitasking model with production spillovers or with prosocial behavior. We show that when mission-related rewards are not feasible, organizations that care about both the mission and the bottom line prefer flat wages to incentives. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a joint multitasking scheduling and common due date assignment problem on a single machine, for which examples can be found in product delivery process in logistics. Multitasking allows the machine to perform multiple tasks. The multitasking phenomenon has been observed in various practical domains, including manufacturing and administration. In multitasking settings, each waiting job interrupts a currently in-processing job, causing an interruption time and a switching time. In common due date assignment problems, the objective is to determine the optimal value of this due date with the purpose of minimising a total penalty function, which is associated with service quality. For the problem with general interruption functions, analytical properties are obtained to reduce the search space of the optimal solutions. For the cases with linear interruption functions, we develop a polynomial-time algorithm. Numerical experiments have been conducted to validate the efficiency of our proposed algorithm. Computational results also demonstrate an interesting phenomenon that in some cases, the optimal solutions under multitasking are superior to the counterparts without multitasking. Besides, we also devise a mixed integer programme for the cases with linear interruption function. [ABSTRACT FROM PUBLISHER]

The central focus of this paper is the behaviour of individuals when multitasking with media, wherein attitudes towards multiple media use and the management strategies employed when media multitasking are examined. A qualitative design is chosen for this in-depth study, using 34 face-to-face interviews in the UK, Germany and Australia. Findings confirm multiple media use as a central feature of everyday life, with a switching method of media multitasking widely acknowledged across all three samples. Broad similarities in attitudes, including the perceived costs and benefits of multitasking with media, are uncovered across the samples, where media hierarchies and media synergies are generated alongside self-imposed restrictions to develop effective personal ‘media multitasking portfolios’. [ABSTRACT FROM PUBLISHER]

Goal-oriented dialogue systems are becoming pervasive in human lives. To facilitate task completion and human participation in a practical setting, such systems must have extensive technical knowledge and social understanding. Politeness is a socially desirable trait that plays a crucial role in task-oriented conversations for ensuring better user engagement and satisfaction. To this end, we propose a novel task of politeness analysis in goal-oriented dialogues. Politeness analysis consists of two sub-tasks: politeness turn identification and phrase extraction. Politeness turn identification is dependent on textual triggers denoting politeness or impoliteness. In this regard, we propose a Bidirectional Encoder Representations from Transformers-Directional Graph Convolutional Network (BERT-DGCN) based multi-task learning approach that performs turn identification and phrase extraction tasks in a unified framework. Our proposed approach employs BERT for encoding input turns and DGCN for encoding syntactic information, in which dependency among words is incorporated into DGCN to improve its capability to represent input utterances and benefit politeness analysis task accordingly. Our proposed model classifies each turn of a conversation into one of the three pre-defined classes, viz. polite, impolite and neutral, and extracts phrases denoting politeness or impoliteness in that turn simultaneously. As there is no such readily available data, we prepare a conversational dataset, PoDial for mental health counseling and legal aid for crime victims in English for our experiment. Experimental results demonstrate that our proposed approach is effective and achieves 2.04 points improvement on turn identification accuracy and 2.40 points on phrase extraction F1- score on our dataset over baselines. • Aim to incorporate social intelligence in goal-oriented dialogue systems. • Politeness facilitates the development of socially-affective conversational agents. • Propose a multi-task learning framework for politeness analysis task in conversations. • It helps comprehend speakers' social conduct and detect phrases conveying politeness. • Sharing of information between tasks improves the performance of our multi-task model. [ABSTRACT FROM AUTHOR]

The practice of using a "second screen" while concurrently watching television (TV) has become a widespread phenomenon. People use a smartphone, a tablet, or a laptop while watching TV to conduct research on the show that they are watching, to communicate with their friends, or to do online shopping. Whereas work on multitasking suggests that TV consumption may lead to lower online sales, research in the area of impulse buying suggests the opposite. Our finding, based on a panel data set following 100,000 consumers in the United States and a Big Data set from browsing behavior, shows on the aggregate and the individual level that second screening can lead to higher sales for low-complexity goods (e.g., beverages, food, detergents) but causes lower sales of highly complex goods like financial products and consumer electronics. If a TV program appeals to a large TV audience, then this results in fewer immediate sales of high-complexity products (1% increase in TV consumption leads to −2.2% sales) and more sales of low-complexity products (1% increase in TV consumption leads to +8.8% sales). The practice of using a "second screen" while concurrently watching television (TV) has become a widespread phenomenon. Whereas work on multitasking suggests that TV consumption may lead to lower online sales, research in the area of impulse buying suggests the opposite. In this paper, we study the impact of concurrent TV consumption on online shopping behavior and try to understand this conflict. Our first study analyzes the impact of TV consumption on online shopping behavior at the aggregate level using a panel of 100,000 U.S. customers over a period of 24 months. We address potential endogeneity problems with an instrumental variable approach. Our second study proposes a novel approach to realizing individual-level analysis over thousands of consumers and products. Examining massive amounts of data, we identify a sample of consumers who watched a certain TV show and who were simultaneously doing online shopping at Amazon.com. We then compare their shopping behavior on the day of the TV show to their shopping behavior one week prior. Both studies reveal that whereas TV consumption is correlated with a higher likelihood of online shopping in general, the causal effect of TV watching differs with respect to the complexity of the products purchased. If a TV program appeals to a large TV audience, then this results in fewer immediate sales of high-complexity products (1% increase in TV consumption leads to −2.2% sales) and more sales of low-complexity products (1% increase in TV consumption leads to +8.8% sales). [ABSTRACT FROM AUTHOR]

Current students of higher education institutions are referred to as digital natives and so they are expected to have a good level of digital competence. The aim of the paper is to examine whether digital native students of non-technical study programs consider themselves ready for the digital transformation of education and whether, from their point of view, they meet the preconditions for such digital education. A survey (questionnaire) was conducted for this purpose. The results showed that most students have appropriate technical support for online education (technical equipment, internet connection and access to the necessary software). Most students have a positive attitude towards the use of digital technologies in general and specifically in education too. This result is consistent with the characteristics of digital natives. Consistency was also found in their self-assessment of their own level of technical skill (77.4% of students do not need help working with digital technologies or only to a small extent and 84.4% of students can actively participate in online education). The results also indicate students' propensity for multitasking as a feature of digital natives (during online education, 77% of students use multiple devices at once and a total of 85.2% of students also engage in other activities during online learning). Most students are also satisfied with the support of the subjects they study in the form of e-courses. However, the preference for a particular form of study materials (printed or digital) is not such ambiguous. [ABSTRACT FROM AUTHOR]