Task Analysis Research Articles

Key elements of Goal-Directed Training for children with cerebral palsy: A qualitative content analysis

Background: Goal-directed training is an evidence-based occupational therapy intervention for children with cerebral palsy targeting motor performance and goal attainment. There is variability and limited description on goal-directed training delivery within the studies who found it a successful intervention. The aim of this study was to establish the who, what, why and how of goal directed training according to practicing occupational therapists. Methods: Occupational therapists currently delivering goal-directed training to children with cerebral palsy were recruited. Qualitative data were collected from structured, open-ended interviews and analysed through content analysis. Themes and consensus answers were elicited and mapped to the Template for Intervention Description and Replication checklist. Results: Four key elements were identified as essential for goal-directed training: goal setting, task analysis, active intervention and evaluation. Within each area, occupational therapists provided rich descriptions of what these elements looked like in practice. Despite having a clear understanding of goal-directed training, some barriers to implementation were identified. Conclusion: Occupational therapists identified key elements and barriers to implementing goal-directed training in their practice. It is recommended that further support and training is provided to ensure that occupational therapists deliver goal-directed training with high fidelity.

VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts

Since funds or tokens in smart contracts are maintained through specific state variables, contract audit, an effective means for security assurance, particularly focuses on these variables and their related operations. However, the absence of publicly accessible source code for numerous contracts, with only bytecode exposed, hinders audit efforts. Recovering variables and their types from Solidity bytecode is thus a critical task in smart contract analysis and audit, yet this is a challenging task because the bytecode loses variable and type information, only with low-level data operated by stack manipulations and untyped memory/storage accesses. The state-of-the-art smart contract decompilers miss identifying many variables and incorrectly infer the types for many identified variables. To this end, we propose VarLifter, a lifter dedicated to the precise and efficient recovery of typed variables. VarLifter interprets every read or written field of a data region as at least one potential variable, and after discarding falsely identified variables, it progressively refines the variable types based on the variable behaviors in the form of operation sequences. We evaluate VarLifter on 34,832 real-world Solidity smart contracts. VarLifter attains a precision of 97.48% and a recall of 91.84% for typed variable recovery. Moreover, VarLifter finishes analyzing 77% of smart contracts in around 10 seconds per contract. If VarLifter is used to replace the variable recovery modules of the two state-of-the-art Solidity bytecode decompilers, 52.4%, and 74.6% more typed variables will be correctly recovered, respectively. The applications of VarLifter to contract decompilation, contract audit, and contract bytecode fuzzing illustrate that the recovered variable information improves many contract analysis tasks.

Cuckoo Search Optimization-Based Influence Maximization in Dynamic Social Networks

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms.

Biomarkers of cognitive and memory decline in psychotropic drug users.

Psychotropic drugs are vital in psychiatry, aiding in the management of mental health disorders. Their use requires an understanding of their pharmacological properties, therapeutic applications, and potential side effects. Ongoing research aims to improve their efficacy and safety. Biomarkers play a crucial role in understanding and predicting memory decline in psychotropic drug users. A comprehensive understanding of biomarkers, including neuroimaging, biochemical, genetic, and cognitive assessments, is essential for developing targeted interventions and preventive strategies. In this narrative review, we performed a comprehensive search on PubMed and Google using review-specific terms. Clinicians should use a multifaceted approach, including neurotransmitter analysis, neurotrophic factors, miRNA profiling, and cognitive tasks for early intervention and personalized treatment. Anxiolytics' mechanisms involve various neurotransmitter systems and emerging targets. Research on biomarkers for memory decline in anxiolytic users can lead to early detection and intervention, enhancing clinical practices and aligning with precision medicine. Mood stabilizer users can benefit from early detection of memory decline through RNA, neurophysiological, and inflammatory biomarkers, promoting timely interventions. Performance-enhancing drugs may boost athletic performance in the short term, but their long-term health risks and ethical issues make their use problematic. Long-term use of psychotropic performance enhancers in athletes shows changes in biomarkers of cognitive decline, necessitating ongoing monitoring and intervention strategies. Understanding these genetic influences on memory decline helps pave the way for personalized approaches to prevent or mitigate cognitive deterioration, emphasizing the importance of genetic screening and early interventions based on an individual's genetic profile. Future research should focus on refining these biomarkers and protective measures against cognitive deterioration. Overall, a comprehensive understanding of biomarkers in psychotropic drug users is essential for developing targeted interventions and preventive strategies.

Making Sense of Multi-threaded Application Performance at Scale with NonSequitur

Modern multi-threaded systems are highly complex. This makes their behavior difficult to understand. Developers frequently capture behavior in the form of program traces and then manually inspect these traces. Existing tools, however, fail to scale to traces larger than a million events. In this paper we present an approach to compress multi-threaded traces in order to allow developers to visually explore these traces at scale. Our approach is able to compress traces that contain millions of events down to a few hundred events. We use this approach to design and implement a tool called NonSequitur. We present three case studies which demonstrate how we used NonSequitur to analyze real-world performance issues with Meta's storage engine RocksDB and MongoDB's storage engine WiredTiger, two complex database backends. We also evaluate NonSequitur with 42 participants on traces from RocksDB and WiredTiger. We demonstrate that, in some cases, participants on average scored 11 times higher when performing performance analysis tasks on large execution traces. Additionally, for some performance analysis tasks, the participants spent on average three times longer with other tools than with NonSequitur.

Control-Flow Deobfuscation using Trace-Informed Compositional Program Synthesis

Code deobfuscation, which attempts to simplify code that has been intentionally obfuscated to prevent understanding, is a critical technique for downstream security analysis tasks like malware detection. While there has been significant prior work on code deobfuscation, most techniques either do not handle control flow obfuscations that modify control flow or they target specific classes of control flow obfuscations, making them unsuitable for handling new types of obfuscations or combinations of existing ones. In this paper, we study a new deobfuscation technique that is based on program synthesis and that can handle a broad class of control flow obfuscations. Given an obfuscated program P, our approach aims to synthesize a smallest program that is a control-flow reduction of P and that is semantically equivalent. Since our method does not assume knowledge about the types of obfuscations that have been applied to the original program, the underlying synthesis problem ends up being very challenging. To address this challenge, we propose a novel trace-informed compositional synthesis algorithm that leverages hints present in dynamic traces of the obfuscated program to decompose the synthesis problem into a set of simpler subproblems. In particular, we show how dynamic traces can be useful for inferring a suitable control-flow skeleton of the deobfuscated program and performing independent synthesis of each basic block. We have implemented this approach in a tool called Chisel and evaluate it on 546 benchmarks that have been obfuscated using combinations of six different obfuscation techniques. Our evaluation shows that our approach is effective and that it produces code that is almost identical (modulo variable renaming) to the original (non-obfuscated) program in 86% of cases. Our evaluation also shows that Chisel significantly outperforms existing techniques.

The Implementation of a MOOC-Based Flipped Classroom Teaching Method in the Context of Oncology Radiotherapy Residency Training.

The objective of this study is to examine the efficacy of the flipped classroom blended teaching method in the context of massive open online courses (MOOCs) for implementing standardized training and teaching of residents in oncology radiotherapy. A total of 48 junior residents who received standardized training at the Oncology Radiology Department of Harbin Medical University Cancer Hospital between September 2021 and August 2023 were randomly divided into two groups-i.e., the research group (24 cases) and the control group (24 cases)-using the random number table method. The control group received conventional didactic training, whereas the research group participated in a blended learning approach based on the MOOC model. The assessment results, along with the evaluations of teaching effectiveness, self-learning ability, and teaching satisfaction questionnaires, were observed and compared for the two groups of students. Compared with the control group, the research group presented significantly higher scores on theoretical foundations, skill operation, and case analysis (P < 0.05). The research group also showed greater outcomes than the control group in terms of improved theoretical knowledge, problem-solving skills, self-learning ability, teamwork, and communication (P < 0.05). The students in the research group presented significantly higher scores on measures of self-motivation beliefs, task analysis, self-monitoring and adjustment, and self-evaluation than those in the control group (P < 0.05). The research group also demonstrated significantly higher levels of satisfaction than the control group in terms of improvements in learning interest and initiative, clinical thinking ability, problem-solving ability, team cooperation ability, and the level of radiotherapy target delineation (P < 0.05). The implementation of MOOC-based flipped classroom blended teaching was shown to have positive effects on the standardized training and teaching of residents in the field of oncology radiotherapy. This approach can undoubtedly enhance students' academic performance, problem-solving abilities, and self-learning aptitudes while effectively stimulating their learning interests and initiative. Therefore, MOOC-based flipped classroom blended teaching is a valuable candidate for clinical application and promotion.

Analysis of tasks and training load during pre-season training in professional basketball

IntroductionIn the quest to improve basketball players' performance during the season, the analysis of the training processes during the pre-season is crucial to successfully face the competition period. The aim of this research was to characterize the training tasks of a professional basketball team of the ACB category during the 2022/23 pre-season, as well as to analyze the relationship between the game situation with the pedagogical variables and External Load variables. Materials and MethodsThe sample consisted of 107 tasks during 20 sessions of a Spanish first division professional basketball team. The dependent variables were the pedagogical variables and the external load. The independent variable was the Game Situation, understood as the organization of the players during the tasks. All variables were recorded using the Integral System for the Analysis of Training Tasks (SIATE) tool. A descriptive and inferential analysis was carried out to determine the relationships between the game situation with the pedagogical and external load variables. ResultsAll indicators of the pedagogical and external load variables show a statistically significant association with the game situation (p<.05). ConclusionsTherefore, the pedagogical and external load variables are conditioned by the game situation. Positioning itself as a variable of great relevance for the planning of the sessions, as its design facilitates the achievement of the proposed objectives and more effective training sessions.

Analysis of existing means of the cutting tool path visualization in the CNC systems

BACKGROUND: Currently, machines for processing metals and other materials are used in various industry sectors. Operation of these machines defined with the CNC is impossible without using the corresponding systems, which ensure performing real-time configuring of the system for solving various technological tasks by means of machine drives control. AIM: Analysis of functional capabilities of the visualization module of the AksiOMA Kontrol CNC system in comparison with other well-known same-type systems with consequent enlarging of capabilities of this module. Based on this, the study aim is formulated as follows: broadening the functional capabilities of the module of cutting tool motion path visualization in the AksiOMA Kontrol CNC system by means of development of the path analysis module. METHODS: For achieving the aim, the next task to broaden the functional capabilities of the module of cutting tool path visualization in the AksiOMA Kontrol CNC system is formulated as follows: analysis of existing means of cutting tool path visualization in the CNC systems. RESULTS: The study result is the fact that the visualization interface is critically important for operation, as it helps the operator to assess the abnormal situation quickly. Mistakes can be found at every stage of part machining setting-up. The mistakes can be made when positioning the part or the tool, at zero-point binding, at axes offset setting-up, etc. When machine equipment is set up, generally, difficulties take place if there is a different set-up for the other order. For these cases, in the CNC systems, such functional capabilities as processing visualization are necessary. This functionality is critically important, as it helps to find a mistake in time, to assess the danger level, and to fix it in time, that helps to prevent both the machine and the tool from failure and excessive mechanical impact. CONCLUSION: The study, which contains solving the tasks of analysis of cutting tool motion path in the AksiOMA Kontrol CNC system, is completed. The analysis conducted in the paper revealed that majority of CNC systems does not have the presented functional capabilities that does not help to conduct the complete analysis of the machining process.

Generalizing OD Maps to Explore Multi-dimensional Geospatial Datasets

ABSTRACT Understanding the mobility of entities in geospatial data is important to many fields, ranging from the social sciences to epidemiology, economics or air traffic control. Visualizing such entities can be challenging as it requires preserving both their explicit properties (spatial trajectories) and their implicit properties (abstract attributes of those trajectories). An existing technique called origin–destination maps preserves both explicit and implicit properties of datasets, using the spatial nesting technique. In this paper, we aim at generalizing this technique beyond an origins-and-destinations dataset (2-attribute datasets), to explore multi-dimensional datasets (N-attribute datasets) with the nesting approach. We present an abstraction framework – we call Gridify – and an interactive open-source tool implementing this framework using several levels of nested maps. We report on several case studies representative of the types of dimensions found in geospatial datasets (quantitative, temporal, discrete, boolean), showing the applicability of this approach to achieve visual exploratory analysis tasks in various application domains.

Creating a specialized dataset for enhanced noise compliance in live concert environments

Regulatory guidelines mandate strict noise levels during concerts, specially for night leisure soundscapes, yet accurate compliance assessment is hindered by the presence of extraneous noises. This paper presents a methodology focused on creating a specialized dataset tailored to address this challenge. We have captured several live concerts, ensuring the dataset's relevance and applicability in noise compliance assessments. This paper exposes the methodology followed since the deployment of the sensors until the labeling of the most relevant parts of it by using the LAeq levels to select the most prominent sound sources. The dataset has been used to use a PANN and incorporate a classification mechanism into a noise monitoring platform for live concerts. By leveraging LAeq levels to identify prominent sound sources, our methodology ensures the inclusion of key acoustic events in the dataset, facilitating subsequent analysis and classification tasks. The integration of a PANN (Pretrained Audio Neural Network) enables the development of a robust classification mechanism tailored to the intricacies of live concert environments. This work underscores the importance of specialized datasets in addressing the challenges of noise compliance in concert settings, offering a modest resource for advancing research and practical applications in this field.

To Map, To Ascertain

This paper offers a reflection on the theme of this special issue, how mapmakers deal with uncertainty, from a digital humanities perspective. For modern geohumanities scholars and digital mapmakers working in the field of history, dealing with the dual uncertainties of historical data and historical societies themselves can be a difficult task. How do they deal with these challenges? Do we use similar solutions to deal with uncertainty? Can we learn from the practices of early modern cartographers? And to what extent is (un)certainty itself a fruitful research topic in the geohumanities? To answer these questions, it is important to consider the historical development of the field of geohumanities and why it has learned to adapt to dealing with uncertain or ambiguous knowledge. Practical examples from my own research demonstrate how modern geohumanities scholars are affected by the notion of uncertainty in different ways. These examples are linked to the contributions and questions raised in this special issue. The engagement with early modern cartographers shows how important it is for geohumanities scholars not only to invest in geospatial analysis tasks, but also to become cartographers themselves.

TeKo: Text-Rich Graph Neural Networks With External Knowledge.

Graph neural networks (GNNs) have gained great prevalence in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants adopt a message-passing principle that obtains network representations by the attribute propagates along network topology, which however ignores the rich textual semantics (e.g., local word-sequence) that exist in numerous real-world networks. Existing methods for text-rich networks integrate textual semantics by mainly using internal information such as topics or phrases/words, which often suffer from an inability to comprehensively mine the textual semantics, limiting the reciprocal guidance between network structure and textual semantics. To address these problems, we present a novel text-rich GNN with external knowledge (TeKo), in order to make full use of both structural and textual information within text-rich networks. Specifically, we first present a flexible heterogeneous semantic network that integrates high-quality entities as well as interactions among documents and entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity descriptions, to gain a deeper insight into textual semantics. Furthermore, we devise a reciprocal convolutional mechanism for the constructed heterogeneous semantic network, enabling network structure and textual semantics to collaboratively enhance each other and learn high-level network representations. Extensive experiments illustrate that TeKo achieves state-of-the-art performance on a variety of text-rich networks as well as a large-scale e-commerce searching dataset.

Upper limb joint kinematics resulting from a model conceived for the analysis of SHAP motor tasks performed by trans-radial amputees: discrepancies with the Vicon upper limb model joint kinematics

Upper limb joint kinematics resulting from a model conceived for the analysis of SHAP motor tasks performed by trans-radial amputees: discrepancies with the Vicon upper limb model joint kinematics

Translating Traditional Folk Poetry: The Perception of Translators and Translation Students and the Reality in Saudi Society

Despite the massive presence and influence of traditional folk poetry (TFP) within Saudi society, the efforts to translate such works remain very limited. While many authors and researchers have examined the impact of this literary genre and illustrated its prominence, academic investigations into the difficulties of translating TFP works are almost nonexistent. This is also true about studies that focus on the overarching sentiment regarding these efforts among those who are associated with the field of language and translation and among members of the general public. This paper thus aims at examining these perspectives and outlining the challenges translation trainees face when performing tasks in this field. The data for this study was collected through the dissemination of a questionnaire and the analysis of a translation task assigned to university students in an academic program for English language and translation. The qualitative and quantitative analysis of the data shows that the level of acceptance of TFP translation efforts is strongly connected to the degree of association with the field of languages and translation, with individuals from outside these circles being less supportive of initiatives in this domain. Additionally, the paper identified several obstacles and challenges encountered by translation students working on TFP texts. The paper concludes with a number of recommendations and suggestions that can, hopefully, contribute to the efforts to raise awareness about translating TFP and to the process of improving the current state of translation within this field.

Exploring large language models for the generation of synthetic training samples for aspect-based sentiment analysis in low resource settings

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in sentiment analysis, aiming to identify sentiment expressed towards specific aspects of an entity. This paper explores the use of Large Language Models (LLMs), specifically GPT-3.5-turbo and Llama-3-70B, for generating annotated data in Aspect-Based Sentiment Analysis (ABSA), aiming to address the scarcity of labeled datasets in the field. Two low-resource scenarios are considered, with 25 and 500 manually annotated examples available. In the 25-example scenario, adding synthetic examples generated through few-shot prompting resulted in F1 scores of 81.33 for Aspect Category Detection (ACD) and 71.71 for Aspect Category Sentiment Analysis (ACSA). For the 500-example scenario, synthetic data augmentation showed a notable gain only for the ACSA task, raising the F1 score from 84.54 to 86.70.

Implicit expression recognition enhanced table-filling for aspect sentiment triplet extraction

Aspect sentiment triplet extraction (ASTE) is a challenging task in aspect-based sentiment analysis (ABSA), involving the identification of aspect terms, opinion terms, and their corresponding sentiment polarities within comments to form triplets. The emergence of more realistic DMASTE datasets, featuring diverse domains, implicit aspect terms, and longer comments, poses challenges for existing methods. In particular, these methods struggle with recognizing implicit expressions effectively and capturing sufficient information. To overcome these hurdles, we propose an implicit expression recognition enhanced table-filling (IERET) method. This approach integrates modeling of overall implicit expression in sentences and employs a bidirectional information aggregation module to capture word pair information comprehensively. During the decoding process, a table-filling method accurately delineates aspect-opinion pair boundaries. Experimental results across in-domain, single-source cross-domain, and multi-source cross-domain on the DMASTE dataset demonstrate that our proposed IERET method achieves state-of-the-art performance.

Engineering flexible machine learning systems by traversing functionally invariant paths

Contemporary machine learning algorithms train artificial neural networks by setting network weights to a single optimized configuration through gradient descent on task-specific training data. The resulting networks can achieve human-level performance on natural language processing, image analysis and agent-based tasks, but lack the flexibility and robustness characteristic of human intelligence. Here we introduce a differential geometry framework—functionally invariant paths—that provides flexible and continuous adaptation of trained neural networks so that secondary tasks can be achieved beyond the main machine learning goal, including increased network sparsification and adversarial robustness. We formulate the weight space of a neural network as a curved Riemannian manifold equipped with a metric tensor whose spectrum defines low-rank subspaces in weight space that accommodate network adaptation without loss of prior knowledge. We formalize adaptation as movement along a geodesic path in weight space while searching for networks that accommodate secondary objectives. With modest computational resources, the functionally invariant path algorithm achieves performance comparable with or exceeding state-of-the-art methods including low-rank adaptation on continual learning, sparsification and adversarial robustness tasks for large language models (bidirectional encoder representations from transformers), vision transformers (ViT and DeIT) and convolutional neural networks.

Risk Analysis on Autonomous Vessels based on Systems Theory – Application of NET-HARMS method

Abstract The shift towards Maritime Autonomous Surface Ships is a significant development in the maritime logistics industry, with the potential to enhance efficiency, safety, and environmental sustainability. However, the integration of autonomous systems also presents new challenges and risks, particularly in the absence of empirical data for traditional risk assessment methodologies. This research tackles this problem by utilizing the Net-worked Hazard Analysis and Risk Management System (Net-HARMS) method, a systems thinking method that hasn’t been previously employed in examining MASS. The method analyses the risks associated with the EC-funded, H2020, MOSES Project, which included a concept for automating the manoeuvring and docking processes with autonomous tugboats. The Net-HARMS method offers a comprehensive and holistic approach to risk assessment, overcoming the limitations of conventional probabilistic models. By constructing a Hierarchical Task Analysis and a task network, the research maps the system’s operational framework and explores task interdependencies. The use of a risk mode taxonomy allows for the identification of task-specific and emergent risks, which are then assessed by utilising the risk matrix of the Risk-Based Assessment Tool developed by DNV, to assess the final risk as a function of the effectiveness of each risk mitigation layer and the severity of the identified task consequences. The findings provide valuable insights into critical tasks requiring enhanced risk control measures and contribute to the development of safety constraints necessary for the successful implementation of autonomous shipping technologies. By applying Net-HARMS method to the realm of autonomous ships, this research not only fills a significant gap in maritime risk analysis but also sets a precedent for future studies in this rapidly evolving field.

WISE: Efficient WSI Selection for Active Learning in Histopathology

Deep neural network (DNN) models have been applied to a wide variety of medical image analysis tasks, often with the successful performance outcomes that match those of medical doctors. However, given that even minor errors in a model can impact patients’ life, it is critical that these models are continuously improved. Hence, active learning (AL) has garnered attention as an effective and sustainable strategy for enhancing DNN models for the medical domain. Extant AL research in histopathology has primarily focused on patch datasets derived from whole-slide images (WSIs), a standard form of cancer diagnostic images obtained from a high-resolution scanner. However, this approach has failed to address the selection of WSIs, which can impede the performance improvement of deep learning models and increase the number of WSIs needed to achieve the target performance.This study introduces a WSI-level AL method, termed WSI-informative selection (WISE). WISE is designed to select informative WSIs using a newly formulated WSI-level class distance metric. This method aims to identify diverse and uncertain cases of WSIs, thereby contributing to model performance enhancement. WISE demonstrates state-of-the-art performance across the Colon and Stomach datasets, collected in the real world, as well as the public DigestPath dataset, significantly reducing the required number of WSIs by more than threefold compared to the one-pool dataset setting, which has been dominantly used in the field.