Core Task Research Articles

Joint friend and item recommendation based on multidimensional feature reciprocal interaction in social e-commerce

Social e-commerce platforms need to undertake the two core tasks of recommending potential social friends and preferred consumption items for users. However, the use of traditional one-dimensional information is no longer able to accurately make personalized recommendations. Early scholars have confirmed that users’ social and consumption behaviors do not exist independently: users with the same interests are more likely to become friends, and there is a high probability of similar consumption behaviors among friends. In this paper, we propose a joint friend and item recommendation model based on multidimensional feature reciprocal interaction (MFRI). Which is based on the user’s social friends and item preference information, extracts the shallow and deep features of the user’s social and consumption behaviors, and utilizes the reciprocity between unusual behaviors to achieve mutual enhancement. The reciprocity between shallow and deep features in similar behaviors is also explored based on the attention mechanism, and the model is trained by a joint loss function. We conducted experiments on real datasets, and the results confirm the effectiveness and robustness of MFRI for potential friend and preference item recommendations.

동시 텍스트를 활용한 국어과 중심 통합 수업 방안 연구

Objectives This study focused on integrated classes, which are the core tasks of school education in today's knowledge convergence era, and attempted to propose a plan for integrated classes centered on the Korean language department using poetry texts. Methods Poetry texts have many elements in common with various subjects in that they are prominent in musicality and conversationality and reflect various aspects of human life, and have excellent potential as a medium for integrated classes. If you run an integrated class using poetry text in the Korean language department, you can expect the effect of effective language learning, total understanding of knowledge, motivating learning and strengthening memory, and solving future social problems through creativity and character enhancement. Results Accordingly, an integrated instructional plan was proposed in consideration of the significance of integrated classes using poetry texts. First, according to what is integrated with the Korean language and literature fields centering on the poem text, it was classified into intra-subject integration, inter-subject integration, and cross-curricular integration with learning topics. In-subject integration is a plan to integrate and operate literature and other areas within the Korean language department, inter-subject integration is a method to integrate the Korean language and literature fields with other subjects, and cross-curricular learning topics. Poetry texts suitable for use in each type were presented, and a Korean language department-centered integrated class was designed and proposed based on this. Conclusions The significance of this study is that it specifically proposed an integrated teaching plan for the Korean language department using poetry texts. It is necessary to refine the integrated teaching plan through continuous revision and supplementation by applying these measures to actual classes.

Dead Men Do Even Tell Filled Tales

Background: Dental identification of deceased individual is a core task in the forensic dentistry. The accurate recording of clinical procedures has become more important over the time because of the increasing trends of lawsuits worldwide. Advances in imaging, root canal anatomy and restorative materials have been consistently emerging in present research and practice. Hence, the purpose of this paper is to provide an update on interrelationship between restorative dentistry and forensic personal identification.Aim: To observe the effects of predetermined incineration temperatures (400 and 800) on unrestored and restored teeth with different restorative materials, and its effect on length of tooth and volume of the pulp under Stereomicroscope and CBCT.Materials and Methods: 48 extracted premolar teeth were divided into 2 groups based on predetermined temperatures and subdivided into 4 subgroups based on restorative material used. Subgroup i- Unrestored teeth, Subgroup ii- class I Amalgam restoration, Subgroup iii- class I Amalgomer restoration and Subgroup iv- class I Cention restoration. The Antemortem and postmortem records were compared using Stereomicroscope and CBCT.Results: Teeth exposed to incineration at different temperatures will be analyzed under Stereomicroscope and CBCT.

Semantic segmentation of large-scale point cloud scenes via dual neighborhood feature and global spatial-aware

As a core task in 3D scene information extraction, point cloud semantic segmentation is crucial for understanding 3D scenes and environmental perception. While extracting local geometric structural features from point clouds, existing research often overlooks the long-range dependencies present in the scene, making it challenging to fully uncover the long-range contextual features hidden within point clouds. On this basis, we propose a segmentation algorithm (DG-Net) that integrates dual neighborhood features with global spatial-aware. Initially, the local structure information encoding module is designed to learn about local geometric shapes by encoding spatial position and directional features, thus supplementing structural information. Subsequently, a dual neighborhood features complementary module is introduced to merge the geometric structural and semantic features within local neighborhoods, learning local dependencies and capturing distinguishable local contextual features. Finally, these features are relayed to a global spatial-aware module equipped with a gated unit, which dynamically adjusts the weights of features at different stages, effectively modeling long-range dependencies between local structures and finely extracting long-range contextual features. We conducted experiments on benchmark datasets of point cloud scenes, and both quantitative and qualitative results demonstrate that our algorithm can accurately identify small-scale objects with complex geometric structures within scenes, surpassing other mainstream networks in segmentation performance. The mIoU on the S3DIS, Toronto3D, and SensatUrban datasets are 71.9 %, 82.1 %, and 59.8 %, respectively.

ESIE-BERT: Enriching sub-words information explicitly with BERT for intent classification and slot filling

Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough in the two tasks. The architecture based on autoencoding (BERT-based model) can optimize the two tasks jointly. However, we note that BERT-based models convert each complex token into multiple sub-tokens by the Wordpiece algorithm, which generates an out-of-alignment between the length of the tokens and the labels. This leads to BERT-based models not performing well in label prediction, limiting model performance improvement. Many existing models can address this issue, but some hidden semantic information is discarded during fine-tuning. We addressed the problem by introducing a novel joint method on top of BERT. This method explicitly models multiple sub-token features after the Wordpiece tokenization, contributing to both tasks. Our proposed method effectively extracts contextual features from complex tokens using the Sub-words Attention Adapter (SAA), preserving overall utterance information. Additionally, we propose an Intent Attention Adapter (IAA) to acquire comprehensive sentence features, assisting users in predicting intent. Experimental results confirm that our proposed model exhibits significant improvements on two public benchmark datasets. Specifically, the slot-filling F1 score improves from 96.5 to 98.2 (an absolute increase of 1.7%) on the Airline Travel Information Systems (ATIS) dataset.

Concluding Remarks: The Magisterium of Pius XII in a Historical Perspective

From the 19th century onwards, ‘teaching’ was increasingly seen as the core task of the popes; the pontificate of Pope Pius XII can be seen here as a preliminary climax of an increasing intensification of the papal teaching office, which also extended to more and more topics. It was a relatively small group of theologians from the Roman universities who determined not only the work of the Holy Office, but also the drafting of papal teaching documents. Professionalisation and a certain standardisation of papal teaching were guided by the attempt to restrict themselves to the area of principles of faith and morals. Anti-modernist tendencies remained dominant; the relationship to the succeeding pontificate and the Second Vatican Council with its divergent objectives thus remains an open question.

How does the impact of a two-stage air pollution control policy on air quality different? Evidence from 258 cities in China

Air pollution control has become a core task in the construction of China's “ecological civilization”. From 2013 to 2020, the Chinese government successively implemented two consecutive environmental management policies targeting air pollution, namely the Air Pollution Prevention and Control Action Plan (APPCAP) and the Blue Sky Defense War (BSDW). To compare and analyze the effects of these two consecutive policies on air pollution control and their differences in governance mechanisms, this paper employs daily air pollution data from 258 cities in China from January 2013 to September 2020 for empirical analysis. We examines the direct impact of the two-stage policies on air pollution and explores the impact mechanisms from three aspects: technological effects, structural effects, and scale effects. The results indicate: (1) Both APPCAP and BSDW significantly reduced AQI, PM2.5, PM10, SO2, nitrogen dioxide, and CO. (2) Compared to APPCAP, BSDW had a stronger effect on reducing nitrogen dioxide and managed O3 in coordination. (3) APPCAP did not have significant impacts on technology, scale, and structure effects, whereas BSDW significantly promoted technological innovation and industrial restructuring but significantly suppressed the scale effect. Our work provides new empirical evidence for the policy heterogeneity in air pollution control and offers useful policy implications for improving air quality and coordinated pollution control.

PatchDetector: Pluggable and non-intrusive patch for small object detection

Object detection is one of the core tasks in computer vision that serves as a crucial underpinning for numerous applications. In recent years, deep learning-based methods have achieved remarkable performance in object detection. However, the performance of small objects still remains unsatisfactory. Therefore, some specific architectures have been proposed to address this issue in certain areas, such as remote-sensing and UAV images. In this paper, we aim to design a pluggable and non-intrusive method, termed as PatchDetector, to improve the performance of small object detection, which can effectively avoid the time and resource overhead of retraining the entire network. To achieve that, we first analyze why the mainstream networks perform poorly on small objects and find out that the fundamental reason is that the features of small are superseded by the background, which leads to a significant semantic gap in multi-level layers. Then, significance analysis is conducted to find the essential features for improving the small object detection. Next, with the located significant features, we devise a pluggable patch network for extracting essential features for small objects, which is non-intrusive to the original network. Experiments on mainstream detectors, including YOLO series and Faster RCNN, show that the proposed PatchDetector achieves 0.4%∼2.0% mAP on small objects while not compromising the performance of medium and large objects.

Large language models facilitate the generation of electronic health record phenotyping algorithms.

Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.

The academic task performance scale: psychometric properties, and measurement invariance across ages, genders and nations

Academic task performance (TP) refers to the proficiency with which students perform in academic tasks through making the right choices and completing core tasks central to their academic studies, on time and to specification. We adapted Koopmans et al.’s task performance scale (TPS) for use within tertiary education and investigated its psychometric properties, internal consistency and measurement invariance across age, gender and national groups in university students (n = 3,265). The results showed that a hierarchical ESEM model with one higher-order task performance factor consisting of time management and task efficiency fitted the data best. The TPS exhibited full measurement invariance across gender and age groups, implying that the latent mean scores can be used to determine differences. However, invariance could only partially be established for national cohorts, implying that cross-national comparisons may not be possible. These findings offer preliminary support for the TPS as a valid instrument for gauging students’ academic task performance.

Preparing medical students for their educational task as physicians: important, desirable and unexplored territory

BackgroundPhysicians engage in educational activities in daily practice and take over an important role in providing information and transferring knowledge to patients and medical students. Therefore, it is important to focus on methods to develop teaching skills during medical school. Peer-teaching is a teaching method that is connected to different positive learning outcomes. This study aims to investigate the perspective of medical students regarding teaching as a core competency of physicians and peer-teaching as an opportunity to acquire educational skills. The study also aims to examine to what extent medical students are prepared for their teaching role at medical schools.MethodsThis cross-sectional study was performed by an online survey amongst Dutch medical students from all medical schools across all years of study. In total, 2666 medical students filled out the survey. The survey was part of the annual online survey of the Dutch medical advocacy group (DeGeneeskundestudent) amongst all medical students in the fall of 2017. The data were analysed with descriptive statistics and statistical tests (chi-squared-test and binomial test).ResultsThe results show that 49% of medical students see teaching as one of the core tasks of a physician. However, only 25% feel well prepared by their medical school for this teaching role. Instead, there are many students who gain experiences and teaching skills on their own outside medical schools. 64% of the respondents agrees that senior medical students can educate junior medical students well.ConclusionsImplementing peer-teaching in the curricular of medical schools could be an effective teaching method to prepare medical students for their future teaching role. It is important that medical schools focus on enhancing educational quality and designing learning environments for best learning outcomes to better prepare medical students for professional life.

Bounded impulse-control hovering formation in elliptical orbits

Satellite formation plays an important role in space missions such as on-orbit services and distributed space facilities. In these missions, hovering in a bounded range is one of the core tasks, but hovering technologies for the chief satellite in an elliptical orbit have been rarely investigated. Given the boundary constraints of relative motion between the chief and deputy satellites, this paper proposes two models for the design and control of hovering formation near an elliptical reference orbit through impulse: (1) the revisiting hovering formation model, which forces the deputy satellite to revisit the same relative position and maintains the formation via impulses; (2) the sequential-impulse-control hovering formation model, which uses a sequence of impulses to ensure the deputy satellite flies within the given boundary. Unlike the revisiting hovering formation model, this model does not require a fixed revisiting point, so the satellite’s trajectory is more flexible. The geometrical properties, formation maintenance strategy, and impulse-control solution for both models are investigated in detail. Finally, numerical simulations are conducted to verify the advantages of the proposed methods over other conventional methods, suggesting their wide applications in practical space missions.

Advancements and Challenges in Robot-Assisted Bone Processing in Neurosurgical Procedures.

Practical applications of nerve decompression using neurosurgical robots remain unexplored. Our ongoing research and development initiatives, utilizing industrial robots, aim to establish a secure and efficient neurosurgical robotic system. The principal objective of this study was to automate bone grinding, which is a pivotal component of neurosurgical procedures. To achieve this goal, we integrated an endoscope system into a manipulator and conducted precision bone machining using a neurosurgical drill, recording the grinding resistance values across 3 axes. Our study encompassed 2 core tasks: linear grinding, such as laminectomy, and cylindrical grinding, such as foraminotomy, with each task yielding unique measurement data. In linear grinding, we observed a proportional increase in grinding resistance values in the machining direction with acceleration. This observation suggests that 3-axis resistance measurements are a valuable tool for gauging and predicting deep cortical penetration. However, problems occurred in cylindrical grinding, and a significant error of 10% was detected. The analysis revealed that multiple factors, including the tool tip efficiency, machining speed, teaching methods, and deflection in the robot arm and jig joints, contributed to this error. We successfully measured the resistance exerted on the tool tip during bone machining with a robotic arm across 3 axes. The resistance ranged from 3 to 8 Nm, with the measurement conducted at a processing speed approximately twice that of manual surgery performed by a surgeon. During the simulation of foraminotomy under endoscopic grinding conditions, we encountered a -10% error margin.

Negotiating for influence and resources: A study of Swedish teachers’ and principals’ experiences of aggressive emails from parents

This article focuses on teachers’ and principals’ experiences of aggressive emails in home–school collaborations with parents. Semi-structured interviews with 31 teachers and principals from primary, lower and upper secondary schools in Sweden were conducted. Three categories were identified in the material which illuminate the phenomenon of cyberaggression towards principals and teachers from parents: ‘aggressive emails as reactions to principals and teachers’ performances’, ‘aggressive emails as a way of imposing power’ and ‘aggressive emails as a source of anxiety, loss of joy, and decreased focus on core tasks’. The study concludes that cyberaggression in emails from parents is centred around the act of negotiating and that email communication opens up spaces for negotiation to take place. The study also concludes that email cyberaggression tends to be instrumental in character, intended to result in a specific outcome rather than to cause harm. However, cyberaggression influences professional practice and can create a vicious circle of administrative obligation.

EnMatch: Matchmaking for Better Player Engagement via Neural Combinatorial Optimization

Matchmaking is a core task in e-sports and online games, as it contributes to player engagement and further influences the game's lifecycle. Previous methods focus on creating fair games at all times. They divide players into different tiers based on skill levels and only select players from the same tier for each game. Though this strategy can ensure fair matchmaking, it is not always good for player engagement. In this paper, we propose a novel Engagement-oriented Matchmaking (EnMatch) framework to ensure fair games and simultaneously enhance player engagement. Two main issues need to be addressed. First, it is unclear how to measure the impact of different team compositions and confrontations on player engagement during the game considering the variety of player characteristics. Second, such a detailed consideration on every single player during matchmaking will result in an NP-hard combinatorial optimization problem with non-linear objectives. In light of these challenges, we turn to real-world data analysis to reveal engagement-related factors. The resulting insights guide the development of engagement modeling, enabling the estimation of quantified engagement before a match is completed. To handle the combinatorial optimization problem, we formulate the problem into a reinforcement learning framework, in which a neural combinatorial optimization problem is built and solved. The performance of EnMatch is finally demonstrated through the comparison with other state-of-the-art methods based on several real-world datasets and online deployments on two games.

A service mesh approach to integrate processing patterns into microservices applications

Cloud is the new enabler of data processing, archiving and analyzing, wherein offered services are built with flexible and low-coupling schemes following a microservice architecture, which is commonly managed by service mesh managers. Microservice architecture allows designers to build microservice systems based on design patterns. However, current service mesh managers are based only on pipeline patterns and delegate the construction of other patterns to virtual container managers. This limitation prevents designers from defining new patterns that can provide microservice systems with different features. This paper presents a new approach for constructing microservices systems that integrate processing patterns following a service mesh strategy. This approach will enable designers to create processing patterns not considered in current service meshes and to build designs based on the combination of patterns. The approach proposes the integration of components for implicit handling of processing patterns; this means that service mesh core tasks such as discovery process, microservice coupling and workload management become transparent, eliminating manager or end-user intervention. Encouraging results were obtained, in terms of performance and execution flexibility, in a case study, where a set of existing applications collaborating in a traditional workflow was converted into a microservice application integrating processing patterns (parallel and distributed) generated following this new approach.

Historical and dispersal processes drive community assembly of multiple aquatic taxa in glacierized catchments in the Qinghai-Tibet plateau

Understanding the relative role of dispersal dynamics and niche constraints is not only a core task in community ecology, but also becomes an important prerequisite for bioassessment. Despite the recent progress in our knowledge of community assembly in space and time, patterns and processes underlying biotic communities in alpine glacierized catchments remain mostly ignored. To fill this knowledge gap, we combined the recently proposed dispersal-niche continuum index (DNCI) with traditional constrained ordinations and idealized patterns of species distributions to unravel community assembly mechanisms of different key groups of primary producers and consumers (i.e., phytoplankton, epiphytic algae, zooplankton, macroinvertebrates, and fishes) in rivers in the Qinghai-Tibet Plateau, the World's Third Pole. We tested whether organismal groups with contrasting body sizes differed in their assembly processes, and discussed their applicability in bioassessment in alpine zones. We found that community structure of alpine river biotas was always predominantly explained in terms of dispersal dynamics and historical biogeography. These patterns are most likely the result of differences in species-specific functional attributes, the stochastic colonization-extinction dynamics driven by multi-year glacier disturbances and the repeated hydrodynamic separation among alpine catchments after the rising of the Qilian mountains. Additionally, we found that the strength of dispersal dynamics and niche constraints was partially mediated by organismal body sizes, with dispersal processes being more influential for microscopic primary producers. Finding that zooplankton and macroinvertebrate communities followed clumped species replacement structures (i.e., Clementsian gradients) supports the notion that environmental filtering also contributes to the structure of high-altitude animal communities in glacierized catchments. In terms of the applied fields, we argue that freshwater bioassessment in glacierized catchments can benefit from incorporating the metacommunity perspective and applying novel approaches to (i) detect the optimal spatial scale for species sorting and (ii) identify and eliminate the species that are sensitive to dispersal-related processes.

A review based knowledge organization supported research framework for digital storytelling from travel records

Purpose This study presents a review based research framework that aims to influence memory institutions in their projects on digital storytelling from digitized ancient travel records. This study aims to influence research and policymaking related to design and delivery of services based on memory institutions’ collections of historical records. Design/methodology/approach The demonstrated research framework has been synthesized using inputs from a review of existing studies on the domain accompanied by a short survey created for collecting the opinion of selected experts. Studies demonstrating utilization of semantic web technologies and those that can influence policymaking related to digital storytelling were primarily reviewed. Findings The core tasks behind digital storytelling vary depending on the project goals. So, a two-part framework had to be proposed that covers the generic fundamental tasks with diverse applicability and digital storytelling related specific tasks separately. Also during the review, it was found that studies demonstrating the use of travel records for digital storytelling were less in number compared to studies using digital storytelling for tourism in general. Originality/value The demonstrated research framework can guide memory institutions in exposing their travel-related holdings to a wider audience using innovative semantic web technologies and open up avenues for future empirical research thereby adding to the novelty of the presented research. Also, reviews of articles on digital storytelling or digital humanities in general exist, but, review of digital storytelling initiatives focusing specifically on tourism and travel literature is scarce.

Solar demulsification: Towards the oil-water interface broken of solar photo-thermo-electro coupling

The stability of the emulsified oil/water mixture depends on the strength and tightness of the interface, which is closely related to the adsorption and close packing of the active components on the interface. The core task of demulsification is to change the interface properties to enhance efficient oil-water separation. In this study, a coupling model of photo field, thermo field, and electro field was constructed to focus on fully considering the particularity of the demulsification, while improving the utilization efficiency of solar energy by presetting and matching a variety of energy flows and sub-processes. Taking oil/water emulsion from the ASP flooding as an example, the oil removal rate of 82.27% can be obtained, and efficient energy modes, namely high-intensity thermo field, uniform photo field and low-intensity electro field, are established. At the same time, the results show that the photo-thermo-electro field changes the properties of the oil-water interface, such as the interfacial tension, zeta potential and modulus. The mechanism of solar demulsification is that the solar thermal effect changes the motion state of the molecules at the interface, the coupling electro effect drives the migration of charged droplets and the stretching or deformation of the polymer chain, which combined with the photo field to degrade the polymer at the interface. Solar demulsification not only enables innovation in the form of energy and promotes its sustainable development, but also provides a green process in which solar thermo, photo, and electro are synergistically combined to achieve demulsification.

SC-Net: Symmetrical conical network for colorectal pathology image segmentation

Background and ObjectiveImage segmentation of histopathology of colorectal cancer is a core task of computer aided medical image diagnosis system. Existing convolutional neural networks generally extract multi-scale information in linear flow structures by inserting multi-branch modules, which is difficult to extract heterogeneous semantic information under multi-level and different receptive field and tough to establish context dependency among different receptive field features. MethodsTo address these issues, we propose a symmetric spiral progressive feature fusion encoder-decoder network called the Symmetric Conical Network (SC-Net). First, we design a Multi-scale Feature Extraction Block (MFEB) matching with the Symmetric Conical Network to obtain multi-branch heterogeneous semantic information under different receptive fields, so as to enrich the diversity of extracted feature information. The encoder is composed of MFEB through spiral and multi-branch arrangement to enhance context dependence between different information flow. Secondly, the information loss of contour, color and others in high-level semantic information through causally stacking MFEB, the Feature Mapping Layer (FML) is designed to map low-level features to high-level semantic features along the down-sampling branch and solve the problem of insufficient global feature extraction in deep levels. ResultsThe SC-Net was evaluated on our self-constructed colorectal cancer dataset, a publicly available breast cancer dataset and a polyp dataset. The results revealed that the mDice of segmentation reached 0.8611, 0.7259 and 0.7144. We compare our model with the state-of-art semantic segmentation UNet++, PSPNet, Attention U-Net, R2U-Net and other advanced segmentation networks. The experimental results demonstrate that we achieve the most advanced performance. ConclusionsThe results indicate that the proposed SC-Net excels in segmenting H&E stained pathology images, effectively preserving morphological features and spatial information even in scenarios with weak texture, poor contrast, and variations in appearance.