Reviewerly/exHarmony huggingface dataset:
Improving distinctiveness in video captioning with text-video similarity

Video captioning is a task of automatically describing visual content of a video with a sentence. Recent works in video captioning focus on improving the performance of sentence accuracy. However, the distinctiveness of sentence, i.e., highlighting unique and accurate details of video, in video captioning is still underexplored. This paper aims to improve sentence distinctiveness by incorporating video retrieval into the training process of the video captioning model. Specifically, the video retrieval will calculate similarity scores between the input text generated by the video captioning and videos. These similarity scores are then incorporated into the training loss of video captioning, which serves as distinctiveness constraint where the generated sentence and its corresponding video should have the highest similarity scores. To further improve the sentence distinctiveness, we additionally use reference scores, i.e., similarity scores between ground truth sentences and videos, as weights to scale the training loss of video captioning. This reference score serves as a target score for the model, indicating the desired level of distinctiveness for the generated sentence on how similar the generated sentence should be to the ground truth sentence for the corresponding video. Our qualitative and quantitative results show that our method improves sentence distinctiveness while simultaneously increasing its accuracy on MSVD and MSR-VTT datasets.
Meta-path infomax joint structure enhancement for multiplex network representation learning

Network representation learning has achieved significant success in homogeneous network data analysis in recent years. Nevertheless, they cannot be directly applied in multiplex networks. To overcome the characteristic of heterogeneity in multiplex networks, several emerging methods utilize the concept of meta-path to denote different types of relations and obtain the node representations for each type of meta-path individually. Despite the remarkable progress, there still exist two important issues in the previous approaches. First, the complementary information between different types of meta-paths that may make the representations more discriminative, is rarely investigated. Second, current studies generally learn multiplex node representations based on the original graph structure, while overlooking the latent relations between nodes. To address the aforementioned issues, in this paper, we propose a novel model with Meta-path Infomax joint Structure Enhancement (MISE) for multiplex network representations. Specifically, we first develop a meta-path infomax mechanism, which maximizes the mutual information between local and global meta-path representations, making the node representation contain more complementary information. Additionally, we propose a graph structure learning module that captures the implicit correlations between nodes to construct the latent graph structure. Such structure enhancement is a simple yet surprisingly effective technique to learn high-quality representations. We sufficiently evaluate the performance of our proposal on both supervised and unsupervised downstream tasks. Comprehensive experimental results show that our MISE achieves a promising boost in performance on a variety of real-world datasets for multiplex network representation learning.
State of The Art Review: Building Computational Thinking on Science Education

Industrial Revolution 4.0 requires individuals to have the ability in the field of technology and use it to solve existing problems. Computational Thinking (CT) is one of the skills needed in dealing with technological developments through a problem-solving process. Many research developments in the field of CT have been carried out, but the theoretical studies presented are still limited to the ability in solving problems using a computer. Whereas in its development, CT theory must be adapted to the scope and purpose of building it. Based on that, new research is needed which aims to test and analyze the truth of these findings and examine the stages of building appropriate CT for science students with state-of-the-art review method. By taking a specific scope that has not been studied by many researchers, namely science education, it is found that CT is the ability in dividing a problem into sub-steps, carry out deeper investigations, analyze and criticize and test the truth of something so that the right solution is obtained. This definition is more specific than the definition of CT in theory because it is adapted to the characteristics of science. Whereas from a state-of-the-art review of the stages of building CT, it was found that the stages of task decomposition, abstraction, generalization, data structures and algorithms were considered to optimalize the CT construction for science students. It is because students could identify tasks or problems and divide the problem into small parts at the task decomposition stage. Therefore, they can be completed one by one.
Analysis of Water Demand Forecasting System Based on GIS Water Diversion in Yellow River Irrigation Area

The Yellow River irrigation area in the lower reaches of the Yellow River has excellent natural conditions and is an important food and cotton production base in China. Monitoring soil moisture in the current period is of great significance for ensuring local agricultural production. Based on this, in order to help the relevant personnel use GIS technology to carry out water demand forecasting for the Yellow River irrigation area, this paper starts from the characteristics of GIS-diversion Yellow River irrigation area water demand forecasting system, describes and explains in detail the operation conditions of system model, system content, system structure, system development, system database and system application.
A comparison of deep and shallow models for the detection of induced seismicity

Abstract Can an interpretable logistic regression model perform comparably to a deep learning model in the task of earthquake detection? In spite of the recent focus in academic seismological research on deep learning, we find there is hope that it can. Using data from the Groningen Gas Field in the Netherlands, relating to low‐magnitude induced seismicity, we build on a recently presented four‐input logistic regression model by adding to it four further statistically derived features. We evaluate the performance of our feature‐enhanced model relative to both the original logistic regression model (shallow machine learning model) and a deep learning model proposed by the same research group. We discover that at the signal‐to‐noise ratio of this earlier work, our enhanced logistic regression model in fact overall outperforms the deep learning model and displays no false negative errors. At the lower signal‐to‐noise ratios also considered here, while the number of false positive errors made by the logistic regression model increases, the number of undetected earthquakes remains zero. Though the number of false positives is for the highest imbalance ratios currently prohibitive, the benefit of our four additional features, which increases as the signal‐to‐noise ratio decreases, suggests that an interpretable model might be made to perform comparably to a more complex deep learning model at real‐world class imbalance ratios if further useful inputs could be identified.
Leading the digitalisation process in K–12 schools – The school leaders’ perspective

Digital technologies have increased worldwide in the last years. In addition, the pandemic has strengthened digital technologies' roles in education, requiring twenty-first-century skills, such as digital competence, and indicating a new normal. Digitalisation in education creates opportunities that could lead to positive effects if digital technologies are applied correctly. However, applying digital technologies can incorrectly trigger a negative development - for example, increasing workload due to nonfriendly user interfaces in software and reducing the motivation to apply digital technologies in education due to a lack of digital competence. Teachers require access to digital technologies and digital competence related to educational issues that influence equality within and among K-12 schools, thus making school leaders' roles crucial in digitalising education. Three group interviews and a survey were used to collect the data in a network of three municipalities in Sweden. The data have been categorised and analysed with thematic analysis. The results show that school leaders describe the digitalisation process in the form of digital competence for teachers, access to hardware and software, and a shared culture. School leaders explain that clear guidelines, collaboration between teachers, and enough time enable digitalisation in education. However, the lack of support and the lack of resources constrain digitalisation in education. At the same time, school leaders do not often discuss their own digital competence. The school leaders' roles in the digitalisation of K-12 schools are important, requiring digital competence for leading the digitalisation process.
Evaluating the Perceptual Properties of Crosscutting Concerns Occurrence Points Specifications in Embedded Software

Crosscutting concerns associated with the non-functional requirements and system constraints contribute to the growing complexity of embedded software. Techniques, such as Aspect-Oriented Software Development, propose units of modularization to encapsulate their handling. A key design issue in embedded software is related to the process of locating, identifying, and specifying the Crosscutting Concerns Occurrence Points (CCOP). This paper reports on an empirical evaluation that compares four notations, namely JPDD, Theme/UML, AspectJ, and Tracematch to specify the selection of CCOPs. A set of metrics to quantify the perceptual properties of these specifications was proposed based on the Physics of Notation (PoN) conceptual framework. Three embedded automation systems have been specified using the mentioned notations and evaluated according to the proposed metrics set. Experimental results show how the used notation impacts the comprehension of the specification, whether in graphical or textual form. The results show poor discriminability in the cognitive effectiveness of visual representations. Hence, using visual notations to specify CCOP selection seems not to facilitate the understanding as one would expect.
Simulation of Timing Attacks and Challenges for Early Side-Channel Security Analysis

Side-channel attacks (SCA) enable attackers to gain access to non-disclosed information by measuring emissions of a system, e.g., timing, electromagnetic waves or power consumption. The emissions of a system can typically only be measured on the final system. As a consequence, the analysis of such security threats is often only possible at a very late stage in the development process. In this paper, we present an approach to simulate timing attacks in early stages of the development process with SystemC and discuss the potentials and limitations of this approach. Our results show that the simulation of SCA in SystemC is generally possible, but currently difficult due to an explanation gap. It is, to the best of our knowledge, not well understood where the causal connection between physical quantities and data, which is exploited in SCA, comes from. This poses a major challenge for the design of precise models that accurately reflect physical insights for early security analysis.
Evangelization Through Education in Korea: From Simple Schooling to Universities

Since its establishment in 1784, the Catholic Church in Korea has strived to evangelize Korean society through education. Over the past 240 years, the Catholic Church has established and operated a range of educational institutions, from small schools within churches to elementary, middle, high, and university schools, as well as kindergartens and special schools for disabilities, in order to provide education tailored to the various ages and circumstances of students. However, this has not been without its challenges. Catholic schools have had to adapt in various ways due to the oppression of the Chosŏn Dynasty and the Japanese Empire, as well as the establishment and changes in the government of the Republic of Korea. In such circumstances, the Catholic Church has worked hard to carry out its mission of evangelization and to meet the demands of the times through its educational projects. These educational projects of the Korean Catholic Church have had an important influence on the history of Korean modern education. Although major variables such as national education policy and social change are still ahead, the efforts of the Korean Catholic Church for evangelization and a well-rounded education will continue.
An explainable deep learning model for prediction of <scp>early‐stage</scp> chronic kidney disease

Abstract Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end‐stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep neural networks (DNN) with an adaptive moment estimation optimization function to predict early‐stage CKD. The health care applications require interpretability over the predictions of the black‐box model to build conviction towards the model's prediction. Hence, the predictions of the DNN‐CKD model are explained by the local interpretable model‐agnostic explainer (LIME). The diagnostic patient data is trained on five layered DNN with three hidden layers. Over the unseen data, the DNN‐CKD model yields an accuracy of 98.75% and a roc_auc score of 98.86% in detecting CKD risk. The explanation revealed by the LIME algorithm echoes the influence of each feature on the prediction made by the DNN‐CKD model over the given CKD data. With its interpretability and accuracy, the proposed system may effectively help medical experts in the early diagnosis of CKD.
Interpretable cost-sensitive regression through one-step boosting

In most practical prediction problems, such as regression and classification, the different types of prediction errors are not equally costly in the decision-making process. Although there exist numerous real-world cost-sensitive regression problems, ranging from loan charge-off forecasting to house price predictions, the literature on cost-sensitive learning mainly focuses on classification and only a few solutions are proposed for regression problems. These regressions are typically characterized by an asymmetric cost structure, where over- and underpredictions of a similar magnitude face vastly different costs. In this paper, we present a one-step boosting method (OSB) for cost-sensitive regression. The proposed methodology leverages a secondary learner to incorporate cost-sensitivity into an already trained cost-insensitive regression model. The secondary learner is defined as a linear function of certain variables deemed interesting for cost-sensitivity. These variables do not necessarily need to be the same as in the already trained model. An efficient optimization algorithm is achieved through iteratively reweighted least squares using the asymmetric cost function. The obtained results become interpretable through bootstrapping, enabling decision makers to distinguish important variables for cost-sensitivity as well as facilitating statistical inference. Applying different cost functions and various initial cost-insensitive learning methods on several public datasets consistently yields a significant reduction in the average misprediction cost, illustrating the excellent performance of our approach.
Bilateral electrodermal asymmetry in competitive tasks

Chapter 5 documents the implementation of the VPSC formulation in the VPSC code and describes the latter in detail: its structure, input/output files, subroutines, and the derivation of the algorithms used.
IDDANet: An Input-Driven Dynamic Adaptive Network ensemble method for edge intelligence

Dynamic pruning and conditional convolution are crucial for reducing the computational complexity of convolutional neural networks, enabling their deployment on resource-limited devices. However, existing methods only focus on one aspect in isolation, ignoring the collaborative potential of devices and the similarity between input samples in edge cluster scenarios, making it difficult to eliminate redundancy in a given network structure. In order to reduce computation costs while enhancing model representation, we propose an input-driven dynamic adaptive network ensemble method, namely IDDANet. Our method is a two-stage model synthesis strategy that first dynamically prunes the network by using sample complexity and similarity to obtain a lightweight model and a set of input-dependent combination coefficients. We then combine the convolution parameters of multiple models to synthesise a more accurate ensemble model. As demonstrated through extensive experiments on different datasets, our method can significantly reduce computation complexity while improving model representation ability compared to state-of-the-art methods. For example, our method can reduce 58.5% of the FLOPs of ResNet-18, while only having a 0.1% Top-1 accuracy drop on CIFAR-10.
Using BERT models for breast cancer diagnosis from Turkish radiology reports

Diagnostic radiology is concerned with obtaining images of the internal organs using radiological imaging procedures. These images are then interpreted by a diagnostic radiologist, who produces a textual report that assists in the diagnosis of illness or injury. Early detection of certain illnesses, particularly cancer, is critical, and the reports produced by diagnostic radiologists play a key role in this process. To develop models for the early detection of cancer, text classification techniques can be applied to radiological reports. However, this process requires access to a dataset of radiology reports, which is not widely available. Currently, radiology report datasets exist for high-resource languages such as English and Dutch, but not for low-resource languages such as Turkish. This article describes the collection of a mammography report dataset for Turkish, consisting of 62 reports from real patients that were manually labeled by an expert for diagnosing breast cancer. Basic machine learning models were applied to this dataset using pre-trained BERT, DistilBERT, and an ensemble learning hard voting approach. The results showed that BERT on Turkish achieved the best performance, with a 91% F1-score. Hard Voting, which combined the results of BERTTurkish, BERTClinical, and BERTMultilingual, achieved the highest F1-score of 93%. The results show that BERT and Hard Voting outperform the other machine learning models for breast cancer diagnosis from Turkish radiology reports.
Edge convolutional networks: Decomposing graph convolutional networks for stochastic training with independent edges

After the success of Graph Convolutional Networks (GCN), many stochastic training methods have been proposed to resolve the scalability and efficiency issues of GCN by sampling. In mini-batch training, a common phase of these methods is to form a small-scale subgraph rooting in the given batch. The subgraph formation leads to heavy time consumption, additional space occupation, and complex implementation. To rectify these issues, we eliminate the subgraph formation phase and propose Edge Convolutional Network (ECN), which is trained with independently sampled edges. It has constant time complexity for sampling, reducing the sampling time by orders of magnitude without compromising convergence speed. Specifically, when there are two convolutional layers, as in the most common situation, GCN can also be trained with the techniques behind ECN, gaining substantial sampling time reduction without trade-offs. We prove that the expressiveness difference between ECN and GCN is theoretically bounded and examine the inference performance of ECN through excessive experiments on real-world, large-scale graphs. Furthermore, we improve ECN with advanced mechanisms of GCN, including skip connection, identity mapping, embedding, and attention. With proper mechanisms integrated, ECN rivals state-of-the-art (SotA) baselines in inductive node classification and produces new SotA accuracy on the dataset of Flickr. The code is available athttps://github.com/cf020031308/ECN.
Avoiding Recursion in the Representation of Subsenses and Subentries in Dictionaries

Abstract Recursion, and recursion-like design patterns, are used in the entry schemas of dictionaries to model subsenses and subentries. Recursion occurs when elements of a given type, such as sense, are allowed to contain elements of the same or similar type, such as sense or subsense. This article argues that recursion unnecessarily increases the computational complexity of entries, making dictionaries less easily processable by machines. The article will show how entry schemas can be simplified by re-engineering subsenses and subentries as relations (like in a relational database) such that we only have flat lists of senses and entries, while the is-subsense-of and is-subentry-of relations are encoded using pairs of unique identifiers. This design pattern losslessly records the same information as recursion (including – importantly – the listing order of items inside an entry) but decreases the complexity of the entry structure and makes dictionary entries more easily machine-processable.
Development of a cochlear implantation-related mechanical injury model in miniature pigs

Introduction To avoid mechanical injuries related to the process of cochlear implantation, the physical properties of the cochlea and cochlear electrodes should be studied. To further explore and determine possible mechanical injury during cochlear implantation, we developed a cochlear implantation-related mechanical injury model in miniature pigs. 2.
Specification of Quality of Context Requirements for Digital Phenotyping Applications

Digital phenotyping applications use sensor data from personal digital devices (e.g., smartphones, smart bands) to quantify moment-to-moment human phenotype at the individual in-situ level. Ensuring the quality and distribution of the data used is essential requirement in the domain of these applications. Context Quality (QoC) refers to the Information Quality (QoI) used and the Quality of Service (QoS) level of information distribution. QoI is measured by parameters that define how reliable the information is. On the other hand, QoS is provided by specifying the quality of service for distributing context data. Some aspects can degrade the QoC of the application, such as information from sensors being imprecise, wireless communication technologies used in the acquisition and distribution of information, scalability problems can cause information delay, and intermittent connection due to user mobility can result in data loss. Therefore, this study conceives a process for incorporating QoC requirements and a Domain-Specific Language (DSL) to specify these requirements in digital phenotyping applications. A case study was carried out where the scenario of an application for monitoring workers’ health was considered. It was possible to prove the expressiveness and simplicity of the proposed language when using it to define the instances of the application classes responsible for the acquisition and distribution of context information.