Search Results (9)

The first-order false belief (FB) test is frequently employed to assess theory of mind (ToM); however, it faces substantial criticism regarding its inadequacies. Critics argue that the responses remain binary and are influenced by the presence and location of the object. This study aims to address these criticisms by manipulating an object’s location through three alternative FB tasks, thereby enhancing the understanding of children’s reasoning strategies (reality, belief, or perceptual access reasoning) and offering a language skill-independent measure of ToM. This study involved 150 children aged 3–6 years who were administered standard and three alternative FB tasks along with a receptive vocabulary acquisition test. The findings revealed that children predominantly utilized reality reasoning, identifying the object’s location as the correct response. However, in a condition where the object was physically removed, the percentage of correct responses increased significantly, and the use of belief reasoning increased. While age and language skills were found to be directly correlated with FB performance, the object’s interference with belief reasoning in younger children was reduced. In light of these findings, the three alternative tasks are posited to offer a promising, more accurate measure of FB understanding, independent of the object’s presence and language skill. Full article

Muscle ultrasound quantification is a valuable complementary diagnostic tool for diabetic peripheral neuropathy (DPN), enhancing physicians’ diagnostic capabilities. Quantitative assessment is generally regarded as more reliable and sensitive than visual evaluation, which often necessitates specialized expertise. This work develops a computer-aided diagnostic (CAD) system based on muscle ultrasound that integrates the bag of features (BOF) and an ensemble subspace k-nearest neighbor (KNN) algorithm for DPN detection. The BOF creates a histogram of visual word occurrences to represent the muscle ultrasound images and trains an ensemble classifier through cross-validation, determining optimal parameters to improve classification accuracy for the ensemble diagnosis system. The dataset includes ultrasound images of six muscles from 53 subjects, consisting of 27 control and 26 patient cases. An empirical analysis was conducted for each binary classifier based on muscle type to select the best vocabulary tree properties or K values for BOF. The result indicates that ensemble subspace KNN classification, based on the bag of features, achieved an accuracy of 97.23%. CAD systems can effectively diagnose muscle pathology, thereby addressing limitations and identifying issues in individuals with diabetes. This research underscores muscle ultrasound as a promising diagnostic tool to aid physicians in making accurate diagnoses, streamlining workflow, and uncovering muscle-related complications in DPN patients. Full article

Considering the increasing demand for more sustainable products across many industries, eco-labels are a useful tool for communicating the sustainability-related performance of a product to the eco-conscious consumer. However, the abundance of different eco-labels and a lack of harmonization concerning their assessment methods can hamper their effectiveness. To address these shortcomings, this paper considers the methods employed by eco-labels in the textile and clothing industry to assess the sustainability-based performance of products. Using a sample of 10 eco-labels from the Ecolabel Index, a new framework for classifying eco-labels based on their assessment methods is developed. The framework includes two categories of label assignments ((i) binary and (ii) different levels of performance) and six types of assessment methods. These types are characterized according to the decision support features employed by the labels, such as lists of mandatory criteria, minimum (average) scores, percentage scores, and the weighting of sub-categories. The proposed framework shows the benefits of cascading decision science notions in the eco-labeling domain. It provides a harmonized vocabulary of components (i.e., a roadmap) to perform a consistent and traceable advancement of eco-labels. Consequently, it can be expanded at present to allow for the classification of other eco-labels in the textile and clothing industry and beyond. Full article

Binary code similarity detection (BCSD) plays a crucial role in various computer security applications, including vulnerability detection, malware detection, and software component analysis. With the development of the Internet of Things (IoT), there are many binaries from different instruction architecture sets, which require BCSD approaches robust against different architectures. In this study, we propose a novel IoT-oriented binary code similarity detection approach. Our approach leverages a customized transformer-based language model with disentangled attention to capture relative position information. To mitigate out-of-vocabulary (OOV) challenges in the language model, we introduce a base-token prediction pre-training task aimed at capturing basic semantics for unseen tokens. During function embedding generation, we integrate directed jumps, data dependency, and address adjacency to capture multiple block relations. We then assign different weights to different relations and use multi-layer Graph Convolutional Networks (GCN) to generate function embeddings. We implemented the prototype of IoTSim. Our experimental results show that our proposed block relation matrix improves IoTSim with large margins. With a pool size of ${10}^3$, IoTSim achieves a recall@1 of 0.903 across architectures, outperforming the state-of-the-art approaches Trex, SAFE, and PalmTree. Full article

Assigning predefined classes to natural language texts, based on their content, is a necessary component in many tasks in organizations. This task is carried out by classifying documents within a set of predefined categories using models and computational methods. Text representation for classification purposes has traditionally been performed using a vector space model due to its good performance and simplicity. Moreover, the classification of texts via multilabeling has typically been approached by using simple label classification methods, which require the transformation of the problem studied to apply binary techniques, or by adapting binary algorithms. Over the previous decade, text classification has been extended using deep learning models. Compared to traditional machine learning methods, deep learning avoids rule design and feature selection by humans, and automatically provides semantically meaningful representations for text analysis. However, deep learning-based text classification is data-intensive and computationally complex. Interest in deep learning models does not rule out techniques and models based on shallow learning. This situation is true when the set of training cases is smaller, and when the set of features is small. White box approaches have advantages over black box approaches, where the feasibility of working with relatively small sets of data and the interpretability of the results stand out. This research evaluates a weighting function of the words in texts to modify the representation of the texts during multilabel classification, using a combination of two approaches: problem transformation and model adaptation. This weighting function was tested in 10 referential textual data sets, and compared with alternative techniques based on three performance measures: Hamming Loss, Accuracy, and macro-$F_1$. The best improvement occurs on the macro-$F_1$ when the data sets have fewer labels, fewer documents, and smaller vocabulary sizes. In addition, the performance improves in data sets with higher cardinality, density, and diversity of labels. This proves the usefulness of the function on smaller data sets. The results show improvements of more than 10% in terms of macro-$F_1$ in classifiers based on our method in almost all of the cases analyzed. Full article

In this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic. StEduCov consists of 16,572 tweets gathered over 15 months, from March 2020 to May 2021, using the Twitter API. The tweets were manually annotated into the classes agree, disagreeor neutral. We performed benchmarking on the dataset using state-of-the-art and traditional machine learning models. Specifically, we trained deep learning models—bidirectional encoder representations from transformers, long short-term memory, convolutional neural networks, attention-based biLSTM and Naive Bayes SVM—in addition to naive Bayes, logistic regression, support vector machines, decision trees, K-nearest neighbor and random forest. The average accuracy in the 10-fold cross-validation of these models ranged from 75% to $84.8$% and from $52.6$% to 68% for binary and multi-class stance classifications, respectively. Performances were affected by high vocabulary overlaps between classes and unreliable transfer learning using deep models pre-trained on general texts in relation to specific domains such as COVID-19 and distance education. Full article

Misinformation is considered a threat to our democratic values and principles. The spread of such content on social media polarizes society and undermines public discourse by distorting public perceptions and generating social unrest while lacking the rigor of traditional journalism. Transformers and transfer learning proved to be state-of-the-art methods for multiple well-known natural language processing tasks. In this paper, we propose MisRoBÆRTa, a novel transformer-based deep neural ensemble architecture for misinformation detection. MisRoBÆRTa takes advantage of two state-of-the art transformers, i.e., BART and RoBERTa, to improve the performance of discriminating between real news and different types of fake news. We also benchmarked and evaluated the performances of multiple transformers on the task of misinformation detection. For training and testing, we used a large real-world news articles dataset (i.e., 100,000 records) labeled with 10 classes, thus addressing two shortcomings in the current research: (1) increasing the size of the dataset from small to large, and (2) moving the focus of fake news detection from binary classification to multi-class classification. For this dataset, we manually verified the content of the news articles to ensure that they were correctly labeled. The experimental results show that the accuracy of transformers on the misinformation detection problem was significantly influenced by the method employed to learn the context, dataset size, and vocabulary dimension. We observe empirically that the best accuracy performance among the classification models that use only one transformer is obtained by BART, while DistilRoBERTa obtains the best accuracy in the least amount of time required for fine-tuning and training. However, the proposed MisRoBÆRTa outperforms the other transformer models in the task of misinformation detection. To arrive at this conclusion, we performed ample ablation and sensitivity testing with MisRoBÆRTa on two datasets. Full article

Dependency parsing has been built upon the idea of using parsing methods based on shift-reduce or graph-based algorithms in order to identify binary dependency relations between the words in a sentence. In this study we adopt a radically different approach and cast full dependency parsing as a pure sequence tagging task. In particular, we apply a linearization function to the tree that results in an output label for each token that conveys information about the word’s dependency relations. We then follow a supervised strategy and train a bidirectional long short-term memory network to learn to predict such linearized trees. Contrary to the previous studies attempting this, the results show that this approach not only leads to accurate but also fast dependency parsing. Furthermore, we obtain even faster and more accurate parsers by recasting the problem as multitask learning, with a twofold objective: to reduce the output vocabulary and also to exploit hidden patterns coming from a second parsing paradigm (constituent grammars) when used as an auxiliary task. Full article

In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to reduce the vocabulary size and improve the loading speed of the vocabulary and tracking accuracy. Secondly, we proposed an offline map construction algorithm based on the map element and keyframe database; then, we designed a fast reposition method of the mobile robot based on the offline map. Finally, we presented an offline visualization method for map elements and mapping trajectories. In order to check the performance of the algorithm in this paper, we built a mobile robot platform based on the EAI-B1 mobile chassis, and we implemented the rapid relocation method of the mobile robot based on improved ORB SLAM2 algorithm by using C++ programming language. The experimental results showed that the improved ORB SLAM2 system outperforms the original system regarding start-up speed, tracking and positioning accuracy, and human–computer interaction. The improved system was able to build and load offline maps, as well as perform rapid relocation and global positioning tracking. In addition, our experiment also shows that the improved system is robust against a dynamic environment. Full article