Context-aware Model Research Articles

Learning to Reuse Distractors to Support Multiple-Choice Question Generation in Education

Multiple choice questions (MCQs) are widely used in digital learning systems, as they allow for automating the assessment process. However, due to the increased digital literacy of students and the advent of social media platforms, MCQ tests are widely shared online, and teachers are continuously challenged to create new questions, which is an expensive and time-consuming task. A particularly sensitive aspect of MCQ creation is to devise relevant distractors, i.e., wrong answers that are not easily identifiable as being wrong. This paper studies how a large existing set of manually created answers and distractors for questions over a variety of domains, subjects, and languages can be leveraged to help teachers in creating new MCQs, by the smart reuse of existing distractors. We built several data-driven models based on context-aware question and distractor representations, and compared them with static feature-based models. The proposed models are evaluated with automated metrics and in a realistic user test with teachers. Both automatic and human evaluations indicate that context-aware models consistently outperform a static feature-based approach. For our best-performing context-aware model, on average 3 distractors out of the 10 shown to teachers were rated as high-quality distractors. We create a performance benchmark, and make it public, to enable comparison between different approaches and to introduce a more standardized evaluation of the task. The benchmark contains a test of 298 educational questions covering multiple subjects & languages and a 77k multilingual pool of distractor vocabulary for future research.

Context-Aware Linguistic Steganography Model Based on Neural Machine Translation

Context-Aware Linguistic Steganography Model Based on Neural Machine Translation

Consumer behavior prediction and marketing strategy optimization based on big data analysis

Abstract A large amount of user data on network platforms contains rich treasures, and the mining and development of user consumption behavior in combination with big data provide new possibilities for enterprise precision marketing. In this paper, a consumption behavior prediction model is constructed based on context-aware data and support vector machine classification algorithm, and interactive information collection is used to collect contextual data of user consumption behavior and consumption behavior cycle data. Combined with the support vector machine classification algorithm, the collected behavioral consumption data is divided into hyperplanes, and for nonlinear data, the method of placing the corresponding behavioral data in hyperplanes in the feature space is proposed. The selection of consumption behavior features is obtained using the min-max normalization process, and behavior prediction is carried out on this basis. The results show that the context-aware behavioral prediction model in this paper has the highest R-value, F1 value and NDCG among all models in Top-10, which are 0.796, 0.645 and 0.878, respectively. The two-stage prediction method in the behavioral prediction model can achieve a 98.15% data capture rate, which can accurately obtain the number of these users so as to formulate an accurate marketing strategy.

SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images

Deep learning has revolutionized digital pathology, allowing automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs are broken into smaller images called tiles, and a neural network encodes each tile. Many recent works use supervised attention-based models to aggregate tile-level features into a slide-level representation, which is then used for downstream analysis. Training supervised attention-based models is computationally intensive, architecture optimization of the attention module is non-trivial, and labeled data are not always available. Therefore, we developed an unsupervised and fast approach called SAMPLER to generate slide-level representations. Slide-level representations of SAMPLER are generated by encoding the cumulative distribution functions of multiscale tile-level features. To assess effectiveness of SAMPLER, slide-level representations of breast carcinoma (BRCA), non-small cell lung carcinoma (NSCLC), and renal cell carcinoma (RCC) WSIs of The Cancer Genome Atlas (TCGA) were used to train separate classifiers distinguishing tumor subtypes in FFPE and frozen WSIs. In addition, BRCA and NSCLC classifiers were externally validated on frozen WSIs. Moreover, SAMPLER's attention maps identify regions of interest, which were evaluated by a pathologist. To determine time efficiency of SAMPLER, we compared runtime of SAMPLER with two attention-based models. SAMPLER concepts were used to improve the design of a context-aware multi-head attention model (context-MHA). SAMPLER-based classifiers were comparable to state-of-the-art attention deep learning models to distinguish subtypes of BRCA (AUC=0.911±0.029), NSCLC (AUC=0.940±0.018), and RCC (AUC=0.987±0.006) on FFPE WSIs (internal test sets). However, training SAMLER-based classifiers was >100 times faster. SAMPLER models successfully distinguished tumor subtypes on both internal and external test sets of frozen WSIs. Histopathological review confirmed that SAMPLER-identified high attention tiles contained subtype-specific morphological features. The improved context-MHA distinguished subtypes of BRCA and RCC (BRCA-AUC=0.921±0.027, RCC-AUC=0.988±0.010) with increased accuracy on internal test FFPE WSIs. Our unsupervised statistical approach is fast and effective for analyzing WSIs, with greatly improved scalability over attention-based deep learning methods. The high accuracy of SAMPLER-based classifiers and interpretable attention maps suggest that SAMPLER successfully encodes the distinct morphologies within WSIs and will be applicable to general histology image analysis problems. This study was supported by the National Cancer Institute (Grant No. R01CA230031 and P30CA034196).

Enhancing local citation recommendation with recurrent highway networks and SciBERT-based embedding

When writing academic papers, referencing statements, claims and previous studies is always an important activity. However, it is considered challenging for scientists to find relevant and appropriate scientific articles which are closely related to their current works in order to reference them in their research. As a consequence of the rapid growth in scientific papers being published every year, researchers might easily get overwhelmed within a huge number of resources. One way to help them find the desired references more easily is to use context-aware citation recommendation. The citation recommender system can automatically provide a list of suitable papers as references based on specified inputs which reflect the researchers’ interests. Among the outstanding achievements of deep learning and natural language processing in recent years, the utilization of deep neural learning architectures have supported to address the problem of citation recommendation. As the result, the neural citation recommendation area has received much attention from the academic community, with the aim of enhancing the precision and correctness of the results of existing citation recommendation systems. Following this research direction, in our paper we present a novel context-aware citation recommendation model, called RHN-DualLCR (Recurrent Highway Networks – Dual Local Citation Recommendation), which integrates Recurrent Highway Networks (RHN), an improved model of the original Bidirectional Long Short-Term Memory (BiLSTM) architecture, and uses a SciBERT-based (Science text of Bidirectional Encoder Representations from Transformers) embedding layer to build up the efficiency of the state-of-the-art local citation recommendation model, which enriches context representation with global information. Our research demonstrates its originality and relevance because we used one of the latest achievements of deep models (RHN model) and natural language processing (SciBERT) applied to the citation recommendation problem. We have conducted experiments on the RHN-DualLCR model on 3 widely known datasets for the citation recommendation problem: ACL-200 (Association for Computational Linguistics), ACL-600 and RefSeer, and also used 2 common evaluation standards Mean Reciprocal Rank (MRR) and the Recall@K (R@K for short) to evaluate the performance of our model. Experimental results show that our proposed model is 3% to 16% better than the original models or state-of-the-art models.

Message from the editor in Chief

*Message from the editor in Chief
 
 The year 2023 promises many exciting developments for the Global Journal of Computer Sciences: Theory and Research (GJCS). In addition, every year, the number of citations continues to rise strongly in proportion to the increase in the number of articles published. In this respect, first of all, we would like to thank all our authors who have contributed to the success of our journal. While reviewing our new issue, we saw articles from different authors from different countries: Turkey, Argentina, Albania, Algeria.
 The multiculturalism policy followed in our globally-focused journal makes us happy with every new issue. We examine the following research areas in the current issue; “A data warehouse architecture proposal and ETL analysis:a case study of an Albanian banking system”, “Applying developed genetic algorithm operators to knapsack problems”, “Developing a new model for context awareness in ambient intelligence”, “Initial value problems: a didactic tool in evolution”, “Remote control of a serial manipulator using a depth camera” .
 We will continue to contribute to literature within the scope of Computer Science with our strong team.
 Thanks to all our authors the contributors to this issue.
 
 Hope to see you in our new issue.
 
 Assoc. Prof. Dr. Ezgi Pelin YILDIZ
 Global Journal of Computer Sciences: Theory and Research (GJCS) CHEF EDITOR

A Graph-Based Context-Aware Model to Understand Online Conversations

Online forums that allow for participatory engagement between users have been transformative for the public discussion of many important issues. However, such conversations can sometimes escalate into full-blown exchanges of hate and misinformation. Existing approaches in natural language processing (NLP), such as deep learning models for classification tasks, use as inputs only a single comment or a pair of comments depending upon whether the task concerns the inference of properties of the individual comments or the replies between pairs of comments, respectively. However, in online conversations, comments and replies may be based on external context beyond the immediately relevant information that is input to the model. Therefore, being aware of the conversations’ surrounding contexts should improve the model’s performance for the inference task at hand. We propose GraphNLI , 1 a novel graph-based deep learning architecture that uses graph walks to incorporate the wider context of a conversation in a principled manner. Specifically, a graph walk starts from a given comment and samples “nearby” comments in the same or parallel conversation threads, which results in additional embeddings that are aggregated together with the initial comment’s embedding. We then use these enriched embeddings for downstream NLP prediction tasks that are important for online conversations. We evaluate GraphNLI on two such tasks - polarity prediction and misogynistic hate speech detection - and find that our model consistently outperforms all relevant baselines for both tasks. Specifically, GraphNLI with a biased root-seeking random walk performs with a macro- F 1 score of 3 and 6 percentage points better than the best-performing BERT-based baselines for the polarity prediction and hate speech detection tasks, respectively. We also perform extensive ablative experiments and hyperparameter searches to understand the efficacy of GraphNLI. This demonstrates the potential of context-aware models to capture the global context along with the local context of online conversations for these two tasks.

A context-aware hybrid deep learning model for the prediction of tropical cyclone trajectories

Tropical cyclones (TCs) are powerful natural disasters that lead to the loss of human lives and destruction of nature. Accurate prediction of the TC trajectory over a long period is crucial for timely warning and safe evacuation. Given the complexity of TCs and the geographical contexts that affects their movements, robust tools are required to process historical TC data and predict their locations. In this study, three deep learning models with flexible topology, namely, the multilayer perceptron (MLP) model, long short-term memory (LSTM) model, and a new data-driven model (MLP-LSTM) for TC trajectory prediction, were developed. They were evaluated using the North Atlantic Ocean TC dataset and contextual information such as wind speed, wind direction, and air pressure. The results showed that the hybrid MLP-LSTM model outperformed the MLP and LSTM models, especially when contextual information was considered. The average prediction distance errors for the next three hours were calculated as 52.73 km, 20.65 km, and 19.54 km for the MLP, LSTM, and MLP-LSTM models, respectively. The prediction enhancements for the next 24 h when contextual information was considered were 166 km, 203 km, and 208 km for the MLP, LSTM, and MLP-LSTM models, respectively.

Enforcing legal information extraction through context-aware techniques: The ASKE approach

To cope with the growing volume, complexity, and articulation of legal documents as well as to foster digital justice and digital law, increasing effort is being devoted to legal knowledge extraction and digital transformation processes. In this paper, we present the ASKE (Automated System for Knowledge Extraction) approach to legal knowledge extraction, based on a combination of context-aware embedding models and zero-shot learning techniques into a three-phase extraction cycle, which is executed a number of times (called generations) to progressively extract concepts representative of the different meanings of terminology used in legal documents chunks. A graph-based data structure called ASKE Conceptual Graph is initially populated through a data preparation step, and it is continuously enriched at each ASKE generation with results of document chunk classification, new extracted terminology, and newly derived concepts. A quantitative evaluation of ASKE knowledge extraction and document classification is provided by considering the EurLex dataset. Furthermore, we present the results of applying ASKE to a real case-study of Italian case law decisions with qualitative feedback from legal experts in the framework of an ongoing national research project.

Context aware Markov chains models

For more than a century, Markov chain models have had a tremendous effect on research and industry alike in various domains such as physics, chemistry, biology, and computer science. However, limited research has been performed on how to incorporate contextual conditions into the modeling phase. Existing approaches are performed in an ad hoc manner or fail to consider multiple contextual dimensions simultaneously. Inspired by the main paradigms in context-aware recommender systems, we suggest five novel approaches for learning contextual Markov chain models. In particular we suggest three contextual pre-filtering models that operate by learning multiple sub-models from various data partitions, each tailored to specific contextual aspects; a contextual model that directly extents the Markovian model with the contextual features; and lastly, a post-filtering model, which serves to refine the predictions made by a traditional non-contextual model by considering the available contextual information. We evaluate the suggested methods in two use cases: analysis of web browsing activities, and attack propagation patterns in honeypot systems. Experiments with massive datasets that contain millions of records indicate that taking the context of modeled sequences into consideration can substantially improve the accuracy of predicting and ranking the next possible element in such sequences. Moreover, we demonstrate the high scalability of the suggested methods, making them suitable for analyzing a vast amount of sequential data.

Context-Aware Auto-Encoded Graph Neural Model for Dynamic Question Generation using NLP

Question generation is an important task in natural language processing that involves generating questions from a given text. This paper proposes a novel approach for dynamic question generation using a context-aware auto-encoded graph neural model. Our approach involves constructing a graph representation of the input text, where each node in the graph corresponds to a word or phrase in the text, and the edges represent the relationships between them. We then use an auto-encoder model to learn a compressed representation of the graph that captures the most important information in the input text. Finally, we use the compressed graph representation to generate questions by dynamically selecting nodes and edges based on their relevance to the context of the input text. We evaluate our approach on four benchmark datasets (SQuAD, Natural Questions, TriviaQA, and QuAC) and demonstrate that it outperforms existing state-of-the-art methods for dynamic question generation. In the experimentation, to evaluate the result four performance metrics are used i.e. BLEU, ROUGE, F1-Score, and Accuracy. The result of the proposed approach yields an accuracy of 92% on the SQuAD dataset, 89% with QuAC, and 84% with TriviaQA. while on the natural questions dataset, the model gives 79% accuracy. Our results suggest that the use of graph neural networks and auto-encoder models can significantly improve the accuracy and effectiveness of question generation in NLP. Further research in this area can lead to even more sophisticated models that can generate questions that are even more contextually relevant and natural-sounding.

Context-aware multi-head self-attentional neural network model for next location prediction

Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual’s next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations’ land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model’s performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.

Visitors’ satisfaction towards indoor environmental quality in Australian hotels and serviced apartments

Tourism is Australia’s fourth-largest exporting sector, yet insufficient research has been done on how satisfied guests are with the indoor environmental quality (IEQ) of Australian guest homes. This project utilised web-mining, natural language processing and aspect-based sentiment analysis to analyse visitors’ IEQ satisfaction in Australian tourist accommodations across ten tourism cities. By analysing 543,213 IEQ-related reviews from 1397 hotels and serviced apartments with two-stars and above at the Booking.com, guests’ text comments were classified by semi-supervised word-embedding based models into nine IEQ dimensions. Using a cutting-edge custom-trained bidirectional context-aware deep sequence model, sentiment scores were computed to estimate the degree of IEQ satisfaction. Results showed that guests were most dissatisfied with facilities, cleanliness and maintenance, and acoustics. IEQ dissatisfaction reduced as the buildings’ star ratings rose, except for thermal, lighting, exterior view, and layout and design dimensions. There was a small effect of seasonal fluctuations on the levels of dissatisfaction with thermal conditions, indoor air quality, and cleanliness and maintenance. All IEQ dimensions, excluding indoor air quality, showed small effects on the percentage of dissatisfied visitors across climate zones. During the pandemic, there was a significant decline in the guests’ evaluation scores. Notwithstanding, the IEQ satisfaction was scarcely affected by COVID-19, except for indoor air quality, cleanliness and maintenance, and facilities. This study’s findings can serve as a rationale for the Australian hospitality industry to enhance visitor satisfaction.

Robust imputation method with context-aware voting ensemble model for management of water-quality data.

Water-quality monitoring and management are crucial for ensuring the safety and sustainability of water resources. However, missing data is a frequent problem in water-quality datasets, which can result in biased results in hydrological modeling and data analysis. While classic statistical methods and emerging machine/deep learning methods have been applied for imputing missing values, most existing studies perform well in specific missing scenarios, but not in universal scenarios. Therefore, existing imputation methods often fail to robustly impute missing values across various scenarios. To address the problem, we propose an imputation method that uses a context-aware voting-ensemble model to dynamically select optimal weights to integrate various imputation models across different missingness scenarios. For first identify the attributes of missingness scenarios that influence imputation accuracy. Then after introducing missing values in collected data according to the missingness scenarios, we measure the accuracy of various imputation models across the missingness scenarios. Weights of imputation models are optimized by estimating non-linear functions with regression model that can capture relationships between missingness scenarios and imputation accuracies of models. The final imputed value of the ensemble model for a missing scenario can be determined by multiplying each imputation model's weight by its imputed value, then summing the products. The method inherits the advantages of state-of-art imputation models, including the ability to learn long-term dependencies in time series, as well as the flexibility of using a dynamic weighting strategy to process various missingness scenarios. To validate the superiority of our method, we evaluate on real-world water-quality data from a river in South Korea. The proposed method achieves higher accuracy and lower variation of imputed values than baseline models across various missingness scenarios. Furthermore, we showed the applicability of our method to various hydrological environment by validating our method on industrial water quality dataset. This study highlights the potential value of the ensemble model with dynamic weighting in robust imputation of water-quality data.

Indoor temperature prediction with context-aware models in residential buildings

This paper presents a novel approach for predicting average indoor temperature in residential buildings, utilizing contextual factors of the rise of the building and geographical location. The proposed approach employs advanced deep learning architectures, such as Long Short-Term Memory (LSTM) and Transformers, to create generalized predictive models applicable to a range of residential buildings. The models are trained using historical data from 18 residential buildings over a period of 6 to 10 years, where the buildings are located in different climate zones. Testing is done on nine different data sets representing three different locations and three different types of buildings. The study demonstrates that incorporating the context of building rise significantly improves the models’ predictive performance. Specifically, the transformer-based models show improvements in R2 of 4%–27% in a 6 h prediction horizon. The proposed approach explicitly using context information significantly improves the accuracy of predicting, making learnt models a good starting point for optimizing district heating distribution.

Toward a Context-Aware Course-Planning Model in Pervasive Learning Environments

This article proposes a context-aware course-planning model for pervasive learning environments. The model considers learners’ preferences, including their learning styles, locations, activities, and devices. The model generates four-dimensional contexts based on these preferences, each dimension being a weighted vector of visual, aural, read/write, and kinesthetic (VARK) features. Using a content-based similarity algorithm, the model adapts the course content to each learner’s context. The courses are created in a sequence of rings, each containing all possible learning materials (LMs) that have the same content in different formats. Each LM is represented by a vector with weights like those of the learners’ context dimensions. The model generates all possible plans based on the predefined contexts of the learner, detects the learner’s actual context, and adapts the content accordingly. The goal of the model is to enable learners to learn what they want, in the way they prefer, and to complete courses efficiently, at any time, place, and during any activity, in the appropriate format.

A Multi-Scale Context Aware Attention Model for Medical Image Segmentation.

Medical image segmentation is critical for efficient diagnosis of diseases and treatment planning. In recent years, convolutional neural networks (CNN)-based methods, particularly U-Net and its variants, have achieved remarkable results on medical image segmentation tasks. However, they do not always work consistently on images with complex structures and large variations in regions of interest (ROI). This could be due to the fixed geometric structure of the receptive fields used for feature extraction and repetitive down-sampling operations that lead to information loss. To overcome these problems, the standard U-Net architecture is modified in this work by replacing the convolution block with a dilated convolution block to extract multi-scale context features with varying sizes of receptive fields, and adding a dilated inception block between the encoder and decoder paths to alleviate the problem of information recession and the semantic gap between features. Furthermore, the input of each dilated convolution block is added to the output through a squeeze and excitation unit, which alleviates the vanishing gradient problem and improves overall feature representation by re-weighting the channel-wise feature responses. The original inception block is modified by reducing the size of the spatial filter and introducing dilated convolution to obtain a larger receptive field. The proposed network was validated on three challenging medical image segmentation tasks with varying size ROIs: lung segmentation on chest X-ray (CXR) images, skin lesion segmentation on dermoscopy images and nucleus segmentation on microscopy cell images. Improved performance compared to state-of-the-art techniques demonstrates the effectiveness and generalisability of the proposed Dilated Convolution and Inception blocks-based U-Net (DCI-UNet).

Targeted Context-Based Attacks on Trust Management Systems in IoT

Trust management systems play an important role in Internet of Things (IoT) by providing a means of finding whether a given device can provide a service to a satisfactory level, and for identifying potentially malicious devices in the network. Context awareness extends trust models by allowing a trustor to filter and aggregate evidence by their relevance to the current situation. Context awareness is important in the formulation of trust in IoT networks due to their heterogeneity and due to the dynamic changes in the capabilities of IoT devices. In this paper, we have proposed a new type of attack on context aware trust models for IoT systems, context-based attacks. In this attack, an adversary manipulates the context to impact a target group of IoT devices, while other devices in non-targeted groups are not even aware of the attack. We have demonstrated the effectiveness of this new type of attack on seven previously proposed trust models through practical simulations and theoretical proofs. This paper also proposes a new trust management system that can mitigate such context-based attacks.

A context-aware multi-agent reasoning based intelligent assistive formalism

In recent years, the rapid proliferation of intelligent smart resource-bounded devices and autonomous software technologies has significantly influenced human life and made human life much smarter, more efficient, flexible, comfortable, and secure but device dependent. These intelligent devices are equipped with the feature of context awareness that often run in a highly decentralized environment with minimal and/or without human intervention. As these systems are often distributed in nature, they exhibit complex adaptive behavior by exchanging contextualized information among different resource-bounded devices and/or systems. However, due to the limitation of resources (computational memory), there are still many challenges for a real-time deployment. In this paper, we present a resource-bounded multi-agent reasoning-based context-aware model that assists users intelligently to take the right action autonomously whenever and wherever needed. The core emphasis is given to the lightweight, efficient reasoning mechanism to manipulate rapidly triggering contextual information with the least utilization of computational resources. We develop algorithms along with the complexity factors to evaluate the system’s efficacy in computational time and memory utilization. To suitably formalize the specification and model the proposed formalism using UPPAAL model checker, we develop the case study of a context-aware intelligent assistant along with its rule-based reasoning strategies and priorities for the execution of prioritized tasks according to the customized needs of the user and verify the correctness properties.

An intelligent context-aware threat detection and response model for smart cyber-physical systems

Smart cities, businesses, workplaces, and even residences have all been converged by the Internet of Things (IoT). The types and characteristics of these devices vary depending on the industry 4.0 and have rapidly increased recently, especially in smart homes. These gadgets can expose users to serious cyber dangers because of a variety of computing constraints and vulnerabilities in the security-by-design concept. The smart home network testbed setup presented in this study is used to evaluate and validate the protection of the smart cyber-physical system. The context-aware threat intelligence and response model identifies the states of the aligned smart devices to distinguish between real-world typical and attack scenarios. It then dynamically writes specific rules for protection against potential cyber threats. The context-aware model is trained on IoT Research and Innovation Lab - Smart Home System (IRIL-SHS) testbed dataset. The labeled dataset is utilized to create a random forest model, which is subsequently used to train and test the context-aware threat intelligence SHS model's effectiveness and performance. Finally, the model's logic is used to gain rules to be included in Suricata signatures and the firewall rulesets for the response system. Significant values of the measuring parameters were found in the results. The presented model can be used for the real-time security of smart home cyber-physical systems and develops a vision of security challenges for Industry 4.0.