Reviewerly/exHarmony huggingface dataset:
Digital competence test for learning in schools: Development of items and scales

Digital competence is one of the key competences in modern society. COVID-19 related remote schooling revealed that the level of digital competence for learning is not at an expected level. While schools try to overcome this issue, there is a need to understand the level of students' digital competence by assessing it. However, previous attempts to design a comprehensive digital competence test have not been entirely successful. One of the reasons might be that the focus of these tests has been too general. Therefore, the aim of this study was to create a test for assessing primary and lower secondary school students' digital competence for learning in ten dimensions identified based on earlier studies. This digital competence for learning assessment test (Digitest) was carried out with 836 third to ninth grade students from Estonian schools. IRT analysis and confirmatory factor analysis were conducted to establish the potential of the test to measure digital competence for learning and discover which latent variables can be differentiated with it. The results showed that nine dimensions describing attitudes, skills and behaviours can be distinguished with the Digitest and that the test items have good fit to assess digital competence for learning. However, the study also revealed that two higher-order dimensions of digital competence for learning could be identified: motivational and cognitive-behavioural. Thus, empirical data collected with the Digitest is initiating discussions for assessing students’ digital competence in a holistic way but also more general discussions on the concept of competence. This can help educators put greater emphasis on areas where students need further improvement.
Privacy-preserving activity recognition using multimodal sensors in smart office

Developing a human activity recognition (HAR) system for employees is essential to incorporate intelligence into smart office environments, enabling various human-centered applications to enhance employees’ well-being. Although remarkable progress has been made for the HAR in the smart office, several issues still exist, including lacking a privacy-preserving and unobtrusive method and demanding enhanced generalization performance across users. Therefore, a novel privacy-preserving HAR method based on multimodal sensors is investigated in this study, containing an infrared array sensor, a sensing chair, and a triaxial accelerometer built in a smartphone. The effectiveness of different multimodal combinations is examined. Moreover, a deep learning model is developed for multimodal data fusion to enhance the generalization performances across users. The model contains a residual 3D convolutional neural network (CNN) and 1D CNN for learning spatial–temporal feature representation of different modalities. Additionally, external memory units and an adaptive decision fusion operation are utilized for multimodal data fusion. Finally, extensive experiments are conducted to examine the performance of the proposed model using a self-collected dataset and the leave-one-subject-out cross-validation approach. The results verify the effectiveness of the proposed model.
Deterministic sampling in heterogeneous graph neural networks

Graphs are typically used to model datasets where any given data point is correlated with only a small number of other data points in the set, i.e. localized correlations. In some datasets, the data points can be of different types, and this requires the use of heterogeneous graphs. Learning methods underpinned by graphs are used for analysis tasks such as node classification and link prediction. To exploit localized correlations in the learning process, sampling the neigbourhood of a candidate root node is typically required. The data from the sampled set of nodes can then be embedded and aggregated for use in an end-to-end neural network architecture. Previous approaches to sampling are stochastic in nature, e.g. random walk with restart. In this work, we propose a new approach to sampling that is deterministic in nature. The deterministic approach is based on the notion of node importance in relation to a root node. The factors that contribute to the importance are: (i) distance (number of edges) from root node; and (ii) centrality measure of the node. In this study, we adopt the Katz measure as the centrality measure. By devising an efficient sampling method together with node embedding and aggregation methods, we propose a Deterministic Heterogeneous Graph Neural Network (D-HetGNN). The application of D-HetGNN to three datasets is presented, and an extensive experimental evaluation demonstrates the superiority of the proposed sampling.
Lung And Colon Cancer Detection Using Weighted Average Ensemble Transfer Learning

Cancer is a deadly disease that is caused by metabolic abnormalities or a confluence of inherited diseases. Lung and colon cancer are considered as two of the most widely spread causes of disability and death in humans today. In the process of determining the best course of therapy for patients, the histological diagnosis of these tumors is the most important aspect. Diagnosis of the cancer in an early stage before spreading even more in the body will reduce the risk of death greatly on either front. Through utilizing the machine learning and deep learning models, this type of cancer diagnosis can be sped, providing researchers a cost-effective way to analyze a larger number of patients in much less time. In this study, we propose an ensemble transfer learning model to rapidly diagnose lung and colon cancer. Through utilizing multiple transfer learning models and ensemble them for a better performance. The proposed model uses the lung and colon histology (LC25000) dataset, Our models has an accuracy for each of, MobileNet V1, Inception V3, and VGG16 98.32%, 98%, and 96.93% for lung and colon cancer detection, respectively, while our ensemble model has an accuracy of 99.44%. This study’s findings indicate that our proposed method outperforms existing models therefor it could be used in clinics to assist medical personnel with the detection of lung and colon cancer.
Comparative Analysis of Membership Inference Attacks in Federated Learning

Given a federated learning model and a record, a membership inference attack can determine whether this record is part of the model's training dataset. Federated learning is a machine learning technique that enables different parties to train a model without the need to centralize or share that data. Membership inference attack risks the private datasets if those datasets are used to train the federated learning model and access to the generated model is available. There is a need for further study in a federated learning environment to develop effective countermeasures against the membership inference attack without compromising the utility of the target model. In this study, we empirically investigated and compared various membership inference attack approaches in a federated learning environment. We also evaluated these attacks on several optimizers and analyzed them with and without countermeasures.
Honey2Fish - A Hybrid Encryption Approach for Improved Password and Message Security

Nowadays, there is an upsurge in reliance on web usage. Many of these web applications make use of password-based authentication. Ensuring a secure password protection mechanism is crucial for maintaining the overall confidentiality of computer systems. Despite extensive research on creating strong, reliable, confidential passwords, several challenges still exist. One such issue is weaker password-based encryption (PBE). This paper proposes Honey2Fish, a hybrid encryption approach that uses Honey encryption (HE) and Twofish for enhanced password and message security. A two-layer protection mechanism protects both the privacy of credentials and the messages. This approach provides improved performance and enhanced security while keeping the system’s overall complexity low for securing passwords and messages. The proposed solution uses honeywords to give bogus but justified data while encrypting with an incorrect password to safeguard against brute force attacks. This paper further examines contemporary honeyword creation algorithms and proposes a user-centric approach to avoid human error while entering passwords. The validation of the research suggests that Honey2Fish is useful and secure in real-life password-based authentications. The result shows Honey2Fish has an average of 50% avalanche property with good throughput.
Deep Learning based MIMO detection in 6G Wireless Communication System

This paper proposes a deep learning algorithm to decode spheres in order to solve the multi-input multi-output (MIMO) receiver’s detection problem. The modified K and K1 sphere decoders achieve high performance in terms of spectral efficiency. This in turn will improve the data rate of next generation wireless communication systems. In order to reduce the computational complexity, the number of codewords needs to be reduced, which can be achieved with a deep learning algorithm based on a neural network. Simulation results depict the effectiveness of the proposed schemes in terms of BER, spectral efficiency, and computational time. Simulations were carried out using the MATLAB tool to support the proposed algorithm and validate the analytical results.
ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors

Image inpainting involves filling missing areas of a corrupted image. Despite impressive results have been achieved recently, restoring images with both vivid textures and reasonable structures remains a significant challenge. Previous methods have primarily addressed regular textures while disregarding holistic structures due to the limited receptive fields of Convolutional Neural Networks (CNNs). To this end, we study learning a Zero-initialized residual addition based Incremental Transformer on Structural priors (ZITS++), an improved model upon our conference work, ZITS (Dong et al. 2022). Specifically, given one corrupt image, we present the Transformer Structure Restorer (TSR) module to restore holistic structural priors at low image resolution, which are further upsampled by Simple Structure Upsampler (SSU) module to higher image resolution. To recover image texture details, we use the Fourier CNN Texture Restoration (FTR) module, which is strengthened by Fourier and large-kernel attention convolutions. Furthermore, to enhance the FTR, the upsampled structural priors from TSR are further processed by Structure Feature Encoder (SFE) and optimized with the Zero-initialized Residual Addition (ZeroRA) incrementally. Besides, a new masking positional encoding is proposed to encode the large irregular masks. Compared with ZITS, ZITS++ improves the FTR's stability and inpainting ability with several techniques. More importantly, we comprehensively explore the effects of various image priors for inpainting and investigate how to utilize them to address high-resolution image inpainting with extensive experiments. This investigation is orthogonal to most inpainting approaches and can thus significantly benefit the community.
A New Action Meta-model and Grammar for a DEMO Based Low-Code Platform Rules Processing Engine

We consider current Design and Engineering Methodology for Organizations (DEMO) Action Rules Specification to be unnecessarily complex and ambiguous. Even while using a “structured English” syntax similar to the one used in Semantics of Business Vocabulary and Business Rules (SBVR), such specifications are: incomplete while not containing enough ontological information to derive a functional implementation; and complex by containing mostly unneeded specifications. We propose a new meta-model for DEMO’s Action Model in the form of an Extended Backus–Naur Form (EBNF) syntax which is being implemented in a prototype that directly executes DEMO models as an Information and Workflow System. This prototype includes an action engine that runs DEMO transactions and the enclosed actions specified in our approach. We are currently integrating Blockly in our solution to allow syntactically correct visual programming of our proposed new Action Rule language that includes constructs to evaluate logical conditions, update the state of internal or external information systems, obtain input and provide output (formatted with a ‘What You See Is What You Get’ (WYSIWYG) template editor) to users, among others.
Semi-Supervised Bolt Anomaly Detection Based on Local Feature Reconstruction

During the process of high-speed railway online maintenance, anomaly detection of targets in grease injection facility is extremely challenging. Environmental uncertainty (noise disturbance, various illumination conditions, changeable camera perspective, etc.) limits the performance of existing unsupervised anomaly detection methods, which tend to struggle in such complex backgrounds without prior knowledge of anomalies. Therefore, we propose a novel semi-supervised anomaly detection algorithm that only learns from anomaly-free samples and annotations while can recognize both anomaly-free and anomaly targets (key-areas) and detecting anomalous targets simultaneously. We firstly propose a key-area recognition module (KRM) to segment anomaly-free targets and predict coordinates of both anomaly-free and anomalous key-areas’ center point (key-points). By using the predicted coordinates, we generated a circular mask to erase features within the key-areas. Based on the erased features, a local feature reconstruction and segmentation module (LFRSM) is proposed to restore the latent embeddings within the key-areas and segment both anomaly-free and anomalous key-areas. The final anomaly results are obtained by analyzing the original segmentation results of KRM and the reconstructed segmentation results of LFRSM. To validate the effectiveness of the proposed algorithm, we develop a dataset, called BOLT-DET, for bolt anomaly detection in complex scenes. The proposed algorithm outperforms other existing state-of-the-art (SOTA) anomaly detection algorithms (SPADE, DFR, CFLOW, Student-Teacher, PatchCore, and DRAEM) with mIoU of 0.66, AUROC of 0.92 and F1-Measure of 0.69 on BOLT-DET. It can accurately detect anomalies and meet the requirement of practical industrial applications.
A Machine Learning approach for Detecting Malicious URL using different algorithms and NLP techniques

A malicious URL is one that was made specifically to attack through spam or fraud. Due to the billions of dollars that are compromised, malicious URLs pose a severe threat to security software. Finding secure and phishing links is therefore crucial. Therefore, machine learning is quite helpful for resolving security-related challenges. In this study, we use about 5 lakh URLs that were retrieved from the Kaggle dataset. We are utilizing three NLP approaches, including the count vectorizer, hash vectorizer, and TF-IDF vectorizer. Six machine learning classifiers, including the decision tree, random forest, K-NN, NB, SVM, and logistic regression, were used in conjunction with all these techniques. The highest accuracy results of 98.2 percent are produced by random forest. To determine whether or not the URL supplied is malicious, we built a web app using Flask.
Survey on IoT Multi-Factor Authentication Protocols: A Systematic Literature Review

The Internet of Things (IoT) is a network of physical objects that is equipped with computers, electronics, applications, sensors, and other devices.As the number of IoT devices and apps increases, a range of security techniques should be applied to strengthen and enhance their immunity to cyber attacks as attackers have more capabilities and tools now focused on targeting the IoT networks.In this context, we study the nature of the Internet of Things infrastructure, especially some different Iot architecture layers, their security challenges and brief proposed solutions.For different IoT layers, authentication is a main security aspect affecting each layer separately.We provide a systematic literature review (SLR) methodology to locate existing multi-factor authentication protocols in the literature and evaluate their effectiveness.Then we provide an analysis of available multi-factor authentication protocols that are intended for IoT environments.Then some recommendations were provided based on the analysis.In conclusion, since the large range of IoT devices are resource-and power-limited, the nature of IoT settings requires lightweight protocols.Any of those proposed protocols can call for ongoing follow-up in the future to figure out upcoming weaknesses.
A Survey of Weakly-supervised Semantic Segmentation

Weakly-supervised image semantic segmentation is a popular technology in computer vision and deep learning today. The main goal of weakly-supervised semantic segmentation is to train a model by images with only coarse or sparse annotations. Specifically, it assigns a label to each pixel through coarse label refinement or sparse label propagation, etc. The existing semantic segmentation has a wide range of applications, which includes pedestrian detection, autonomous driving, medical image segmentation, etc. However, fully-supervised semantic segmentation requires pixel-level annotation, which is expensive in manpower and time, and more and more works have focused on weakly-supervised semantic segmentation in recent years. Thus, this paper provides a review of weakly supervised semantic segmentation. Firstly, this paper summarizes the state-of-the-art research results of weakly-supervised semantic segmentation. Secondly, the widely-used datasets and semantic segmentation models are introduced. Finally, this paper analyzes the existing problems and future development directions in the field of weakly-supervised semantic segmentation.
FedGraph-KD: An Effective Federated Graph Learning Scheme Based on Knowledge Distillation

Graph Neural Networks (GNNs) have achieved success in a variety of domains due to their potent graph-data processing skills. However, gathering graph structure data from various universities and applying GNNs for centralized training is highly challenging due to privacy concerns and regulatory limitations. As a solution, Federated Graph Neural Networks (Fed-GNNs) do not require sharing data but support collaborative training of public models by sharing parameters or features between multiple parties. Thus, Fed-GNNs has gained more attention recently. However, existing Fed-GNNs schemes do not consider the problem of participants of the public model often having different private GNN models, i.e., the model heterogeneity problem, which can lead to failure in the model heterogeneity scenario. To address this issue, this paper explores an effective and novel Federated Graph Learning scheme Based on Knowledge Distillation models (FedGraph-KD). On one hand, each client trains its local models through knowledge distillation. On the other hand, this paper uses a federated learning framework to update the shared model parameters. Extensive experiments and analyses on several different graph classification datasets demonstrate the effectiveness of our approach.
A Remote View into the Classroom: Analyzing Teacher Use of Digitally Enhanced Educative Curriculum Materials in Support of Student Learning

When integrated into online curriculum modules for students, educative curriculum materials (ECMs) can enhance teachers’ enactment of these modules. This study investigated (1) the use of digitally enhanced ECMs built into an online plate tectonics curriculum module by teachers with different backgrounds and teaching experience, (2) the relationship between teachers’ use of ECMs and student learning gains, and (3) teacher reflections on the value of the ECMs they used. We studied 26 teachers who taught middle and high school students (n = 1,098) by analyzing teacher log files automatically generated by the ECMs, teacher reflections collected with post-implementation surveys and interviews, student log files, and student learning gains from pretest to posttest. Results indicate that (1) there were large variations in the amounts and types of ECM features teachers accessed, (2) middle school teachers accessed significantly more ECM features than high school teachers p < .01, (3) students of teachers who used ECMs during class time made significantly higher learning gains than students of teachers who used them only before and/or between class time, p < .05, and (4) teachers most valued ECM features on student assessment. An overall non-significant, but positive, correlation between the total teacher interactions with ECMs and student learning gains was observed, r = 0.20, p = .32.
Users' photos of items can reveal their tastes in a recommender system

Recommender Systems (RS) are based on the generalization of the observed interactions of a population of users with a collection of items. Collaborative Filters (CF) give good results, but they degrade when there are few interactions to learn from. The alternative would be to observe some features of the users that could be linked to their tastes. However, specific information on users or items is often not available. In this research work, we explore how to exploit the photos of items taken by users. Our aim is to assign similar meanings to the photos of items with which the same group of users interacted. For this purpose, we define a multi-label classification task from images to sets of users. The classifier uses a general-purpose convolutional neural network to extract the basic visual features, followed by additional layers necessary to accomplish the learning task. To evaluate our proposal we compared it with CFs, using two tourism datasets that include: restaurants of six cities and points of interest of three locations. According to the experimentation carried out, the poor results achieved by CFs are outperformed by our proposal, which takes into account the visual and taste semantics of the available photos.
MR-Net: Multiresolution sinusoidal neural networks

We present MR-Net, a general architecture for multiresolution sinusoidal neural networks, and a framework for imaging applications based on this architecture. We extend sinusoidal networks, and we build an infrastructure to train networks to represent signals in multiresolution. Our coordinate-based networks, namely L-Net, M-Net, and S-Net, are continuous both in space and in scale as they are composed of multiple stages that progressively add finer details. Currently, band-limited coordinate networks (BACON) are able to represent signals at multiscale by limiting their Fourier spectra. However, this approach introduces artifacts leading to an image with a ringing effect. We show that MR-Net can represent more faithfully what is expected of sequentially applying low-pass filters in a high-resolution image. Our experiments on the Kodak Dataset show that MR-Net can reach comparable Peak Signal-to-Noise Ratio (PSNR) to other architectures, on image reconstruction, while needing fewer additional parameters for multiresolution. Along with MR-Net, we detail our architecture's mathematical foundations and general ideas, and show examples of applications to texture magnification, minification, and antialiasing. Lastly, we compare our three MR-Net subclasses.