Reviewerly/exHarmony huggingface dataset:
Formation of Universal Competencies of Undergraduates during Development of the Plot of Web-Quest

The paper tackles the issue of using the educational web-quest technology in forming universal competencies of students of higher educational establishments.The aim of the paper is to offer a theoretical grounding and to experimentally test the efficiency of forming the theory of web-quest which helps to form students' universal competencies and improve the quality of higher education on the whole.The methodology involves the analysis of works devoted to issues and the prospects of educational informational support, stating the potential of web-quest technology for forming the necessary competencies and personal qualities.For the experimental assessment, empirical methods were used (observation, analysis of the content of web-quests, results from completing tasks, and the role of this approach in forming universal competencies).Research results.The authors specify the structure-forming and meaningful role of the story line as an element of a web-quest, the prospect of its creating to increase the quality of teaching undergraduates, its advantages and disadvantages in forming the needed competencies and personal qualities of future specialists (training program 44.04.01 Pedagogical education, profile Informatization of education (master's degree).The conclusion proves that working over forming a web-quest story line contributes to forming the students' universal competencies and necessary personal qualities, as well as to improving the quality of teaching on the whole.
Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation Learning

Despite the great progress in video understanding made by deep convolutional neural networks, feature representation learned by existing methods may be biased to static visual cues. To address this issue, we propose a novel method to suppress static visual cues (SSVC) based on probabilistic analysis for self-supervised video representation learning. In our method, video frames are first encoded to obtain latent variables under standard normal distribution via normalizing flows. By modelling static factors in a video as a random variable, the conditional distribution of each latent variable becomes shifted and scaled normal. Then, the less-varying latent variables along time are selected as static cues and suppressed to generate motion-preserved videos. Finally, positive pairs are constructed by motion-preserved videos for contrastive learning to alleviate the problem of representation bias to static cues. The less-biased video representation can be better generalized to various downstream tasks. Extensive experiments on publicly available benchmarks demonstrate that the proposed method outperforms the state of the art when only single RGB modality is used for pre-training.
Givens QR Decomposition over Relational Databases

This paper introduces Figaro, an algorithm for computing the upper-triangular matrix in the QR decomposition of the matrix defined by the natural join over a relational database. The QR decomposition lies at the core of many linear algebra techniques and their machine learning applications, including: the matrix inverse; the least squares; the singular value decomposition; eigenvalue problems; and the principal component analysis.
Face Recognition in Single Sample Per Person Fusing Multi-Scale Features Extraction and Virtual Sample Generation Methods

Although face recognition has received a lot of attention and development in recent years, it is one of the research hotspots due to the low efficiency of Single Sample Per Person (SSPP) information in face recognition. In order to solve this problem, this article proposes a face recognition method based on virtual sample generation and multi-scale feature extraction. First, in order to increase the training sample information, a new NMF-MSB virtual sample generation method is proposed by combining the Non-negative Matrix Factorization (NMF) reconstruction strategy with Mirror transform(M), Sliding window(S), and Bit plane(B) sample extension methods. Second, a feature extraction method (named WPD-HOG-P) based on Wavelet Packet Decomposition, Histograms of Oriented Gradients, and image Pyramid is proposed. The proposed WPD-HOG-P method is beneficial to multi-scale facial image feature extraction. Finally, based on the extracted WPD-HOG-P features, the recognition model is established by using a grid search optimization support vector machine. Experimental results on ORL and FERET data sets show that the proposed method has higher recognition rates and lower computational complexity than the benchmark methods.
Can pre-trained Transformers be used in detecting complex sensitive sentences? - A Monsanto case study

Each and every organisation releases information in a variety of forms ranging from annual reports to legal proceedings. Such documents may contain sensitive information and releasing them openly may lead to the leakage of confidential information. Detection of sentences that contain sensitive information in documents can help organisations prevent the leakage of valuable confidential information. This is especially challenging when such sentences contain a substantial amount of information or are paraphrased versions of known sensitive content. Current approaches to sensitive information detection in such complex settings are based on keyword-based approaches or standard machine learning models. In this paper, we wish to explore whether pretrained transformer models are well suited to detect complex sensitive information. Pretrained transformers are typically trained on an enormous amount of text and therefore readily learn grammar, structure and other linguistic features, making them particularly attractive for this task. Through our experiments on the Monsanto trial data set, we observe that the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) transformer model performs better than traditional models. We experimented with four different cat-egories of documents in the Monsanto dataset and observed that BERT achieves better F2 scores by 24.13% to 65.79% for GHOST, 30.14% to 54.88% for TOXIC, 39.22% for CHEMI, 53.57 % for REGUL compared to existing sensitive information detection models.
Diagrams based on the Hexagonal and Triangular Grids

In this study we show that sometimes it is worth to consider a non-traditional regular grid in diagrams and proofs by diagrams instead of the traditional rectangular arrangement.We refer to the hexagonal and triangular grids as non-traditional regular grids.Particularly, we show that not only triangular numbers can be arranged in triangular shape, but also, it is natural to represent the square numbers via triangles on the triangular grid.On the other hand, we show that hexagonal arrangement is useful to represent binomial coefficients, and also to solve jug puzzles.The triangular and hexagonal grids are dual of each other and share many properties including symmetric properties.Based on our study, we propose to consider them more frequently in education and diagrammatic applications as well.This would be fruitful also for critical thinking, design and problem solving in mathematics, engineering and informatics including artificial and computational intelligence.
Continual Learning With Speculative Backpropagation and Activation History

Continual learning is gaining traction these days with the explosive emergence of deep learning applications. Continual learning suffers from a severe problem called catastrophic forgetting. It means that the trained model loses the previously learned information when training with new data. This paper proposes two novel ideas for mitigating catastrophic forgetting: Speculative Backpropagation (SB) and Activation History (AH). The SB enables performing backpropagation based on past knowledge. The AH enables isolating important weights for the previous task. We evaluated the performance of our scheme in terms of accuracy and training time. The experiment results show a 4.4% improvement in knowledge preservation and a 31% reduction in training time, compared to the state-of-the-arts (EWC and SI).
The Classification of Oral Squamous Cell Carcinoma (OSCC) by Means of Transfer Learning

Patients that are diagnosed with oral cancer has more than an 83% survival chance if it is detected in its early stages. However, through conventional labour-intensive means, only 29% of cases are detected. It is worth mentioning that 90% of oral cancer is Oral Squamous Cell Carcinoma (OSCC) and is often caused by smoking and alcohol consumption. Computer-aided diagnostics could further increase the rate of detection of this form of oral cancer. The present study sought to employ a class of deep learning techniques known as transfer learning. The Inception V3 pre-trained convolutional neural network model is used to extract the features from texture-based images. Consequently, the malignant and benign nature of the cancer is identified from three different machine learning models, i.e., Support Vector Machine (SVM), k-Nearest Neighbors (kNN) and Random Forest (RF). It was shown from the study that an average of 91% classification accuracy was obtained from the test and validation dataset from the Inception V3-RF pipeline. The outcome of the present study could serve useful in an objective-based automatic diagnostic of OSCC and hence could possibly increase its detection.
A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

Abstract Unconstrained face images are interfered by many factors such as illumination, posture, expression, occlusion, age, accessories and so on, resulting in the randomness of the noise pollution implied in the original samples. In order to improve the sample quality, a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary. First, the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary. Then, a block cooperation framework is presented to perform sparse coding for different local structures of human face, and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients. Finally, by synthesising the sparse representation results of all visual salient block dictionaries, the global coding residual is obtained and the class label is given. The experimental results on four databases, that is, AR, extended Yale B, LFW and PubFig, indicate that the combination of visual saliency dictionary, block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition.
ConLearn: Contextual-knowledge-aware Concept Prerequisite Relation Learning with Graph Neural Network

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)ConLearn: Contextual-knowledge-aware Concept Prerequisite Relation Learning with Graph Neural NetworkHao Sun, Yuntao Li, and Yan ZhangHao Sun, Yuntao Li, and Yan Zhangpp.118 - 126Chapter DOI:https://doi.org/10.1137/1.9781611977172.14PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Prerequisite relations among concepts are important for a wide range of educational applications, such as intelligent tutoring and curriculum planning. However, concept prerequisite relation learning is not trivial due to the sparsity of prerequisite relations. In this paper, we propose a contextual-knowledge-aware concept prerequisite relation learning approach called ConLearn. Four unique properties of the proposed approach are: (1) It transfers knowledge from large language model BERT to improve contextual representations of concepts; (2) It captures concept prerequisite transition patterns by applying graph neural network on concept prerequisite graph; (3) It is equipped with self-attention mechanism to fuse information from related concepts for target concept prerequisite relation classification; (4) No handcrafted features are used in our model, which makes our model easy to implement in downstream applications. Extensive experiments on three representative datasets demonstrate that our approach significantly outperforms the state-of-the-art methods. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
A Whole-Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and labor intensive. We developed an open-source library dealing with recurrent tasks in the processing of WSI and helping with the training and evaluation of neuronal networks for classification tasks. Methods: Two histopathology use-cases were selected and only hematoxylin and eosin (H&amp;E) stained slides were used. The first use case was a two-class classification problem. We trained a convolutional neuronal network (CNN) to distinguish between dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG), two neuropathological low-grade epilepsy-associated tumor entities. Within the second use case, we included four clinicopathological disease conditions in a multilabel approach. Here we trained a CNN to predict the hormone expression profile of pituitary adenomas. In the same approach, we also predicted clinically silent corticotroph adenoma. Results: Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. For the second use case, the best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma, and 0.98 for gonadotroph adenoma. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive and fastai-compatible library is helpful to standardize the workflow and minimize the burden of training a CNN. Indeed, our trained CNNs extracted neuropathologically relevant information from the WSI. This approach will supplement the clinicopathological diagnosis of brain tumors, which is currently based on cost-intensive microscopic examination and variable panels of immunohistochemical stainings.
Robust coherent control in three-level quantum systems using composite pulses

Here, we focus on using composite pulses to realize high-robustness and high-fidelity coherent control in three-level quantum systems. We design the dynamic parameters (Rabi frequency and detuning) for three-level Hamiltonians for high-fidelity quantum state control using five well-known coherent control techniques including a composite adiabatic passage (CAP). Furthermore, we compare their performance against the Rabi frequency and systematic errors, and accordingly show that the CAP is the most robust against them. It features a broad range of high efficiencies above 99.9%. Thus, it provides an accurate approach for manipulating the evolution of quantum states in three-level quantum systems.
Maritime English: Teaching English for Maritime Sciences or Teaching Maritime Sciences in English?

Maritime English is a “scourge” for maritime science students at universities engaged in maritime science. English is a foreign language in Indonesia may cause the students cannot understand Maritime English well. A phenomenon occurs that Maritime English teachers do not have basic knowledge about the maritime sciences. This has an impact on the teaching and learning process of Maritime English. This study seeks to reveal whether Maritime English is English teaching intended for maritime science or Maritime English is actually maritime science materials taught using English. The researcher uses a qualitative descriptive method by using a learning module adopted from the IMO Model Course 3.17 to conduct research. The results of the study found that the teachers never obtained many materials related to the maritime world. However, there are also materials that these teachers obtained during their undergraduate and master’s degree courses, such as grammar, reading comprehension, and listening comprehension. This means that Maritime English is indeed English intended for maritime science students (English for Special Purposes) and maritime sciences delivered using English.
DeFi and Start-Ups: Revolution in Finance

Decentralized finance (DeFI) is a new financial system that was built from scratch over blockchain technology. The decentralization and openness of smart contracts have created conditions suited to building financial services that do not rely on traditional institutions or middlemen. Lending, borrowing, trading, funding, asset management, derivatives such as options and synthetics and insurance are some of the services that DeFi offers. New paradigms in such important businesses that seek massive gains of cryptopioneers are attracting much attention and investment. Additionally, a new way of funding and ruling startups is mixed with the traditional venture capital and startup ecosystem. An overview of blockchain concepts, DeFi key functioning and the relation with startups is then presented.
Generating phone-quality records to train machine learning models for smartphone-based earthquake early warning

Earthquake early warning (EEW) system detects earthquakes and sends an early warning to areas likely to be affected, which plays a significant role in reducing earthquake risk. In recent years, as with the widespread distribution of smartphones, as well as their powerful computing ability and advanced built-in sensors, a new interdisciplinary research method of smartphone-based earthquake early warning has emerged. Some smartphone-based earthquake early warning systems have applied signal processing techniques and machine learning algorithms to the sensor data recorded by smartphones for better monitoring earthquakes. But it is challenging to collect abundant phone-recorded seismic data for training related machine learning models and selecting appropriate features for these models. One alternative way to solve this problem is to transform the data recorded by seismic networks into phone-quality data. In this paper, we propose such a transformation method by learning the differences between the data recorded by seismic networks and smartphones, in two scenarios: phone fixed and free located on tables, respectively. By doing this, we can easily generate abundant phone-quality earthquake data to train machine learning models used in EEW systems. We evaluate our transformation method by conducting various experiments, and our method performs much better than existing methods. Furthermore, we set up a case study where we use the transformed records to train machine learning models for earthquake intensity prediction. The results show that the model trained by using our transformed data produces superior performance, suggesting that our transformation method is useful for smartphone-based earthquake early warning.
A Game Theory-based Virtual Machine Placement Algorithm in Hybrid Cloud Environment

This paper deals with the problem of virtual machine placement in hybrid cloud situation from an economic and QoS perspectives.Because excessive investment of resources in cloud computing environment will result in resources waste, and too few resources can generate QoS issues.This paper uses a game theory model to describe the problem and find the balance between these contradictions.Based on this model, a virtual machine placement algorithm for scheduling virtual resources is proposed.Compared to the traditional game theory, our LBOGT algorithm proposes a game between tripartite sides: users, individual providers and provider groups.Experiments show that our proposed algorithm reduces physical machines energy consumption by 6.16%, and increases by 10.6% in profit provider under the premise of users' QoS.
A Novel Model for Resisting Side Channel Attack by Masking of Gates

The XOR gate is often used in cryptography modules. These cells' hardware implementations are subject to power analysis attacks. Correlation power attacks (CPAs) allow an attacker to estimate a highly correlated hypothetical value from the actual hidden value. By masking the input and unmaking the output, the present countermeasure technology randomises the power consumption pattern and increases the number of cells. We proposed the mask XOR gate in this study because they do not require cell unmasking and demonstrate a smaller correlation between power traces and input data.
Noise-Robust Quantum Teleportation With Counterfactual Communication

Direct counterfactual communication (DCC) based on the dynamic quantum Cheshire cat effect is an interesting protocol for particle-less information transfer.In this paper, we propose a noise-robust, completely counterfactual, teleportation scheme based on the modified DCC.We show that our scheme does not require any a priori shared phase reference; an essential requirement for conventional teleportation schemes.For our modified DCC, we investigate the noise acting on the remotely controlled properties of a photon, i.e., path and polarization.We show that our proposed modification and resulting teleportation scheme are robust against channel delay and dephasing noise.While for photonic loss, depolarizing, and bit-flip noise, it is possible to obtain the correct state given only the channel state information.
Towards Personalized User Training for Secure Use of Information Systems

Information Systems (IS) represent an integral part of our lives, both in the organizational and personal sphere. To use them securely, users must be properly trained. The main problem is that most training processes still use the one-size-fits-all approach where users receive the same kind of learning material. In addition, personalized training may be a more suitable approach however a comprehensive process for IS user profiling and personalized IS user training improvement has not been introduced yet. This paper proposes a novel approach for personalized user training for secure use of IS to fill in this gap. The proposed approach focuses on three key dimensions (i.e., the personalization process, selection of training tools and materials, and participants) and is composed of five phases covering the identification of key IS security elements, IS user profiling and personalization of IS security training. It is scalable to all company sizes and aims to lower both the IS training costs and optimization of outcomes. As a side-effect, it also helps to lower user resistance to participation in IS security training.
Cooperative Co-evolution and Adaptive Team Composition for a Multi-rover Resource Allocation Problem

In this paper, we are interested in ad hoc autonomous agent team composition using cooperative co-evolutionary algorithms (CCEA). In order to accurately capture the individual contribution of team agents, we propose to limit the number of agents which are updated in-between team evaluations. However, this raises two important problems with respect to (1) the cost of accurately estimating the marginal contribution of agents with respect to the team learning speed and (2) completing tasks where improving team performance requires multiple agents to update their policies in a synchronized manner. We introduce a CCEA algorithm that is capable of learning how to update just the right amount of agents’ policies for the task at hand. We use a variation of the El Farol Bar problem, formulated as a multi-robot resource selection problem, to provide an experimental validation of the algorithms proposed.