Reviewerly/exHarmony huggingface dataset:
What are the determinants of rural-urban divide in teachers’ digital teaching competence? Empirical evidence from a large sample

Abstract The digital divide between rural and urban areas is becoming the key factors resulting educational imbalance, which might be exacerbated by differences in teachers’ digital teaching competence. Therefore, it was crucial to explore the divide and determinants of digital teaching competence between rural and urban teachers. A large-scale survey was conducted with 11,784 K–12 teachers in China (43.40% from rural schools and 56.60% from urban schools). First, this study investigated potential factors for teachers’ digital teaching competence, including information and communication technology (ICT) attitude, ICT skills, and data literacy. Second, the data indicated the digital divide existed, i.e., the ICT attitude, ICT skills, data literacy, and digital teaching competence of rural teachers were significantly lower than those of urban teachers. Third, the Blinder-Oaxaca decomposition method demonstrated that data literacy and ICT skills were the most important determinants of the divide in digital teaching competence between rural and urban teachers. Hence, our research provided important insights for policymakers, school leaders and teachers to bridge the digital divide.
Dynamical behaviors of vertical-cavity surface-emitting lasers with an embedded saturable absorber subjected to an on-off keying (OOK) current modulation

Abstract We numerically study the dynamics of VCSEL-SA when modulated by an analog on-off keying (OOK) current. It is found that the laser gives a pulse-like response when operated below the threshold current. After the threshold current, which is in the excitable phase, the laser generates single spikes (phasic spiking) and a series of spikes (tonic spiking) wherein the amplitude of the spikes varies as the form of the current. Complex dynamics such as bursting oscillations and chaotic dynamics are successfully generated at low frequencies (few MHz). After this excitable phase, the laser copies the OOK current with underdamped oscillations generated during the OFF phase of the OOK current.
A Real-time Chaos-based Audio Encryption Scheme

This work introduces a chaos based audio encryption scheme, along with its implementation, which works in real time. Initially, a novel chaotic map is introduced, and using this map a Pseudo-Random Bit generator is defined. The encryption scheme is defined based on the above and utilizes parallel computing to encrypt recorded audio "on the fly", rather than collecting the complete recording and encrypting afterwards, making the method suitable for real-time encryption. The security of the proposed method is evaluated via statistical tests. Finally, an Open-Source implementation of the proposed method using Python is given.
Progressive Multi-Secret Sharing of Color Images Using Lorenz Chaotic System

Multi-Secret Image Sharing (MSIS) systems share multiple images to multiple participants in unintelligible forms that can be recovered using all the shares. This paper employs the concept of progressive secret sharing with MSIS to introduce a new system, where the number of used shares in the recovery process defines the quality of the recovered secrets. The proposed system works for any number of secret color images, and is lossless when all the shares are present. The Lorenz chaotic system, which is numerically solved using Euler method, is used as source of randomness to encrypt the secret images. Image encryption utilizes a long system key to perform the substitution and permutation stages. The system passes all security tests, including statistical analysis and key sensitivity, and it is also robust to noise and crop attacks. The analysis results are within the required ranges for a good encryption system, and they are better than those of the compared MSIS systems.
Application of Quantum Walk for Signal Processing

Signal processing is important for modern technologies such as digital communication systems and sensor networks, and is used to enhance and optimize the quality of transmitted signals. However, traditional methods and design frameworks are insufficient for the demands of future communication networks, which are expected to be highly complex and constantly changing. Our research focuses on a mathematical model known as a Markov model, which represents a system as a series of states linked by transitions. This model can be applied to various signal processing challenges in telecommunications, such as the modeling of communication channels, sources of information, and the correction of distortion and noise in transmitted signals. We examine the random walk algorithm, a simple and efficient way to simulate the behavior of a Markov chain, as it can provide valuable insights into the performance of communication systems and help to optimize their design. However, it may not be able to accurately model systems with complex transitions or systems that exhibit long-term correlations. In order to address the challenges and limitations of classical random walk, we propose the use of quantum random walk, which is a quantum mechanical version of classical random walk. It describes the motion of particles in a probabilistic way, similar to classical random walk, but with the added complexity of quantum mechanics. We also provide quantum walk algorithm with Hadamard gate implementation for coin operator.
GLRP: Guided by Layer-wise Relevance Propagation – Selecting Crucial Neurons in Artificial Neural Networks

Artificial neural networks (ANN) are used in critical application domains like autonomous driving or medicine. As a result of an attack or due to faults in the hardware, an ANN might make wrong predictions which might lead to a dangerous malfunction.Using layer-wise relevance propagation, we identify crucial neurons in trained ANNs. Hardening techniques, independent of our approach, can then be used to protect crucial neurons. Mathematically as well as with empirical experiments, we show that hardening the crucial neurons reduces or even eliminates the number of wrong predictions made by the ANN.
A Vision-based Deep Learning Platform for Human Motor Activity Recognition

To track the body movement of patients with movement disorders, sensors such as Kinect cameras are not easily accessible. Recently-developed deep learning models, as a subset of Artificial Intelligence (AI), can analyze patients’ behavior from RGB images of smartphones. The Stacked Hourglass model is a novel pose estimation deep learning model which can accurately determine the location of body joints and a long short-term memory network (LSTM) can determine the corresponding action by analyzing the kinematic behavior of the body joints. This study develops a deep learning model that uses RGB images from the UT-Kinect dataset as input and determines the action performed with 84.14 % accuracy. Specifically, our contributions are: (i) developed the preprocessing pipeline to use stack hourglass model on the UT-kinect dataset (ii) finetuning of the model to handle 20 joints (iii) Added a human action recognition component to accurately classify the actions performed. Our method can be an efficient replacement for the hardly-accessible Kinect cameras and can be used to analyze various diseases with movement disorders.
Improving the Accuracy of Detecting Texture Areas in Multi-Channel Images with Noise

A method for detecting textural objects on multi-channel images with noise is proposed. The method is based on splitting multi-bit digital images into bit planes and representing the bit planes of two channels by three-dimensional Markov chains with two states. The method has two steps. At the first step, the white Gaussian noise is effectively removed due to a more accurate calculation of the statistical characteristics of the image within the three-dimensional sliding window and the effective use of statistical redundancy of multi-channel images. At the second step, the selection of extended textural fragments with homogeneous statistical characteristics is performed. Transition probabilities for three-dimensional Markov chains are used as texture features. The estimation of textural features is carried out using a binary image of one of the higher, most informative, digits of the digital image, which allows us to reduce computing resources. Modeling results confirming the effectiveness of combined image processing of two spectral channels for the detection of textural objects are presented.
Parsing a Sentence Using Clustering Algorithms

The application of machine learning in linguistics has reached a very high level. The most powerful multilayer neural networks are used for machine translation, analysis and text generation. However, there are prospects for using surface neural networks for sentence analysis. The combination of a two-layer graph convolutional neural network (GCN) and the traditional phrasal approach to machine translation gives good results. The article discusses an original approach to the development of a machine translation system for processing specialized texts in the construction industry for translation from English into Russian based on machine learning. Clustering methods that are used at different stages of sentence processing to find the most adequate principle for splitting a sentence into sentence members with subsequent construction of a dependency tree are considered in this paper. The results of applying clustering based on the usual k-means method, as well as spectral clustering, are compared.
An Approach to Optimize Replay Buffer in Value-Based Reinforcement Learning

Reinforcement Learning (RL) has seen numerous advancements in recent years, particularly in the area of value-based algorithms. A key component of these algorithms is the Replay Buffer, which stores past experiences to improve learning. In this paper, the authors explore an optimization method for the Replay Buffer that increases the learning efficiency of an agent by prioritizing experiences based on their training value (T). The authors test the proposed approach in two environments, a maze and Cartpole-v1, comparing it to traditional Q-learning and Deep Q-Networks (DQN) algorithms. The results demonstrate improvements in learning efficiency and training effects, showing potential for the application of the method in various RL scenarios.
Using Deep Convolutional Neural Networks to Abstract Obstacle Avoidance for Indoor Environments

In this paper, an approach to learning an obstacle avoidance program for an autonomous robot is presented. A deep learning network, which matches one that was successfully used in the past for a classification task, was replicated and used to classify ten categories in the CIFAR10 dataset. This trained network was then altered by replacing the final fully-connected feed-forward network with a new one that was initiated with random weights. Using a new database made up of images labeled with the actions taken by an operator as he remotely drove the robot, the network learned the proper action for each image. In previous work, we reported that this network operating on the actual robot successfully moved through the desired path in the training environment while avoiding obstacles. Now we have expanded this work by showing that the obstacle avoidance control program is generalized enough that it was successful when tested in three environments not seen during training.
Fuzzy Linguistic Assessment Based Dilemma Evaluation in Confrontation Analysis

The strategic conflict is always an important means for countries and enterprises to maintain competitiveness in a complex environment, and it confronts with a large amount of cognitive information. The confrontation analysis can take the subjective cognitive information in a strategic conflict into consideration. The decision-making is subject to resource constraints, and the key problems should be solved first. However, due to the complexity of objective things and the fuzziness of human thinking, the current research is difficult to evaluate the key dilemmas in the conflict process. Therefore, we propose a method which is based on fuzzy linguistic assessment to evaluate the key dilemmas in the confrontation analysis. First, it analyzes the elements of the dilemma according to the generating criterion of dilemma. Second, it describes the clarity of the doubt and the gap between position and intention based on fuzzy language. Finally, it evaluates the existing dilemmas, so as to identify the key dilemmas and assist countries or enterprises to make decisions under resource constraints. The feasibility and applicability of this method are illustrated by a case study.
Information Technologies in the Implementation of Scientific Organizations Publication Programs

In modern conditions, international scientific communication is very important for the scientist professional activity. Each scientist in the process of his activity relies on the results of predecessors and contemporaries research. The article describes and systematizes the main information technologies in the implementation of the scientific organizations publication programs. Electronic resources are considered and analyzed for posting metadata of scientific materials and scientists identifiers. Organizational conclusions about the provision of scientific materials to publishing organizations are made.
Handcrafted features vs deep-learned features: Hermite Polynomial Classification of Liver Images

Liver cancer is one of the most common types of cancer according to World Health Statistics. Computer-aided diagnosis (CAD) systems are used in medical imaging for liver tumor identification and classification. Texture is a type of feature that can provide measurements of properties such as smoothness and regularity of the image. Handcraft techniques based on fractal geometry allow quantifying self-similarity properties present in images. However, new studies have shown that using information obtained from deep-learned feature maps can maximize the results of classical classifiers. This work presents an approach that investigates descriptors obtained by handcrafted and deep learning features, feature selection methods and the Hermite polynomial (HP) algorithm to classifier liver histological images. The results were evaluated using metrics such as accuracy (ACC) and the imbalance accuracy metric (IAM). The association with fractal features, Lasso regularization and the HP algorithm achieved 0.98 of IAM and 99.53% ACC, which was relevant when evaluated with other studies in the literature.
Ilities for Responsive Manufacturing: A Case from Offshore Wind Turbine Manufacturing

We present here a discussion of responsiveness and related ilities in an Offshore Wind Manufacturer. This is stated first by the understanding that these terms may be vague and hard to define. To tackle this issue, a prescriptive semantic is used, which gives us a qualitative and quantitative starting point to define responsiveness in terms of reactivity an agility time. This is followed by a sports analogy, which gives insight for more ilities. A framework for categorization of these extra ilities is presented, based on detection, interpretation, computation, enforcement and adaptation time. A real case study in manufacturing from a production task (joint bolting) is used. Here, we discuss the initial problem, the solution, the consequences of the solution, with pros (e.g. visibility) and cons (e.g. wrong traceability). A discussion on an improved solution using digital tools is commented, as well as a list of improved ilities compared to the initial case. We finish the paper with a call for peers for more work in ilities using real and concrete examples from the industry.
An Ensemble Approach for Inconsistency Detection in Medical Bills: A Case Study

Auditing medical bills is a complex task for large health insurance companies, such as Unimed-BH, which serves over 1.5 million patients. To overcome this challenge, our study proposes an ensemble-based tool that ranks medical bills based on their likelihood of being inconsistent. Our ensemble comprises multiple machine learning algorithms and employs a Multi-Armed Bandit Algorithm to optimally combine its constituents. Using data collected from November 2022 to March 2023, we created a dataset with 81,173 hospital operations (with up to 5,972 items) and 508,304 outpatient operations (with up to 2,402 items). The proposed method proved effective in detecting inconsistencies in both scenarios. By ranking all bills and flagging the top of the ranking as potentially inconsistent, we achieved a recall of. 852 at the 5% threshold. Our proposed solution was deployed in the real world, and over a five-month period, it recovered R$1, 571,146 (USD$349, 610) which would have gone unaudited. Our findings suggest that the proposed approach is effective and can help free up expert auditors to handle more complex tasks, increases efficiency, and reduce costs while detecting inconsistencies in medical bills.
Automatic ICD-10 Code Association: A Challenging Task on French Clinical Texts

Automatically associating ICD codes with electronic health data is a well-known NLP task in medical research. NLP has evolved significantly in recent years with the emergence of pre-trained language models based on Transformers architecture, mainly in the English language. This paper adapts these models to automatically associate the ICD codes. Several neural network architectures have been experimented with to address the challenges of dealing with a large set of both input tokens and labels to be guessed. In this paper, we propose a model that combines the latest advances in NLP and multi-label classification for ICD-10 code association. Fair experiments on a Clinical dataset in the French language show that our approach increases the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> -score metric by more than 55% compared to state-of-the-art results.
PsyBERTpt: A Clinical Entity Recognition Model for Psychiatric Narratives

Mental disorders are among the most complex disorders to treat due to the scarcity of biomarkers that identify and quantify the severity of the disease, as is commonly available in other areas of medicine. The practice of psychiatry uses semi-structured and unstructured data to record the mental and behavioral states of patients, which are impressions of the physician about the patient, and therefore important information for prognosis. Most of this data lacks standardization, making it difficult to use for quantitative analysis through computational tools since clinical decision models are based on structured data. In this work, a team of psychiatrists and computer scientists developed a methodology based on Natural Language Processing to extract relevant information from admission clinical notes of a psychiatric emergency service. With the use of BERT, we developed psyBERTpt, a prediction model capable of extracting multiple types of information considered relevant to psychiatric practice.
Comparison of deep learning architectures for colon cancer mutation detection

Colorectal cancer is responsible of the death of hundred of thousands of people worldwide each year. The histopathological features of the tumor are generally identified from the analysis of tissue taken from a biopsy providing information for selecting the adequate treatment. With the advent of digital pathology, slides of tissue are increasingly available as Whole Slide Images (WSI) allowing their analysis using artificial intelligence algorithms. Additionally, genetic sequencing of the tumor can also be performed to identify specific mutations (e.g. KRAS) and their subtypes (e.g. G12C). While sequencing can take time and is costly, it provides key information on the specific type of cancer. In this paper, we target the identification of key genes and variants using only stained histopathology images using deep neural networks. Predicting gene information only from histopathology images could be easily used routinely and would allow saving the time and cost of tumor sequencing. Experiments performed on 45 colorectal cancer WSI from 45 patients revealed that deep models can correctly classify 90% of patients with the KRAS G12C mutation. Furthermore, we also used CAM and LIME methods to explore the interpretability of the results highlighting which parts of the images were used by the models to predict the presence of specific mutations.
A Vendor Neutral Archive with MONAI for Automatic Medical Image Analysis

Computer-assisted diagnosis has been advancing significantly with the proliferation of intelligent image analysis algorithms. These tools enhance medical image screening and diagnostic workflow by highlighting relevant features for doctors, resulting in faster and more accurate diagnoses. However, developing these tools requires large annotated image datasets for achieving acceptable performance. Moreover, developing the analysis algorithms requires knowledge about these technologies, often limited to medical staff. Finally, the integration of those solutions in clinical environments is still a hard barrier due to interoperability issues. This work proposes a solution that extends a Vendor-Neutral Archive for supporting the integration of AI tools for research and production. It makes use of two open-source components, Dicoogle and MONAI, resulting in a Web and DICOM compliant platform that uses active learning strategies for automatic image annotation and inference services.
Secure and Modular Sensor Network for an Ambient Assisted Living Field Study

For the field study of an ambient assisted living research project a secure and modular sensor network is required. The design considerations, requirements and implementation of this sensor network are presented in the paper. The system collects data from various health and smart home sensors installed in 68 test households. The data is transmitted to the research institute and stored in a central database. A special focus hereby is finding the balance between the demand of the research project to gather as much data as possible and strict privacy demands. The paper focuses on the technical design and implementation of the sensor network. Besides the technical implementation, a short description and evaluation of the field study with 76 participants is presented. The analysis and interpretation of the collected data is not in the scope of this work.
A Machine Learning-based Method for Cyber Risk Assessment

Cyber risk assessment is one of the top priorities of modern organizations and companies, owing to the massive amount of data they process on a daily basis and to the increasing number of successful cyber attacks. The probability of occurrence of these cyber incidents can be estimated by means of statistical tools, which exploit numerical categories to compute the probability that the organization will be breached by one or more cyber attacks. However, these approaches heavily rely on experts' estimates and/or on past data, which are not always available. In this paper we show that, by exploiting machine learning tools, cyber risk can be assessed by using some easily obtainable parameters (called maturity, complexity, attractiveness) representing the cyber posture of the organization under exam. To validate the method we propose, we apply it to three organizations in the healthcare sector having different values of maturity and complexity. The results highlight how the model can be successfully used to assign each organization a class of cyber risk, even in a crucial sector such as healthcare.