Reviewerly/exHarmony huggingface dataset:
A novel cascade machine learning pipeline for Alzheimer’s disease identification and prediction

Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning.In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve.Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification.The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.
Forecasting the chaotic dynamics of external cavity semiconductor lasers

Chaotic time series prediction has been paid intense attention in recent years due to its important applications. Herein, we present a single-node photonic reservoir computing approach to forecasting the chaotic behavior of external cavity semiconductor lasers using only observed data. In the reservoir, we employ a semiconductor laser with delay as the sole nonlinear physical node. By investigating the effect of the reservoir meta-parameters on the prediction performance, we numerically demonstrate that there exists an optimal meta-parameter space for forecasting optical-feedback-induced chaos. Simulation results demonstrate that using our method, the upcoming chaotic time series can be continuously predicted for a time period in excess of 2 ns with a normalized mean squared error lower than 0.1. This proposed method only utilizes simple nonlinear semiconductor lasers and thus offers a hardware-friendly approach for complex chaos prediction. In addition, this work may provide a roadmap for the meta-parameter selection of a delay-based photonic reservoir to obtain optimal prediction performance.
Integrating a fiber cavity into a wheel trap for strong ion–cavity coupling

We present an ion trap with an integrated fiber cavity, designed for strong coupling at the level of single ions and photons. The cavity is aligned to the axis of a miniature linear Paul trap, enabling simultaneous coupling of multiple ions to the cavity field. We simulate how charges on the fiber mirrors affect the trap potential, and we test these predictions with an ion trapped in the cavity. Furthermore, we measure micromotion and heating rates in the setup.
A Unified Framework for Monitoring Social Distancing and Face Mask Wearing Using Deep Learning: An Approach to Reduce COVID-19 Risk

Corona Virus Disease 2019 (COVID-19) is caused by Severe Acute Syndrome Corona Virus 2 (SARS-COV-2). It has become a pandemic disease of the 21st century, killing many lives. During this pandemic situation, precautious measures like social distancing and wearing face mask are being followed globally to break the COVID chain. A pre-programmed viewing system is needed to monitor whether these COVID-19 appropriate behaviours are being followed by the commoners and to ensure COVID-19 preventive measures are followed appropriately. In this work, a deep learning based predictive model and live risk analysis application has been proposed, which detects the high-risk prone areas based on social distancing measures among individuals and face mask wearing tendency of the commoners. The proposed system utilizes ImageNet-1000 dataset for human detection using You Only Look Once (YOLOv3) object detection algorithm; Residual Neural Network (ResNet50v2) uses Kaggle dataset and Real-World Masked Face Dataset (RMFD) for detecting if the persons are face masked or not. Detected human beings (in side-view) are transformed to top view using Top-View Transform Model (TVTM) followed by the calculation of interpersonal distance between the pedestrians and categorized them into three classes include high risk, medium risk, low risk. This unified predictive model provided an accuracy of 97.66%, precision of 97.84%, and F1-Score of 97.92%.
A Trigger-Based Approach for Optimizing Camera Placement Over Time

We contribute a new approach for in situ automation of camera placement over time. Our approach incorporates triggers, regularly evaluating the current camera placement and searching for a new camera placement when a trigger fires. We evaluate our approach running in situ with five data sets from two simulation codes, considering camera placement quality (evaluated using a viewpoint quality metric) and overhead (number of camera positions evaluated). We find that our approach has a significant benefit — reduced overhead with similar quality — compared to the naïve approach of searching for a new camera placement each cycle.
Effect of Cycle GAN in Melanoma Classification

Skin cancer is a common form of cancer that affects many. Melanoma, the deadliest type of skin cancer, takes thousands of lives every year. However, if Melanoma is diagnosed early, treatment can be provided to prevent further harm. Lesions can specifically recognize melanoma on the skin. Skin with lesions can be visually surveyed to identify Melanoma. In this paper, we explore a method to alter skin images to make it easier to identify whether a person has Melanoma. For example, if an image of a mole has a great deal of hair throughout the image that obscures the area, this method would increase the visibility of the mole by removing the hair. By doing so, Melanoma can be diagnosed more quickly and then receive medical care. For this, we will use the deep learning technique, Cycle GAN. Cycle GAN is trained through unpaired images. So, accessing data to train the algorithm will not be as problematic as finding paired images. Cycle GAN functions by translating an image into a new domain and translating an image again to recreate the original image. We are planning to implement this method onto data. We expect it to translate an image to make the image clearer such that performance metrics of classification methods (CNN, RNN, and XG-Boost) may be improved.
Code-free machine learning for classification of central nervous system histopathology images

Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its currently limited application in routine clinical practice. This discrepancy is in part due to the extent of informatics expertise typically required for implementation of ML. Therefore, we assessed the suitability of 2 publicly accessible code-free ML platforms (Microsoft Custom Vision and Google AutoML), for classification of histopathological images of diagnostic central nervous system tissue samples. When trained with typically 100 to more than 1000 images, both systems were able to perform nontrivial classifications (glioma vs brain metastasis; astrocytoma vs astrocytosis, prediction of 1p/19q co-deletion in IDH-mutant tumors) based on hematoxylin and eosin-stained images with high accuracy (from ∼80% to nearly 100%). External validation of the predicted accuracy and negative control experiments were found to be crucial for verification of the accuracy predicted by the algorithms. Furthermore, we propose a possible diagnostic workflow for pathologists to implement classification of histopathological images based on code-free machine platforms.
The implementation of chatbot-mediated immediacy for synchronous communication in an online chemistry course

Low student engagement and motivation in online classes are well-known issues many universities face, especially with distance education during the COVID-19 pandemic. The online environment makes it even harder for teachers to connect with their students through traditional verbal and nonverbal behaviours, further decreasing engagement. Yet, addressing such problems with 24/7 synchronous communication is overly demanding for faculty. This paper details an automated Question-Answering chatbot system trained in synchronous communication and instructor immediacy techniques to determine its suitability and effectiveness in attending to students undergoing an online Chemistry course. The chatbot is part of a new wave of affective focused chatbots that can benefit students' learning process by connecting with them on a relatively more humanlike level. As part of the pilot study in the development of this chatbot, qualitative interviews and self-report data capturing student-chatbot interactions, experiences and opinions have been collected from 12 students in a Singaporean university. Thematic analysis was then employed to consolidate these findings. The results support the chatbot's ability to display several communication immediacy techniques well, on top of responding to students at any time of the day. Having a private conversation with the chatbot also meant that the students could fully focus their attention and ask more questions to aid their learning. Improvements were suggested, in relation to the chatbot's word detection and accuracy, accompanied by a framework to develop communication immediacy mechanics in future chatbots. Our findings support the potential of this chatbot, once modified, to be used in a similar online setting.The online version contains supplementary material available at 10.1007/s10639-023-11602-1.
Robust AISMC-neural network observer-based control of high-speed autonomous vehicles with unknown dynamics

Substantial challenges still exist in designing path-tracking control systems for autonomous vehicles, particularly at speed limits or under varying operating conditions. Such problems arise for various reasons, such as the nonlinear characteristics of vehicular components, system-component interactions, constraints on the states and control inputs, and more. This paper focuses on designing a robust adaptive control system for high-speed autonomous vehicles in case the system dynamics are unknown or unavailable. For this purpose, an intelligent NN-based estimation system’s universal approximation potential will be leveraged, coupled to an adaptive integral sliding mode controller (AISMC). Unlike previously reported studies, the present paper considers the entire dynamics of the autonomous vehicle unknown rather than solely a part of the system or external disturbances merely. The Lyapunov stability theorem is employed to guarantee the asymptotic stability of the developed framework and to obtain the adaptation laws. A critical maneuver explores the effectiveness and robustness of the suggested framework under severe disturbances, parametric uncertainties, and high speeds. The obtained results indicate that the developed framework holds the capacity to navigate the vehicle alongside the desired trajectory and outperforms other reported studies in the literature subject to various external disturbances.
Stop: A Dataset for Spoken Task Oriented Semantic Parsing

End-to-end spoken language understanding (SLU) predicts intent directly from audio using a single model. It promises to improve the performance of assistant systems by leveraging acoustic information lost in the intermediate textual representation and preventing cascading errors from Automatic Speech Recognition (ASR). Further, having one unified model has efficiency advantages when deploying assistant systems on-device. However, the limited number of public audio datasets with semantic parse labels hinders the research progress in this area. In this paper, we release the Spoken Task-Oriented semantic Parsing (STOP) dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , the largest and most complex SLU dataset publicly available. Additionally, we define low-resource splits to establish a benchmark for improving SLU when limited labeled data is available. Furthermore, in addition to the human-recorded audio, we are releasing a TTS-generated versions to benchmark the performance for low-resource and domain adaptation of end-to-end SLU systems.
Protection of Noise Squeezing in a Quantum Interferometer with Optimal Resource Allocation

Interferometers are crucial for precision measurements, including gravitational waves, laser ranging, radar, and imaging. The phase sensitivity, the core parameter, can be quantum-enhanced to break the standard quantum limit (SQL) using quantum states. However, quantum states are highly fragile and quickly degrade with losses. We design and demonstrate a quantum interferometer utilizing a beam splitter with a variable splitting ratio to protect the quantum resource against environmental impacts. The optimal phase sensitivity can reach the quantum Cramér-Rao bound of the system. This quantum interferometer can greatly reduce the quantum source requirements in quantum measurements. In theory, with a 66.6% loss rate, the sensitivity can break the SQL using only a 6.0 dB squeezed quantum resource with the current interferometer rather than a 24 dB squeezed quantum resource with a conventional squeezing-vacuum-injected Mach-Zehnder interferometer. In experiments, when using a 2.0 dB squeezed vacuum state, the sensitivity enhancement remains at ∼1.6 dB via optimizing the first splitting ratio when the loss rate changes from 0% to 90%, indicating that the quantum resource is excellently protected with the existence of losses in practical applications. This strategy could open a way to retain quantum advantages for quantum information processing and quantum precision measurement in lossy environments.
Recent Advances on Deep Learning based Knowledge Tracing

Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge.
Anomaly detection for fault detection in wireless community networks using machine learning

Machine learning has received increasing attention in computer science in recent years and many types of methods have been proposed. In computer networks, little attention has been paid to the use of ML for fault detection, the main reason being the lack of datasets. This is motivated by the reluctance of network operators to share data about their infrastructure and network failures. In this paper, we attempt to fill this gap using anomaly detection techniques to discern hardware failure events in wireless community networks. For this purpose we use 4 unsupervised machine learning, ML, approaches based on different principles. We have built a dataset from a production wireless community network, gathering traffic and non-traffic features, e.g. CPU and memory. For the numerical analysis we investigated the ability of the different ML approaches to detect an unprovoked gateway failure that occurred during data collection. Our numerical results show that all the tested approaches improve to detect the gateway failure when non-traffic features are also considered. We see that, when properly tuned, all ML methods are effective to detect the failure. Nonetheless, using decision boundaries and other analysis techniques we observe significant different behavior among the ML methods.
SOCIAL VARIABLES OF CYBER SECURITY EDUCATIONAL PROGRAMMES

As the need for cybersecurity is growing exponentially along with the development of new technologies, people's resistance to change determine a gap between people's real digital competences and the ideal profile that would allow their optimal functioning in the emerging society. In this article we study how people' perception of technology can be influenced in an era where the use of Internet of Things, social networks, big data is important but the protection of personal data is a must. While Romania introduced coding and IT&amp;C knowledge in gymnasium curricula, the majority of population do not have the chance to attend such courses. The few existent courses in this direction are not enough customised for different social category. Therefore, any attempt to influence the way in which culture is formed must take into account the matching between all the knowledge, opinions, values, norms, signs and symbols that are conveyed in a society with the development stage of that society and with the system of values that its members have internalized. Therefore, comparing traditional teaching and e-learning we will propose a specific blended cybersecurity educational model for building a proper cyber security culture among Romanian population having in mind the complexity of the factors that influence the results of an effective cyber security strategy. Using such a model it will be possible to transmit basic knowledge to the average citizen and to determine him to adopt a behaviour that will allow him the optimal functioning in present days' society.
NOVEL ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR ENHANCED RECRUITMENT CAMPAIGNS USING SOCIAL MEDIA

The recent digital development forced many market leaders to adopt advanced artificial intelligence (AI) technologies to facilitate the transition from rigid processes to more flexible, outcome-driven approaches. The use of AI presents numerous benefits. First of all, the AI technology helps enable the analysis of unstructured data from a variety of sources across social media channels. Also, AI allows companies not only to automate certain processes but also to reconfigure them to complement and, more importantly, augment the skills and expertise of employees. The new era of artificial intelligence-systems gather information, analyze, recommend, plan and more importantly, learn. This paper presents an innovative approach to recruitment. According to recent studies, business intelligence and analytics showed to be the most crucial area that AI can deliver results. We analyze how software companies are more prone to integrate AI applications in marketing because of the new market trend - digital marketing. Services companies, on the other hand, are more interested in the use of AI in human resources. Our case study applies sentiment analysis (SA) techniques to improve the recruitment processes aiming to increase the efficiency of internship campaigns by ensuring a better match between the candidates' professional skills and the hiring company fields of activity. We demonstrate that AI can improve the recruitment process and assist in hiring the right persons. Thus, the HR staff has the opportunity to select the internship candidates based on their skills and determine the right position where abilities of an individual candidate are most needed. This novel approach not only increases the company productivity but also motivates candidates to improve their skills.
Preface: Artificial intelligence (AI), machine learning ML) and digital pathology integration are the next major chapter in our diagnostic pathology and laboratory medicine arena

This timely captivating topic is organized and presented in this special issue of the journal of Seminar in diagnostic pathology. This special issue will be dedicated to the utilization of machine learning within the digital pathology and laboratory medicine fields. Special thanks to all the authors whose contributions to this review series has not only enhanced our overall understanding of this exciting new field but will also enrich the reader's understanding of this important discipline.
An Emoticon-Based Sentiment Aggregation on Metaverse Related Tweets

The metaverse is referred to as a virtual reality space, integrating the real world with the digital world. People have gained interest in this as many well-known companies like Facebook are entering this world of the metaverse. Most people are curious about the possibilities and future of the metaverse considering the various prospective applications it is going to offer like virtual meetings, learning environments and many more. However, there are also worries about potential negative effects as in the metaverse signals from the real world can be transmitted in the virtual world. In this regard, people express their feelings on various social media platforms. The various data obtained from this social media platform is a popular study subject as it gives an important insight into the thoughts of society towards a new event. Thus, in this work twitter, which is one of the extensive social media platforms internationally used to express thoughts, is chosen as the platform of study. Tweets are analyzed to assess people’s attitudes and tendencies towards an event. In the present generation, people express themselves not only with words but also emojis. Therefore, in this paper tweets compromising of both text and emojis are considered for analyzing human sentiments towards the metaverse and the potential risk associated with it. The dataset is compromises of tweets having the word the metaverse and hashtags the metaverse. Observed analysis shows an improvement of over 2.92% for the proposed method.
EAI-Stereo: Error Aware Iterative Network for Stereo Matching

Current state-of-the-art stereo algorithms use a 2D CNN to extract features and then form a cost volume, which is fed into the following cost aggregation and regularization module composed of 2D or 3D CNNs. However, a large amount of high-frequency information like texture, color variation, sharp edge etc. is not well exploited during this process, which leads to relatively blurry and lacking detailed disparity maps. In this paper, we aim at making full use of the high-frequency information from the original image. Towards this end, we propose an error-aware refinement module that incorporates high-frequency information from the original left image and allows the network to learn error correction capabilities that can produce excellent subtle details and sharp edges. In order to improve the data transfer efficiency between our iterations, we propose the Iterative Multiscale Wide-LSTM Network which could carry more semantic information across iterations. We demonstrate the efficiency and effectiveness of our method on KITTI 2015, Middlebury, and ETH3D. At the time of writing this paper, EAI-Stereo ranks $${1^{st}}$$ on the Middlebury leaderboard and $${1^{st}}$$ on the ETH3D Stereo benchmark for 50% quantile metric and second for 0.5px error rate among all published methods. Our model performs well in cross-domain scenarios and outperforms current methods specifically designed for generalization. Code is available at https://github.com/David-Zhao-1997/EAI-Stereo .