Reviewerly/exHarmony huggingface dataset:
DeepStreamOS: Fast open-Set classification for convolutional neural networks

Convolutional Neural Networks (CNNs) can achieve state of the art results for visual recognition problems when the train and test data distributions are the same and when all classes in the test set are present in the training data. This is not representative of the real-world where the data evolves; existing classes change and new classes emerge. A traditional neural network only has the capacity to label instances with classes it has been trained on and cannot identify unknown classes. This can be of serious consequence in safety critical systems. The research field of Open-Set Classification provides potential solutions to overcome the identification of unknown classes in deep neural networks. In safety-critical systems, the speed with which unknown classes can be identified is also essential. Our system, termed DeepStreamOS, brings together the use of deep neural network activations with a stream-based outlier detection method for fast identification of instances that belong to unknown classes. DeepStreamOS uses all layers of a CNN to get a trajectory of the activations and applies a stream-based analysis method to determine if an instance belongs to an unknown class. We use CIFAR-10 and Fashion-MNIST datasets, withholding classes to apply as unknown data on VGG16 and MobileNet deep neural networks. Our system is compared with leading open-set classification methods: OpenMax and EVM. We show that DeepStreamOS outperforms OpenMax and EVM in most open-set classification scenarios and by a large margin on speed in all scenarios.
How to leverage the multimodal EHR data for better medical prediction?

Healthcare is becoming a more and more important research topic recently.With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of medical service.However, the complexity of electronic health records (EHR) data is a challenge for the application of deep learning.Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature, and unstructured data like free text and laboratory measurements.Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration.Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear.Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction.The results on two medical prediction tasks show that our fused model with different data outperforms the state-of-the-art method that without clinical notes, which illustrates the importance of our fusion method and the value of clinical note features.Our code is available at https: //github.com/emnlp-mimic/mimic.
A new representation of scene layout improves saliency detection in traffic scenes

It has been well established that scene context plays an important role in directing visual attention. A robust representation of scene layout is expected to facilitate further analysis of traffic scenes, especially under challenging visual conditions like nighttime and/or on a small-scale dataset. In this work, a new layout representation for traffic scenes is proposed and applied to the popular visual task of saliency detection. First, a general layout representation for traffic scenes is defined as a combination of an original point (Vanishing Point, VP) and two axes along roadsides. Then, a simple algorithm is proposed to build a robust layout representation for traffic scenes, along with an improved VP detection method. Finally, to verify the contribution of the proposed layout representation, a layout-guided saliency detection framework is proposed to improve existing methods by integrating layout-guided prior learned from human fixations collected with an eye-tracking recorder. Experimental results show that the proposed layout representation can significantly improve the performance of various saliency detection methods including classical bottom-up methods and deep-learning-based methods. Moreover, compared with the deep-learning methods, the layout-guided method has an obvious advantage in terms of robustness when only a small-scale dataset is available and under varying visual scenes.
Optical see-through head-mounted display including transmittance-variable display for high visibility

This paper presents an optical see-through head-mounted display (OST-HMD) that can provide a highly immersive augmented reality (AR) environment. Because visibility of objects is degraded by external light and because projection optics cannot represent black color, the reality of AR content is reduced. To solve this problem, we adopted an additional transparent display to adjust the transmittance of exterior light. This adjustment is achieved using a guest–host liquid crystal (GHLC) to provide transparency in the initial state and opacity when voltage is applied. To verify the display’s usefulness, we measured ambient contrast ratio (ACR) and color gamut under various ambient light conditions after attaching the GHLC panel to the existing OST-HMD (Microsoft HoloLens). Under typical office illumination, use of the GHLC panel increased the ACR by 4.67 times, and the color gamut by 2.87 times compared to the OST-HMD without it. Under high illumination the panel increased the ACR by 1.54 times and the color gamut by 16.16 times. The proposed method significantly improved the ambient contrast ratio and color expression, and yielded a flexible way to customize existing OST-HMDs by adding a detachable display.
A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

INTRODUCTION: Breast cancer is the most hazardous disease among women worldwide.A simple, cost-effective, and efficient screening called mammographic imaging is used to find the breast abnormalities to detect breast cancer in the early stages so that the patient's health can be improved.OBJECTIVES: The main challenge is to extract the features by using a novel technique called Advanced Gray-Level Cooccurrence Matrix (AGLCM) from pre-processed images and to classify the images using machine learning algorithms.METHODS: To achieve this, we proposed a four-step process: image acquisition, pre-processing, feature extraction, and classification.Initially, a pre-processing technique called Contrast Limited Advanced Histogram Equalization (CLAHE) is used to increase the contrast of images and the features are retrieved using AGLCM which extracts texture, intensity and shape-based features as these are important to identify the abnormality.RESULTS: In our framework, a classifier called eXtreme Gradient Boosting (XGBoost) is applied on mammograms and the results are compared with other classifiers such as Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM).The experiments are done on the Mammographic Image Analysis Society (MIAS) dataset.CONCLUSION: The outcome achieved with CLAHE+ AGLCM+ XGBoost classifier is better than the existing methods.In future, we experiment on large datasets and also concentrate on optimal features selection to increase the classification.
Application of ASTER Data for Differentiating Carbonate Minerals and Evaluating MgO Content of Magnesite in the Jiao-Liao-Ji Belt, North China Craton

Numerous reports have successfully detected or differentiated carbonate minerals such as calcite and dolomite by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). However, there is a need to determine whether existing methods can differentiate magnesite from other carbonate minerals. This study proposes optimal band ratio combinations and new thresholds to distinguish magnesite, dolomite, and calcite using ASTER shortwave-infrared (SWIR) data. These were determined based on the spectral and chemical analysis of rock samples collected from Liaoning, China and Danchon, North Korea and the reflectance values from ASTER images. The results demonstrated that the simultaneous use of thresholds 2.13 and 2.015 for relative absorption band depths (RBDs) of (6 + 8)/7 and (7 + 9)/8, respectively, was the most effective for magnesite differentiation. The use of RBDs and band ratios to discriminate between dolomite and calcite was sufficiently effective. However, talc, tremolite, clay, and their mixtures with dolomite and calcite, which are commonly found in the study area, hampered the classification. The assessment of the ASTER band ratios for magnesite grade according to magnesium oxide content indicated that a band ratio of 5/6 was the most effective for this purpose. Therefore, this study proved that ASTER SWIR data can be effectively utilized for the identification and grade assessment of magnesite on a regional scale.
Automatic Key Update Mechanism for Lightweight M2M Communication and Enhancement of IoT Security: A Case Study of CoAP Using Libcoap Library

The ecosystem for an Internet of Things (IoT) generally comprises endpoint clients, network devices, and cloud servers. Thus, data transfers within the network present multiple security concerns. The recent boom in IoT applications has accelerated the need for a network infrastructure that provides timely and safe information exchange services. A shortcoming of many existing networks is the use of static key authentication. To enable the use of automatic key update mechanisms in IoT devices and enhance security in lightweight machine-to-machine (M2M) communications, we propose a key update mechanism, namely, double OTP (D-OTP), which combines both one-time password (OTP) and one-time pad to achieve an IoT ecosystem with theoretically unbreakable security. The proposed D-OTP was implemented into the Constrained Application Protocol (CoAP) through the commonly used libcoap library. The experimental results revealed that an additional 8.93% latency overhead was required to obtain an unbreakable guarantee of data transfers in 100 CoAP communication sessions.
The potential role of the geosciences in contributing to the UN’s Sustainable Development Goals

In 2015, the United Nations adopted a series of 17 Sustainable Development Goals (SDGs) and 169 individual targets with the aim of achieving these within 15 years, i.e., by 2030.These ambitious goals include ending poverty and hunger, facilitating sus tainable economic growth and social development, and protecting the environment.Using Gill and Smith (2021) as a major source, this paper outlines the potential role that the geosciences and geoscientists as geopractitioners can play in contributing to the achievement of the SDGs.
A Survey of Big Data, High Performance Computing, and Machine Learning Benchmarks

In recent years, there has been a convergence of Big Data (BD), High Performance Computing (HPC), and Machine Learning (ML) systems. This convergence is due to the increasing complexity of long data analysis pipelines on separated software stacks. With the increasing complexity of data analytics pipelines comes a need to evaluate their systems, in order to make informed decisions about technology selection, sizing and scoping of hardware. While there are many benchmarks for each of these domains, there is no convergence of these efforts. As a first step, it is also necessary to understand how the individual benchmark domains relate. In this work, we analyze some of the most expressive and recent benchmarks of BD, HPC, and ML systems. We propose a taxonomy of those systems based on individual dimensions such as accuracy metrics and common dimensions such as workload type. Moreover, we aim at enabling the usage of our taxonomy in identifying adapted benchmarks for their BD, HPC, and ML systems. Finally, we identify challenges and research directions related to the future of converged BD, HPC, and ML system benchmarking.
Prediction of the Facial Growth Direction is Challenging

Facial dysmorphology or malocclusion is frequently associated with abnormal growth of the face. The ability to predict facial growth (FG) direction would allow clinicians to prepare individualized therapy to increase the chance for successful treatment. Prediction of FG direction is a novel problem in the machine learning (ML) domain. In this paper, we perform feature selection and point the attribute that plays a central role in the abovementioned problem. Then we successfully apply data augmentation (DA) methods and improve the previously reported classification accuracy by 2.81%. Finally, we present the results of two experienced clinicians that were asked to solve a similar task to ours and show how tough is solving this problem for human experts.
Rotation-aware correlation filters for robust visual tracking

Recent years have witnessed several modified discriminative correlation filter (DCF) models exhibiting excellent performance in visual tracking. A fundamental drawback to these methods is that rotation of the target is not well addressed which leads to model deterioration. In this paper, we propose a novel rotation-aware correlation filter to address the issue. Specifically, samples used for training of the modified DCF model are rectified when rotation occurs, rotation angle is effectively calculated using phase correlation after transforming the search patch from Cartesian coordinates to the Log-polar coordinates, and an adaptive selection mechanism is further adopted to choose between a rectified target patch and a rectangular patch. Moreover, we extend the proposed approach for robust tracking by introducing a simple yet effective Kalman filter prediction strategy. Extensive experiments on five standard benchmarks show that the proposed method achieves superior performance against state-of-the-art methods while running in real-time on single CPU.
Efficient joint model learning, segmentation and model updating for visual tracking

The Tracking-by-segmentation framework is widely used in visual tracking to handle severe appearance change such as deformation and occlusion. Tracking-by-segmentation methods first segment the target object from the background, then use the segmentation result to estimate the target state. In existing methods, target segmentation is formulated as a superpixel labeling problem constrained by a target likelihood constraint, a spatial smoothness constraint and a temporal consistency constraint. The target likelihood is calculated by a discriminative part model trained independently from the superpixel labeling framework and updated online using historical tracking results as pseudo-labels. Due to the lack of spatial and temporal constraints and inaccurate pseudo-labels, the discriminative model is unreliable and may lead to tracking failure. This paper addresses the aforementioned problems by integrating the objective function of model training into the target segmentation optimization framework. Thus, during the optimization process, the discriminative model can be constrained by spatial and temporal constraints and provides more accurate target likelihoods for part labeling, and the results produce more reliable pseudo-labels for model learning. Moreover, we also propose a supervision switch mechanism to detect erroneous pseudo-labels caused by a severe change in data distribution and switch the classifier to a semi-supervised setting in such a case. Evaluation results on OTB2013, OTB2015 and TC-128 benchmarks demonstrate the effectiveness of the proposed tracking algorithm.
A POA Theory-based Network Teaching Mode for English Course in Higher Vocational College

As the world economy develops quickly, more and more overseas-funded enterprises have swarmed into the markets of different countries. Therefore, English teaching is very essential because English is the main communication language. However, students in higher vocational colleges with poor English foundation, weak learning ability, and low enthusiasm and interest in English learning, seriously affecting students 'English practical ability and their employment. Despite the adoption of Internet information technology in some schools to carry out English teaching, the teaching content is limited to textbooks and instructed by single teaching methods, which is ineffective to improve students' English practical ability. In view of this, in order to enhance the English classroom teaching effect in higher vocational colleges, improve students’ English learning and practical ability, stimulate their interest in English learning and initiative, a POA (production-oriented approach) theory-based network teaching mode was introduced in this paper. Firstly, based on POA teaching theory, the application in English teaching in higher vocational college was analyzed, and then student independent learning evaluation system was designed using AHP. In order to promote students' autonomous learning, SPOC mode was also introduced to provide students with a wealth of learning resources. Practical teaching results have shown that the POA theory-based network teaching mode can yield significant effects on improving students’ English practical ability, stimulating students' interest and enthusiasm in English learning.
Machine learning of transcripts and audio recordings of spontaneous speech for diagnosis of Alzheimer’s disease

Abstract Background Language deficits may occur in early stages of Alzheimer’s disease (AD). Analysis of individuals’ language changes has been shown to predict the diagnosis and severity of AD. This work explored machine learning methods to detect AD using voice recordings of the Cookie Theft picture description tasks. Method An open‐source dataset from Dementia Bank, which included manual transcripts and audio recordings of the Cookie Theft picture description task from 309 individuals with AD and 243 cognitively healthy older adults, was employed in the classification analysis. We analyzed transcripts generated using three different approaches (Figure 1), including a) automatically generated transcripts based on denoised voice recording; b) automatically generated transcripts without the denoising process; and c) manuscript transcripts. Denoising was implemented using noisereduce , a Python library to minimize background noise (CITATION). Automatic transcripts were produced using two toolkits: CMUSpinx pocketsphinx interpreter (PS) and Mozilla DeepSpeech (DS). The Small‐Bidirectional Encoder Representations from Transformers (BERT) model was implemented to extract high dimensional feature vectors to provide contextual representation of transcripts for classification analysis. The experiment was designed to compare the performance of transcript type (manual vs. automatic), recording quality (noise vs. denoise), as well as speech recognition methods (DS vs. PS). A neural network model was constructed to classify between those with AD and healthy controls. The classification analysis was conducted using 10‐fold cross validation. The classification was repeated 20 times and the averaged results were reported (see Table 1). Result The performance of each approach is shown as Table 1. Overall, the model that used the features extracted from DS transcripts with denoised speech recordings achieved the best performance which had 92.72% accuracy for AD diagnosis validation results. The comparison between raw and denoised speech recordings showed conflicting performances. Using features from PS transcripts with noisy speech recordings obtained better performance than the denoised one, which was different with features from DS transcripts. Conclusion The analysis approach which combined transcription driven recognition and transfer learning indicated the potential of better performance in AD diagnosis. Further investigation is needed to understand the reason for its superior performance.
The Analysis of Public Opinion in Colleges and Universities Oriented to Wireless Networks under the Application of Intelligent Data Mining

In recent years, the incidence of public opinion in colleges and universities has been high. Monitoring, forecasting, and responding to public opinion of students in colleges and universities have increasingly become the work that education management departments at all levels attach great importance to. For each university, how to understand the sensation of teachers and students in real time in the era of informationization entering the intelligent campus has become an urgent problem. How to collect college campus network information, analyze and manage this information, and find hot topics from it has a profound impact on the reform of colleges and universities. Hence, in this paper, we propose a public opinion analysis framework based on intelligent data mining technique. Its advantage lies in the fact that it can withdraw the needed and unknown knowledge and regularities from the massive network data and host log data. It is a new attempt to use data mining in achieving public opinion. At present, data mining algorithm applied to public opinion analysis mainly has four basic patterns: association, sequence, classification, and clustering. Data mining technology is advanced for: it can process large amount of data. It does not need the users’ subjective evaluation and is more likely to discover the ignored and hidden information. Here, initially, the dataset is collected, which is preprocessed and divided into a training set and test set. Feature extraction of the text is done using Linear Discriminant Analysis (LDA). After that, text cosine similarity calculation is performed to compute the similarity between text vectors obtained from the LDA. Convolutional neural network (CNN) is used for classification purpose. We proposed Krill Herd Harmony Search Optimization Algorithm (KHHSOA) for optimizing the CNN and classifying the text into positive and negative opinion. The proposed system is simulated using MATLAB simulation tool, and the performance is analyzed in terms of metrics like accuracy, precision, recall, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> -measure, kappa coefficient, and error rate. The proposed method is proved to be better when compared with the existing techniques.
Using data-driven learning in language teaching

This chapter considers the use of data-driven learning (DDL) in the language classroom. It highlights its pedagogical functions and describes the main issues to bear in mind when choosing a corpus and a query tool to do DDL. It also provides an overview of the way DDL may be operationalised and shows how the DDL activities should be adapted to the learning context, learners’ level and topic being investigated. The chapter then reviews studies that have evaluated participants’ attitudes towards DDL, learners’ practices and the efficiency of DDL, revealing the promises that DDL holds for language teaching. It also highlights some of the limitations of DDL which account for its relative lack of implementation in the classroom, and gives some indications as to how DDL should evolve to become a viable pedagogical approach.