Reviewerly/exHarmony huggingface dataset:
Weakly supervised histopathological image representation learning based on contrastive dynamic clustering

Feature representations of histopathology whole slide images (WSIs) are crucial to the downstream applications for computer-aided cancer diagnosis, including whole slide image classification, region of interest detection, hash retrieval, prognosis analysis, and other high-level inference tasks. State-of-the-art methods for whole slide image feature extraction generally rely on supervised learning algorithms based on fine-grained manual annotations, unsupervised learning algorithms without annotation, or directly use pre-trained features. At present, there is a lack of research on weakly supervised feature learning methods that only utilize WSI-level labeling. In this paper, we propose a weakly supervised framework that learns the feature representations of various lesion areas from histopathology whole slide images. The proposed framework consists of a contrastive learning network as the backbone and a designed contrastive dynamic clustering (CDC) module to embedding the lesion information into the feature representations. The proposed method was evaluated on a large scale endometrial whole slide image dataset. The experimental results have demonstrated that our method can learn discriminative feature representations for histopathology image classification and the quantitative performance of our method is close to the fully-supervision learning methods. The code is available at <a href="https://github.com/junl21/cdc">https://github.com/junl21/cdc</a>.
A Deep Learning Program to Predict Acute Kidney Injury

Acute kidney injury is a dangerous and sometime fatal clinical situation, which can cause irreversible damage. If we can predict it earlier and make appropriate prevention before its outbreak, kidney injury could be avoided. One challenge of early recognition of AKI is that the most e-alerts have focused on creatinine-based algorithms, but the elevation of serum creatinine lags behind renal injury. We use recurrent neural network (RNN) to make data mining on laboratory results of MIMIC-III Database. At first, we transfer the case data into Pandas DataFrame of series framed for supervised learning. Then we can use RNN predicts the next serum creatinine values (SCr) based on the last laboratory test results after emergency admissions. We train the RNN on whole dataset (i.e. multi-cases prediction) with LSTM. As the result shown, this prototype can predict criteria (SCr) of AKI with a RMSE (Root Mean Square Error) of 0.017mg/dL.
Personality Prediction Based on Twitter Tweets

The Myers–Briggs Personality Type Indicator (MBTI) is used to identify people’s personalities using a questionnaire, which takes a long time and effort. Due to the abundance of data about a person present in social networks, finding their type utilizing this data will yield better results. By using machine learning techniques, this data can be used to identify various aspects of a person’s personality. The MBTI dataset from Kaggle was used for building the model. The naive Bayes technique is adapted for making the classification model that gives a person’s MBTI type. Synthetic Minority Over-Sampling Technique (SMOTE) and random under-sampling are used to sample the data to attain a balance. The hundred recent tweets of the user are scraped, preprocessed, and applied to the model, which gives their MBTI type and its description.
Unsupervised Video-based Person Re-identification Based on The Joint Global-local Metrics

At present, supervised video-based person re-identification has achieved excellent performance. However, the initial video data obtained from real scenes are often unlabeled. Labelling such data is very time-consuming. If unsupervised learning can be effectively applied to these data, so much cost will be saved. In this paper, based on the joint global and local metric, an unsupervised video-based person re-identification method is proposed, which takes both the global information of a video sequence and the local information between the video frames into account to better distinguish different appearances of the same pedestrian. The global similarity and local similarity are calculated using global and local features, respectively. Meanwhile, a diversity constraint is used as an aid for cluster merging and evaluation. In the training process, the network is optimized by combining cluster mutual exclusion loss and center loss, which reduces the within-class differences and enlarges the between-class differences. Experiments on two benchmark datasets, MARS and DukMTMC-VideoReID, the results show that this method has higher accuracy and stabilityshow that the proposed method can achieve higher accuracy and is more stable than most state-of-the-art unsupervised methods.
Identification of New Clusters from Labeled Data Using Mixture Models

Nowadays attempts to segment classes or groups are often found in various fields. Especially, one of emerging issues in biological and medical areas is identification of new subtypes of biological samples or patients. For the identification, we often need to find new subtypes from known classes. In such cases, we usually use clustering techniques. However, usual clustering methods could mix up the labels of the known classes in clustering outcomes and it might lead to wrong interpretation for the identified clusters. Also, they do not use the information about known classes. Thus, this study proposes a Gaussian mixture model-based approach for identifying new clusters from known classes while it maintains them. The performance of the proposed model is verified through simulations and it is applied to a breast cancer data set.
Experimental demonstration of an extreme learning machine based on Fabry Perot lasers for parallel neuromorphic processing

We present experimental results regarding dispersion equalization in IM-DD transmission systems with an extreme learning machine based on a Fabry Perot laser. The exploitation of two longitudinal modes yields enhanced computational power and processing speed.
In and out: production mechanisms in Human Beatboxing

Human Beatboxing (HBB) is a musical technique produced with vocal tract movements. In this study we investigate HBB production mechanisms of 9 drum imitations produced in isolation by 5 beatboxers. Based on aerodynamic (intraoral pressure and oral airflow), acoustic and laryngoscopic data we were able to identify gestures. Results show beatboxers use a wider range of production mechanisms compared to speech production. Indeed, we found both ingressive and egressive airflow initiated either in the mouth, the larynx or the lungs. For a same sound we found that participants were able to use different mechanisms such as a combination of glottalic and pulmonic airstreams. This raises questions concerning the coordination of initiatory mechanisms and its planification. HBB differs from speech production because it is a musical system that does not rely on linguistic constraints such as intelligibility of semantic meaning so patterns are not as restricted as in speech. Human Beatboxing is a great paradigm to explore the vocal tract capacities during production. Beatboxers have an extended control of their vocal tract to combine different initiation and articulation mechanisms. HBB offers new perspectives on articulatory complexity, phonetic diversity of the world’s languages and potential application for speech pathology.
Multi Robot Welding Path Planning Based on Improved Ant Colony Algorithm

In view of the unreasonable path planning of multiple robots in the existing BIW Welding process, the welding process of multiple robots is analyzed, the mathematical model is established, and the improved ant colony algorithm is used to calculate, so as to obtain the optimal solder joint task allocation and the optimal welding path. The program is written in Python language to solve the algorithm. The calculation results show that the improved ant colony algorithm can effectively allocate the solder joints of multiple welding robots, and plan a reasonable welding path according to the assigned solder joints.
Infrastructure, Modulation, Portal

Following Foucault’s remarks on the importance of architecture to disciplinary power, this paper offers a typology of power relations expressed in different models of Internet governance. Infrastructure governance understands the Internet as a common pool or public resource, on the model of traditional infrastructures like roads and bridges. Modulation governance, which I study by way of Net Neutrality debates in the U.S., understands Internet governance as traffic shaping. Portal governance, which I study by way of data collection policies of dominant platform companies, understands the Internet as creating a user experience that facilitates data mining. The latter two are forms of architectural disciplinary power that undermine the first. I then argue that the rise of portal and modulation governance primarily serves to remake parts of civil society by fostering market norms of consumption and entrepreneurialism. In that sense, efforts to shape Internet architecture need to be understood as techniques of subjectification.
A Health Care Image Compression Scheme using Discrete Wavelet Transform and Convolution Neural Network

Medical image processing is an important field that directly impacts the health care system. It recognizes disease and also provides information for diagnosis and surgical process. The objectives of medical image compression are to reduce the computational complexity, storage size, and transmission bandwidth. This research has proposed an image compression scheme (MIC-DWT-CNN) based on discrete wavelet transform and convolutional neural networks. Region-growing and otsu-thresholding methods have separated the interested area and non-interested area of the medical image. The DWT has compressed the region of interest, and CNN has compressed the non-interested area in the medical image. The MIC-DWT-CNN scheme has experimented on the images of the medical image dataset using the python platform. The research objective is to achieve better compression efficiency and image quality. The performance of the MIC-DWT-CNN method has been evaluated using Mean square error (MSE), Peak Signal to Noise Ratio (PSNR), and Compression Ratio (CR). The existing techniques have been used to compare with the MIC-DWT-CNN method. The MIC-DWT-CNN method has achieved a better compression performance than the existing methods. The MIC-DWT-CNN method has achieved a higher CR, i.e., 25.01, than existing methods. Also, the model has provided the required level of MSE and PSNR values.
Joint Prediction of Truecasing and Punctuation for Conversational Speech in Low-Resource Scenarios

Capitalization and punctuation are important cues for comprehending written texts and conversational transcripts. Yet, many ASR systems do not produce punctuated and case-formatted speech transcripts. We propose to use a multi-task system that can exploit the relations between casing and punctuation to improve their prediction performance. Whereas text data for predicting punctuation and truecasing is seemingly abundant, we argue that written text resources are inadequate as training data for conversational models. We quantify the mismatch between written and conversational text domains by comparing the joint distributions of punctuation and word cases, and by testing our model cross-domain. Further, we show that by training the model in the written text domain and then transfer learning to conversations, we can achieve reasonable performance with less data.
Real-Time Vision-Based Chinese Sign Language Recognition with Pose Estimation and Attention Network

Currently, communication between deaf and hearing people is still facing great problems both at the research level and at the application level. Also, practical automatic sign language interpretation methods have become a relatively large demand. In this paper, we will recognize video sign language based on computer vision to capture skeletal key point information using multi-head attention mechanism and long and short-term memory mechanism. We construct a database of Chinese video sign language consisting of 30 words, with a total data volume of more than 1200. Meanwhile, the experiments of our proposed framework on this dataset achieved 85% accuracy rate. The experimental results show that our proposed method has the characteristics of high accuracy and light weight in the problem of Chinese sign language recognition.
Multi-Level Statistical Model for Forecasting Solar Radiation

As a substitute for conventional energy sources, Solar energy is quickly becoming a popular source of renewable energy. Various entities ranging from small households and businesses to large firms and MNCs are currently making plans on investing resources in the generation of solar energy. Thus, accurate prediction of solar radiation has become a necessity in the present scenario. Due to limitations like the unavailability of proper measuring equipment and a small number of meteorological departments, accurate prediction of solar radiation is not possible in many places around the world. This paper focuses on forecasting solar radiation using machine learning techniques. Solar radiation depends upon various natural factors, which are easier to measure, and these factors can help forecast solar radiation. This paper explores the available data to identify the various factors which affect solar radiation. Based on these factors, the paper investigates the performance of different standard regression models based on solar radiation prediction. Next, multi-level statistical models are proposed, which stack multiple standard models into layers, and the R<sup>2</sup> scores of these custom models is compared with the R<sup>2</sup> scores of the standard models.
Cryptocurrency Price Trend Analysis via Machine Learning Methods

Bitcoin (cryptocurrencies) is the hottest economic product in recent years. However, due to its highly volatile trend, it is difficult for investors to invest in a targeted manner in the direction of its market trend. And the correlation among cryptocurrencies is often overlooked. In this paper, to solve this problem, the currency data of the past year has been used to put into four regression models to predict and analyze the top five currencies(ranked by market cap) on the market. Among the models, the KNN model has the highest accuracy, reaching 0.923.
Reciprocal GAN Through Characteristic Functions (RCF-GAN)

The integral probability metric (IPM) equips generative adversarial nets (GANs) with the necessary theoretical support for comparing statistical moments in an embedded domain of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">critic</i> , while stabilising their training and mitigating the mode collapse issues. For enhanced intuition and physical insight, we introduce a generalisation of IPM-GANs which operates by directly comparing probability distributions rather than their moments. This is achieved through characteristic functions (CFs), a powerful tool that uniquely comprises all information about any general distribution. For rigour, we first theoretically prove the ability of the CF loss to compare probability distributions, and proceed to establish the physical meaning of the phase and amplitude of CFs. An optimal sampling strategy is then developed to calculate the CFs, and an equivalence between the embedded and data domains is proved under the reciprocal theory. This makes it possible to seamlessly combine IPM-GAN with an auto-encoder structure by an advanced anchor architecture, which adversarially learns a semantic low-dimensional manifold for both generation and reconstruction. This efficient reciprocal CF GAN (RCF-GAN) structure, uses only two modules and a simple training strategy to achieve the state-of-the-art bi-directional generation. Experiments demonstrate the superior performance of RCF-GAN on both regular (images) and irregular (graph) domains.
Meta-study of success-related factors of SaaS providers based on a cloud computing ecosystem perspective

Due to the commercial launch of cloud computing, traditional value chains in IT service provision were expanded and partially replaced by networked relations which formed an increasingly incomprehensible business ecosystem. Consequently, both incumbent IT service providers and start-ups were able to assume new roles. Software as a Service (SaaS) providers occupy the largest segment and offer ready-to-use software applications. Numerous studies have been conducted with the aim of detecting which factors determine the success of SaaS providers. However, these studies fail to provide a holistic overview of the current state of research. Under these circumstances, inadequate support is given to the executives of SaaS providers when it comes to their decision-making processes. In this book chapter, the results from a comparative analysis of studies on success-related factors of SaaS providers are presented. The analysis is based on the Passau Cloud Computing Ecosystem model, a role-based model of the cloud ecosystem.
The Influence of Ways of Organizing Learning Interactions on the Development of Communicative and Reflexive Abilities of Children 6—10 Years Old

The features of the development of communicative and reflexive abilities of children aged 6—10 years, depending on the ways of organizing educational interactions, are discussed. Basic social competencies defined by the requirements of the current Federal State Educational Standard of Primary General Education are considered as indicators of the development of abilities in the learning environment. Based on the selected indicators, a variant of the “Puzzle” diagnostic method has been developed, which allows to study the relationship between the development of social competencies in younger schoolchildren, and the joint way they perform while solving experimental problems. A comparative analysis of students' social competencies allows to prove the influence of ways of organizing educational interactions on the development of communicative and reflexive abilities of primary school children.
Highlighting Object Category Immunity for the Generalization of Human-Object Interaction Detection

Human-Object Interaction (HOI) detection plays a core role in activity understanding. As a compositional learning problem (human-verb-object), studying its generalization matters. However, widely-used metric mean average precision (mAP) fails to model the compositional generalization well. Thus, we propose a novel metric, mPD (mean Performance Degradation), as a complementary of mAP to evaluate the performance gap among compositions of different objects and the same verb. Surprisingly, mPD reveals that previous methods usually generalize poorly. With mPD as a cue, we propose Object Category (OC) Immunity to boost HOI generalization. The idea is to prevent model from learning spurious object-verb correlations as a short-cut to over-fit the train set. To achieve OC-immunity, we propose an OC-immune network that decouples the inputs from OC, extracts OC-immune representations, and leverages uncertainty quantification to generalize to unseen objects. In both conventional and zero-shot experiments, our method achieves decent improvements. To fully evaluate the generalization, we design a new and more difficult benchmark, on which we present significant advantage. The code is available at https://github.com/Foruck/OC-Immunity.
Parallelising 2D-CNNs and transformers: A Cognitive-based approach for Automatic Recognition of Learners’ English Proficiency

Learning English as a foreign language requires an extensiveuse of cognitive capacity, memory, and motor skills in order to orallyexpress one’s thoughts in a clear manner. Current speech recognition in-telligence focuses on recognising learners’ oral proficiency from fluency,prosody, pronunciation, and grammar’s perspectives. However, the ca-pacity of clearly and naturally expressing an idea is a high level cognitivebehaviour which can hardly be represented by these detailed and segmen-tal dimensions, which indeed do not fulfil English learners and teachersrequirements. This work aims to utilise the state-of-the-art deep learningtechniques to recognise English speaking proficiency at a cognitive level,i. e., a learner’s ability to clearly organise their own thoughts when ex-pressing an idea in English as a foreign language. For this, we collectedthe “Oral English for Japanese Learners” Dataset (OEJL-DB), a corpusof recordings by 82 students of a Japanese high school expressing theirideas in English towards 5 different topics. Annotations concerning theclarity of learners’ thoughts are given by 5 English teachers according to2 classes: clear and unclear. In total, the dataset includes 7.6 hours ofaudio data with an average length for each oral English presentation of66 seconds. As initial cognitive-based method to identify learners’ speak-ing proficiency, we propose an architecture based on the paralelizationof CNNs and Transformers. With the strengthening of the CNNs in spa-tial feature representation and the Transformer in sequence encoding,we achieve a 89.4 % accuracy and 87.6 % Unweighted Average Recall(UAR), results which outperform those from the ResNet architectures(89.2 % accuracy and 86.3 % UAR). Our promising outcomes reveal thatspeech intelligence can be efficiently applied to “grasp” high level cog-nitive behaviours, a new area of research which seems to have a greatpotential for further investigation.
An Efficient Model to Detect and Prevent SQL Injection Attack

SQL injection attack (SQLIA) is considered one of most threats used to attack web applications. Therefore, attackers used SQL injection vulnerability to gain ultimate access to databases that belong to applications and expose their sensitive information. Thus, attackers use SQL injections vulnerability to manipulate data also it could be used to take full control of the target machine. Accordingly, several methods were proposed in the literature to address this vulnerability widely because of its importance and high impact on the security of web applications. Thus, we propose a model to detect and prevent SQL injection attacks, which uses runtime validation to detect the occurrence of such attacks, our proposed model is adaptable to any existing system, with no need to modify the client or server and either no need to know web application source code. Furthermore, the modification independence is done by adding additional middleware between client and server. Thus, any check process is done on this middleware, and it is represented as a proxy that can do sanitize the inputs for detecting and preventing SQLIA. Furthermore, our proposed model accuracy reaches 86:6% for detecting and preventing SQLIA.