Reviewerly/exHarmony huggingface dataset:
Fusion of Self-supervised Learned Models for MOS Prediction

We participated in the mean opinion score (MOS) prediction challenge, 2022. This challenge aims to predict MOS scores of synthetic speech on two tracks, the main track and a more challenging sub-track: out-of-domain (OOD). To improve the accuracy of the predicted scores, we have explored several model fusion-related strategies and proposed a fused framework in which seven pretrained self-supervised learned (SSL) models have been engaged. These pretrained SSL models are derived from three ASR frameworks, including Wav2Vec, Hubert, and WavLM. For the OOD track, we followed the 7 SSL models selected on the main track and adopted a semi-supervised learning method to exploit the unlabeled data. According to the official analysis results, our system has achieved 1st rank in 6 out of 16 metrics and is one of the top 3 systems for 13 out of 16 metrics. Specifically, we have achieved the highest LCC, SRCC, and KTAU scores at the system level on main track, as well as the best performance on the LCC, SRCC, and KTAU evaluation metrics at the utterance level on OOD track. Compared with the basic SSL models, the prediction accuracy of the fused system has been largely improved, especially on OOD sub-track.
An attention-based convolutional neural network for recipe recommendation

The boom in cuisine websites has accumulated a wealth of recipe data, as well as interaction data between users and recipe. Based on these data, users can get recommendation that meet their tastes on recommendation algorithm. In this paper, we propose an attention-based convolutional neural network for recipe recommendation. Specifically, we use attention mechanism to capture users’ preferences for different ingredients. At the same time, we use multi-perspectives convolution neural network to extract user features and recipe features at higher level. Furthermore, a multi-layer neural network is used to model the interaction between users and recipes according to their features. The experimental results show that our method achieves the better recommendation results compared with other traditional methods.
Computerized Analysis of Mammogram Images for Early Detection of Breast Cancer

Breast cancer is widespread worldwide and can be cured if diagnosed early. Using digital mammogram images and image processing with artificial intelligence can play an essential role in breast cancer diagnosis. As many computerized algorithms for breast cancer diagnosis have significant limitations, such as noise handling and varying or low contrast in the images, it can be difficult to segment the abnormal region. These challenges could be overcome by proposing a new pre-processing model, exploring its impact on the post-processing module, and testing it on an extensive database. In this research work, the three-step method is proposed and validated on large databases of mammography images. The first step corresponded to the database classification, followed by the second step, which removed the pectoral muscle from the mammogram image. The third stage utilized new image-enhancement techniques and a new segmentation module to detect abnormal regions in a well-enhanced image to diagnose breast cancer. The pre-and post-processing modules are based on novel image processing techniques. The proposed method was tested using data collected from different hospitals in the Qassim Health Cluster, Qassim Province, Saudi Arabia. This database contained the five categories in the Breast Imaging and Reporting and Data System and consisted of 2892 images; the proposed method is analyzed using the publicly available Mammographic Image Analysis Society database, which contained 322 images. The proposed method gives good contrast enhancement with peak-signal to noise ratio improvement of 3 dB. The proposed method provides an accuracy of approximately 92% on 2892 images of Qassim Health Cluster, Qassim Province, Saudi Arabia. The proposed method gives approximately 97% on the Mammographic Image Analysis Society database. The novelty of the proposed work is that it could work on all Breast Imaging and Reporting and Data System categories. The performance of the proposed method demonstrated its ability to improve the diagnostic performance of the computerized breast cancer detection method.
Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning

Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.
A chaotic self-adaptive JAYA algorithm for parameter extraction of photovoltaic models

In order to have the highest efficiency in real-life photovoltaic power generation systems, how to model, optimize and control photovoltaic systems has become a challenge. The photovoltaic power generation systems are dominated by photovoltaic models, and its performance depends on its unknown parameters. However, the modeling equation of the photovoltaic model is nonlinear, leading to the difficulty in parameter extraction. To extract the parameters of the photovoltaic model more accurately and efficiently, a chaotic self-adaptive JAYA algorithm, called AHJAYA, was proposed, where various improvement strategies are introduced. First, self-adaptive coefficients are introduced to change the priority of information from the best search agent and the worst search agent. Second, by combining the linear population reduction strategy with the chaotic opposition-based learning strategy, the convergence speed of the algorithm is improved as well as avoid falling into local optimum. To verify the performance of the AHJAYA, four photovoltaic models are selected. The experimental results prove that the proposed AHJAYA has superior performance and strong competitiveness.
Explaining the Predictions of Unsupervised Learning Models

Abstract Unsupervised learning is a subfield of machine learning that focuses on learning the structure of data without making use of labels. This implies a different set of learning algorithms than those used for supervised learning, and consequently, also prevents a direct transposition of Explainable AI (XAI) methods from the supervised to the less studied unsupervised setting. In this chapter, we review our recently proposed ‘neuralization-propagation’ (NEON) approach for bringing XAI to workhorses of unsupervised learning such as kernel density estimation and k-means clustering. NEON first converts (without retraining) the unsupervised model into a functionally equivalent neural network so that, in a second step, supervised XAI techniques such as layer-wise relevance propagation (LRP) can be used. The approach is showcased on two application examples: (1) analysis of spending behavior in wholesale customer data and (2) analysis of visual features in industrial and scene images.
Smart Contract Application for Managing Land Administration System Transactions

Land administration systems are of great importance for a large number of stakeholders. One of the key problems related to land administration systems is the problem of the correctness of their state, meaning that data stored in land administration systems are not in concordance with the actual legal, spatial and topographic situation. The main causes of land administration systems’ incorrect state are data collection and compilation, data processing, and data misuse. In this paper, we discuss the problems of data tampering, the long time needed for registering land administration system’s transactions, and the possibility of double spending, which all can add incorrectness in a land administration system. Our research is based on the hypothesis that these problems may be addressed by means of distributed ledger technology, or to be more precise, by means of blockchain technology. The solution is presented in a form of a smart contract written in Solidity programing language that can cover even those more specific use cases in land administration systems such as sharing of ownership, transferring part of ownership, splitting or merging of real estate, and limiting the possibility of trading a real estate. The proposed smart contract represents an implementation of a programming interface that was created based on both ERC-20 and ERC-721 token standards, to satisfy the specific needs of land administration systems.
Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension

Code review is an essential practice in software engineering to spot code defects in the early stages of software development. Modern code reviews (e.g., acceptance or rejection of pull requests with Git) have become less formal than classic Fagan's inspections, lightweight, and more reliant on individuals (i.e., reviewers). However, reviewers may encounter mentally demanding challenges during the code review, such as code comprehension difficulties or distractions that might affect the code review quality. This work proposes a novel approach that evaluates the quality of code reviews in terms of bug-finding effectiveness and provides the reviewers with a clear message of whether the review should be repeated, indicating the code regions that may not have been well-reviewed. The proposed approach utilizes biometric information collected from the reviewer during the review process using non-intrusive biofeedback devices (e.g., smartwatches). Biometric measures such as Heart Rate Variability (HRV) and task-evoked pupillary response are captured as a surrogate of the cognitive state of the reviewer (e.g., mental workload) and inexpensive desktop eye-trackers compatible with the software development settings. This work uses Artificial Intelligence techniques to predict the cognitive load from the extracted biomarkers and classify each code region according to a set of features. The final evaluation considers various factors such as code complexity, time of the code review, the experience level of the reviewer, and other factors. Our experimental results show the approach could predict the review quality with 87.77%±4.65 accuracy and a Spearman correlation coefficient of 0.85 (p-value < 0.001) between the predicted and the actual review performance. This evaluation validates the cognitive load measurement using electroencephalography (EEG) signals as ground truth for the HRV and pupil signals.
Natural Language Processing of Radiology Reports to Detect Complications of Ischemic Stroke

Abstraction of critical data from unstructured radiologic reports using natural language processing (NLP) is a powerful tool to automate the detection of important clinical features and enhance research efforts. We present a set of NLP approaches to identify critical findings in patients with acute ischemic stroke from radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI). We trained machine learning classifiers to identify categorical outcomes of edema, midline shift (MLS), hemorrhagic transformation, and parenchymal hematoma, as well as rule-based systems (RBS) to identify intraventricular hemorrhage (IVH) and continuous MLS measurements within CT/MRI reports. Using a derivation cohort of 2289 reports from 550 individuals with acute middle cerebral artery territory ischemic strokes, we externally validated our models on reports from a separate institution as well as from patients with ischemic strokes in any vascular territory. In all data sets, a deep neural network with pretrained biomedical word embeddings (BioClinicalBERT) achieved the highest discrimination performance for binary prediction of edema (area under precision recall curve [AUPRC] > 0.94), MLS (AUPRC > 0.98), hemorrhagic conversion (AUPRC > 0.89), and parenchymal hematoma (AUPRC > 0.76). BioClinicalBERT outperformed lasso regression (p < 0.001) for all outcomes except parenchymal hematoma (p = 0.755). Tailored RBS for IVH and continuous MLS outperformed BioClinicalBERT (p < 0.001) and linear regression, respectively (p < 0.001). Our study demonstrates robust performance and external validity of a core NLP tool kit for identifying both categorical and continuous outcomes of ischemic stroke from unstructured radiographic text data. Medically tailored NLP methods have multiple important big data applications, including scalable electronic phenotyping, augmentation of clinical risk prediction models, and facilitation of automatic alert systems in the hospital setting.
Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier

Development of abnormal cells in the skin is known as skin cancer or melanoma, which can spread other parts of the body. Melanoma rarely occurs in eye, mouth and intestines. In this study, the classification of melanoma using shearlet transform coefficients and naïve Bayes classifier is discussed. The melanoma images are decomposed by the shearlet transform. Then, from the shearlet coefficients, predefined number of (50, 75 and 100) coefficients are selected from the decomposed subbands. The selected subband coefficients are directly applied to the naïve Bayes classifier. Performance of skin cancer classification system is measured in terms of accuracy. Results show that a better classification accuracy of 90.5 % is achieved at 3rd level with 100 coefficients of shearlet transform and naïve Bayes classifier for skin image classification system.
Face Emotion Identification System for Visually Challenged Persons using Machine Learning

ABSTARCTIn recent years, Machine Learning has regularly grown to be fast research, however, humans have an exclusive method of expressing their emotions and beneath the impact of clarity, heritage, and different factors, and there are a few problems in facial features recognition.In this paper, mainly focusing the visually challenged persons is struggling to identify the emotions of the people in front of us, due to the lack of non-visual information in their nearby environment.The proposed system is to collect the face features to checking the population's influence on the results of emotion reputation like body movements or voice indicators and additionally video series further to photograph in the emotion reputation system.The face features are extracted by using the Haar classifier which detects the face and then obtains the characteristic value.Six kinds of specific emotional categories are obtained through the Neural Network classifier training.The proposed experiment result is taken from the analysis and achieves reasonable accuracy, dependent on the test set, and test emotions.
BYOD Secured Solution Framework

This research focuses on Secured design architecture of BYOD solution. Bring your Own Device is a project driven in most of the enterprises to provide internet access to employee personal devices and Guest users. It has Resulted in a trend to give Access to Employees Smart phones , tablets and personal to Improve employee productivity and address changing working preferences in todays digital age , The Enterprises are Benefiting by providing lesser Devices to their Employees , this is one of the major reasons for the Enterprise to invest in BYOD Infrastructure. Slowly BYOD becomes a rule rather exception. The BYOD Infrastructure provides their Employees an access to the Internet while being a trusted user accessing the Enterprise infrastructure , which is intended to be Secure . The BYOD Infrastructure also provides Employees to grant Guest User Internet Access to their Visiting Partners as well. Connecting external devices in Corporate network increases the cyber security risk and data leakage incident. Using BYOD services users can do malicious activities, try to gain unauthorized access to internal network which can lead into major security breach. Providing Internet access to the external devices using internal corporate network has resulted in a challenge in addressing significant Security risk , data theft and Shadow IT because of unsecured design architecture that compromise the security policy. Installing malware in BYOD and connecting to Internet network can also lead into serious damage and major security risk. A major concern is also to revoke internet access while employee is no more in the system. Incase if employee left and access does not get revoked on time also can be a risk. Poor BYOD design significantly lead into organization Cyber security risk. The Research Paper has Demonstrated the Use Case for Trusted User access and Untrusted Guest User access and Monitoring of the BYOD-User activity and Audit compliance .In this research paper, a secured Design Architecture, Implementation and analysis is conducted. A practical analysis is conducted regarding the design and a Secure BYOD model is demonstrated. This study also highlights the Secured model of BYOD implementation maintaining organization laid down security policy so that enterprise CEO, CIO, CISO can address concerns for securing the Enterprises BYOD Infrastructure.
Extractive Text Summarization using Deep Natural Language Fuzzy Processing

Text summarization is most trending research areas in a modern context. The main aim of this project is to reduce text size while preserving the information underlying into it. In summary construction level, in general, given complex task which are basically will involve with deep natural language fuzzy processing methodologies. In general, an extractive based summary method is the very simple original text of subset of which will not guarantee as best narrative coherence output, because they are most conveniently representing an approximate summarized content from given text-based only on relevance judgment. In an automatic process of fuzzy summarization which is divided into the following steps: Pre-processing (sentence segmentation, tokenization, stop words removal), Feature Extraction, Sentence Scoring, Sentence Ranking and Summary Extraction.
The clothing image classification algorithm based on the improved Xception model

In recent years, the clothing industry develops rapidly under the great influence of internet. A large number of clothing images have been produced and how to accurately make a classification of such a wide range of clothing has become a research focus. In this paper, we propose a clothing image classification algorithm based on the improved Xception model. Firstly, the last fully connected layer of the original network is replaced with another fully connected layer to recognise eight classes instead of 1,000 classes. Secondly, the activation function we employ in our network adopts both exponential linear unit (ELU) and rectified linear unit (ReLU), which can improve the nonlinear and learning characteristics of the networks. Thirdly, in order to enhance the anti-disturbance capability of the network we employ a L2 regularisation method. Fourthly, we perform data augmentation on the training images to reduce over-fitting. Finally, the learning rate is set to zero in the layers of the first two modules of our network and we fine-tune the network. The experimental results show that the top-1 accuracy by the algorithm proposed in this paper is 92.19%, which is better than the state-of-the-art models of Inception-v3, Inception-ResNet-v2 and Xception.
Malaria Cell Image Classification using Deep Learning

Malaria caused by the Plasmodium parasites, is a blood disorder, which is transmitted through the bite of a woman Anopheles mosquito. With almost 240 million cases mentioned each year, the sickness puts nearly forty percentage of the global populace at danger. Macroscopic usually take a look at thick and thin blood smears to identify a disease or a cause and figure it out what weakens them. However, the accuracy depends upon smear quality and awareness in classifying and counting parasite and non-parasite cells. Manual evaluation, which is the gold standard for diagnosis requires various steps to be performed. Moreover, this process leads to overdue and misguided analysis, even when it comes to the hands of expertise. In our project, we aim at building a robust, minimized reliance of humans, sensitive model for automated analysis of Malaria. A category of deep learning models, namely Convolutional Neural Networks, guarantee especially versatile and advanced outcome with end-to-cease attribute extraction and categorization. The precision, unwavering quality, velocity and cost of the methods utilized for malaria examination are key to the diseases’ cure and ultimate eradication. In this study, we compare the overall performance of pre- trained CNN primarily based DL model as characteristic extractors closer to classifying parasite and non-parasite cells to aid in progressed sickness screening. The highest quality model layers for attribute extraction from the underlying records, is determined experimentally. The dataset has a variety of Parasite and Non-Parasite pictures of blood samples. To achieve accurate outcome, we have selected certain dominating features such as size, color, shape and cell count from the images which will help in the categorization process. Pre-trained CNNs are used as a promising tool for attribute extraction; this can be determined by the outcome of its statistical validation. Given these developments, automated microscopy could be a very good deal in the chase towards a low-priced, effortless, and dependable method for diagnosing malaria.
An Application-oriented Review of Deep Learning in Recommender Systems

The development in technology has gifted huge set of alternatives.In the modern era, it is difficult to select relevant items and information from the large amount of available data.Recommender systems have been proved helpful in choosing relevant items.Several algorithms for recommender systems have been proposed in previous years.But recommender systems implementing these algorithms suffer from various challenges.Deep learning is proved successful in speech recognition, image processing and object detection.In recent years, deep learning has been also proved effective in handling information overload and recommending items.This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications.The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.