Reviewerly/exHarmony huggingface dataset:
Developing A Web based System for Breast Cancer Prediction using XGboost Classifier

In today's world cancer is the most common diseases which lead to greatest number of death.Cancer is not one disease; it is a group of more than 100 different and distinctive diseases.Cancer can involve in any tissue of the body and have many different forms and in each body part.Breast Cancer is a grim disease and it is the only type of cancer that is widespread among women worldwide.As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer.So in this project we are developing a web based diagnosis system for which we have done the comparative study of the supervised machine learning classifiers to get to know which classifier is giving the best accuracy.For that we have taken dataset from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms.In which we will use following classification techniques of machine learning like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Adaboost Classifier and XGboost Classifier for the classification of benign and malignant tumor in which the machine is learned from the past data and can predict the category of new input.
Deep Learning Could Diagnose Diabetic Nephropathy with Renal Pathological Immunofluorescent Images

Artificial Intelligence (AI) imaging diagnosis is developing, making enormous steps forward in medical fields. Regarding diabetic nephropathy (DN), medical doctors diagnose them with clinical course, clinical laboratory data and renal pathology, mainly evaluate with light microscopy images rather than immunofluorescent images because there are no characteristic findings in immunofluorescent images for DN diagnosis. Here, we examined the possibility of whether AI could diagnose DN from immunofluorescent images. We collected renal immunofluorescent images from 885 renal biopsy patients in our hospital, and we created a dataset that contains six types of immunofluorescent images of IgG, IgA, IgM, C3, C1q and Fibrinogen for each patient. Using the dataset, 39 programs worked without errors (Area under the curve (AUC): 0.93). Five programs diagnosed DN completely with immunofluorescent images (AUC: 1.00). By analyzing with Local interpretable model-agnostic explanations (Lime), the AI focused on the peripheral lesion of DN glomeruli. On the other hand, the nephrologist diagnostic ratio (AUC: 0.75833) was slightly inferior to AI diagnosis. These findings suggest that DN could be diagnosed only by immunofluorescent images by deep learning. AI could diagnose DN and identify classified unknown parts with the immunofluorescent images that nephrologists usually do not use for DN diagnosis.
Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

Abstract Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent predictive systems in various application areas including smartphone apps. For an effective context-aware system, context pre-modeling is considered as a key issue and task, as the representation of contextual data directly influences the predictive models. This paper mainly explores the role of major context pre-modeling tasks, such as context vectorization by defining a good numerical measure through transformation and normalization, context generation and extraction by creating new brand principal components, context selection by taking into account a subset of original contexts according to their correlations, and eventually context evaluation , to build effective context-aware predictive models utilizing multi-dimensional contextual data. For creating models, various popular machine learning classification techniques such as decision tree, random forest, k-nearest neighbor, support vector machines, naive Bayes classifier, and deep learning by constructing a neural network of multiple hidden layers, are used in our study. Based on the context pre-modeling tasks and classification methods, we experimentally analyze user-centric smartphone usage behavioral activities utilizing their contextual datasets. The effectiveness of these machine learning context-aware models is examined by considering prediction accuracy, in terms of precision, recall, f-score, and ROC values, and has been made an empirical discussion in various dimensions within the scope of our study.
An algorithm for decoding skew Reed–Solomon codes with respect to the skew metric

After giving a new interpretation of the skew metric defined in [8], we show that the decoding algorithm of [4] for skew Reed–Solomon codes in the Hamming metric remains valid with respect to the skew metric. This enables us to make a first step towards a list decoding algorithm in the skew metric.
Slide Reduction, Revisited—Filling the Gaps in SVP Approximation

We show how to generalize Gama and Nguyen's slide reduction algorithm [STOC '08] for solving the approximate Shortest Vector Problem over lattices (SVP) to allow for arbitrary block sizes, rather than just block sizes that divide the rank n of the lattice. This leads to significantly better running times for most approximation factors. We accomplish this by combining slide reduction with the DBKZ algorithm of Micciancio and Walter [Eurocrypt '16]. We also show a different algorithm that works when the block size is quite large—at least half the total rank. This yields the first non-trivial algorithm for sublinear approximation factors. Together with some additional optimizations, these results yield significantly faster provably correct algorithms for $$\delta $$ -approximate SVP for all approximation factors $$n^{1/2+\varepsilon } \le \delta \le n^{O(1)}$$ , which is the regime most relevant for cryptography. For the specific values of $$\delta = n^{1-\varepsilon }$$ and $$\delta = n^{2-\varepsilon }$$ , we improve the exponent in the running time by a factor of 2 and a factor of 1.5 respectively.
General-Purpose User Embeddings based on Mobile App Usage

In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage. User behaviors on mobile app usage, including retention, installation, and uninstallation, can be a good indicator for both long-term and short-term interests of users. For example, if a user installs Snapseed recently, she might have a growing interest in photographing. Such information is valuable for numerous downstream applications, including advertising, recommendations, etc. Traditionally, user modeling from mobile app usage heavily relies on handcrafted feature engineering, which requires onerous human work for different downstream applications, and could be sub-optimal without domain experts. However, automatic user modeling based on mobile app usage faces unique challenges, including (1) retention, installation, and uninstallation are heterogeneous but need to be modeled collectively, (2) user behaviors are distributed unevenly over time, and (3) many long-tailed apps suffer from serious sparsity. In this paper, we present a tailored Auto Encoder-coupled Transformer Network (AETN), by which we overcome these challenges and achieve the goals of reducing manual efforts and boosting performance. We have deployed the model at Tencent, and both online/offline experiments from multiple domains of downstream applications have demonstrated the effectiveness of the output user embeddings.
Visual Quality of Compressed Mesh and Point Cloud Sequences

With the development of immersive video, the delivery and storage of 3D content have become important research areas. While compression methods for meshes and point clouds, the two main representations for 3D content, are actively studied, there are few studies of their perceptual compression quality and none that consider observation distance. In this paper, we study the perceptual quality of compressed 3D sequences, for both point cloud compression and mesh-based compression. We explore the impact of bit rate and observation distance on perceptual quality. Evaluation of perceptual quality is carried out both by collecting viewer opinion scores of the compressed sequences separately, and with a side-by-side comparison. A functional model for mesh and point cloud compression quality is estimated to predict Mean Opinion Score (MOS) which yields high Pearson correlation and rank correlation scores with measured MOS.
A multi-site study examining the usability of a virtual reality game designed to improve retention of sterile catheterization skills in nursing students

A usability study of a Virtual Reality Sterile Urinary Catheter Insertion Game (VR SUCIG) was conducted to understand user needs in regards to this game. Background: Learning and retention of psychomotor skills in health care is essential to safe clinical practice. Bauman suggests games are most useful when they are part of a layered-learning approach; in other words, they support various forms of learning and serve as cognitive aids ( Bauman et al., 2014 ). Intervention: The VR Sterile Urinary Catheter Insertion Game (VRSUCIG) was created by nurses and a computer gaming developer to provide nursing students with a cost-effective way to practice sterile catheter insertion skills in a systematic, evidence-based manner. A usability study and user reaction survey were conducted to gain a deep understanding of user’s needs. Methods: Three hundred nursing students, from 9 US nursing schools participated. Participants played the VR SUCIG and completed the System Usability Scale (SUS) and a User Reaction Survey (URS). Results: The SUS for the 2nd generation of the VR SUCIG was 57, or medium usability. The URS demonstrated the game motivated them to keep practicing. The VR SUCIG promoted repetitive practice of the skill and visually accentuated the concept of sterility. Conclusions. User reactions indicate that nursing students were eager and excited to utilize this technology. Usability scores indicate further refinement of technology is needed.
A novel method for speed training acceleration of recurrent neural networks

Although recurrent neural networks (RNNs) perfectly solve many difficult problems, their computational complexity significantly increases training time. Therefore, the primary problem with applying RNNs is to shorten the time needed to train and operate a network. An effective solution to this problem is to use parallel processing. In the paper, a particular approach for the Jordan network will be shown, however, the presented idea is applicable to other RNN structures. This type of network is characterized by natural parallelism, and in the paper, this feature is used to significantly accelerate the learning process. High-performance learning has been achieved using a novel parallel three-dimensional architecture. The presented solutions can be implemented in digital hardware.
On the Ability and Limitations of Transformers to Recognize Formal Languages

Transformers have supplanted recurrent models in a large number of NLP tasks.However, the differences in their abilities to model different syntactic properties remain largely unknown.Past works suggest that LSTMs generalize very well on regular languages and have close connections with counter languages.In this work, we systematically study the ability of Transformers to model such languages as well as the role of its individual components in doing so.We first provide a construction of Transformers for a subclass of counter languages, including well-studied languages such as n-ary Boolean Expressions, Dyck-1, and its generalizations.In experiments, we find that Transformers do well on this subclass, and their learned mechanism strongly correlates with our construction.Perhaps surprisingly, in contrast to LSTMs, Transformers do well only on a subset of regular languages with degrading performance as we make languages more complex according to a well-known measure of complexity.Our analysis also provides insights on the role of self-attention mechanism in modeling certain behaviors and the influence of positional encoding schemes on the learning and generalization abilities of the model.
Multilingual Argument Mining: Datasets and Analysis

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets.However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between.In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation.We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation.In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model.Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a humangenerated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.
A Bilingual Generative Transformer for Semantic Sentence Embedding

Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences.Bilingual data offers a useful signal for learning such embeddings: properties shared by both sentences in a translation pair are likely semantic, while divergent properties are likely stylistic or language-specific.We propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors.Our proposed approach differs from past work on semantic sentence encoding in two ways.First, by using a variational probabilistic framework, we introduce priors that encourage source separation, and can use our model's posterior to predict sentence embeddings for monolingual data at test time.Second, we use high-capacity transformers as both data generating distributions and inference networkscontrasting with most past work on sentence embeddings.In experiments, our approach substantially outperforms the state-of-the-art on a standard suite of unsupervised semantic similarity evaluations.Further, we demonstrate that our approach yields the largest gains on more difficult subsets of these evaluations where simple word overlap is not a good indicator of similarity. 1
Holographic patient tracking after bed movement for augmented reality neuronavigation using a head-mounted display

Holographic neuronavigation has several potential advantages compared to conventional neuronavigation systems. We present the first report of a holographic neuronavigation system with patient-to-image registration and patient tracking with a reference array using an augmented reality head-mounted display (AR-HMD).Three patients undergoing an intracranial neurosurgical procedure were included in this pilot study. The relevant anatomy was first segmented in 3D and then uploaded as holographic scene in our custom neuronavigation software. Registration was performed using point-based matching using anatomical landmarks. We measured the fiducial registration error (FRE) as the outcome measure for registration accuracy. A custom-made reference array with QR codes was integrated in the neurosurgical setup and used for patient tracking after bed movement.Six registrations were performed with a mean FRE of 8.5 mm. Patient tracking was achieved with no visual difference between the registration before and after movement.This first report shows a proof of principle of intraoperative patient tracking using a standalone holographic neuronavigation system. The navigation accuracy should be further optimized to be clinically applicable. However, it is likely that this technology will be incorporated in future neurosurgical workflows because the system improves spatial anatomical understanding for the surgeon.
Self-Embedding Fragile Watermarking Scheme to Detect Image Tampering Using AMBTC and OPAP Approaches

Research on self-embedding watermarks is being actively conducted to solve personal privacy and copyright problems by image attack. In this paper, we propose a self-embedded watermarking technique based on Absolute Moment Block Truncation Coding (AMBTC) for reconstructing tampered images by cropping attacks and forgery. AMBTC is suitable as a recovery bit (watermark) for the tampered image. This is because AMBTC has excellent compression performance and image quality. Moreover, to improve the quality of the marked image, the Optimal Pixel Adjustment Process (OPAP) method is used in the process of hiding AMBTC in the cover image. To find a damaged block in a marked image, the authentication data along with the watermark must be hidden in the block. We employ a checksum for authentication. The watermark is embedded in the pixels of the cover image using 3LSB and 2LSB, and the checksum is hidden in the LSB. Through the recovering procedure, it is possible to recover the original marked image from the tampered marked image. In addition, when the tampering ratio was 45%, the image (Lena) could be recovered at 36 dB. The proposed self-embedding method was verified through an experiment, and the result was the recovered image showed superior perceptual quality compared to the previous methods.
Sentiment and Emotion in Social Media COVID-19 Conversations: SAB-LSTM Approach

Sentiment and Emotion detection in social media conversations remains a challenge and analyzing the people emotion emerged as an important task in this unprecedented time of COVID-19.People sentiment and emotions are affected by lockdowns, social distancing, travel, work-from-home, wearing mask, reading social media posting.Most of them are feeling sad, anger, depressed, and some of them are neutral and happy.The most recent Sentiment Analysis (SA) researches are done using Twitter dataset (short-text) and rule-based (sentiment lexicon) approach, the outcome of these SA models' results is not showing the consistent prediction of people sentiment about COVID-19.To mitigate and overcome limitations of lexicon approach, processing unstructured social media long text posting, getting context based sentiment score, model overfitting, performance problems of sentiment models, authors' proposed and built a novel multi-class SA model using extension of Bidirectional LSTM (SAB-LSTM) with additional layers.In this experiment SAB-LSTM model has been used to process long text of social media posting, news articles text dataset.Experiment result showed, SAB-LSTM model performance is better than traditional LSTM and BLSTM.Compared SAB-LSTM performance metric of Precision, Recall, F1 Score and sentiment score with traditional LSTM and BLSTM.For this experiment collected COVID-19 related dataset from various social media sources such as Twitter, Facebook, YouTube, News articles blogs and collected data from friends and family.
Rewarding reviews with tokens: An Ethereum-based approach

Recommender Systems (RSs) are becoming increasingly popular in the last years. They collect reviews concerning several types of items (e.g., shops, professionals, services, songs or videos) in order to rank them according to a given criterion, and to suggest the most relevant ones to their users. However, most of the currently used RSs exhibit two main drawbacks: they are based on a centralized control model and they do not provide reward mechanisms to encourage the participation of users. To deal with these challenges, the architectures of current RSs could be enhanced through blockchain technology, thus providing novel solutions to decentralize them. As a matter of fact, the blockchain technology could be successfully adopted in this context because smart contracts would allow the decentralization of system control, while cryptocurrency and tokens could be used to implement the reward mechanism. In the light of the above considerations, this manuscript presents a decentralized rating framework aimed to support the users of RSs based on blockchain technology, providing a token-based reward mechanism that remunerates users submitting their reviews to incentivize their participation. Moreover, the proposed system provides a flexible strategy to rank items, allowing users to choose among different functions to combine reviews to obtain item ranking. The performance and the cost of using the proposed system have been evaluated on the Ropsten Ethereum test network. For instance, our experiments have shown that the median time required to store a batch of 35 ratings is about 47 s, while the average time required to obtain the score of an item having 6000 ratings is less than 2.5 s.
Are Technological Innovation and Foreign Direct Investment a Way to Boost Economic Growth? An Egyptian Case Study Using the Autoregressive Distributed Lag (ARDL) Model

Both technological innovation and foreign direct investment have received widespread attention in the literature on their role in promoting economic growth. Therefore, this study aims to test the relationship between foreign direct investment, technological innovation, and economic growth of the Egyptian economy during the period between 1990–2019 using the autoregressive distributed lag model simultaneous integration test. Our findings show of the ARDL (Autoregressive Distributed Lag) model estimation a joint complementary relationship between the rate of growth of per capita gross domestic product (GDP) in US dollars and the independent variables in the model in the long and short term, which are statistically significant results. We found a positive significant relationship between the variables of incoming foreign direct investment and share of total capital formation in economic growth. Therefore, in the long term, the rate of inflation and the innovation index had a negative impact in the long term and the speed of adjustment towards equilibrium was very large, as it was estimated at 1.5 years (1/0.651). Furthermore, the study also provides valuable lessons and a strategic vision for the Egyptian government, which aspires to advance technology and attract more foreign direct investment.
Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations. Additionally, a supervised a machine learning technique is implemented for performing concept-level sentiment analysis. Experimental results depict the effectiveness of the proposed system in terms of improved efficiency (P: 88%, R: 88%, F-score: 88%, and A: 87.5%).