Reviewerly/exHarmony huggingface dataset:
Labels in a haystack: Approaches beyond supervised learning in biomedical applications

Recent advances in biomedical machine learning demonstrate great potential for data-driven techniques in health care and biomedical research. However, this potential has thus far been hampered by both the scarcity of annotated data in the biomedical domain and the diversity of the domain's subfields. While unsupervised learning is capable of finding unknown patterns in the data by design, supervised learning requires human annotation to achieve the desired performance through training. With the latter performing vastly better than the former, the need for annotated datasets is high, but they are costly and laborious to obtain. This review explores a family of approaches existing between the supervised and the unsupervised problem setting. The goal of these algorithms is to make more efficient use of the available labeled data. The advantages and limitations of each approach are addressed and perspectives are provided.
Implementation Of Data Mining On Glasses Sales Using The Apriori Algorithm

The Optik Nasional is one of the most important optik in Tasikmalaya. Many of his everyday sales transactions, even longer sales, will increase as a result of his activities through the process of purchasing and selling glasses with diverse brands. If left to its own devices, sales transaction data devolves into useless junk. With the help of technological advancements, the ability to collect and process data is improving. Data mining is the process of extracting information and expertise from large amounts of data. Transaction data may be used and analysed to generate helpful information for improved sales and promotional goods, in addition to serving as an archive for the firm. The author utilises the Apriori algorithm to obtain information on the most popular and in-demand goods in a database transaction at a national optical firm, so that the results may be utilised to improve the growth of eyeglass sales and marketing.
ICT Motivation in Sixth-Grade Students in Pandemic Times—The Influence of Gender and Age

Information and communication technology (ICT) is being immersed in people’s daily lives at an increasingly younger age. It has been key for adolescents to pursue distance education, and their use and mastery of technological means and tools with Internet access has increased. In this study, the motivation, specifically in the interest, digital competence, autonomy, and social interaction, generated by ICTs in the daily lives of adolescents during the pandemic caused by COVID-19 was analyzed. In the study, the objective was to study the motivation, use and commitment generated by ICTs in these students in relation to their gender and age after their confinement to the classroom caused by the first wave of incoming students. An experimental method of descriptive and correlative design was used along with a quantitative method to analyze the data. The data were obtained in the year 2020 through a validated questionnaire committed to the ICT scale used by PISA (Programme for International Student Assessment). A total of 924 students from the sixth grade of primary education in the autonomous city of Ceuta (Spain), aged between 10 and 13, participated in the sample. The results reveal that the motivation and commitment to ICT in these age groups were medium in relation to the total mean of results on a Likert-5 scale. Boys scored higher in all the variables analyzed, and both age and gender show correlations, in addition to the factor of prediction. In conclusion, students in the sixth year of primary education, after the confinement period, were medium-high in their use and engagement of ICT. In addition, gender and age affected ICT use and engagement.
Improving feature extraction from histopathological images through a fine-tuning ImageNet model

Due to lack of annotated pathological images, transfer learning has been the predominant approach in the field of digital pathology. Pre-trained neural networks based on ImageNet database are often used to extract “off-the-shelf” features, achieving great success in predicting tissue types, molecular features, and clinical outcomes, etc. We hypothesize that fine-tuning the pre-trained models using histopathological images could further improve feature extraction, and downstream prediction performance. We used 100 000 annotated H&E image patches for colorectal cancer (CRC) to fine-tune a pre-trained Xception model via a 2-step approach. The features extracted from fine-tuned Xception (FTX-2048) model and Image-pretrained (IMGNET-2048) model were compared through: (1) tissue classification for H&E images from CRC, same image type that was used for fine-tuning; (2) prediction of immune-related gene expression, and (3) gene mutations for lung adenocarcinoma (LUAD). Five-fold cross validation was used for model performance evaluation. Each experiment was repeated 50 times. The extracted features from the fine-tuned FTX-2048 exhibited significantly higher accuracy (98.4%) for predicting tissue types of CRC compared to the “off-the-shelf” features directly from Xception based on ImageNet database (96.4%) (P value = 2.2 × 10−6). Particularly, FTX-2048 markedly improved the accuracy for stroma from 87% to 94%. Similarly, features from FTX-2048 boosted the prediction of transcriptomic expression of immune-related genes in LUAD. For the genes that had significant relationships with image features (P < 0.05, n = 171), the features from the fine-tuned model improved the prediction for the majority of the genes (139; 81%). In addition, features from FTX-2048 improved prediction of mutation for 5 out of 9 most frequently mutated genes (STK11, TP53, LRP1B, NF1, and FAT1) in LUAD. We proved the concept that fine-tuning the pretrained ImageNet neural networks with histopathology images can produce higher quality features and better prediction performance for not only the same-cancer tissue classification where similar images from the same cancer are used for fine-tuning, but also cross-cancer prediction for gene expression and mutation at patient level.
Detecting Adversarial Examples in Deep Neural Networks using Normalizing Filters

Deep neural networks are vulnerable to adversarial examples which are inputs modified with unnoticeable but malicious perturbations.Most defending methods only focus on tuning the DNN itself, but we propose a novel defending method which modifies the input data to detect the adversarial examples.We establish a detection framework based on normalizing filters that can partially erase those perturbations by smoothing the input image or depth reduction work.The framework gives the decision by comparing the classification results of original input and multiple normalized inputs.Using several combinations of gaussian blur filter, median blur filter and depth reduction filter, the evaluation results reaches a high detection rate and achieves partial restoration work of adversarial examples in MNIST dataset.The whole detection framework is a low-cost highly extensible strategy in DNN defending works.
Analytical structures and stability analysis of the simplest Takagi-Sugeno fuzzy two-term controllers

This paper deals with the simplest Takagi-Sugeno fuzzy two-term controllers. The analytical structures of the simplest fuzzy controllers were developed using a modified rule base. The number of tuning parameters of the controllers is reduced by introducing a novel rule base consisting of two rules. These controllers are termed as 'the simplest' since minimal (two) number of fuzzy sets is used for fuzzification. Algebraic product (AP)/minimum (min) triangular norm, bounded sum (BS)/maximum (max) triangular co-norm, different universes of discourses (UoDs) of inputs, and centre of gravity (CoG) defuzzification method are chosen to derive the mathematical models of the fuzzy controllers. The simplest fuzzy controller with modified rule base is equivalent to a (nonlinear) variable gain/structure PI/PD controller. The BIBO stability of the closed loop control system is investigated using the small gain theorem. The gain of the controller either varies or remains constant in different regions of the input plane. The gain variations and the computational burden of the fuzzy controllers are also studied. Two examples of nonlinear dynamical systems are considered to validate the developed models of the fuzzy controllers.
Characteristic of enterprise collaboration system and its implementation issues in business management

Collaboration is an extremely useful area for the most of the enterprise systems particularly within Web 2.0 and Enterprise 2.0. The collaboration provides help in enterprise collaboration system (ECS) to achieve the desired goal by unifying completed tasks of employees or people working on a similar or the same task. Thus, the collaboration systems have witnessed significant attention. The ECS provides consistent and off-the-shelf support to processes and managements within organisations. Management techniques of the ECS may be useful to a community which manages ECS systems for collaboration. In this context, this paper focuses on enterprise collaboration system and answers critical questions related to ECS including: 1) what does collaboration really means for an enterprise system; 2) how can the collaboration help to improve internal processes and management of the system; 3) how it is helpful to improve interactions with customers and partners?
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction

Few-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation.Some recent works have introduced relation information (i.e., relation labels or descriptions) to assist model learning based on Prototype Network.However, most of them constrain the prototypes of each relation class implicitly with relation information, generally through designing complex network structures, like generating hybrid features, combining with contrastive learning or attention networks.We argue that relation information can be introduced more explicitly and effectively into the model.Thus, this paper proposes a direct addition approach to introduce relation information.Specifically, for each relation class, the relation representation is first generated by concatenating two views of relations (i.e., [CLS] token embedding and the mean value of embeddings of all tokens) and then directly added to the original prototype for both train and prediction.Experimental results on the benchmark dataset FewRel 1.0 show significant improvements and achieve comparable results to the state-of-theart, which demonstrates the effectiveness of our proposed approach.Besides, further analyses verify that the direct addition is a much more effective way to integrate the relation representations and the original prototypes. 1 2
Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.
Neural Subgraph Counting with Wasserstein Estimator

Subgraph counting is a fundamental graph analysis task which has been widely used in many applications. As the problem of subgraph counting is NP-complete and hence intractable, approximate solutions have been widely studied, which fail to work with large and complex query graphs. Alternatively, Machine Learning techniques have been recently applied for this problem, yet the existing ML approaches either only support very small data graphs or cannot make full use of the data graph information, which inherently limits their scalability, estimation accuracies and robustness.
Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT

This paper proposes a new network framework, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. First, we use EfficientNetB4 as the encoder to extract more feature information during the encoding stage. Then, an attention gate is introduced in the skip connection to eliminate irrelevant regions and highlight features of a specific segmentation task. Finally, to alleviate the problem of gradient vanishment, we replace the traditional convolution of the decoder with a residual block to improve the segmentation accuracy. We verified the proposed method on the LiTS17 and SLiver07 datasets and compared it with classical networks such as FCN, U-Net, attention U-Net, and attention Res-U-Net. In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics. Meanwhile, in the LiTS17 assessment, the best performance is obtained except for a slight inferior on RVD. The proposed method’s qualitative and quantitative results demonstrated its applicability in liver segmentation and proved its good prospect in computer-assisted liver segmentation.
A novel oversampling and feature selection hybrid algorithm for imbalanced data classification

Traditional approaches tend to cause classier bias in the imbalanced data set, resulting in poor classification performance for minority classes. In particular, there are many imbalanced data in financial fraud, network intrusion, and fault detection, where recognition rate of minority classes is pertinent than the classification performance of majority classes. Therefore, there is pressure on developing efficient algorithms to solve the class imbalance problem. To this end, this article presents a novel hybrid algorithm Negative Binary General (NBG), to improve the performance of imbalanced classifications by combining oversampling and a feature selection algorithm. A novel oversampling algorithm, Negative-positive Synthetic Minority Oversampling Technique (NPSMOTE), improves sample generation’s practicability while the Binary Ant Lion Optimizer (BALO) algorithm extracts the most significant features to improve the classification performance. Simulation experiments carried out using seven benchmark imbalanced data sets demonstrate that, the proposed NBG algorithm significantly outperforms the classification of imbalanced small-sample data sets compared to nine other existing and six recently published algorithms.
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers

On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that enable a safer internet is an important research area. Moreover, the web is a highly multilingual, cross-cultural community that develops its own lingo over time. As such, it is crucial to develop models that are effective across a diverse range of languages, usages, and styles. In this paper, we present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model that is applicable across a range of languages, domains, and tasks. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings. We additionally outline the techniques employed to make such a byte-level model efficient and feasible for productionization. Through extensive experiments on multilingual toxic comment classification benchmarks derived from real API traffic and evaluation on an array of code-switching, covert toxicity, emoji-based hate, human-readable obfuscation, distribution shift, and bias evaluation settings, we show that our proposed approach outperforms strong baselines. Finally, we present our findings from deploying this system in production.
SMORE

Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and are a crucial component in many AI systems. There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query. Embedding-based methods solve both tasks by first computing an embedding for each entity and relation, then using them to form predictions. However, existing scalable KG embedding frameworks only support single-hop knowledge graph completion and cannot be applied to the more challenging multi-hop reasoning task. Here we present Scalable Multi-hOp REasoning (SMORE), the first general framework for both single-hop and multi-hop reasoning in KGs. Using a single machine SMORE can perform multi-hop reasoning in Freebase KG (86M entities, 338M edges), which is 1,500x larger than previously considered KGs. The key to SMORE's runtime performance is a novel bidirectional rejection sampling that achieves a square root reduction of the complexity of online training data generation. Furthermore, SMORE exploits asynchronous scheduling, overlapping CPU-based data sampling, GPU-based embedding computation, and frequent CPU--GPU IO. SMORE increases throughput (i.e., training speed) over prior multi-hop KG frameworks by 2.2x with minimal GPU memory requirements (2GB for training 400-dim embeddings on 86M-node Freebase) and achieves near linear speed-up with the number of GPUs. Moreover, on the simpler single-hop knowledge graph completion task SMORE achieves comparable or even better runtime performance to state-of-the-art frameworks on both single GPU and multi-GPU settings.
GNSS/Inertial Navigation/Wireless Station Fusion UAV 3-D Positioning Algorithm With Urban Canyon Environment

Unmanned aerial vehicles (UAVs) have become the core infrastructure of smart cities because of their fast, flexible, and strong environmental adaptability. However, signal occlusion caused by the urban canyon effect will seriously affect its global navigation satellite system (GNSS) reliability. The fusion algorithm represented by Kalman Filter cannot meet the real-time and stability requirements of UAV high maneuvering flight positioning due to its high complexity. In this article, integrated GNSS, inertial navigation, and wireless base station navigation, a 3-D UAV positioning method called GIW-UP, based on information geometry, is proposed. It converts the information of various types of navigation sources into probability density functions, and then the fusion is realized from the perspective of information probability. Given the differences in the information output time and navigation parameters of various navigation sources, the proposed GIW-UP method is compared with the least squares (LS) method, the unscented Kalman filter (UKF) method, and the neural network-based multisensor two-stage fusion (MTFA) method in three aspects: stability, convergence speed, and computational complexity. The results show that the GIW-UP can effectively reduce the fusion computational complexity and improve positioning stability.
Artificial Intelligence for Metaverse: A Framework

The metaverse is attracting considerable attention recently. It aims to build a virtual environment that people can interact with the world and cooperate with each other. In this survey paper, we re-introduce metaverse in a new framework based on a broad range of technologies, including perception which enables us to precisely capture the characteristics of the real world, computation which supports the large computation requirement over large-scale data, reconstruction which builds the virtual world from the real one, cooperation which facilitates long-distance communication and teamwork between users, and interaction which bridges users and the virtual world. Despite its popularity, the fundamental techniques in this framework are still immature. Innovating new techniques to facilitate the applications of metaverse is necessary. In recent years, artificial intelligence (AI), especially deep learning, has shown promising results for empowering various areas, from science to industry. It is reasonable to imagine how we can combine AI with the framework in order to promote the development of metaverse. In this survey, we present the recent achievement by AI for metaverse in the proposed framework, including perception, computation, reconstruction, cooperation, and interaction. We also discuss some future works that AI can contribute to metaverse.
Superfast and large-depth-range sinusoidal fringe generation for multi-dimensional information sensing

Among many multi-dimensional information sensing methods such as structured-light and single-pixel imaging technologies, sinusoidal fringe generation is general and crucial. Current methods of sinusoidal fringe generation force concessions in either the speed or the depth range. To mitigate this trade-off, we have simultaneously achieved both speed breakthrough and depth range enhancement by improving both the optical projection system and binary coding algorithm based on an off-the-shelf projector. Specifically, we propose a multifocal projection system and oblique projection method, which essentially eliminates the existence of a single focal plane in the conventional axisymmetric system and utilizes its anisotropy characteristics to achieve a superior filtering effect. Furthermore, the optimal pulse width modulation technique is introduced to modulate the square binary pattern for eliminating specific harmonics. To the best of our knowledge, the proposed method, for the first time, simultaneously achieved superfast (9524 frames per second) and large-depth-range (560 mm, about three times that of the conventional method) sinusoidal fringe generation with consistently high accuracy. Experimental results demonstrate the superior performance of the proposed method in multi-dimensional information sensing such as 3D, 4D, and [ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m3"> <mml:mrow> <mml:mi>z</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m4"> <mml:mrow> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> ; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m5"> <mml:mrow> <mml:mi>s</mml:mi> </mml:mrow> </mml:math> (strain)].