Reviewerly/exHarmony huggingface dataset:
Assessment of deep learning assistance for the pathological diagnosis of gastric cancer

Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may the benefits of DL be achieved. A fully crossed multireader multicase study was conducted to evaluate DL assistance with pathologists' diagnosis of gastric cancer. A total of 110 whole-slide images (WSI) (50 malignant and 60 benign) were interpreted by 16 board-certified pathologists with or without DL assistance, with a washout period between sessions. DL-assisted pathologists achieved a higher area under receiver operating characteristic curve (ROC-AUC) (0.911 vs. 0.863, P = 0.003) than unassisted in interpreting the 110 WSIs. Pathologists with DL assistance demonstrated higher sensitivity in detection of gastric cancer than without (90.63% vs. 82.75%, P = 0.010). No significant difference was observed in specificity with or without deep learning assistance (78.23% vs. 79.90%, P = 0.468). The average review time per WSI was shortened with DL assistance than without (22.68 vs. 26.37 second, P = 0.033). Our results demonstrated that DL assistance indeed improved pathologists' accuracy and efficiency in gastric cancer diagnosis and further boosted the acceptance of this new technique.
Towards Lightweight Transformer Via Group-Wise Transformation for Vision-and-Language Tasks

Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost. However, compressing Transformer remains as an open problem due to its internal complexity of the layer designs, i.e., Multi-Head Attention (MHA) and Feed-Forward Network (FFN). To address this issue, we introduce Group-wise Transformation towards a universal yet lightweight Transformer for vision-and-language tasks, termed as LW-Transformer1. LW-Transformer applies Group-wise Transformation to reduce both the parameters and computations of Transformer, while also preserving its two main properties, i.e., the efficient attention modeling on diverse subspaces of MHA, and the expanding-scaling feature transformation of FFN. We apply LW-Transformer to a set of Transformer-based networks, and quantitatively measure them on three vision-and-language tasks and six benchmark datasets. Experimental results show that while saving a large number of parameters and computations, LW-Transformer achieves very competitive performance against the original Transformer networks for vision-and-language tasks. To examine the generalization ability, we apply LW-Transformer to the task of image classification, and build its network based on a recently proposed image Transformer called Swin-Transformer, where the effectiveness can be also confirmed.
MAVE

Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product ranking, retrieval and recommendations. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we introduce MAVE, a new dataset to better facilitate research on product attribute value extraction. MAVE is composed of a curated set of 2.2 million products from Amazon pages, with 3 million attribute-value annotations across 1257 unique categories. MAVE has four main and unique advantages: First, MAVE is the largest product attribute value extraction dataset by the number of attribute-value examples. Second, MAVE includes multi-source representations from the product, which captures the full product information with high attribute coverage. Third, MAVE represents a more diverse set of attributes and values relative to what previous datasets cover. Lastly, MAVE provides a very challenging zero-shot test set, as we empirically illustrate in the experiments. We further propose a novel approach that effectively extracts the attribute value from the multi-source product information. We conduct extensive experiments with several baselines and show that MAVE is an effective dataset for attribute value extraction task. It is also a very challenging task on zero-shot attribute extraction. Data is available at \urlhttps://github.com/google-research-datasets/MAVE .
RATIONAL-CRITICAL THINKING AS THE “FOUNDATION OF BEING” OF A PERSON

The article proposes an explication of the concept of “rational-critical thinking”, distinguishes the key skills and attitudes that make it up. It is shown that in modern conditions rational-critical thinking is the most important resource of human existence, providing both the opportunity to achieve genuine knowledge and protection from social manipulation. A characteristic of rationalcritical thinking as a synthetic “epistemic virtue” is proposed. The results of an experiment that assessed the basic logical culture of a student audience as a primary marker of rational-critical thinking are presented.
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation

Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart.It aims to extract relations from multiple sentences at once.In this paper, we propose a semi-supervised framework for DocRE with three novel components.Firstly, we use an axial attention module for learning the interdependency among entitypairs, which improves the performance on twohop relations.Secondly, we propose an adaptive focal loss to tackle the class imbalance problem of DocRE.Lastly, we use knowledge distillation to overcome the differences between human annotated data and distantly supervised data.We conducted experiments on two DocRE datasets.Our model consistently outperforms strong baselines and its performance exceeds the previous SOTA by 1.36 F1 and 1.46 Ign_F1 score on the DocRED leaderboard. 1
Federated Deep Learning Approaches for the Privacy and Security of IoT Systems

Using federated learning, which is a distributed machine learning approach, a machine learning model can train on a distributed data set without having to transfer any data between computers. Instead of using a centralised server for training, the model uses data stored locally on the device itself. After that, the server uses this model to create a jointly trained model. Federated learning asserts that privacy is preserved because no data is sent. Botnet attacks are detected using on-device decentralised traffic statistics and a deep autoencoder. This proposed federated learning approach addresses privacy and security concerns about data privacy and security rather than allowing data to be transferred or relocated off the network edge. In order to get the intended results of a previously centralised machine learning technique while also increasing data security, computation will be shifted to the edge layer. Up to 98% accuracy is achieved in anomaly detection with our proposed model using features like MAC IP and source/destination/IP for training. Our solution outperforms a standard centrally managed system in terms of attack detection accuracy, according to our comparative performance analysis.
A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems

Arithmetic Optimization Algorithm (AOA) is a physically inspired optimization algorithm that mimics arithmetic operators in mathematical calculation. Although the AOA has an acceptable exploration and exploitation ability, it also has some shortcomings such as low population diversity, premature convergence, and easy stagnation into local optimal solutions. The Golden Sine Algorithm (Gold-SA) has strong local searchability and fewer coefficients. To alleviate the above issues and improve the performance of AOA, in this paper, we present a hybrid AOA with Gold-SA called HAGSA for solving industrial engineering design problems. We divide the whole population into two subgroups and optimize them using AOA and Gold-SA during the searching process. By dividing these two subgroups, we can exchange and share profitable information and utilize their advantages to find a satisfactory global optimal solution. Furthermore, we used the Levy flight and proposed a new strategy called Brownian mutation to enhance the searchability of the hybrid algorithm. To evaluate the efficiency of the proposed work, HAGSA, we selected the CEC 2014 competition test suite as a benchmark function and compared HAGSA against other well-known algorithms. Moreover, five industrial engineering design problems were introduced to verify the ability of algorithms to solve real-world problems. The experimental results demonstrate that the proposed work HAGSA is significantly better than original AOA, Gold-SA, and other compared algorithms in terms of optimization accuracy and convergence speed.
Label Semantics for Few Shot Named Entity Recognition

We study the problem of few shot learning for named entity recognition.Specifically, we leverage the semantic information in the names of the labels as a way of giving the model additional signal and enriched priors.We propose a neural architecture that consists of two BERT encoders, one to encode the document and its tokens and another one to encode each of the labels in natural language format.Our model learns to match the representations of named entities computed by the first encoder with label representations computed by the second encoder.The label semantics signal is shown to support improved state-of-the-art results in multiple few shot NER benchmarks and onpar performance in standard benchmarks.Our model is especially effective in low resource settings.
Gender and Perception: Implementation of Web-based Character Assessment on Students’ Character Outcomes

This study aims to find out how the differences in students' perceptions of the use of web-based character assessments were analyzed by gender and to determine the differences in the acquisition of students' perception assessments of the use of webbased character assessments in junior high schools in Batanghari Regency, Indonesia.The population in this study was a junior high school in Batanghari Regency, Jambi Province, Indonesia with a sample of 322 students using purposive sampling technique.The quantitative method used in this research is descriptive and inferential analysis method to obtain assumption testing (normality and homogeneity) and hypothesis testing (Anova).The results showed that in the variable of student perceptions of the use of web-based character assessment, there was a significant average difference between female students and male students.Then on the student character variable there is also a significant average difference between female students and male students.
Sentiment analysis and opinion mining on educational data: A survey

Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning–teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention.​ In this review article, (1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and aspect level, (2) sentiment annotation techniques including lexicon-based and corpus-based approaches for unsupervised annotations are explored, (3) the role of AI in sentiment analysis with methodologies like machine learning, deep learning, and transformers are discussed, (4) the impact of sentiment analysis on educational procedures to enhance pedagogy, decision-making, and evaluation are presented. Educational institutions have been widely invested to build sentiment analysis tools and process their student feedback to draw their opinions and insights. Applications built on sentiment analysis of student feedback are reviewed in this study. Challenges in sentiment analysis like multi-polarity, polysemous, negation words, and opinion spam detection are explored and their trends in the research space are discussed. The future directions of sentiment analysis in education are discussed.
Event-Based Fusion for Motion Deblurring with Cross-modal Attention

Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution, providing valid image degradation information within the exposure time. In this paper, we rethink the event-based image deblurring problem and unfold it into an end-to-end two-stage image restoration network. To effectively fuse event and image features, we design an event-image cross-modal attention module applied at multiple levels of our network, which allows to focus on relevant features from the event branch and filter out noise. We also introduce a novel symmetric cumulative event representation specifically for image deblurring as well as an event mask gated connection between the two stages of our network which helps avoid information loss. At the dataset level, to foster event-based motion deblurring and to facilitate evaluation on challenging real-world images, we introduce the Real Event Blur (REBlur) dataset, captured with an event camera in an illumination-controlled optical laboratory. Our Event Fusion Network (EFNet) sets the new state of the art in motion deblurring, surpassing both the prior best-performing image-based method and all event-based methods with public implementations on the GoPro dataset (by up to 2.47 dB) and on our REBlur dataset, even in extreme blurry conditions. The code and our REBlur dataset are available at https://ahupujr.github.io/EFNet/ .
NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution.
Algorithmically Bypassing Censorship on Sina Weibo with Nondeterministic Homophone Substitutions

Like traditional media, social media in China is subject to censorship. However, in limited cases, activists have employed homophones of censored keywords to avoid detection by keyword matching algorithms. In this paper, we show that it is possible to scale this idea up in ways that make it difficult to defend against. Specifically, we present a non-deterministic algorithm for generating homophones that create large numbers of false positives for censors, making it difficult to locate banned conversations. In two experiments, we show that 1) homophone-transformed weibos posted to Sina Weibo remain on-site three times longer than their previously censored counterparts, and 2) native Chinese speakers can recover the original intent behind the homophone-transformed messages, with 99% of our posts understood by the majority of our participants. Finally, we find that coping with homophone transformations is likely to cost the Sina Weibo censorship apparatus an additional 15 hours of human labor per day, per censored keyword. To conclude, we reflect briefly on the opportunities presented by this algorithm to build interactive, client-side tools that promote free speech.
Predicting Speech Acts in MOOC Forum Posts

Students in a Massive Open Online Course (MOOC) interact with each other and the course staff through online discussion forums. While discussion forums play a central role in MOOCs, they also pose a challenge for instructors. The large number of student posts makes it difficult for an instructor to know where to intervene to answer questions, resolve issues, and provide feedback. In this work, we focus on automatically predicting speech acts in MOOC forum posts. Our speech act categories describe the purpose or function of the post in the ongoing discussion. Specifically, we address three main research questions. First, we investigate whether crowdsourced workers can reliably label MOOC forum posts using our speech act definitions. Second, we investigate whether our speech acts can help predict instructor interventions and assignment completion and performance. Finally, we investigate which types of features (derived from the post content, author, and surrounding context) are most effective for predicting our different speech act categories.
Improving Hierarchical Planning Performance by the Use of Landmarks

In hierarchical planning, landmarks are tasks that occur on any search path leading from the initial plan to a solution. In this work, we present novel domain-independent planning strategies based on such hierarchical landmarks. Our empirical evaluation on four benchmark domains shows that these landmark-aware strategies outperform established search strategies in many cases.
Towards Generating Novel Games Using Conceptual Blending

We sketch the process of creating a novel video game by blending two video games specified in the Video Game Description Language (VGDL), following the COINVENT computational model of conceptual blending. We highlight the choices that need to be made in this process, and discuss the prospects for a computational game designer based on blending.
Exploring Player Trace Segmentation for Dynamic Play Style Prediction

Existing work on player modeling often assumes that the play style of players is static. However, our recent work shows evidence that players regularly change their play style over time. In this paper we propose a novel player modeling framework to capture this change by using episodic information and sequential machine learning techniques. In particular, we experiment with different trace segmentation strategies for play style prediction. We evaluate this new framework on gameplay data gathered from a game-based interactive learning environment. Our results show that sequential machine learning techniques that incorporate predictions from previous segments outperform non-sequential techniques. Our results also show that too fine (minute-by-minute) or too coarse (whole trace) segmentation of traces decreases performance.
Ultigesture: A wristband-based platform for continuous gesture control in healthcare

In recent years, wearable sensor-based gesture recognition is proliferating in the field of healthcare. It could be used to enable remote control of medical devices, contactless navigation of X-ray display and Magnetic Resonance Imaging (MRI), and largely enhance patients’ daily living capabilities. However, even though a few commercial or prototype devices are available for wearable gesture recognition, none of them provides a combination of (1) fully open API for various healthcare application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. In addition, the existing gesture recognition algorithms are mainly designed for discrete gestures. Accurate recognition of continuous gestures is still a significant challenge, which prevents the wide usage of existing wearable gesture recognition technology. In this paper, we present Ultigesture wristband, a hardware/software platform for gesture recognition and remote control. Due to its affordability, small size, and comfortable profile, Ultigesture wristband is an attractive option for mass consumption. Ultigesture wristband provides full open API access for third party research and application development. In addition, it employs a novel continuous gesture segmentation and recognition algorithm, which accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously.
Recurrent Face Aging with Hierarchical AutoRegressive Memory

Modeling the aging process of human faces is important for cross-age face verification and recognition. In this paper, we propose a Recurrent Face Aging (RFA) framework which takes as input a single image and automatically outputs a series of aged faces. The hidden units in the RFA are connected autoregressively allowing the framework to age the person by referring to the previous aged faces. Due to the lack of labeled face data of the same person captured in a long range of ages, traditional face aging models split the ages into discrete groups and learn a one-step face transformation for each pair of adjacent age groups. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transitional states. In this way, the intermediate aged faces between the age groups can be generated. Towards this target, we employ a recurrent neural network whose recurrent module is a hierarchical triple-layer gated recurrent unit which functions as an autoencoder. The bottom layer of the module encodes the input to a latent representation, and the top layer decodes the representation to a corresponding aged face. The experimental results demonstrate the effectiveness of our framework.
Scene Categorization Using Deeply Learned Gaze Shifting Kernel

Accurately recognizing sophisticated sceneries from a rich variety of semantic categories is an indispensable component in many intelligent systems, e.g., scene parsing, video surveillance, and autonomous driving. Recently, there have emerged a large quantity of deep architectures for scene categorization, wherein promising performance has been achieved. However, these models cannot explicitly encode human visual perception toward different sceneries, i.e., the sequence of humans sequentially allocates their gazes. To solve this problem, we propose deep gaze shifting kernel to distinguish sceneries from different categories. Specifically, we first project regions from each scenery into the so-called perceptual space, which is established by combining color, texture, and semantic features. Then, a novel non-negative matrix factorization algorithm is developed which decomposes the regions' feature matrix into the product of the basis matrix and the sparse codes. The sparse codes indicate the saliency level of different regions. In this way, the gaze shifting path from each scenery is derived and an aggregation-based convolutional neural network is designed accordingly to learn its deep representation. Finally, the deep representations of gaze shifting paths from all the scene images are incorporated into an image kernel, which is further fed into a kernel SVM for scene categorization. Comprehensive experiments on six scenery data sets have demonstrated the superiority of our method over a series of shallow/deep recognition models. Besides, eye tracking experiments have shown that our predicted gaze shifting paths are 94.6% consistent with the real human gaze allocations.