Reviewerly/exHarmony huggingface dataset:
Genetic programming for structural similarity design at multiple spatial scales

The growing production of digital content and its dissemination across the worldwide web require eficient and precise management. In this context, image quality assessment measures (IQAMs) play a pivotal role in guiding the development of numerous image processing systems for compression, enhancement, and restoration. The structural similarity index (SSIM) is one of the most common IQAMs for estimating the similarity between a pristine reference image and its corrupted variant. The multi-scale SSIM is one of its most popular variants that allows assessing image quality at multiple spatial scales. This paper proposes a two-stage genetic programming (GP) approach to evolve novel multi-scale IQAMs, that are simultaneously more effective and efficient. We use GP to perform feature selection in the first stage, while the second stage generates the final solutions. The experimental results show that the proposed approach outperforms the existing MS-SSIM. A comprehensive analysis of the feature selection indicates that, for extracting multi-scale similarities, spatially-varying convolutions are more effective than dilated convolutions. Moreover, we provide evidence that the IQAMs learned for one database can be successfully transferred to previously unseen databases. We conclude the paper by presenting a set of evolved multi-scale IQAMs and providing their interpretation.
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection

The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance.However, the deployment of current uncertainty estimation methods in 3D object detection remains challenging due to timing and computational constraints.To tackle this issue, we propose LiDAR-MIMO, an adaptation of the multiinput multi-output (MIMO) uncertainty estimation method to the LiDAR-based 3D object detection task.Our method modifies the original MIMO by performing multi-input at the feature level to ensure the detection, uncertainty estimation, and runtime performance benefits are retained despite the limited capacity of the underlying detector and the large computational costs of point cloud processing.We compare LiDAR-MIMO with MC dropout and ensembles as baselines and show comparable uncertainty estimation results with only a small number of output heads.Further, LiDAR-MIMO can be configured to be twice as fast as MC dropout and ensembles, while achieving higher mAP than MC dropout and approaching that of ensembles.
Technology Assisted Device for Bilingual Learning Environment in Higher Education in Taiwan

Internationalization is the primary goal of the development of education in Taiwan today. There are foreign students in almost every school in Taiwan. Although they would go to a language school for Chinese classes before coming to Taiwan, each teacher's speaking speed and accent are different, and many technical terms are not taught in a language school. This causes the efficiency of learning to be greatly reduced. However, in order to allow foreign students to understand the content of the class, the switch to teaching in English has caused local students with poor English proficiency to be unable to understand the content of the class. Similarly, in Taiwan, not every subject taken by foreign students is taught in English. The official language, Chinese, is still the main communication tool in Taiwan. In order to improve the learning effectiveness of foreign students and local students in a bilingual environment and to deal with the learning problems caused by insufficient language skills, we have proposed solutions. This solution uses voice recognition and text translation technology, through the teacher&#x2019;s microphone for the real-time simultaneous translation and recording of class content. The method solves the above-mentioned problems. Both foreign and local students can choose the source language and destination language at the same time for their better understanding and learning efficiency.
Detection and Classification of Lung Cancer Cells Using Swin Transformer

Lung cancer is one of the greatest threats to human health.It is a very effective way to detect lung cancer by pathological pictures of lung cancer cells.Therefore, improving the accuracy and stability of diagnosis is very important.In this study, we develop an automatic detection scheme for lung cancer cells based on convolutional neural networks and Swin Transformer.Microscopic images of patients' lung cells are first segmented using a Mask R-CNNbased network, resulting in a separate image for each cell.Part of the background information is preserved by Gaussian blurring of surrounding cells, while the target cells are highlighted.The classification model based on Swin Transformer not only reduces the computation but also achieves better results than the classical CNN model, ResNet50.The final results show that the accuracy of the method proposed in this paper reaches 96.16%.Therefore, this method is helpful for the detection and classification of lung cancer cells.
Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and Clustering

Sentiment classification constitutes an important topic in the field of Natural Language Processing, whose main purpose is to extract the sentiment polarity from unstructured texts. The label propagation algorithm, as a semi-supervised learning method, has been widely used in sentiment classification due to its describing sample relation in a graph-based pattern. Whereas, current graph developing strategies fail to use the global distribution and cannot handle the issues of polysemy and synonymy properly. In this paper, a semi-supervised learning methodology, integrating the tripartite graph and the clustering, is proposed for graph construction. Experiments on E-commerce reviews demonstrate the proposed method outperform baseline methods on the whole, which enables precise sentiment classification with few labeled samples.
Transforming science through cyberinfrastructure

NSF's vision for the U.S. cyberinfrastructure ecosystem for science and engineering in the 21 st century.
Developing an Accessibility Metric for VR Games Based on Motion Data Captured Under Game Conditions

Virtual Reality (VR) games are not as accessible as conventional video games because they heavily rely on the motion of the body as the main means of input. This causes large accessibility issues because it prevents some physically impaired players from using them. It also makes it more difficult to develop accessibility tools to address the issues. Given these challenges, it is of particular importance that an impaired player can determine whether they will be able to play a game before they buy it. We propose the first prototype of a metric that aims at visually presenting the important aspects of the body motion that a game requires. Instead of manual classification, the metric is based on data captured from able-bodied players that play the game as designed. In this work, we introduce the metric itself, demonstrate how it differentiates six popular VR games based on data we collected in a user study, and discuss limitations and routes for further development.
Spammer detection using multi-classifier information fusion based on evidential reasoning rule

Abstract Spammer detection is essentially a process of judging the authenticity of users, and thus can be regarded as a classification problem. In order to improve the classification performance, multi-classifier information fusion is usually used to realize the automatic detection of spammers by utilizing the information from multiple classifiers. However, the existing fusion strategies do not reasonably take the uncertainty from the results of different classifiers (views) into account, and the relative importance and reliability of each classifier are not strictly distinguished. Therefore, in order to detect spammers effectively, this paper develops a novel multi-classifier information fusion model based on the evidential reasoning (ER) rule. Firstly, according to the user's characterization strategy, the base classifiers are constructed through the profile-based, content-based and behavior-based. Then, the idea of multi-classifier fusion is combined with the ER rule, and the results of base classifiers are aggregated by considering their weights and reliabilities. Extensive experimental results on the real-world dataset verify the effectiveness of the proposed model.
A Simplex Method-Based Bacterial Colony Optimization Algorithm for Data Clustering Analysis

Data clustering is the task of separating data samples into a set of clusters. [Formula: see text]-means is a popular partitional clustering algorithm. However, it has a lot of weaknesses, including sensitivity to initialization and the ability to become stuck in local optima. Hence, nature-inspired optimization algorithms were applied to the clustering problem to overcome the limitations of the [Formula: see text]-means algorithm. However, due to the high-dimensionality of a search space, the nature-inspired optimization algorithm suffers from local optima and poor convergence rates. To address the mentioned issues, this paper presents a simplex method-based bacterial colony optimization (SMBCO) algorithm. The simplex method is a stochastic variant approach that improves population diversity while increasing the algorithm’s local searching ability. The potential and effectiveness of the proposed SMBCO clustering algorithm are assessed using a variety of benchmark machine learning datasets and the generated groups were evaluated using different performance measures. When compared to several well-known nature-inspired algorithms, the experimental results reveal that the SMBCO model produces superior clustering efficiency and a faster convergence rate.
Future-proofing students in higher education with unmanned aerial vehicles technology: A knowledge management case study

In this paper we report experiences in implementing a new course ‘Understanding Drone &amp; Robotics Technology – History, Usage, Ethics &amp; Legal Issues’ at the Singapore Management University framed as a strategic knowledge management (KM) initiative in an institution of higher learning aimed at capturing, sharing and creating new knowledge about disruptive technologies such as unmanned aerial vehicles. We posit the new course as a knowledge innovation initiative (similar to a KM-enabled business case in a corporate setting) in support of the university’s mission and vision so as to deliver new value to students and to stay ahead of the latest technological developments. In line with a ‘normal’ KM initiative, we examine how the new learning and teaching initiative was conceived, pushed forward and eventually launched, creating a new multi-disciplinary learning experience for students, instructors and other stakeholders. We explain the knowledge strategy of the course and use I. Nonaka’s SECI framework to shed light on selected aspects of the pedagogical approach towards achieving the desired learning outcomes. Overall, the paper intends to make a case for more collaborative knowledge leadership as a strategic enabler of multi-disciplinary knowledge innovation in a rapidly changing higher education landscape.
Research on Behavior Analysis of Real-Time Online Teaching for College Students Based on Head Gesture Recognition

The accessibility of online teaching makes it popular in various teaching scenarios. Most of these researches about online interaction mainly focuse on the communication network stability, facial expression/gesture recognition algorithm and statistical description analysis, and its expertise mainly comes from the fields of computer technology and algorithm engineering. The evaluation and analysis of the effect of online teaching is an important test for the adaptability of such tools in the field of education. However, from the perspective of teachers, there is still a lack of literature on data interpretation after the application of this technology. An experiment based on real online teaching was carried out in this paper. This study uses image recognition technology to process video and extract five kinds of head movement data from dozens of student samples, and then develop statistical description interpretation. Some novel and interesting conclusions indicate that diversified behaviors occurred in real-time online learning. This study obtained data of five high-frequency online learning behaviors, including blinking, yawning, nodding, shaking head and leaving. These behaviors are related to learning state and time. Teaching features, students’ personal characteristics and learning environment have a comprehensive impact on online learning behaviors. The result provides a basis for personalized learning and teaching scheme design in the future. It also helps to enrich online teaching evaluation methods and accelerate the construction of online education framework and rules.
A Hybrid Approach for Task Scheduling in the Cloud Environment

In today’s world, Cloud Computing has been considered as the best concept for the virtualization of various resources. There are various approaches that have been available for improvising the load balancing and also to improvise the job scheduling in the concept of cloud. Cloud Computing has offers the on-demand allocation of resources to users and this feature of cloud makes it a best among various technologies. Cloud computing provides great performance in less maintenance cost. So, Task scheduling has become a very important factor in enhancing the performance of resources dynamically and at low cost, which is the most crucial part of cloud computing. In other words, we can say that allocation of resources and task scheduling are the two major factors that are considered for the better performance and they must be organized precisely. The author has focused on enhancing the task scheduling process by creating a hybrid optimization algorithm and named as Cuckoo Harmony Search Algorithm (CHSA) to remove the task scheduling problem.
Robust Face-Swap Detection Based on 3D Facial Shape Information

Maliciously-manipulated images or videos - so-called deep fakes - especially face-swap images and videos have attracted more and more malicious attackers to discredit some key figures. Previous pixel-level artifacts based detection techniques always focus on some unclear patterns but ignore some available semantic clues. Therefore, these approaches show weak interpretability and robustness. In this paper, we propose a biometric information based method to fully exploit the appearance and shape feature for face-swap detection of key figures. The key aspect of our method is obtaining the inconsistency of 3D facial shape and facial appearance, and the inconsistency based clue offers natural interpretability for the proposed face-swap detection method. Experimental results show the superiority of our method in robustness on various laundering and cross-domain data, which validates the effectiveness of the proposed method.
Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis

Exploiting rich linguistic information in raw text is crucial for expressive text-to-speech (TTS). As large scale pre-trained text representation develops, bidirectional encoder representations from Transformers (BERT) has been proven to embody semantic information and employed to TTS recently. However, original or simply fine-tuned BERT embeddings still cannot provide sufficient semantic knowledge that expressive TTS models should take into account. In this paper, we propose a word-level semantic representation enhancing method based on dependency structure and pre-trained BERT embedding. The BERT embedding of each word is reprocessed considering its specific dependencies and related words in the sentence, to generate more effective semantic representation for TTS. To better utilize the dependency structure, relational gated graph network (RGGN) is introduced to make semantic information flow and aggregate through the dependency structure. The experimental results show that the proposed method can further improve the naturalness and expressiveness of synthesized speeches on both Mandarin and English datasets.
CutPaste: Self-Supervised Learning for Anomaly Detection and  Localization

We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects. We bring the improvement upon previous arts by 3.1 AUCs when learning representations from scratch. By transfer learning on pretrained representations on ImageNet, we achieve a new state-of-theart 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localizing defective areas without annotations during training.
Great~Truths~are ~Always ~Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models

Commonsense reasoning in natural language is a desired ability of artificial intelligent systems.For solving complex commonsense reasoning tasks, a typical solution is to enhance pre-trained language models (PTMs) with a knowledge-aware graph neural network (GNN) encoder that models a commonsense knowledge graph (CSKG).Despite the effectiveness, these approaches are built on heavy architectures, and can't clearly explain how external knowledge resources improve the reasoning capacity of PTMs.Considering this issue, we conduct a deep empirical analysis, and find that it is indeed relation features from CSKGs (but not node features) that mainly contribute to the performance improvement of PTMs.Based on this finding, we design a simple MLP-based knowledge encoder that utilizes statistical relation paths as features.Extensive experiments conducted on five benchmarks demonstrate the effectiveness of our approach, which also largely reduces the parameters for encoding CSKGs.
Research on Dyslexia Detection based on Eye Tracking

At present, the eye movement research of reading has accumulated rich research results and formed a more representative eye movement model, which mainly depends on the eye movement instrument to track the eye movement, but there is no popular software and hardware equipment for the detection of dyslexia in the market. This paper designs and implements a dyslexia detection model. The model detects the pupil movement of children through computer vision, tracks the eye movement trajectory, obtains the eye movement coordinates and interval time, establishes two indicators of reading speed and eye jump amplitude, analyzes the eye movement indicators using TOPSIS entropy weight method, and scientifically and reasonably distributes the weight comprehensive score. So as to identify readers with dyslexia and give scientific reading training suggestions and medical advice. It realizes the early screening of dyslexia patients. After practical test, our model achieves an accuracy of 89.7%. Therefore, our results lay a foundation for automatic detection of dyslexia in natural reading.
RIT Boston at SemEval-2022 Task 5: Multimedia Misogyny Detection By Using Coherent Visual and Language Features from CLIP Model and Data-centric AI Principle

Detecting MEME images to be misogynous or not is an application useful on curbing online hateful information against women.In the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) challenge, we designed a system using two simple but effective principles.First, we leverage on recently emerging Transformer models pretrained (mostly in a self-supervised learning way) on massive data sets to obtain very effective visual (V) and language (L) features.In particular, we used the CLIP (Radford et al., 2021) model provided by OpenAI to obtain coherent V and L features and then simply used a logistic regression model to make binary predictions.Second, we emphasized more on data rather than tweaking models by following the data-centric AI principle.These principles were proven to be useful and our final macro-F1 is 0.778 for the MAMI task A and ranked the third place among participant teams.
Unsupervised morphological segmentation in a language with reduplication

We present an extension of the Morfessor Baseline model of unsupervised morphological segmentation (Creutz and Lagus, 2007) that incorporates abstract templates for reduplication, a typologically common but computationally underaddressed process.Through a detailed investigation that applies the model to Māori, the Indigenous language of Aotearoa New Zealand, we show that incorporating templates improves Morfessor's ability to identify instances of reduplication, and does so most when there are multiple minimally-overlapping templates.We present an error analysis that reveals important factors to consider when applying the extended model and suggests useful future directions.
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables

Different from previous fact extraction and verification tasks that only consider evidence of a single format, FEVEROUS brings further challenges by extending the evidence format to both plain text and tables.Existing works convert all candidate evidence into either sentences or tables, thus often failing to fully capture the rich context in their original format from the converted evidence, let alone the context information lost during conversion.In this paper, we propose a Dual Channel Unified Format fact verification model (DCUF), which unifies various evidence into parallel streams, i.e., natural language sentences and a global evidence table, simultaneously.With carefullydesigned evidence conversion and organization methods, DCUF makes the most of pre-trained table/language models to encourage each evidence piece to perform early and thorough interactions with other pieces in its original format.Experiments show that our model can make better use of existing pre-trained models to absorb evidence of two formats, thus outperforming previous works by a large margin.Our code and models are publicly available 1 .