Reviewerly/exHarmony huggingface dataset:
What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.
NTIRE 2023 Challenge on Night Photography Rendering

This paper presents a review of the NTIRE 2023 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year’s competition, participants were not provided with a large training dataset for the target sensor. Instead, this time they were given images of a color checker illuminated by a known light source. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. The highest ranking solutions were further ranked by Richard Collins, a renowned photographer. The top ranking participants’ solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://nightimaging.org/
NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance.
Differential Evolution with exponential crossover can be also competitive on numerical optimization

Differential Evolution (DE) is a powerful population-based evolutionary algorithm for global optimization, and it is widely used in many scientific and engineering applications. There are two commonly used crossover schemes in the literature: one is binomial crossover and the other is exponential crossover. The majority of DE researchers believe that DE variants employing binomial crossover usually obtain superior performance than the ones employing exponential crossover on numerical optimization and DE variants with exponential crossover are good at tackling optimization problems with linkages among neighboring variables. On the contrary, here in this paper, a new perspective is proposed that DE variant with exponential crossover can obtain competitive performance with the ones employing binomial crossover on numerical optimization regardless of whether there are linkages among the variables or not after discovering the proper crossover rate Cr and its corresponding parameter control, and the main contributions of the paper can be summarized as follows: (1) The first powerful DE variant with exponential crossover, namely the DE-EXP algorithm, which is superior to the recent winner DE variants, e.g. LSHADE, iLSHADE and jSO, in Congress on Evolutionary Computation (CEC) competitions, is developed for numerical optimization; (2) A novel parameter control of crossover rate Cr is developed for exponential crossover and the value of Cr can be automatically generated not only in the initialization stage but also during the evolution. (3) A black-box model illustrating the fitness-value-dependency weakness of the recent winner DE variants in CEC competitions is given and a novel fitness-value-independent adaptation scheme for scale factor F is proposed in the DE-EXP algorithm to overcome the fitness-value-dependency weakness. A larger test suite containing 88 benchmarks is used for the validation of our DE-EXP algorithm, and experiment results show its superiority in comparison with several state-of-the-art DE variants.
Beyond ChatGPT: A conceptual framework and systematic review of speech-recognition chatbots for language learning

The diversification of chatbot technology, such as the emergence of large language models and their incorporation into various technologies, necessitates a conceptual framework for a comprehensive understanding of different chatbot types and their possibilities for educational use. However, despite the fact that chatbots with different characteristics can provide learners with different interaction experiences, previous research has drawn on a loose conceptualization of chatbots, ignoring the common or unique design features of different chatbots and the educational affordances that are provided accordingly. In response to this concern, this review aims to further our understanding of different types of speech-recognition chatbots for language learning and the affordances provided by the chatbots. Based on an analysis of 37 empirical studies on uses of chatbots ranging from those with predefined dialogue systems to those utilizing artificial intelligence technology, this review proposes a conceptual framework that comprises three key components of a chatbot system: goal-orientation, embodiment, and multimodality. Using this framework as an analytical tool, eight chatbot types are identified and defined. Additionally, a total of 12 affordances are derived from the presence and absence of each component of the framework. Analysis of the studies through the framework also offers specific insights into how future chatbot research and development should be pursued in terms of goal-orientation, embodiment, and multimodality. Finally, we discuss the potential of the framework as a relevant model for understanding chatbots in adjacent disciplines and types other than speech-recognition chatbots, including ChatGPT and other large language models.
Learning Discriminative Representations for Skeleton Based Action Recognition

Human action recognition aims at classifying the category of human action from a segment of a video. Recently, people have dived into designing GCN-based models to extract features from skeletons for performing this task, because skeleton representations are much more efficient and robust than other modalities such as RGB frames. However, when employing the skeleton data, some important clues like related items are also discarded. It results in some ambiguous actions that are hard to be distinguished and tend to be misclassified. To alleviate this problem, we propose an auxiliary feature refinement head (FR Head), which consists of spatial-temporal decoupling and contrastive feature refinement, to obtain discriminative representations of skeletons. Ambiguous samples are dynamically discovered and calibrated in the feature space. Furthermore, FR Head could be imposed on different stages of GCNs to build a multi-level refinement for stronger supervision. Extensive experiments are conducted on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets. Our proposed models obtain competitive results from state-of-the-art methods and can help to discriminate those ambiguous samples. Codes are available at https://github.com/zhysora/FR-Head.
Inversion-based Style Transfer with Diffusion Models

The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes, including semantic elements and object shapes. Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. Pre-trained text-to-image synthesis diffusion probabilistic models have achieved remarkable quality but often require extensive textual descriptions to accurately portray the attributes of a particular painting. The uniqueness of an artwork lies in the fact that it cannot be adequately explained with normal language. Our key idea is to learn the artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions. Specifically, we perceive style as a learnable textual description of a painting. We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image, thus capturing and transferring the artistic style of a painting. We demonstrate the quality and efficiency of our method on numerous paintings of various artists and styles. Codes are available at https://github.com/zyxElsa/InST.
Spherical Transformer for LiDAR-Based 3D Recognition

LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse distant points. In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones. We design radial window self-attention that partitions the space into multiple non-overlapping narrow and long windows. It overcomes the disconnection issue and enlarges the receptive field smoothly and dramatically, which significantly boosts the performance of sparse distant points. Moreover, to fit the narrow and long windows, we propose exponential splitting to yield fine-grained position encoding and dynamic feature selection to increase model representation ability. Notably, our method ranks 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> on both nuScenes and SemanticKITTI semantic segmentation benchmarks with 81.9% and 74.8% mIoU, respectively. Also, we achieve the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> place on nuScenes object detection benchmark with 72.8% NDS and 68.5% mAP. Code is available at https://github.com/dvlab-research/SphereFormer.git.
What Can Human Sketches Do for Object Detection?

Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited to that of image retrieval. In this paper, for the first time, we cultivate the expressiveness of sketches but for the fundamental vision task of object detection. The end result is a sketch-enabled object detection framework that detects based on what you sketch – that “zebra” (e.g., one that is eating the grass) in a herd of zebras (instance-aware detection), and only the part (e.g., “head” of a “zebra”) that you desire (part-aware detection). We further dictate that our model works without (i) knowing which category to expect at testing (zero-shot) and (ii) not requiring additional bounding boxes (as per fully supervised) and class labels (as per weakly supervised). Instead of devising a model from the ground up, we show an intuitive synergy between foundation models (e.g., CLIP) and existing sketch models build for sketch-based image retrieval (SBIR), which can already elegantly solve the task – CLIP to provide model generalisation, and SBIR to bridge the (sketch→photo) gap. In particular, we first perform independent prompting on both sketch and photo branches of an SBIR model to build highly generalisable sketch and photo encoders on the back of the generalisation ability of CLIP. We then devise a training paradigm to adapt the learned encoders for object detection, such that the region embeddings of detected boxes are aligned with the sketch and photo embeddings from SBIR. Evaluating our framework on standard object detection datasets like PASCAL-VOC and MS-COCO outperforms both supervised (SOD) and weakly-supervised object detectors (WSOD) on zero-shot setups. Project Page: https://pinakinathc.github.io/sketch-detect
3D Human Pose Estimation via Intuitive Physics

Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the scene. A physics engine can be used to enforce physical plausibility, but these are not differentiable, rely on unrealistic proxy bodies, and are difficult to integrate into existing optimization and learning frameworks. In contrast, we exploit novel intuitive-physics (IP) terms that can be inferred from a 3D SMPL body interacting with the scene. Inspired by biomechanics, we infer the pressure heatmap on the body, the Center of Pressure (CoP) from the heatmap, and the SMPL body's Center of Mass (CoM). With these, we develop IPMAN, to estimate a 3D body from a color image in a “stable” configuration by encouraging plausible floor contact and overlapping CoP and CoM. Our IP terms are intuitive, easy to implement, fast to compute, differentiable, and can be integrated into existing optimization and regression methods. We evaluate IPMAN on standard datasets and MoYo, a new dataset with synchronized multi-view images, ground-truth 3D bodies with complex poses, body-floor contact, CoM and pressure. IPMAN produces more plausible results than the state of the art, improving accuracy for static poses, while not hurting dynamic ones. Code and data are available for research at https://ipman.is.tue.mpg.de.
The opportunities and challenges of ChatGPT in education

ABSTRACTThe launch of OpenAI ChatGPT's language-generation model has raised alarms within many sectors, especially the academic sector. Several academicians have urged universities to develop new forms of assessment after the launch of ChatGPT, which solves academic questions in less than a few minutes. Academic cheating is not a new phenomenon, and the use of AI-generated text to cheat on assignments is a new type of cheating that poses unique challenges. This study used the Latent Dirichlet Allocation (LDA) method for topic modeling and the Valence Aware Dictionary for Sentiment Reasoning (VADER) method for sentiment analysis. After data preprocessing, 3870 tweets were still available out of the originally 10,000 tweets that were extracted for the study. The VADER sentiment analysis results revealed that 2013 tweets were categorized as “positive,” with the remaining 804 and 1053 tweets categorized as “negative” and “neutral.” The analysis's findings indicate that the majority of people have favorable things to say about ChatGPT. As a result, educational institutions can mitigate the disruptive effects of this technology and promote academic integrity by developing clear policies and guidelines and designing assessments that include limited AI-generated text.KEYWORDS: ChatGPTAIuniversitycheatingplagiarism Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsIbrahim AdesholaIbrahim Adeshola is an experienced multidisciplinary researcher who has had work published in prestigious journals. He has an in-depth knowledge of a variety of research fields, including management information systems, educational technology, marketing, cloud computing, organizational management, business and environmental sustainability, technological innovation, renewable energy, organizational culture, and business development.Adeola Praise AdepojuAdeola Praise Adepoju is currently a Ph.D. scholar in business administration at Cyprus International University. Her research interests include consumer behavior, business strategy, organizational behavior, and student wellbeing.
Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT

Educational feedback has been widely acknowledged as an effective approach to improving student learning. However, scaling effective practices can be laborious and costly, which motivated researchers to work on automated feedback systems (AFS). Inspired by the recent advancements in the pre-trained language models (e.g., ChatGPT), we posit that such models might advance the existing knowledge of textual feedback generation in AFS because of their capability to offer natural-sounding and detailed responses. Therefore, we aimed to investigate the feasibility of using ChatGPT to provide students with feedback to help them learn better. Our results show that i) ChatGPT is capable of generating more detailed feedback that fluently and coherently summarizes students' performance than human instructors; ii) ChatGPT achieved high agreement with the instructor when assessing the topic of students' assignments; and iii) ChatGPT could provide feedback on the process of students completing the task, which might benefit students developing learning skills.
Differential bond energy algorithm for optimal vertical fragmentation of distributed databases

Distributed database systems are gaining importance due to the production in massive amount of data. The efficacy of such systems is highly dependent upon the design of the system. To increase the effectiveness and efficiency of distributed databases, two processes are mainly employed i.e. fragmentation and allocation. Fragmentations can be vertical or horizontal. This work focuses on vertical fragmentation design methods. In this paper, a novel differential bond energy (DBE) algorithm is proposed with objective to determine optimal partition point. The performance of proposed algorithm is compared with classical bond energy algorithm (BEA) on basis of global affinity measure (GAM) value. Results are depicted in form of line graphs. The mean difference in GAM values for both algorithms are also illustrated. The experimental results portrays that DBE is suitable for vertical fragmentation of high dimensional problems as it attain high GAM value as compared to BEA on various datasets.
Joint Future Semantic and Instance Segmentation Prediction

The ability to predict what will happen next from observing the past is a key component of intelligence. Methods that forecast future frames were recently introduced towards better machine intelligence. However, predicting directly in the image color space seems an overly complex task, and predicting higher level representations using semantic or instance segmentation approaches were shown to be more accurate. In this work, we introduce a novel prediction approach that encodes instance and semantic segmentation information in a single representation based on distance maps. Our graph-based modeling of the instance segmentation prediction problem allows us to obtain temporal tracks of the objects as an optimal solution to a watershed algorithm. Our experimental results on the Cityscapes dataset present state-of-the-art semantic segmentation predictions, and instance segmentation results outperforming a strong baseline based on optical flow.
Enhancing students’ conceptual understanding of electricity using learning media-based augmented reality

Some contents of physics provide abstract concepts (e.g. electricity) so they are demanding to be visualized and students are challenging to understand. However, the recent development in technology namely augmented reality can be used as a learning media to depict the some abstract concepts in physics. This study aimed to examine the implementation of learning media-based augmented reality that was developed in electricity topics to enhance students' conceptual understanding. The research method used in this study was pre-experiment with one group pretest-posttest design. The participant of this study was 30 students in the first year of one public vocational high school in Garut. Students' conceptual understanding was snapped using an instrument test (26 item questions) developed in multiple choices. In addition, average gain and normalized gain were used to analyze the enhancing of students' conceptual understanding. The result of this study portrayed that improvement of conceptual understanding was in medium category. This case was illustrated by average gain (29.0) and normalized gain (0.56). According to result findings of this study can be inferred that learning media-based augmented reality developed in electricity topics could enhance students' conceptual understanding.
Data Preprocessing: A preliminary step for web data mining

In recent years immense growth of data i.e. big data is observed resulting in a brighter and more optimized future.Big Data demands large computational infrastructure with high-performance processing capabilities.Preparing big data for mining and analysis is a challenging task and requires data to be preprocessed to improve the quality of raw data.The data instance representation and quality are foremost.Data preprocessing is preliminary data mining practice in which raw data is transformed into a format suitable for another processing procedure.Data preprocessing improves the data quality by cleaning, normalizing, transforming and extracting relevant feature from raw data.Data preprocessing significantly improve the performance of machine learning algorithms which in turn leads to accurate data mining.Knowledge discovery from noisy, irrelevant and redundant data is a difficult task therefore precise identification of extreme values and outlier, filling up missing values poses challenges.This paper discusses various big data pre-processing techniques in order to prepare it for mining and analysis tasks.
Effects of iLearning Media on Student Learning Motivation

As we know today, Indonesia has entered the era of revolution 4.0 which in that era had a major influence on the changes that took place in all fields. Including in the field of education, in particular, the changes that occur in the world of education today are so rapid with the abandonment of learning methods that still use conventional methods. Doing tasks with books, face-to-face communication, collecting assignments in hard copy, which will certainly cause a lot of losses in a certain period of time, such as many assignments that are long buried and difficult to find when needed. This method is certainly considered a boring and time-consuming method, where students cannot explore in the learning process which will take a long time to do a manual search for the number of files that have been collected. At present, the application of learning methods with concepts organizes many reports on the work of assignments on a website content management system. This learning method is made to maximize the way to organize student assignment reports for time management efficiency, therefore this learning method can be applied as the management of education in higher education. It is expected that the existence of this method can improve time management in learning so that it motivates to explore abilities in the existing learning process