Reviewerly/exHarmony huggingface dataset:
Visualization of Flake Knapping Sequence with Analyzing Assembled Chipped Stone Tools

For studying stone tools, repeating assembly and separation of stone tools is an inevitable process. The instructions of this process, however, are ambiguous when traditional 2D illustrations are used. The 3D exploded view is an effective way for instructing the assembly, and it has been widely used in many fields, while it has seldom been used in archaeology. We apply this powerful presentation technique to stone tool models, and study methods of generating exploded graphs and assembly sequences with point clouds of stone tools by computer graphics algorithms. Moreover, lithic knapping methods and relic excavation reports are analyzed to evaluate the difference of flake knapping sequences between the contents of report and automatic generation by using an algorithm. In addition to presentation of principles for restoring stone tools, we design the functions and algorithms of our system based on archaeological rules. The experiment results of stone tool assembly show effectiveness and practicability of the proposed method. The user evaluation indicates that 3D visualization technology can assist in efficient research of flake knapping sequence instruction for chipped stone tools.
A Robot Motion Learning Method Using Broad Learning System Verified by Small-Scale Fish-Like Robot

The widespread application of learning-based methods in robotics has allowed significant simplifications to controller design and parameter adjustment. In this article, robot motion is controlled with learning-based methods. A control policy using a broad learning system (BLS) for robot point-reaching motion is developed. A sample application based on a magnetic small-scale robotic system is designed without detailed mathematical modeling of the dynamic systems. The parameter constraints of the nodes in the BLS-based controller are derived based on Lyapunov theory. The design and control training processes for a small-scale magnetic fish motion are presented. Finally, the effectiveness of the proposed method is demonstrated by convergence of the artificial magnetic fish motion to the targeted area with the BLS trajectory, successfully avoiding obstacles.
Quantum computers: what are they good for?

For now, absolutely nothing. But researchers and firms are optimistic about the applications. For now, absolutely nothing. But researchers and firms are optimistic about the applications.
Large language models and the emergence phenomena

<h2>Abstract</h2> This perspective explores the potential of emergence phenomena in large language models (LLMs) to transform data management and analysis in radiology. We provide a concise explanation of LLMs, define the concept of emergence in machine learning, offer examples of potential applications within the radiology field, and discuss risks and limitations. Our goal is to encourage radiologists to recognize and prepare for the impact this technology may have on radiology and medicine in the near future.
Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning

Online platforms face pressure to keep their communities civil and respectful. Thus, banning problematic online communities from mainstream platforms is often met with enthusiastic public reactions. However, this policy can lead users to migrate to alternative fringe platforms with lower moderation standards and may reinforce antisocial behaviors. As users of these communities often remain co-active across mainstream and fringe platforms, antisocial behaviors may spill over onto the mainstream platform. We study this possible spillover by analyzing 70,000 users from three banned communities that migrated to fringe platforms: r/The_Donald, r/GenderCritical, and r/Incels. Using a difference-in-differences design, we contrast co-active users with matched counterparts to estimate the causal effect of fringe platform participation on users' antisocial behavior on Reddit. Our results show that participating in the fringe communities increases users' toxicity on Reddit (as measured by Perspective API) and involvement with subreddits similar to the banned community---which often also breach platform norms. The effect intensifies with time and exposure to the fringe platform. In short, we find evidence for a spillover of antisocial behavior from fringe platforms onto Reddit via co-participation.
Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.
Students’ Perceptions toward the Use of Grammarly in Checking Grammar in Assignments

This study aimed to find out about the students’ perceptions of the use of Grammarly in checking grammar in written assignments and the students’ obstacles when using Grammarly. This quantitative study used questionnaire to collect the data. The participants of the study were ten students from semester sixth of the English Department at a private university in Banda Aceh, Indonesia. The results of the study showed that many students gave positive responses toward using Grammarly to learn and check their grammar in assignments. In addition, the study found that the students’ obstacles when using Grammarly included some issues such as problems in the internet connection and lack of understanding of Grammarly suggestions. The study suggests that Grammarly to be applied for grammar checking among EFL students.
Quantum mean estimation for lattice field theory

We demonstrate the quantum mean estimation algorithm on Euclidean lattice field theories. This shows a quadratic advantage over Monte Carlo methods which persists even in presence of a sign problem, and is insensitive to critical slowing down. The algorithm is used to compute $\pi$ with and without a sign problem, a toy U(1) gauge theory model, and the Ising model. The effect of $R_{Z}$-gate synthesis errors on a future fault-tolerant quantum computer is investigated.
Efficient hybrid digital image watermarking

Abstract Nowadays, multimedia security is a major issue. Images, video, audio, and text files lose credibility in most times, because they face several attacks related to illegal distribution, duplication, and manipulation of the information conveyed by them. Digital watermarking is an important tool for protecting digital content. This paper introduces an efficient hybrid digital image watermarking scheme. It consists of two stages of Singular Value Decomposition (SVD). The first one is the embedding stage, which is performed by using SVD followed by block-based SVD (B-SVD). The second stage is the extraction stage, which depends on the SVD on the whole image and then B-SVD to get the original watermarks. In this scheme, the watermarking requirements are satisfied, while capacity is increased. Furthermore, watermark detection, robustness, and security are improved. The performance of the proposed scheme is evaluated by the correlation coefficient C r between the original and extracted watermarks, and the Peak Signal-to-Noise Ratio ( PSNR ) between the original and watermarked images. The experimental results demonstrate that the proposed scheme has a good performance, where C r reaches 0.9975 and PSNR reaches 45.8605. It is more secure than the previous schemes, when subjected to attacks. In addition, the proposed scheme is compared to the most recent schemes, revealing its superiority.
Feature-Sniffer: Enabling IoT Forensics in OpenWrt based Wi-Fi Access Points

The Internet of Things is in constant growth, with millions of devices used every day in our homes and workplaces to ease our lives. Such a strict coexistence between humans and smart devices makes the latter digital witnesses of our everyday lives through their sensor systems. This opens up to a new area of digital investigation named IoT Forensics, where digital traces produced by smart devices (network traffic, in primis) are leveraged as evidences for forensic purposes. It is therefore important to create tools able to capture, store and possibly analyse easily such digital traces to ease the job of forensic investigators. This work presents one of such tools, named Feature-Sniffer, which is thought explicitly for Wi-Fi enabled smart devices used in Smart Building/Smart Home scenarios. Feature-Sniffer is an add-on for OpenWrt-based access points and allows to easily perform online traffic feature extraction, avoiding to store large PCAP files. We present Feature-Sniffer with an accurate description of the implementation details, and we show its possible uses with practical examples for device identification and activity classification from encrypted traffic produced by IoT cameras. We release Feature-Sniffer publicly for reproducible research.
Potentialities of Applied Translation for Language Learning in the Era of Artificial Intelligence

Artificial Intelligence (AI) and AI-powered machine translation bring opportunities and challenges for L2 educators and students. Most recently, the emergence of AI-based chatbots, such as ChatGPT, has led to calls for a revision of traditional teaching methods to prioritize reflective reasoning and critical thinking. This article studies the potentialities of Applied Translation (AT) to promote essential critical thinking skills needed to engage effectively with AI-based tools in the L2 classroom. We present the IMI+ framework (Integration, Multimodality, and Interaction) for integrating AT in language education, which helps support the development of digital literacy and critical thinking in L2 classrooms. Furthermore, given the challenges related to privacy and ethics inherent in these new technologies, we propose applying a Critical Ecological Approach (CEA) to AT to help learners navigate those challenges by identifying power imbalances and societal inequities. Finally, we explain how the seven articles in this special issue showcase the potential applications of AT in Spanish language education.
Online Random Feature Forests for Learning in Varying Feature Spaces

In this paper, we propose a new online learning algorithm tailored for data streams described by varying feature spaces (VFS), wherein new features constantly emerge and old features may stop to be observed over various time spans. Our proposed algorithm, named Online Random Feature Forests for Feature space Variabilities (ORF3V), provides a strategy to respect such feature dynamics by generating, updating, pruning, as well as online re-weighing an ensemble of what we call feature forests, which are generated and updated based on a compressed and storage efficient representation for each observed feature. We benchmark our algorithm on 12 datasets, including one novel real-world dataset of government COVID-19 responses collected through a crowd-sensing program in Spain. The empirical results substantiate the viability and effectiveness of our ORF3V algorithm and its superior accuracy performance over the state-of-the-art rival models.
Let Graph Be the Go Board: Gradient-Free Node Injection Attack for Graph Neural Networks via Reinforcement Learning

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security standards, such as product recommendation and user behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and further downgrading its performance become extremely incentive for adversaries. Previous attackers mainly focus on structural perturbations or node injections to the existing graphs, guided by gradients from the surrogate models. Although they deliver promising results, several limitations still exist. For the structural perturbation attack, to launch a proposed attack, adversaries need to manipulate the existing graph topology, which is impractical in most circumstances. Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model. To bridge these gaps, in this paper, we study the problem of black-box node injection attack, without training a potentially misleading surrogate model. Specifically, we model the node injection attack as a Markov decision process and propose Gradient-free Graph Advantage Actor Critic, namely G2A2C, a reinforcement learning framework in the fashion of advantage actor critic. By directly querying the victim model, G2A2C learns to inject highly malicious nodes with extremely limited attacking budgets, while maintaining a similar node feature distribution. Through our comprehensive experiments over eight acknowledged benchmark datasets with different characteristics, we demonstrate the superior performance of our proposed G2A2C over the existing state-of-the-art attackers. Source code is publicly available at: https://github.com/jumxglhf/G2A2C.
THE RISE OF AUGMENTED REALITY BROWSERS: TRENDS, CHALLENGES AND OPPORTUNITIES (Review Paper)

An augmented reality browser is an alternative of a web browser in augmented spaces, which aimed at navigating and publishing overlaid interactive contents. This enabled end users to explore digital information on existing points of interests (POIs) and the application developers to publish overlaid information in the form of layers and channels, etc. These browsing interfaces were vital for publishing and locating contents to unfold the potential of augmented spaces. This paper contributed an analytical review on the recent emergence of mobile augmented reality browsers from the perspective of technological information publishing and usage scenarios. This comparative analysis of the augmented reality browsers would help researchers and industrial scientists to identify the most relevant augmented reality browser for specific applications like object tracking, content overlaying, and ensuring realism up to major extent. This study would also provide guidelines for next generation enriched, seamless and adaptive augmented reality browsers.
The Rogue Scholar weekly newsletter launches on Wednesday

The Rogue Scholar science blog archive is growing nicely, reaching more than 1,500 blog posts this week, and 5-10 new posts every week. You can subscribe to all the blogs you are interested in via an RSS Reader and use various strategies to find and read interesting content, but some people prefer to receive regular email updates instead.
MVP: Multi-task Supervised Pre-training for Natural Language Generation

Pre-trained language models (PLMs) have achieved remarkable success in natural language generation (NLG) tasks.Up to now, most NLG-oriented PLMs are pre-trained in an unsupervised manner using the large-scale general corpus.In the meanwhile, an increasing number of models pre-trained with labeled data (i.e., "supervised pre-training") showcase superior performance compared to unsupervised pre-trained models.Motivated by the success of supervised pre-training, we propose Multitask superVised Pre-training (MVP) for natural language generation.We collect a large-scale natural language generation corpus, MVPCorpus, from 77 datasets over 11 diverse NLG tasks.Then we unify these examples into a general text-to-text format to pre-train the text generation model MVP in a supervised manner.For each task, we further pre-train specific soft prompts to stimulate the model's capacity to perform a specific task.Our MVP model can be seen as a practice that utilizes recent instruction tuning on relatively small PLMs.Extensive experiments have demonstrated the effectiveness and generality of our MVP model in a number of NLG tasks, which achieves state-of-the-art performance on 13 out of 17 datasets, outperforming BART by 9.3% and Flan-T5 by 5.8%.
TextBox 2.0: A Text Generation Library with Pre-trained Language Models

To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs).To be comprehensive, our library covers 13 common text generation tasks and their corresponding 83 datasets and further incorporates 45 PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs.We also implement 4 efficient training strategies and provide 4 generation objectives for pre-training new PLMs from scratch.To be unified, we design the interfaces to support the entire research pipeline (from data loading to training and evaluation), ensuring that each step can be fulfilled in a unified way.Despite the rich functionality, it is easy to use our library, either through the friendly Python API or command line.To validate the effectiveness of our library, we conduct extensive experiments and exemplify four types of research scenarios.The project is released at the link: https://github.com/RUCAIBox/TextBox#2.0.
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension

Machine Reading Comprehension (MRC) has become one of the essential tasks in Natural Language Understanding (NLU) as it is often included in several NLU benchmarks (Liang et al., 2020;Wilie et al., 2020).However, most MRC datasets only have answerable question type, overlooking the importance of unanswerable questions.MRC models trained only on answerable questions will select the span that is most likely to be the answer, even when the answer does not actually exist in the given passage (Rajpurkar et al., 2018).This problem especially remains in medium-to low-resource languages like Indonesian.Existing Indonesian MRC datasets (Purwarianti et al., 2007;Clark et al., 2020) are still inadequate because of the small size and limited question types, i.e., they only cover answerable questions.To fill this gap, we build a new Indonesian MRC dataset called I(n)don'tKnow-MRC (IDK-MRC) by combining the automatic and manual unanswerable question generation to minimize the cost of manual dataset construction while maintaining the dataset quality.Combined with the existing answerable questions, IDK-MRC consists of more than 10K questions in total.Our analysis shows that our dataset significantly improves the performance of Indonesian MRC models, showing a large improvement for unanswerable questions 1 .
The added value of text from Dutch general practitioner notes in predictive modeling

Abstract Objective This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting. Materials and methods We trained and validated prediction models for 4 common clinical prediction problems using various sparse text representations, common prediction algorithms, and observational GP electronic health record (EHR) data. We trained and validated 84 models internally and externally on data from different EHR systems. Results On average, over all the different text representations and prediction algorithms, models only using text data performed better or similar to models using structured data alone in 2 prediction tasks. Additionally, in these 2 tasks, the combination of structured and text data outperformed models using structured or text data alone. No large performance differences were found between the different text representations and prediction algorithms. Discussion Our findings indicate that the use of unstructured data alone can result in well-performing prediction models for some clinical prediction problems. Furthermore, the performance improvement achieved by combining structured and text data highlights the added value. Additionally, we demonstrate the significance of clinical natural language processing research in languages other than English and the possibility of validating text-based prediction models across various EHR systems. Conclusion Our study highlights the potential benefits of incorporating unstructured data in clinical prediction models in a GP setting. Although the added value of unstructured data may vary depending on the specific prediction task, our findings suggest that it has the potential to enhance patient care.
Machine learning for administrative health records: A systematic review of techniques and applications

Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.