Reviewerly/exHarmony huggingface dataset:
Autonomous vehicles and the issue of liability for damage caused by the movement of such a vehicle

The increased progress in technology and the use of artificial intelligence has resulted in the development of many sectors of the economy.These include the road transport sector, where the implementation of modern technologies has contributed to the increase in the automation of vehicles.Currently, the market already offers automated vehicles equipped with a number of driver assistance systems, which improves road safety and reduces the number of collisions and accidents.Highly automated and fully automated vehicles are expected to be in service by the end of this decade.And although fully automated vehicles are not yet widely available, there is already a need to discuss the development of a model of civil liability for damage caused by an automated or fully automated vehicle.This article presents an overview of current national, EU and international regulations regarding autonomous vehicles, and attempts to answer the question of which model of liability for damage caused by the movement of an autonomous vehicle will guarantee the victims of traffic incidents the greatest EWA JĘDRZEJEWSKA WyżSza Szkoła GoSpodarki euroreGionalneJ iM.alcide de GaSperi W JózefoWie 628 legal protection.Beyond the scope of this article are ethical problems related to transferring responsibility for the life and health of the driver, passengers, and other traffic participants to artificial intelligence.
A Multi-Strategy Computer-Assisted EFL Writing Learning System With Deep Learning Incorporated and Its Effects on Learning: A Writing Feedback Perspective

Language learning has increasingly benefited from Computer-Assisted Language Learning (CALL) technologies, especially with Artificial Intelligence involved in recent years. CALL in writing learning acknowledged as the core of language learning is being realized by technologies like Automated Writing Evaluation (AWE), and Automated Essay Scoring (AES), which have developed considerably in both computer and language education fields. AWE has effectively enhanced EFL students’ writing performance to some extent, but such technology can only provide an evaluation in the form of scores, the majority of which are based on holistic scoring, resulting in the inability to provide comprehensive and detailed content-based feedback. In order to provide not only the writing multiple trait-specific evaluation scores, but also detailed writing feedback, we proposed a computer-assisted EFL writing learning system incorporating the neural network models and a couple of semantic-based NLP techniques, MsCAEWL, which fully meets the requirements of writing feedback theory, i.e., multiple, continuous, timely, clear, and multi-aspect guidance interactive feedback. The results of comparison experiments with the AWE baseline models and human raters demonstrated the superiority and the high correlation contained by the proposed system. The independent-sample t-test and paired-sample t-test results of the experiments on MsCAEWL effect validation suggested the significant impact of our proposed system in enhancing students’ EFL writing proficiency.
Hate speech and abusive language detection in Indonesian social media: Progress and challenges

Nowadays Hate Speech and Abusive Language (HSAL) have spread extensively over social media. The easy use of social media allows people to abuse the media to spread HSAL. Hate speech and abusive language in social media must be detected because they can trigger conflict among citizens. Not only in social media, but HSAL also often trigger conflict in real life. In recent years, many scholars have researched HSAL detection in various languages and media. However, there are still many tasks on HSAL detection that need to be done to develop a better HSAL detection system. This paper discusses a summary of Indonesian HSAL detection research, conducted by utilizing the Kitchenham systematic literature review method. Based on our summary, we found that most Indonesian HSAL research still uses the classic machine-learning approach with classic text representation features that experimented on the Twitter text dataset. We also found several challenges and tasks that need to be addressed to build a better HSAL detection system in Indonesian social media that can detect the hate speech target, category, and levels; and the hate speech buzzer, thread starter, and fake account spreader.
Rotational Convolution: Rethinking Convolution for Downside Fisheye Images

It has long been recognized that the standard convolution is not rotation equivariant and thus not appropriate for downside fisheye images which are rotationally symmetric. This paper introduces Rotational Convolution, a novel convolution that rotates the convolution kernel by characteristics of downside fisheye images. With the four rotation states of the convolution kernel, Rotational Convolution can be implemented on discrete signals. Rotational Convolution improves the performance of different networks in semantic segmentation and object detection markedly, harming the inference speed slightly. Finally, we demonstrate our methods’ numerical accuracy, computational efficiency, and effectiveness on the public segmentation dataset THEODORE and our self-built detection dataset SEU-fisheye. Our code is available at: https://github.com/wx19941204/Rotational-Convolution-for-downside-fisheye-images.
Document Forgery Detection in the Context of Double JPEG Compression

In this paper, we propose a strategy to train a CNN to detect document manipulations in JPEG documents under data scarcity scenario. As it comes to scanned PDF documents, it is common that the document consists of a JPEG image encapsulated into a PDF. Indeed, if the document before tampering was a JPEG image, its manipulation will lead to double compression artefacts within the resulting tampered JPEG image. In contrast to related methods that are based on handcrafted histograms of DCT coefficients, we propose a double compression detection method using a one-hot encoding of the DCT coefficients of JPEG images. We can use accordingly a CNN model to compute co-occurrence matrices and avoid handcrafted features such as histograms. Using simulated frauds on Perlin noise, we train our network and then test it on textual images against a state-of-the-art CNN algorithm trained on natural images. Our approach has shown an encouraging generalization on both the database used in the paper and on a stream of synthetic frauds on real documents used in the company Yooz.
Promonads and String Diagrams for Effectful Categories

Premonoidal and Freyd categories are both generalized by non-cartesian Freyd categories: effectful categories.We construct string diagrams for effectful categories in terms of the string diagrams for a monoidal category with a freely added object.We show that effectful categories are pseudomonoids in a monoidal bicategory of promonads with a suitable tensor product.
Reading Strategies, Motivation, and Self-efficacy of Chinese EFL College Students

English teaching emphasizes refining students’ reading skills, particularly evident in China’s education system where the value of reading is consistently highlighted throughout various stages and exams. However, the existing pedagogical methods aimed at improving reading aptitude have not yielded the expected results. Graduates often fail to meet the professional reading standards required by employers, indicating a significant research gap that needs attention. This study aimed to address this gap by investigating how reading strategies, motivation, and self-efficacy impact English comprehension among college students. Using a descriptive correlational approach, data was collected from 468 non-English major Chinese college students through a survey. The findings revealed that female participants and those with over a decade of experience employed more effective reading strategies and demonstrated higher reading motivation. Additionally, students from the Computer Studies department and those with more than ten years of experience exhibited stronger self-belief. The study showed a highly significant correlation between these three variables, indicating that improved reading tactics are associated with increased reading motivation and self-belief. Moreover, higher motivation levels among students were linked to enhanced self-confidence. In conclusion, the study recommends a reading program to enhance the reading comprehension of college students learning English as a foreign language.
Deep Learning Model for Answering Why-Questions in Arabic

The subfield of natural language processing (NLP) known as question answering (QA) involves providing answers to questions posed in natural language. Answering “why” questions has long been a challenging task for QA systems, given the complexity of the reasoning involved. In this paper, we propose a deep learning model for answering “why” questions in Arabic. Recent advances in neural network models have yielded promising results across a range of tasks, particularly with the integration of attention and memory mechanisms. Our proposed model is based on the dynamic memory network (DMN), an architecture that utilizes attention and memory mechanisms to locate and extract relevant information for answering a question. We evaluate the performance of our DMN-based model in answering Arabic “why” questions using the LEMAZA dataset, achieving an F-score of 78.61%. Our findings suggest that DMN-based models hold promise for addressing the challenge of answering “why” questions in Arabic and other languages.
Performance Analysis of SIFT/SURF Algorithms in Neural Networks for Optimized Feature Detection

This paper is an experiment on the implementation of scale-invariant feature transform (SIFT) and speeded up robust features (SURF) algorithms into multi-dimensional neural networks. We are attempting to perform a comparative performance evaluation by using different scale factors of the SIFT algorithm in multi-layered neural networks. This method will help us to understand the best way of implementing the above algorithms in neural networks and from a given sample, extracting distinctive invariant features and finding points of interests. Hence performing a large data set computation would be made much easier because of the neural network implementation. The conventional method of performing SIFT has computational limitations and we aim to achieve best possible way of performing the feature detection when using SIFT and neural network combined, hence transcending computational limitations that SIFT previously had. This approach to recognition of features can robustly find results much faster on bigger dataset and at the same time have the benefits of SIFT algorithm.
Decomposition of Pre-β- Irresolute Maps and g- Closed Sets in Topological Space

This paper explores the decomposition of pre--irresolute (pre--irr) maps and their connection to generalized closed sets (g-closed) in topological space (top. sp.). We provide the necessary background on top. sp., continuity, closed, and related concepts. We introduce the definitions of pre--irr maps and g-closed and discuss their basic properties. The decomposition theorem is presented, stating that every pre--irr map can be decomposed into a composition of a pre--continuous (pre- -cont.) map and a b-irr map. Examples and applications are provided to illustrate the behavior of pre--irr maps and the practical implications of the decomposition theorem. We examine the relationships between pre--cont. maps and pre--irr maps, exploring the conditions under which a pre--irr map is also -irr, and provide a proof for this relationship. The properties of pre--cont. maps, - maps, g- pre--cont. maps, and g--cont. maps are discussed, along with proofs and examples to illustrate their applicability. This paper contributes to the understanding of pre--irr maps, their decomposition, and their relationships with g-closed, providing a foundation for further research in top. sp. and map decompositions. Keywords: pre-β-irresolute maps, g-closed, pre-β-cont. maps, b-cont. maps
Accident Attention System for Somnambulism Patients

Promising technologies such as sensors, networking, and edge have led to many smart healthcare solutions to monitor and track patient health status. The health sector is now experiencing a significant transformation from conventional patient care to a smart healthcare environment. Smart health care allows medical professionals to monitor patients remotely and visualize the disease prognosis effectively. The Internet of medical things connect patients, doctors, and medical equipment via wireless networking technologies to process the data with Artificial Intelligence models. One of the domains of automated health care systems is to alert the caregivers and hospital on emergency conditions. This research study is a novel work that aims to help the caregivers of somnambulism patients attend to them in case of emergency. Sleep quality improves the health and work efficiency of any person. The caregivers of sleepwalking patients suffer from lack of sleep as the patient gets active during the night hours. The model is based on fall detection and sleep detection from wearable sensor data. The fall detection model includes feature selection by LASSO and classification by ensemble classifier. The proposed methodology shows improved performance for the fall detection model for all ensemble machine learning classifiers.
The Analysis of Linguistics Features of Ecovacs Vacuum Robot Deebot Ozmo 920 Manual Book: A Procedure Text

Manual book is a guidebook made to convey information related to the product purchased by consumers. This information is usually detailed regarding warranty information, safety instructions, installation instructions, problem identification, setup instructions, general specifications about the product and maintenance and troubleshooting instructions. The objective of this qualitative research was to analyze schematic structure in a procedure text of Ecovacs Vacuum Robot Deebot Ozmo 920 manual book, especially in Important safety instruction text. There are 44 utterances in text of Important Safety Instruction that are picked up randomly as some samples of analysis. Before they are analyzed, the utterances were grouped with the same linguistics categories, those are simple present tense, imperative sentence, declarative sentence, conjunction, and material process. In material process, there are two categories to discuss, they are passive voice and active voice. Then using schematic structure form, the data were analyzed one by one. The findings of this study show that the language used in Important safety instruction text in manual book is concise, as evidenced by the low percentage of conjunctions (4%). Another is the language used in the text of the Important safety instruction text is effective and direct, as shown by the high percentages of declarative (20%) and imperative sentences (27%), this reduces the possibility of misunderstandings or misinterpretations moreover, the pattern makes the information being conveyed directly easy to understand. Moreover, the study only discusses linguistic features to explain how to describe the use of language in a straightforward procedural tex
A Signal Temporal Logic Motion Planner for Bird Diverter Installation Tasks With Multi-Robot Aerial Systems

This paper addresses the problem of task assignment and trajectory generation for installing bird diverters using a fleet of multi-rotors.The proposed solution extend our previous motion planner to compute feasible and constrained trajectories, considering payload capacity limitations and recharging constraints.Signal Temporal Logic (STL) specifications are employed to encode the mission objectives and temporal requirements.Additionally, an event-based replanning strategy is introduced to handle unforeseen failures.An energy minimization term is also employed to implicitly save multi-rotor flight time during installation operations.The effectiveness and validity of the approach are demonstrated through simulations in MATLAB and Gazebo, as well as field experiments carried out in a mock-up scenario.
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training

Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios.Modern learning-based approaches require large labeled datasets and tend to perform poorly outside the training domain.Our first contribution is to develop a robust corrector module that corrects pose estimates using depth information, thus enabling existing methods to better generalize to new test domains; the corrector operates on semantic keypoints (but is also applicable to other pose estimators) and is fully differentiable.Our second contribution is an ensemble self-training approach that simultaneously trains multiple pose estimators in a selfsupervised manner.Our ensemble self-training architecture uses the robust corrector to refine the output of each pose estimator; then, it evaluates the quality of the outputs using observable correctness certificates; finally, it uses the observably correct outputs for further training, without requiring external supervision.As an additional contribution, we propose small improvements to a regression-based keypoint detection architecture, to enhance its robustness to outliers; these improvements include a robust pooling scheme and a robust centroid computation.Experiments on the YCBV and TLESS datasets show the proposed ensemble self-training performs on par or better than fully supervised baselines while not requiring 3D annotations on real data.Code and pre-trained models are available on the project web page 1 .
A Deep Learning Onion Peeling Approach to Measure Oral Epithelium Layer Number

Head and neck squamous cell carcinoma (HNSCC), specifically in the oral cavity (oral squamous cell carcinoma, OSCC), is a common, complex cancer that significantly affects patients’ quality of life. Early diagnosis typically improves prognoses yet relies on pathologist examination of histology images that exhibit high inter- and intra-observer variation. The advent of deep learning has automated this analysis, notably with object segmentation. However, techniques for automated oral dysplasia diagnosis have been limited to shape or cell stain information, without addressing the diagnostic potential in counting the number of cell layers in the oral epithelium. Our study attempts to address this gap by combining the existing U-Net and HD-Staining architectures for segmenting the oral epithelium and introducing a novel algorithm that we call Onion Peeling for counting the epithelium layer number. Experimental results show a close correlation between our algorithmic and expert manual layer counts, demonstrating the feasibility of automated layer counting. We also show the clinical relevance of oral epithelial layer number to grading oral dysplasia severity through survival analysis. Overall, our study shows that automated counting of oral epithelium layers can represent a potential addition to the digital pathology toolbox. Model generalizability and accuracy could be improved further with a larger training dataset.
Structured World Models from Human Videos

In this paper, we tackle the problem of learning complex, general behaviors directly in the real world.We propose an approach for robots to efficiently learn manipulation skills using only a handful of real-world interaction trajectories from many different settings.Inspired by the success of learning from large-scale datasets in the fields of computer vision and natural language, our belief is that in order to efficiently learn, a robot must be able to leverage internet-scale, human video data.Humans interact with the world in many interesting ways, which can allow a robot to not only build an understanding of useful actions and affordances but also how these actions affect the world for manipulation.Our approach builds a structured, human-centric action space grounded in visual affordances learned from human videos.Further, we train a world model on human videos and finetune on a small amount of robot interaction data without any task supervision.We show that this approach of affordance-space world models enables different robots to learn various manipulation skills in complex settings, in under 30 minutes of interaction.Videos can be found at https://human-world-model.github.io
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation

Fig. 1: FurnitureBench: reproducible real-world furniture assembly benchmark.Benchmarking furniture assembly poses to address many robotic manipulation challenges: long-horizon planning, dexterous control, and visual perception.FurnitureBench is designed to be easy-to-reproduce and easy-to-use with the 3D printable furniture models, robot control software stack, environment setup guide, and large demonstration data.(Left) A decorated room in the real world with furniture models our robot assembled.(Right) A suite of 8 furniture models in our benchmark.Abstract-Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks.However, these approaches have been limited to learning simple behaviors in current real-world manipulation benchmarks, such as pushing or pick-and-place.To enable more complex, longhorizon behaviors of an autonomous robot, we propose to focus on real-world furniture assembly, a complex, long-horizon robot manipulation task that requires addressing many current robotic manipulation challenges to solve.We present FurnitureBench, a reproducible real-world furniture assembly benchmark aimed at providing a low barrier for entry and being easily reproducible, so that researchers across the world can reliably test their algorithms and compare them against prior work.For ease of use, we provide 200+ hours of pre-collected data (5000+ demonstrations), 3D printable furniture models, a robotic environment setup guide, and systematic task initialization.Furthermore, we provide FurnitureSim, a fast and realistic simulator of FurnitureBench.We benchmark the performance of offline RL and IL algorithms on our assembly tasks and demonstrate the need to improve such algorithms to be able to solve our tasks in the real world, providing ample opportunities for future research.
iPlanner: Imperative Path Planning

The problem of path planning has been studied for years.Classic planning pipelines, including perception, mapping, and path searching, can result in latency and compounding errors between modules.While recent studies have demonstrated the effectiveness of end-to-end learning methods in achieving high planning efficiency, these methods often struggle to match the generalization abilities of classic approaches in handling different environments.Moreover, end-to-end training of policies often requires a large number of labeled data or training iterations to reach convergence.In this paper, we present a novel Imperative Learning (IL) approach.This approach leverages a differentiable cost map to provide implicit supervision during policy training, eliminating the need for demonstrations or labeled trajectories.Furthermore, the policy training adopts a Bi-Level Optimization (BLO) process, which combines network update and metric-based trajectory optimization, to generate a smooth and collision-free path toward the goal based on a single depth measurement.The proposed method allows task-level costs of predicted trajectories to be backpropagated through all components to update the network through direct gradient descent.In our experiments, the method demonstrates around 4× faster planning than the classic approach and robustness against localization noise.Additionally, the IL approach enables the planner to generalize to various unseen environments, resulting in an overall 26-87% improvement in SPL performance compared to baseline learning methods.