Reviewerly/exHarmony huggingface dataset:
Designing a Shared Workspace for Learning Using Augmented Reality and Social Robots

Augmented Reality (AR) is a novel technology utilized for merging real and virtual elements, enhancing the physical world. Developing AR applications in Robotics has been of interest in recent years. The current paper proposes a shared workspace for learning using Augmented Reality. The objective is to implement this approach using robots, not to execute collaborative robotic tasks but to establish a collaborative learning environment among students and social robots. We use an Augmented Reality application to superimpose virtual objects to the users' real world, aiming to achieve joint attention at a common point of interest among humans and social robots. The social robot perceives what occurs in the augmented environment and interacts (e.g., sharing information, making comments and gestures, giving constructive feedback to the users, etc.), intending to support humans to achieve the learning goals of the activities. The virtual objects are approached through AR with the aims and scopes of educational material in a learning process. The environment that this paper describes will be the testbed for exploring and further researching the proposed approach; handling virtual objects as educational material and aiming to joint attention at a common point of interest among humans and robots in this Augmented Reality shared world. In future studies, research and analysis of the characteristics and effectiveness of this approach will be conducted.
A Vision-Based System Design and Implementation for Accident Detection and Analysis via Traffic Surveillance Video

In this work, we aim to investigate the problem of detecting and analyzing traffic accidents automatically and effectively through surveillance videos and implement the whole framework on an AI demo board. First, the technique of motion interaction field (MIF) that has the potential to detect crashes in a video is adopted to locate the crashed vehicles based on the interactions between multiple moving objects. Second, the YOLO v3 model is employed to identify the crashed vehicles within the appropriate location. In order to recover the vehicle trajectories before the collision, a hierarchical clustering approach is used, and the corresponding trajectories are obtained. Third, to facilitate the judgment of traffic police, the trajectory is projected to a vertical view by using a perspective transformation. The vehicle velocity is estimated accordingly with the unbiased finite impulse response (UFIR) approach that does not require statistical knowledge of the external noise. Then, the estimated velocity and the obtained collision angle from the vertical view can be utilized to analyze the traffic accident. Finally, to show the effectiveness and implementation performance of the proposed approach, an experiment is carried out based on a Huawei AI demo board named HiKey970 that is used for coding all the mentioned algorithms. Several accident surveillance videos act as the input of the demo board. The accidents are detected successfully, and the corresponding vehicle trajectories are recovered.
Data-Driven Smart e-Learning for English for Specific Purposes

In a globalized world of increasing specialization, language teachers and students alike are faced with the diverse and constantly changing needs of different communicative and professional contexts. English for Specific Purposes (ESP) was invented to help educators better prepare students for such needs, but its pedagogical effectiveness has been inhibited by the lack of proper theoretical grounding and methodology. In this paper, we argue for a smart e-learning approach that welds together the theoretical framework of Construction Grammar and the methodology of Data-Driven Learning. As the core component for implementing such an integrated approach, we propose a smart e-learning system specifically designed for data-driven teaching and learning of ESP constructions. The system adopts corpus-based, NLP-enabled technology to allow ESP students to actively explore large amounts of multilayered, authentic corpora across various specializations, fostering genre awareness and facilitating the development of self-regulated learning skills. By utilizing smart e-learning technology integrated into a smart learning environment, the proposed approach to ESP pedagogy can help cultivate smart language learners equipped to meet the challenges of the future.
Attention-to-Embedding Framework for Multi-instance Learning

We present an attention-to-embedding framework that explicitly addresses the challenge posed by multi-instance learning (MIL) classification tasks, where learning objects are bags containing various numbers of instances. Two key issues of this work are to extract relevant information by determining the relationship between the bag and its instances, and to embed the bag into a new feature space. To respond to these problems, a network with the popular attention mechanism is designed that assigns a new representation and a class probability vector to a given instance in the bag. In addition, compared with the traditional MIL methods, we offer a new embedding function according to the assigned results of instances to process the bag embedding that is unrelated to the distance metric. As a result, MIL challenges will be reduced to single-instance learning (SIL) problems that can be solved using basic machine learning algorithms such as SVM. Extensive experiments on thirty-four data sets demonstrate that our proposed method has the best overall performance over other state-of-the-art MIL methods. This strategy, in particular, has a substantial advantage on web data sets and better stability. Source codes are available at https://github.com/InkiInki/AEMI .
Research Data Management in the Image Lifecycle: A Study of Current Behaviors

Research data management (RDM) practices are critical for ensuring research success. Data can assume diverse formats and data in image format have been understudied in RDM. To understand image management habits in research, we have conducted semi-structured interviews with researchers from four research domains. Most researchers do not formally manage their images, nor do they develop RDM plans. They assume that image management is not a topic discussed at project meetings. In turn, they tend to perform some individual practices, depending on the context and their own opinion, such as creating captions to describe the images and organizing and storing the images in specific locations. However, they see these habits as necessary and admit that they will start to do so in a formal and collaborative way with the working group. These results provide valuable information on practical aspects of the use and production of images in research.
Adapt Burstable Containers to Variable CPU Resources

In the age of the cloud-native, container technology, referred as OS-level virtualization, is increasingly adopted to deploy cloud applications. Compared with virtual machines, containers are lightweight and flexible in resource management. An important quality-of-service (QoS) class in container management is burstable container, whose resource limits are higher than the actual requests allowing a container to expand whenever demands ramp up and additional resources become available. However, efficiently managing burstable containers is challenging, especially for CPU resources. On the one hand, burstable containers should maintain sufficient concurrency, in the form of threads, to utilize extendable CPU resources. On the other hand, the degree of concurrency necessary for utilizing peak CPU resources leads to suboptimal performance when a container's CPU allocation is constrained. In this paper, we recommend that the number of threads in burstable containers should always be set to the CPU limit to guarantee extensibility. However, modern operating systems (OSes) fall short of efficiently managing thread oversubscription. First, the OS CPU scheduler is inefficient for scheduling excessive threads and lacks container awareness. Second, the existing blocking synchronization supported by the OS kernel is inefficient in handling the sleep and wakeup of excessive threads. Finally, the non-blocking synchronization may waste CPUs performing busy waiting when more than one thread in the run queue. To this end, we present a user-level adaptive container scheduler and two OS mechanisms, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">virtual blocking</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">busy-waiting detection</i> , to avoid inefficiency in managing burstable containers without requiring program code changes. Experimental results show that our approaches can keep burstable containers efficient while allowing the applications in containers to take advantage of additional CPUs. The performance gain under high system load is up to 29.7×.
Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation

This study developed a recursive training strategy to train a deep learning model for nuclei detection and segmentation using incomplete annotation. A dataset of 141 H&E stained breast cancer pathologic images with incomplete annotation was randomly split into training/validation set and test set of 89 and 52 images, respectively. The positive training samples were extracted at each annotated cell and augmented with affine translation. The negative training samples were selected from the non-cellular regions free of nuclei using a histogram-based semi-automatic method. A U-Net model was initially trained by minimizing a custom loss function. After the first stage of training, the trained U-Net model was applied to the images in the training set in an inference mode. The U-Net segmented objects with high quality were selected by a semi-automated method. Combining the newly selected high quality objects with the annotated nuclei and the previously generated negative samples, the U-Net model was retrained recursively until the stopping criteria were satisfied. For the 52 test images, the U-Net trained with and without using our recursive training method achieved a sensitivity of 90.3% and 85.3% for nuclei detection, respectively. For nuclei segmentation, the average Dice coefficient and average Jaccard index were 0.831±0.213 and 0.750±0.217, 0.780±0.270 and 0.697±0.264, for U-Net with and without recursive training, respectively. The improvement achieved by our proposed method was statistically significant (P < 0.05). In conclusion, our recursive training method effectively enlarged the set of annotated objects for training the deep learning model and further improved the detection and segmentation performance.
Teaching Teachers First and Then Student: Hierarchical Distillation to Improve Long-Tailed Object Recognition in Aerial Images

Remote sensing data distribution generally exposes the long-tail characteristic. This will limit the object recognition performance of existing deep models when they are trained with such unbalanced data. In this paper, we propose a novel hierarchical distillation framework to address the long-tailed object recognition in aerial images. Firstly, we notice that not only student model should learn feature representations from teachers, but also teacher models should learn feature representations from each other. Therefore, we build hierarchical teacher-wise distillation to improve the feature representations of the teacher models trained with middle and tail data, which is achieved by distilling the feature representations of the teacher model trained with head data. Secondly, we notice that the feature representations of the middle and tail classes can not be effectively distilled from the teacher to the student, since too little middle and tail data can be used to learn. Thus, we propose self-calibrated sampling learning that enforces the student to strengthen the learning of the middle and tail data, thereby improving the student&#x2019; feature learning ability. Extensive experiments on two widely-used DOTA and FGSC-23 datasets demonstrate superior performance of the proposed method compared with state-of-the-art methods. Model and code are publicly available at: https://github.com/wdzhao123/T2FTS.
A Technique for Rapid Development of Declarative Knowledge Bases for Aircraft Diagnostics Based on Decision Tables

Diagnostics of aircraft malfunctions remains an area of research that requires the use and creation of artificial intelligence methods and systems to support decision-making. The paper describes an approach to the rapid development of knowledge bases of intelligent aircraft fault diagnosis systems. At the same time, knowledge is presented in a declarative form as logical rules describing the relationships between external signs of malfunction, possible causes, and operations for their localization and elimination. The technology for prototyping rule-based expert systems based on transformations (PESoT) is used as a methodological basis. This technology implements some End-User Development principles, in particular, visual and wizard-based programming, and model transformations. By PESoT we developed domain models, transformed them to decision tables of a special type, filled them with information from troubleshooting manuals and fault accounting cards, transform filled tables to the elements of a knowledge base with consequent testing. The proposed methodology was also evaluated on a time criterion which showed its applicability for prototyping.
A logic-based tractable approximation of probability

Abstract We provide a logical framework in which a resource-bounded agent can be seen to perform approximations of probabilistic reasoning. Our main results read as follows. First, we identify the conditions under which propositional probability functions can be approximated by a hierarchy of depth-bounded belief functions. Second, we show that under rather palatable restrictions, our approximations of probability lead to uncertain reasoning which, under the usual assumptions in the field, qualifies as tractable.
Breast Tumor Computer-Aided Detection System Based on Magnetic Resonance Imaging Using Convolutional Neural Network

Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based on VGG16, Inception V3, and ResNet50 has higher performance than mainstream CAD systems. Among them, the system built based on VGG16 and ResNet50 has outstanding performance. We further explore the impact of the secondary migration on the experimental results in the VGG16 system, and these results show that the migration can improve system performance of the proposed framework. Conclusion: The accuracy of CNN represented by VGG16 is as high as 91.25%, which is more accurate than traditional machine learning models. The F1 score of the three basic networks that join the secondary migration is close to 1.0, and the performance of the VGG16-based breast tumor CAD system is higher than Inception V3, and ResNet50.
Classifying the Social Media Author Profile Through a Multimodal Representation

The author profiling task refers to extracting as much of an author through what he writes, such as gender, age, nationality, location, among others. Although this task arose a few decades ago, the explosion in social networks has made the task of author profiling mainly focus on digital media. Typically, previous works have used only the text of the users of social networks to determine specific demographic characteristics. However, due to the nature of social networks, it is possible to have access to another type of information that could be useful. This work faces the author profiling in a social networks domain with text and images information. This kind of information is also called multimodal information. The solution proposed uses an approach to extract data from social media messages and users’ images. The proposal shows evidence of the complementarity of the modalities by merging these two information sources. The idea consists of transforming images into texts and having the same representation framework for both types of information, which allows them to be merged. The work explores distinct approaches to extracting information from text and images. The results show that the text constructed from the images contains helpful information, increasing this task’s precision.
Physical Assets Tokenization for Blockchain Market

Asset tokenization is a technique widely adopted in blockchain-based platforms that enables vendors to sell their assets in competitive markets characterized by direct participation and accessibility. Digital assets are natively based on information as, for instance, licenses, tickets, certificates or financial products. As a consequence, in order to perform asset tokenization, all the information must be previously stored, such that each single asset can be correctly identified. Tokens of digital assets are indeed defined as digital objects in which the relevant information are stored to guarantee a single, unique matching between asset and token. The benefit of this approach is represented by the public matching verification. In case of physical assets related to physical goods, the store and verification of all the relevant information to bind each asset to a specific token is a complex process. Moreover, unlike digital assets which only operate on digital domain, physical assets are subject to the regulation of the country where they are located and this asymmetry limits its use. Starting from this scenario, this work presents an architecture to support physical assets tokenization for digital markets. This is achieved through the definition of a process architecture with a specific trade-off between off-chain and on-chain tokenization.
Synchronous Inference for Multilingual Neural Machine Translation

Multilingual neural machine translation allows a single model to translate between multiple language pairs, which greatly reduces the cost of model training and receives much attention recently. Previous studies mainly focus on training stage optimization and improve positive knowledge transfer among languages with different levels of parameter sharing, but ignore the multilingual knowledge transfer during inference although the translation in one language may help the generation of other languages. This work enhances knowledge sharing among multiple target languages in the inference phase. To achieve this, we propose a synchronous inference method that can simultaneously generate translations in multiple languages. During generation, the model predicts the next word of each language not only based on source sentence and previously predicted segments, but also based on predicted words of other target languages. To maximize the inference stage knowledge sharing, we design a cross-lingual attention module which allows the model to dynamically select the most relevant information from multiple target languages. The synchronous inference model requires multi-way parallel training data which is scarce. We therefore propose to adopt multi-task learning to incorporate large-scale bilingual data. We evaluate our method on three multilingual translation datasets and prove that the proposed method significantly improve the translation quality and the decoding efficiency compared to strong bilingual and multilingual baselines.
Depth Map Super-Resolution via Joint Local Gradient and Nonlocal Structural Regularizations

Depth maps have been widely used in many real world applications, such as human-computer interaction and virtual reality. However, due to the limitation of current depth sensing technology, the captured depth maps usually suffer from low resolution and insufficient quality. In this paper, we propose a depth map super-resolution method via joint local gradient and nonlocal structural regularizations. Depth maps contain mainly smooth areas separated by textures which demonstrate distinct geometry direction characteristic. Motivated by this, we classify depth map patches according to their geometrical directions and learn a compact online dictionary in each class. We further introduce two regularization terms into the sparse representation framework. Firstly, a multi-directional total variation model is proposed to characterize the local patterns in the gradient domain. Secondly, a nonlocal autoregressive model is introduced to provide nonlocal constraint to the local structures, which can effectively restore image details and suppress noise. Quantitative and qualitative evaluations compared with state-of-the-art methods demonstrate that the proposed method achieves superior performance for various configurations of magnification factors and datasets.
Relevance of Frequent Pattern (FP)-Growth-Based Association Rules on Liver Diseases

Finding the most frequent itemsets and association rules is becoming a key focus of medical research as the demand for medical data processing grows. The association rule-based analysis assists researchers and analysts in determining the relationships and dependencies among distinct parameters in a liver dataset. This information can assist the patient in determining the source of their illness. There have been extensive researches conducted in order to find optimal association rule mining algorithm. As a result, this paper investigated and critically analyses an Apriori and FP-growth association rule discovery strategies and shows good accuracy. The objective of this work is to discover the strong association rules from liver disease dataset which increases the patient protective measures in order to preserve valuable people’s lives.
SmartFilter: An Edge System for Real-Time Application-Guided Video Frames Filtering

Given the limited bandwidth available in distributed camera systems, it is nearly impossible for cameras to transmit their entire feed to the server in real time. Furthermore, as the number of camera units increases, the processing overheads on the server also increase, resulting in excessive latencies. This article introduces SmartFilter, a new Edge-to-Cloud filtering solution for video analytics. SmartFilter exploits the feedbacks from the running server-side application to filter directly on the camera, frames that are likely to produce the same application result as the previously offloaded ones. Because of its unique filtering mechanism, SmartFilter improves the system’s throughput, latency, and network usage and reduces the server’s processing overhead while maintaining the overall accuracy. SmartFilter is typically a fast and lightweight binary classifier that examines changes within frames to decide when these changes are significant enough to alter the application output. Experiments with various video data sets and in a real-world scenario demonstrate that our solution can achieve 40 FPS on a commodity camera while delivering a filtering efficiency of more than 90%.