Reviewerly/exHarmony huggingface dataset:
A Twofold Convolutional Regression Tracking Network With Temporal and Spatial Mechanism

In recent years, convolutional regression trackers have shown increasing attention for visual object tracking due to their favorable performance and easy implementation. However, most of them are restricted to features from a certain layer and hardly benefit from temporal spatial information, which limits the potential to significant appearance changes. In this work, we go beyond the traditional deep regression trackers and build a novel twofold tracking network, which exploits rich hierarchical features and incorporates both temporal and spatial information to boost the tracking performance. The proposed network is composed of two streams, i.e., an appearance stream and a semantic stream, each stream is independently learned from different convolutional layers. Specially, we propose temporal and spatial mechanism for robust target representation by considering historical information in previous frames as well as spatial information. By design, the proposed twofold convolutional regression tracking network with spatial and temporal mechanism can better tolerate the target appearance changes and improve the tracking accuracy. Extensive experimental results on the benchmarks OTB-2015, Temple-Color, UAV123, and VOT-2018 demonstrate the effectiveness of our method, as compared with a number of state-of-the-art trackers.
Insulator defect detection based on YOLO and SPP-Net

With the continuous construction of smart grids, drones are gradually being used in routine maintenance and inspection of transmission lines. Aiming at the problem of few defect insulator samples and complex background, a detection method of defect insulator based on YOLO and SPP-Net is proposed. This paper uses the original samples to train the YOLOv5s model, and the cropped samples to fine-tune the classification network composed of the pre-trained VGG16 network and SPP-Net, and then cascade the two models. After positioning and cutting the insulators, YOLOv5s sent them to the classification network for defect detection. The final insulator detection accuracy reached 89%.
Geochemical mapping in stream sediments of the Carajás Mineral Province, part 2: Multi-element geochemical signatures using Compositional Data Analysis (CoDA)

The geological imprint on the geochemical background in stream sediment of the Itacaiúnas River Watershed (IRW), located in the most prominent mining area of Brazil, the Carajás Mineral Province, was evaluated. The stream sediment dataset from the Itacaiúnas Geochemical Mapping and Background Project of the Instituto Tecnológico Vale was used to address multi-element associations and for the definition of surface geochemical compartments by applying a series of multivariate geostatistics tools and geoprocessing techniques, through the Compositional Data Analysis (CoDA) perspective. In this study, the geochemical dataset was processed for centered log-ratio (clr) transformation prior to the Principal Factor Analysis (PFA). The spatial distribution of factor scores derived from PFA indicates a pronounced control on stream sediment geochemistry by the underlying geological setting and lithology. Relevant hidden correlations were observed among the nine extracted factors. An unsupervised classification based on k-means clustering algorithm was performed for the factor scores of 761 stream sediment samples. A multiproxy approach that combines the cluster classification and geoprocessing tools revealed a strong similarity in the spatial distribution of different geochemical compartments and geological domains of the IRW. Threshold values of 51 elements were calculated by a variety of statistical methods for references to the main four geological domains of the IRW defined using the geological framework and the geochemical compartments. These values were then compared to the background concentrations of the entire study area and to the investigation levels proposed by the Brazilian environmental agencies for potentially toxic elements. The new threshold values for the geochemical compartments of the IRW can be used for improving the assessment of environmental impact by anthropic activities, and to establish more realistic threshold values for different chemical elements in stream sediments. This study provides useful information for new mineral development projects and ore prospects, with potential benefit to the mineral sector and industry.
“Grip-that-there”: An Investigation of Explicit and Implicit Task Allocation Techniques for Human-Robot Collaboration

In ad-hoc human-robot collaboration (HRC), humans and robots work on a task without pre-planning the robot's actions prior to execution; instead, task allocation occurs in real-time. However, prior research has largely focused on task allocations that are pre-planned - there has not been a comprehensive exploration or evaluation of techniques where task allocation is adjusted in real-time. Inspired by HCI research on territoriality and proxemics, we propose a design space of novel task allocation techniques including both explicit techniques, where the user maintains agency, and implicit techniques, where the efficiency of automation can be leveraged. The techniques were implemented and evaluated using a tabletop HRC simulation in VR. A 16-participant study, which presented variations of a collaborative block stacking task, showed that implicit techniques enable efficient task completion and task parallelization, and should be augmented with explicit mechanisms to provide users with fine-grained control.
Animated Video Media Based on Adobe After Effects (AEF) Application: An Empirical Study for Elementary School Students

The development of the world of digitization is so fast that changes in the education sector also have an effect on the growing learning model. This study aims to determine the use of Adobe After Effects (AEF) based animation video media in my living environment material in grade IV in elementary school. The initial stage of media testing was carried out on 3 media experts, 3 material experts, 5 class teachers from five schools in different regions. The number of samples in this study amounted to 132 students. The data were collected using a Likert scale questionnaire 1 to 5. Based on the data obtained, it shows the value of: (1) the average value of the media expert test (89.71%) including the criteria "Very Valid". (2) the average value of the material expert test is (84.88%) including the criteria "Very Valid" (3) the average product trial feasibility by the teacher is (90.25%) including the criteria "Very Good" and (4) the average result of product testing by students is (91.79%) are categorized as "Very Good". The conclusion is that animation video media is very good for use in curriculum learning and is able to construct patterns of thought so as to motivate students in learning. For further research, a development model can be carried out.
Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention

Mammography is the primary imaging modality used for early detection and diagnosis of breast cancer. X-ray mammogram analysis mainly refers to the localization of suspicious regions of interest followed by segmentation, towards further lesion classification into benign versus malignant. Among diverse types of breast abnormalities, masses are the most important clinical findings of breast carcinomas. However, manually segmenting breast masses from native mammograms is time-consuming and error-prone. Therefore, an integrated computer-aided diagnosis system is required to assist clinicians for automatic and precise breast mass delineation. In this work, we present a two-stage multi-scale pipeline that provides accurate mass contours from high-resolution full mammograms. First, we propose an extended deep detector integrating a multi-scale fusion strategy for automated mass localization. Second, a convolutional encoder-decoder network using nested and dense skip connections is employed to fine-delineate candidate masses. Unlike most previous studies based on segmentation from regions, our framework handles mass segmentation from native full mammograms without any user intervention. Trained on INbreast and DDSM-CBIS public datasets, the pipeline achieves an overall average Dice of 80.44% on INbreast test images, outperforming state-of-the-art. Our system shows promising accuracy as an automatic full-image mass segmentation system. Extensive experiments reveals robustness against the diversity of size, shape and appearance of breast masses, towards better interaction-free computer-aided diagnosis.
Secure and dynamic access control for the Internet of Things (IoT) based traffic system

Today, the trend of the Internet of Things (IoT) is increasing through the use of smart devices, vehicular networks, and household devices with internet-based networks. Specifically, the IoT smart devices and gadgets used in government and military are crucial to operational success. Communication and data sharing between these devices have increased in several ways. Similarly, the threats of information breaches between communication channels have also surged significantly, making data security a challenging task. In this context, access control is an approach that can secure data by restricting unauthorized users. Various access control models exist that can effectively implement access control yet, and there is no single state-of-the-art model that can provide dynamicity, security, ease of administration, and rapid execution all at once. In combating this loophole, we propose a novel secure and dynamic access control (SDAC) model for the IoT networks (smart traffic control and roadside parking management). Our proposed model allows IoT devices to communicate and share information through a secure means by using wired and wireless networks (Cellular Networks or Wi-Fi). The effectiveness and efficiency of the proposed model are demonstrated using mathematical models and discussed with many example implementations.
DAL: A Deep Depth-Aware Long-term Tracker

The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run. We reformulate deep discriminative correlation filter (DCF) to embed the depth information into deep features. Moreover, the same depth-aware correlation filter is used for target redetection. Comprehensive evaluations show that the proposed tracker achieves state-of-the-art performance on the Princeton RGBD, STC, and the newly-released CDTB benchmarks and runs 20 fps.
Can we benchmark Code Review studies? A systematic mapping study of methodology, dataset, and metric

Code Review (CR) is the cornerstone for software quality assurance and a crucial practice for software development. As CR research matures, it can be difficult to keep track of the best practices and state-of-the-art in methodology, dataset, and metric. This paper investigates the potential of benchmarking by collecting methodology, dataset, and metric of CR studies. A systematic mapping study was conducted. A total of 112 studies from 19,847 papers published in high-impact venues between the years 2011 and 2019 were selected and analyzed. First, we find that empirical evaluation is the most common methodology (65% of papers), with solution and experience being the least common methodology. Second, we highlight 50% of papers that use the quantitative method or mixed-method have the potential for replicability. Third, we identify 457 metrics that are grouped into sixteen core metric sets, applied to nine Software Engineering topics, showing different research topics tend to use specific metric sets. We conclude that at this stage, we cannot benchmark CR studies. Nevertheless, a common benchmark will facilitate new researchers, including experts from other fields, to innovate new techniques and build on top of already established methodologies. A full replication is available at https://naist-se.github.io/code-review/.
Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query for a particular task.Although the most common formulation of text ranking is search, instances of the task can also be found in many text processing applications.This tutorial provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) is the best-known example.These models produce high quality results across many domains, tasks, and settings.This tutorial, which is based on the preprint (Lin et al., 2020a) of a forthcoming book to be published by Morgan and & Claypool under the Synthesis Lectures on Human Language Technologies series, provides an overview of existing work as a single point of entry for practitioners who wish to deploy transformers for text ranking in real-world applications and researchers who wish to pursue work in this area.We cover a wide range of techniques, grouped into two categories: transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that perform ranking directly.
Hybrid SLAM-based Exploration of a Mobile Robot for 3D Scenario Reconstruction and Autonomous Navigation

SLAM can be categorized into two groups: laser-based SLAM and visual-based SLAM.They are used to identify surrounded objects of a robot.This paper proposes a combination of visual-based SLAM algorithm and laser-based SLAM.The purpose is to reduce effort but still provides the high quality 3D-reconstructed map.First, this paper presents visual-based SLAM and laser-based SLAM separately.Then, two techniques are integrated into one system.In addition, bi-direction RRT* path planning algorithm is developed to create a feasible and optimal trajectory.A self-tuning Fuzzy-PID controller also is introduced for driving the robot to follow the trajectory precisely.The simulations and real experiments are conducted in order to illustrate the superiority of the proposed approach.
Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining

This paper describes UTokyo's submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas.We present a lowresource machine translation system that improves translation accuracy using cross-lingual language model pretraining.Our system uses an mBART implementation of FAIRSEQ to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Asháninka, Guaraní, Wixarika, Náhuatl, Hñähñu, Quechua, Shipibo-Konibo, and Rarámuri.On average, our system achieved BLEU scores that were 1.64 higher and CHRF scores that were 0.0749 higher than the baseline.
Minimization of a Short Computer-Based Test in Reading

Formative tests and assessments have high potential in supporting learning, especially for students with special educational needs. One approach to gain assessment information on student learning is to monitor learning progress. For that, multiple repeated tests are often performed by practitioners. In order to be useful in practice, tests must meet various interdependent quality criteria. A property of tests that touches various criteria as the utility and economy is the length. A test has to be long enough to give a meaningful, reliable and comparable measure but short enough to be usable in classroom situations. An approach to evaluate and minimize the length of a computer-based test on sentence comprehension is introduced. It is shown that the test can be shortened from eight to 5 min while the estimation of the student´s abilities remains relatively stable for a random item order and a fixed item order variant. The consequences of test development of progress monitoring and the procedure for test time reduction for the different quality criteria are outlined. An approach to evaluate and minimize the length of a computer-based test by using a one parameter logistic model on a test of sentence comprehension ( N = 761) is introduced. The data and the syntax is published in the OSF project https://osf.io/hnbs8/ .
Pre-training with Meta Learning for Chinese Word Segmentation

Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS).However, PTMs used in previous works usually adopt language modeling as pre-training tasks, lacking task-specific prior segmentation knowledge and ignoring the discrepancy between pre-training tasks and downstream CWS tasks.In this paper, we propose a CWS-specific pre-trained model METASEG, which employs a unified architecture and incorporates meta learning algorithm into a multi-criteria pre-training task.Empirical results show that METASEG could utilize common prior segmentation knowledge from different existing criteria and alleviate the discrepancy between pre-trained models and downstream CWS tasks.Besides, METASEG can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in lowresource settings.
Comparing Effectiveness of Paper Based and Augmented Reality Instructions for Manual Assembly and Training Tasks

Augmented Reality (AR) systems have seen a rapid adaptation in both training and in virtual assembly instructions. AR systems assist the operator by enhancing user perception of reality, reducing the defects, and lead-time. However, there is a significant lack in the existing literature to compare AR systems and conventional work instructions. The aim of this research is to provide an empirical evidence of comparing task completion time, number of errors, workload index and system usability of AR and conventional paper-based work instructions. For this purpose, we designed an experiment where participants use paper based instructions and AR instructions to assemble a planetary gearbox. The task was assessed using NASA-TLX and System Usability Scale (SUS) tests, which allowed further analysis using descriptive statistics. Moreover, the qualitative interview at the end of the experiment gave more insights about participant’s overall experience.
Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology

Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM® standard specifies information object definitions (IODs) and services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with datasets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface (API) for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers and researchers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source at https://github.com/herrmannlab/highdicom .
Adversarial Self-Supervised Learning for Out-of-Domain Detection

Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system.Previous unsupervised OOD detection methods only extract discriminative features of different in-domain intents while supervised counterparts can directly distinguish OOD and in-domain intents but require extensive labeled OOD data.To combine the benefits of both types, we propose a selfsupervised contrastive learning framework to model discriminative semantic features of both in-domain intents and OOD intents from unlabeled data.Besides, we introduce an adversarial augmentation neural module to improve the efficiency and robustness of contrastive learning.Experiments on two public benchmark datasets show that our method can consistently outperform the baselines with a statistically significant margin. 1 * Weiran Xu is the corresponding author.
FlexOS

OS design is traditionally heavily intertwined with protection mechanisms. OSes statically commit to one or a combination of (1) hardware isolation, (2) runtime checking, and (3) software verification early at design time. Changes after deployment require major refactoring; as such, they are rare and costly. In this paper, we argue that this strategy is at odds with recent hardware and software trends: protections break (Meltdown), hardware becomes heterogeneous (Memory Protection Keys, CHERI), and multiple mechanisms can now be used for the same task (software hardening, verification, HW isolation, etc). In short, the choice of isolation strategy and primitives should be postponed to deployment time.
Lifelong Learning of Hate Speech Classification on Social Media

Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined.However, the amount of data in social media increases every day, and the hot topics changes rapidly, requiring the classifiers to be able to continuously adapt to new data without forgetting the previously learned knowledge.This ability, referred to as lifelong learning, is crucial for the realword application of hate speech classifiers in social media.In this work, we propose lifelong learning of hate speech classification on social media.To alleviate catastrophic forgetting, we propose to use Variational Representation Learning (VRL) along with a memory module based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural Network).Experimentally, we show that combining variational representation learning and the LB-SOINN memory module achieves better performance than the commonly-used lifelong learning techniques.