Miracle-dz/UniFAR-FTUs huggingface dataset:
OCR-based Inventory Management Algorithms Robust to Damaged Images

Accurate and fast inventory management algorithms are essential in the modern distribution industry. However, the configuration process of inventory management algorithms is very expensive, and the direct comprehensive management of inventory procedures is labor intensive and inaccurate. Therefore, in this paper, we propose an optical character recognition (OCR)-based inventory management algorithm to resolve these practical issues. The main purpose of our inventory management algorithm is to automatically inspect whether a list of items and the actual items match. To this end, our method consists of three steps, namely, text detection, text recognition, and text matching. In addition, to expand our algorithm to real-world applications, we propose adversarial training to ensure robustness against various damaged images, including corruption, blur, and inappropriate viewpoints. To train the network, we construct a new inventory management dataset (IMD) consisting of 10,000 sheets in real retail store environments. We verify our algorithm on the public dataset and our new IMD. As a result, we experimentally demonstrate that our method is not only robust against various damaged images but is also easily applicable in both large and small scale distributions stores at a low cost. Our code and dataset is available at https://blog.airlab. re.kr/Deform-and-Recover/.
MultiPoint: Cross-spectral registration of thermal and optical aerial imagery

: While optical cameras are ubiquitous in robotics, some robots can sense the world in several sections of the electromagnetic spectrum simultaneously, which can extend their capabilities in fundamental ways. For instance, many ﬁxed-wing UAVs carry both optical and thermal imaging cameras, potentially allowing them to detect temperature difference-induced atmospheric updrafts, map their locations, and adjust their ﬂight path accordingly to increase their time aloft. A key step for unlocking the potential offered by multi-spectral data is generating consistent, multi-spectral maps of the environment. In this work, we introduce MultiPoint, a novel data-driven method for generating interest points and associated descriptors for registering optical and thermal image pairs without knowledge of the relative camera viewpoints. Existing pixel-based alignment methods are accurate but too slow to work in near-real time, while feature-based methods such as SuperPoint are fast but produce poor-quality cross-spectral matches due to interest point instability in thermal images. MultiPoint capitalizes on the strengths of both approaches. An ofﬂine mutual information-based procedure is used to align cross-spectral image pairs from a training set, which are then processed by our generalized multi-spectral homographic adaptation stage to generate highly repeatable interest points that are invariant across viewpoint changes in both spectra. These are used to train a MultiPoint deep neural network by exposing this model to both same-spectrum and cross-spectral image pairs. This model is then deployed for fast and accurate online interest point detection. We show that Mul-tiPoint outperforms existing techniques for feature-based image alignment using a dataset of real-world thermal-optical imagery captured by a UAV during ﬂights in different conditions and release this dataset, the ﬁrst of its kind.
Design Choices in Building an MSR Tool: The Case of Kaiaulu (short paper)

Background: Since Alitheia Core was proposed and subsequently retired, tools that support empirical studies of software continue to be proposed, such as Codeface, Codeface4Smells, GrimoireLab and SmartSHARK, but they all make different design choices with overlapping functionality. Aims: We seek to understand the design decisions adopted on these tools– good and bad–and their consequences to understand why their authors reinvented functionality already present in other tools, and to help inform the design of future tools. Method: We used action research to evaluate the tools, and determine principles and anti-patterns to motivate a new tool design. Results: We identified 7 major design choices among the tools: 1) Abstraction Debt, 2) the use of Project Configuration Files, 3) the choice of Batch or Interactive Mode, 4) Minimal Paths to Data, 5) Familiar Software Abstractions, 6) Licensing and 7) the Perils of Code Reuse. Building on the observed good and bad design decisions, we created our own architecture and implemented it as an R package. Conclusions: Tools should not require onerous setup for users to obtain data. Authors should consider the conventions and abstractions used by their chosen language and build upon these instead of redefining them. Tools should encourage best practices in experiment reproducibility by leveraging self-contained and readable schemas that are used for tool automation, and reuse must be done with care to avoid depending on dead code.
A Comparison of Machine Learning-Based Text Classifiers for Mapping Source Code to Architectural Modules

A mapping between a system’s implementation and its software architecture is mandatory in many architecture consistency checking techniques. Creating such a mapping manually is a non-trivial task for most complex software systems. Machine learning-based text classification may be an highly effective tool for automating this task. How to make use of this tool most effectively has not been thoroughly investigated yet. This article presents a comparative analysis of three classifiers applied to map the implementations of five open-source systems to their architectures. The performance of the classifiers is evaluated for different extraction and preprocessing settings as well as different training set sizes. The results suggest that Logical Regression and Support Vector Machines both outperform Naive Bayes unless information about coarse-grained implementation structures cannot be exploited. Moreover, initial manual mappings of more than 15% of all source code files, or 10 files per module, do not seem to lead to a significantly better classification.
Understanding Light- and Elevated Temperature-Induced Degradation in Silicon Wafers Using Hydrogen Effusion Mass Spectroscopy

Hydrogen has been long known for its ability to passivate defects in silicon devices. However, multiple recent studies on understanding the mechanism behind light- and elevated temperature-induced degradation (LeTID) have proposed that hydrogen plays an important role in this degradation mechanism. Despite its important role in photovoltaic applications, the quantitative assessment of hydrogen is difficult and seldom reported. In this work, we applied hydrogen effusion mass spectroscopy to quantify the hydrogen released from hydrogenated silicon nitride (SiNx:H) and atomic layer deposited (ALD) aluminum oxide (AlOx) dielectric films at elevated temperatures. We demonstrate that the amount of hydrogen effused from these layers strongly correlates with the extent of LeTID observed in the multicrystalline silicon wafers passivated with these monolayers and their stacks. It is shown that the hydrogen effusion scales linearly with the SiNx:H thickness, similar as the extent of LeTID. The effusion measurements on the AlOx/SiNx:H stack revealed that the presence of the AlOx film modifies the total amount of hydrogen that is effused, whereas it was found to slow the hydrogen in-diffusion. This result is consistent with the LeTID extent determined after contact firing where ALD AlOx layers were found to act as a hydrogen diffusion barrier, strongly reducing LeTID when placed in between c-Si and SiNx:H and increasing LeTID when placed on top of SiNx:H.
Entropy Bounds for Grammar-Based Tree Compressors

The definition of <inline-formula> <tex-math notation="LaTeX">$\text {k}^{th}$ </tex-math></inline-formula>-order empirical entropy of strings is extended to node-labelled binary trees: A notion of <inline-formula> <tex-math notation="LaTeX">$\text {k}^{th}$ </tex-math></inline-formula>-order empirical entropy for node-labelled binary trees is proposed that is able to capture regularities in both labels and structure of a tree. A suitable binary encoding of tree straight-line programs (that have been used for grammar-based tree compression before) is shown to yield binary tree encodings of size bounded by the <inline-formula> <tex-math notation="LaTeX">$\text {k}^{th}$ </tex-math></inline-formula>-order empirical entropy plus some lower order terms. This result is then extended from node-labelled binary trees to node-labelled unranked trees. This generalizes recent results for grammar-based string compression to grammar-based tree compression. Additionally, experimental results with real XML document trees are presented, in which the proposed notion of <inline-formula> <tex-math notation="LaTeX">$\text {k}^{th}$ </tex-math></inline-formula>-order empirical tree entropy is computed and compared to the performance of grammar-based tree compressors for those XML document trees.
The Effect of Spread Spectrum Modulation for a Buck Converter Coupled with a Single Wired Communication Link

This paper explores the effect of using a Spread Spectrum modulation technique with a Buck Converter on a communication cable coupled at the input voltage. The EMI created by the switching of the power converter generates damped oscillations on the data cable that can have great impact on the quality of the communication channel. Frequency domain analysis shows lower EMI levels in the power cable when compared to the standard deterministic modulation. However, there is no real improvement to the actual communications as there in an increase in the measured Bit Error Rate when evaluated experimentally.
Next Generation “Zen 3” Core

This presentation contains forward-looking statements concerning Advanced Micro Devices, Inc. (AMD) including, but not limited to, the features, functionality, availability, timing, expectations and expected benefits of AMD future products, including Ryzen™ 5000 Series CPUs and Socket AM4, which are made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are commonly identified by words such as “would,” “may,” “expects,” “believes,” “plans,” “intends,” “projects” and other terms with similar meaning. Investors are cautioned that the forward-looking statements in this presentation are based on current beliefs, assumptions and expectations, speak only as of the date of this presentation and involve risks and uncertainties that could cause actual results to differ materially from current expectations. Such statements are subject to certain known and unknown risks and uncertainties, many of which are difficult to predict and generally beyond AMD’s control, that could cause actual results and other future events to differ materially from those expressed in, or implied or projected by, the forward-looking information and statements. Investors are urged to review in detail the risks and uncertainties in AMD’s Securities and Exchange Commission filings, including but not limited to AMD’s Quarterly Report on Form 10-Qfrom the quarter ending on June 26, 2021.
Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks

Predicting students’ academic performance at an early stage of a semester is one of the most crucial research topics in the field of Educational Data Mining (EDM). Students are facing various difficulties in courses like “Programming” and “Data Structures” through undergraduate programs, which is why failure and dropout rates in these courses are high. Therefore, EDM is used to analyze students’ data gathered from various educational settings to predict students’ academic performance, which would help them to achieve better results in their future courses. The main goal of this paper is to explore the efficiency of deep learning in the field of EDM, especially in predicting students’ academic performance, to identify students at risk of failure. A dataset collected from a public 4-year university was used in this study to develop predictive models to predict students’ academic performance of upcoming courses given their grades in the previous courses of the first academic year using a deep neural network (DNN), decision tree, random forest, gradient boosting, logistic regression, support vector classifier, and K-nearest neighbor. In addition, we made a comparison between various resampling methods to solve the imbalanced dataset problem, such as SMOTE, ADASYN, ROS, and SMOTE-ENN. From the experimental results, it is observed that the proposed DNN model can predict students’ performance in a data structure course and can also identify students at risk of failure at an early stage of a semester with an accuracy of 89%, which is higher than models like decision tree, logistic regression, support vector classifier, and K-nearest neighbor.
Machine Learning Techniques for Biomedical Natural Language Processing: A comprehensive Review

The widespread use of electronic health records (EHR) systems in health care provides a large amount of real-world data, leading to new areas for clinical research. Natural language processing (NLP) techniques have been used as an artificial intelligence strategy to extract information from clinical narratives in electronic health records since they include a great amount of valuable clinical information. However, in a free-form text such as electronic health records, many clinical data are still hidden in a clinical narrative format. Therefore, the performance of biomedical NLP techniques is required to unlock the full potential of EHR data to convert a clinical narrative text automatically into structured clinical data. In this way, biomedical NLP applications can be used to direct clinical decisions, identify medical problems, and effectively postpone or avoid the occurrence of a disease. This review discusses the current literature on the secondary use of electronic health record data for clinical research on chronic diseases and addresses the potential, challenges, and applications of biomedical NLP techniques. We review some of the biomedical NLP methods and systems used over EHRs and give an overview of machine learning and deep learning methodologies used to process EHRs and improve the understanding of the patient’s clinical records and the prediction of chronic diseases risk, providing a great chance to extract previously unknown clinical information. Moreover, this review summarizes the utilizing of Deep Learning and Machine Learning techniques in biomedical NLP tasks based on chronic diseases related EHR data. Finally, this review presents the future trends and challenges in the biomedical NLP.
Reciprocally Rotating Magnetic Actuation and Automatic Trajectory Following for Wireless Capsule Endoscopy

Active wireless capsule endoscopy (WCE) under magnetic actuation is a promising technology to reduce the inspection time and relieve the burden of physicians. In this paper, we propose a reciprocally rotating magnetic actuation method for trajectory following of a capsule and develop its dynamic model. For the trajectory following task, we investigate the closed-loop tracking control strategies based on different controllers to actuate the capsule in the complex environments. The effectiveness of our method is validated in extensive experiments in a simulation environment as well as in an ex-vivo pig colon. The results demonstrate that the proposed method can accurately and efficiently actuate the capsule to follow the desired trajectory in the complex environments, achieving tracking errors on the order of millimeter. Moreover, the experiments on the ex-vivo pig colon show that the proposed reciprocally rotating magnetic actuation method has the potential to reduce the clinical risks and improve the safety and clinical acceptability of this technology.
Deep Neural Backdoor in Semi-Supervised Learning: Threats and Countermeasures

Semi-Supervised Learning (SSL) is a powerful derivative for humans to discover the hidden knowledge, and will be a great substitute for data taggers. Although the availability of unlabeled data rises up a huge passion to SSL, the untrustness of unlabeled data leads to many unknown security risks. In this paper, we first identify an insidious backdoor threat of SSL where unlabeled training data are poisoned by backdoor methods migrated from supervised settings. Then, to further exploit this threat, a Deep Neural Backdoor (DeNeB) scheme is proposed, which requires less data poisoning budgets and produces stronger backdoor effectiveness. By poisoning a fraction of our unlabeled training data, the DeNeB achieves the illegal manipulation on the trained model without modifying the training process. Finally, an efficient detection-and-purification defense (DePuD) framework is proposed to thwart the proposed scheme. In DePuD, we construct a deep detector to locate trigger patterns in the unlabeled training data, and perform secured SSL training with purified unlabeled data where the detected trigger patterns are obfuscated. Extensive experiments based on benchmark datasets are performed to demonstrate the huge threatening of DeNeB and the effectiveness of DePuD. To the best of our knowledge, this is the first work to achieve the backdoor and its defense in semi-supervised learning.
Electromagnetically Interfered Energy Metering Resulting from Droop of Current Transducers

Non-linear equipment is increasingly being used in household situations to increase the efficiency of the power consumption of equipment. This results in conducted electromagnetic interference problems due to the switching of the equipment, which draws pulsed currents with high peak values and fast rising slopes. Accordingly, static energy meters, used to measure the energy consumption in households, show misreadings in conjunction with such pulsed currents. Therefore, a control meter is under construction which is used to validate the readings of an installed static energy meter. When validating the control meter, energy metering errors were found resulting from current droop of the current transducers. The droop in the current response multiplied with the voltage resulted in large energy metering errors of 38%. While no issues where found for linear, resistive, loads. This shows the need for pulsed immunity tests to validate energy measurement systems, because linear tests have not shown to be problematic.
Vanishing Point Aided LiDAR-Visual-Inertial Estimator

In this paper, we propose a vanishing point aided LiDAR-Visual-Inertial estimator to achieve real-time, low-drift and robust pose estimation. The proposed method is mainly composed of 3 sequential modules, namely IMU-aided vanishing point (VP) detection module, voxel-map based feature depth association module, and visual inertial fixed-lag smoother module. The IMU-aided VP detection module will detect feature points, line segments and vanishing points to establish robust correspondences in successive frames. In particular, we propose to use 1-line RANSAC method to provide stable VP hypotheses and polar grid to accelerate vanishing point hypothesis validation. After that, we propose a novel voxel-map based feature depth association method, to retrieve depth and assign depth to visual feature efficiently. Finally, the visual inertial fixed-lag smoother module is proposed to jointly minimize error terms. Experiments show that our method outperforms the state-of-the-art visual-inertial odometry and LiDAR-visual estimator in both indoor and outdoor environments.
VirtualHome2KG: Constructing and Augmenting Knowledge Graphs of Daily Activities Using Virtual Space

Daily living studies usually require a physical environment such as cameras, sensor networks, or experimental space. Moreover, it is diﬃcult to collect data by ﬂexibly changing the conditions. In the future, for the analysis of daily life, it is necessary to combine data obtained from a physical space that can acquire real data and a virtual space that can ﬂexibly change conditions and perform many experiments. In this study, using virtual space to enable various analyses of daily living activities, we propose a method to construct and augment knowledge graphs (KGs) based on the simulation results of daily living.
Visual Localization and Mapping with Hybrid SFA

: Visual localization is a crucial requirement in mobile robotics, ﬁeld and service robotics, and self-driving cars. Recently, unsupervised learning with Slow Feature Analysis (SFA) has shown to produce spatial representations that enable localization from holistic images. The approach is faster and much less complex than state-of-the-art monocular visual SLAM methods while achieving similar localization performance in small-scale environments. However, the holistic ap-proach’s performance drops signiﬁcantly for highly complex, large-scale environments due to scene variations occurring during a training phase. Instead of using holistic images, an alternative is to perform localization relative to unique regions present in a scene. Therefore, in this paper, we add a new component to the SFA localization pipeline that leverages state-of-the-art CNN to identify unique image regions. Hence we propose a hybrid approach that ﬁrst learns such regions with a pre-trained CNN and then uses SFA for unsupervised pose estimation relative to each region. We present the experimental results from an autonomous robot in two different outdoor environments of varying complexity and size. The experiments show the proposed hybrid approach outperforms holistic SFA w.r. t localization accuracy in both environments, but beneﬁts are more pronounced in the large-scale environment.
EXI-Net: EXplicitly/Implicitly Conditioned Network for Multiple Environment Sim-to-Real Transfer

: Sim-to-real transfer is attractive for robot learning, as it avoids the high cost of collecting data with real robots, but transferring agents from simulation to the real world is challenging. Previous studies have presented promising methods to solve this problem, but they may fail when a wider range of dynamics has to be considered. In this study, we propose a network architecture with explicit and implicit dynamics parameters for sim-to-real transfer from multiple environments. Using this method, we can estimate the dynamics of the real world and optimize the action in various kinds of environments. The core novelty lies in the dynamics estimation and action optimization, as well as the use of explicit (physically quantiﬁable) and implicit (latent) dynamics parameters to condition the network input. We apply our method to the object pushing task and verify its effectiveness by comparing it with previous methods and real-world experiments.