Reviewerly/exHarmony huggingface dataset:
Exploring the auxiliary learning for long-tailed visual recognition

Real-world visual data often exhibits a long-tailed distribution, where some “head” classes have a large number of samples, yet only a few samples are available for “tail” classes. The fundamental problem of learning with the imbalanced data is that insufficient training samples easily lead to the over-fitting of feature extractor and classifier for tail classes, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training. On the other hand, oversampling tail classes makes the network prone to over-fitting, since head class samples are often consequently under-represented. To resolve this dilemma, in this paper, we propose an effective auxiliary learning approach. The key idea is to split a network into a classifier part and a feature extractor part, and then employ different training strategies for each part in an auxiliary learning manner. Specifically, to promote the awareness of tail-classes, a class-balanced sampling scheme is utilised for training both the classifier and the feature extractor as the primary task. For the feature extractor, we also introduce an auxiliary training task, which is to train a classifier under the regular random sampling scheme. In this way, the feature extractor is jointly trained from both sampling strategies and thus can take advantage of all training data and avoid the over-fitting issue. Apart from this basic auxiliary task, we further explore the benefits of different types of auxiliary tasks for improving the generality of learned features, including self-supervised learning and class-wise re-weighting. Without using any bells and whistles, our model compares favourably over state-of-the-art solutions.
Towards Guidelines for Designing Human-in-the-Loop Machine Training Interfaces

Supervised machine learning approaches commonly require good availability and quality of training data. In applications that depend on human-labeled data, especially from experts, or that depend on contextual knowledge for training data sets, the human-in-the-loop presents a serious bottleneck to the scalability of training efforts. Even if human labeling is generally feasible, sustained human performance and high-quality labels in larger quantities are challenging. Interactive Machine Learning can help solve usability problems in traditional machine learning by giving users agency in deciding how systems learn from data. Yet, the field lacks clear design guidelines for such interfaces, specifically regarding the scaling of training processes. In this paper, we present results from a pilot study in which participants interacted with several interface variants of a recommender engine and evaluated them on interaction and efficiency parameters. Based on the performance of these different learning system implementations we propose design guidelines for the design of such systems and a score for comparative evaluation, in which we combine interaction experience and system learning efficiency into one relative scoring unit.
Second order Takagi-Sugeno fuzzy model with domain adaptation for nonlinear regression

• A second order Takagi-Sugeno (TS 2 ) fuzzy model is proposed. • The proposed model performs better than the original TS fuzzy model in second order nonlinear regression tasks . • Domain adaptation in transfer learning is proved to be effective when optimizing the proposed model. In the regression analysis, Takagi-Sugeno fuzzy model gives a way of exploiting fuzzy logic to tackle nonlinear issues. However, the general Takagi-Sugeno fuzzy model encounters challenges when facing second order regression problems because of its insufficient fitting ability. In this study, the second order Takagi-Sugeno fuzzy model called TS 2 fuzzy model is proposed to extend the application scope of the original model. Moreover, domain adaptation in transfer learning is applied to the proposed model by using space transformation. It aims to further reduce the model’s cumulative error. The experimental results indicate that the proposed model has a better performance with not much extra processing time when dealing with second order nonlinear regression tasks.
Limit Theorems as Blessing of Dimensionality: Neural-Oriented Overview

As a system becomes more complex, at first, its description and analysis becomes more complicated. However, a further increase in the system’s complexity often makes this analysis simpler. A classical example is Central Limit Theorem: when we have a few independent sources of uncertainty, the resulting uncertainty is very difficult to describe, but as the number of such sources increases, the resulting distribution gets close to an easy-to-analyze normal one—and indeed, normal distributions are ubiquitous. We show that such limit theorems often make analysis of complex systems easier—i.e., lead to blessing of dimensionality phenomenon—for all the aspects of these systems: the corresponding transformation, the system’s uncertainty, and the desired result of the system’s analysis.
ReadsRE: Retrieval-Augmented Distantly Supervised Relation Extraction

Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). To address the noisy label problem, most models have adopted the multi-instance learning paradigm by representing entity pairs as a bag of sentences. However, this strategy depends on multiple assumptions (e.g., all sentences in a bag share the same relation), which may be invalid in real-world applications. Besides, it cannot work well on long-tail entity pairs which have few supporting sentences in the dataset. In this work, we propose a new paradigm named retrieval-augmented distantly supervised relation extraction (ReadsRE), which can incorporate large-scale open-domain knowledge (e.g., Wikipedia) into the retrieval step. ReadsRE seamlessly integrates a neural retriever and a relation predictor in an end-to-end framework. We demonstrate the effectiveness of ReadsRE on the well-known NYT10 dataset. The experimental results verify that ReadsRE can effectively retrieve meaningful sentences (i.e., denoise), and relieve the problem of long-tail entity pairs in the original dataset through incorporating external open-domain corpus. Through comparisons, we show ReadsRE outperforms other baselines for this task.
Crowdsourcing Scholarly Discourse Annotations

The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface's usability and the participant's attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.
PTEMagnet: fine-grained physical memory reservation for faster page walks in public clouds

The last few years have seen a rapid adoption of cloud computing for data-intensive tasks. In the cloud environment, it is common for applications to run under virtualization and to share a virtual machine with other applications (e.g., in a virtual private cloud setup). In this setting, our work identifies a new address translation bottleneck caused by memory fragmentation stemming from the interaction of virtualization, colocation, and the Linux memory allocator. The fragmentation results in the effective cache footprint of the host PT being larger than that of the guest PT. The bloated footprint of the host PT leads to frequent cache misses during nested page walks, increasing page walk latency.
Real-Time Survivor Detection in UAV Thermal Imagery Based on Deep Learning

Unmanned Aerial Vehicles (UAVs) uses evolved significantly due to its high durability, lower costs, easy implementation, and flexibility. After a natural disaster occurs, UAVs can quickly search the affected area to save more survivors. Dataset is crucial in developing a round-the-clock rescue system applying deep learning methods. In this paper, we collected a new thermal image dataset captured by UAV for post-disaster search and rescue (SAR) activities. After that, we employed several different deep convolutional neural networks to train the pedestrian detection models on our datasets, including YOLOV3, YOLOV3-MobileNetV1 and YOLOV3-MobileNetV3. Because the onboard microcomputer has limited computing capacity and memory, for balancing the inference time and accuracy, we find optimal points to prune and fine-tune the network based on the sensitivity of convolutional layers. We validate on NVIDIA's Jetson TX2 and achieve 26.60 FPS (Frames per second) real-time performance.
WeMos IoT Controller-Based Low-Cost Fall Detection System for Elderly Users

Fall detection systems for the elderly are very important to protect this type of users. The early detection of the fall of the elderly has a major impact on saving their lives and avoiding the deterioration of the negative medical effects resulting from the effect of the patient falling on a hard surface. One of the constraints in fall detection systems are false-negative errors (no fall detection) or false-positive errors (sending a false warning without real fall accident). These errors have to be reduced significantly. In this paper, an innovative method to reduce fall detection system errors is proposed. The system consists of two orientation detection sensors to track the body orientation instead of using a single sensor in the previous systems which enhances the system accuracy and reduces the false-negative and false-positive errors. The system uses a small size IoT-based controller to process the sensor's information and make the alarm decision based on specific thresholds. The output alarm of the system includes an email sent to the caregivers via the embedded Wi-Fi ESP8266 module as well as an SMS message to the caregivers’ phones via GSM modules to ensure that the alarm message arrives in the absence of internet coverage for the patient or the caregiver. The system is powered by a small lithium-Ion battery. All sensors and modules of the system are combined in a small rubber box that can be fixed in a waist belt or the chest rejoin of the user body. Several tests have been made in different procedures. The tests revealed that the new approach improves the accuracy of the system and reduces the possibility of triggering wrong alarms.
Disease profiling in pharmaceutical E-commerce

• Unlabeled purchase data are turned into partially labeled data with heuristics. • A prudent iterative naïve Bayesian (PINB) algorithm is proposed. • PINB demonstrates high performance in predicting users’ disease label. Pharmaceutical e-commerce platforms need to know users’ disease for product recommendation. However, symptom-based disease prediction is not applicable in this context due to the lack of symptom data. In this study, utilizing only purchase data, a prudent and iterative naïve Bayesian algorithm is proposed for disease prediction with the following advantages. First, it utilizes the freely available drug descriptions to identify some positive samples of a disease, hence converts the problem into a positive and unlabeled learning problem and avoids the costly training samples construction. Second, it proposes a prudent process to refine the quality of potentially positive samples in an iterative Bayesian learning process. This process also involves only one classifier while prior methods often involve multiple classifiers. This process enriches the scant strategies for positive sample selection in positive and unlabeled learning. The test on three liver diseases and three cardiovascular diseases indicate that the proposed algorithm could achieve a precision of 98.64% and recall of 90.90% across six diseases, which are superior to most of the benchmark algorithms.
Thyroid Nodule Detection using Artificial Neural Network

Thyroid gland is one of the largest endocrine gland and is located below the skin and muscles at front of neck. It has an important role in maintaining the metabolism of body. Modalities like Ultrasonography, Computer Tomography (CT),Magnetic Resonance Imaging(MRI) and Computer Aided Diagnosis(CAD) are used for identification and classification of abnormalities in thyroid gland. This paper proposes a computer based system for classifying nodules in thyroid as benign or malignant in ultrasound images depending upon the extracted features. The information related to texture is calculated from both Region of Interest (ROI) blocks. Enhancement is done to improve the image followed by segmentation using Active contour without edge(ACWE).With help of this extracted features, the Multi-layer perceptron(MLP) classifies the nodule as malignant or benign. The classification results obtained the height of accuracy, sensitivity, specificity, predicted positive value and predicted negative value at 93.84%, 97.82%, 84.21%,93.75% and 94.11% respectively. These results suggests that proposed scheme accomplished the classification of ultrasound thyroid nodules as benign or malignant.
Attainable Moment Set Optimization to Support Configuration Design: A Required Moment Set Based Approach

In this paper, we discuss an attainable moment set (AMS) optimization methodology considering a system’s required moment set (RMS). The AMS describes the achievable moments from a system, given its input limits. An RMS, like an AMS, is a convex set in the moment space, describing the required moments for a system to meet the designed mission profile and disturbance rejection requirements. Given the configuration of a system, its mission requirements, and the derived RMS, the proposed optimization maximizes coverage of the AMS onto the RMS, thus ensuring the system possesses the guaranteed controllability to fulfill its required missions from a design level. To achieve this goal, the variables to optimize are chosen as effector settings, such as the installation position and angle of propellers and control surfaces, which effectively change the AMS without a vast impact on major design parameters, such as mass and moment of inertia. Since the optimization includes massive geometry operations of rays intersecting polyhedron, an efficient intersection solver is proposed to speed up the optimization process. The described method is applied to an electric-vertical-take-of-landing vehicle (eVTOL) with eight hover propellers, which delivers a highly improved coverage of the RMS compared to its initial design.
Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders

Previous work mainly focuses on improving cross-lingual transfer for NLU tasks with a multilingual pretrained encoder (MPE), or improving the performance on supervised machine translation with BERT.However, it is under-explored that whether the MPE can help to facilitate the cross-lingual transferability of NMT model.In this paper, we focus on a zero-shot cross-lingual transfer task in NMT.In this task, the NMT model is trained with parallel dataset of only one language pair and an off-the-shelf MPE, then it is directly tested on zero-shot language pairs.We propose SixT, a simple yet effective model for this task.SixT leverages the MPE with a two-stage training schedule and gets further improvement with a position disentangled encoder and a capacity-enhanced decoder.Using this method, SixT significantly outperforms mBART, a pretrained multilingual encoderdecoder model explicitly designed for NMT, with an average improvement of 7.1 BLEU on zero-shot any-to-English test sets across 14 source languages.Furthermore, with much less training computation cost and training data, our model achieves better performance on 15 any-to-English test sets than CRISS and m2m-100, two strong multilingual NMT baselines.
Development of a transperineal prostate biopsy robot guided by MRI‐TRUS image

Abstract Background In the transrectal ultrasound (TRUS)‐guided transperineal prostate biopsy, doctors determine the biopsy target by observing the prostate region in TRUS images. However, ultrasound images with low imaging quality make doctors easy to be interfered when determining the biopsy route, which reduces the biopsy success rate. Methods This paper introduces the guidance method of magnetic resonance image (MRI) registration to ultrasound image and develops a 5‐degrees of freedom robot for prostate biopsy guided by MRI‐TRUS image. The robot uses a structure attached to the ultrasound probe to reduce the space occupied. By registering the posture relationship between MRI, TRUS image, ultrasonic probe and the robot base, the accurate localization of the suspected lesion area can be achieved with the preoperative MRIs. Results The prostate phantom biopsy based on the robotic biopsy system in this paper, the average biopsy error is 1.44 mm, and the maximum biopsy error is 2.23 mm. Conclusions We build a robotic biopsy platform with prostate phantom, and evaluate the biopsy accuracy of MRI‐TRUS guided prostate biopsy robot, the results meet clinical prostate biopsy requirements.
Research on the Visual Recognition Method of Pointer Water Meter Reading

For a long time, water companies have adopted manual meter reading methods when they settle water bills. Manual meter reading is inefficient, and there are often large subjective errors in it. Therefore, that water companies identify water meter readings automatically by computer technology and image processing technology is the future trend. This paper designed a kind of method to identify the water meter reading accurately. The method preprocessed collected water meter dial images by two-dimensional Gaussian filtering firstly. Then the pointer areas were separated from them by threshold and area growth method based on the difference of gray value. Finally, using Bresenham algorithm to calculate deflection angle between pointer center line and the zero scale which realizes the recognition of the reading. To enhance the algorithm's robustness to lighting, this method added the dynamic threshold mechanism and the pixel repair mechanism in the recognition process. This method was deployed on the Raspberry Pi 4B development board equipped with Raspbian GNU/Linux 10 "Buster" operating system, and the relationship between image resolution and reading accuracy was discussed.
A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques

The writer identification (WI) of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist organizations. It is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies, including old national and religious archives. In this study, we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on blocks. This modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis of the histogram of binary images. Also, propose a new framework for correct text rotation that will help us to establish a segmentation method that can facilitate the extraction of text from its background. Image projections and the radon transform are used and improved using machine learning based on a co-occurrence matrix to produce binary images. The training stage involves taking a number of images for model training. These images are selected randomly with different angles to generate four classes (0–90, 90–180, 180–270, and 270–360). The proposed segmentation approach achieves a high accuracy of 98.18%. The study ultimately provides two major contributions that are ranked from top to bottom according to the degree of importance. The proposed method can be further developed as a new application and used in the recognition of handwritten Arabic text from small documents regardless of logical combinations and sentence construction.
Codebook Design for Noncoherent Communications Using Autoencoders

The problem of joint optimization of coding and modulation for noncoherent communications is considered. To minimize block error rate, an autoencoder network is used. The network architecture is designed to mimic the end-to-end operation of a communication system with noncoherent correlation receiver. Signal codebooks with various spectral efficiencies are obtained for several small block lengths. Achieved pairwise correlations between signals of codebooks are compared with some known results. Also, minimum distances and power efficiencies of the obtained codebooks are analyzed. The obtained results show that autoencoder networks can be successfully used to design signal codebooks tailored to specific operating conditions.
Fixed-time adaptive general type-2 fuzzy logic control for air-breathing hypersonic vehicle

This paper proposes a novel fixed-time adaptive general type-2 fuzzy logical control (FAGT2FLC) scheme for an air-breathing hypersonic vehicle (AHV) with uncertainties. Firstly, the AHV dynamic model is transformed into a strict feedback form. Then, the FAGT2FLC is designed based on the transformed model to improve robustness and guarantee fixed-time convergence of the closed-loop system. The general type-2 fuzzy logic system (GT2FLS) is utilized to approximate the model uncertainties; for the purpose of designing adaptive laws, the [Formula: see text]-plane method is employed to represent the GT2FLS. A parameter projection operator is used to solve the possible singularity problem of parameter adaption. Besides, a fixed-time differentiator is used to deal with the “explosion of terms” inherent in backstepping method. Theoretical analysis based on relevant lemmas shows that the closed-loop system will converge into a small error band in fixed time. Lastly, detailed simulations are carried out to demonstrate the effectiveness and superiority of the proposed control scheme.
Impact of African leaders’ characteristics and regime transitions on economic growth in Africa: A dynamic model approach

This paper examined the extent to which leaders' characteristics and regime transitions impact on economic growth. A unique panel dataset, comprising 44 sub-Sahara African countries from 1970 to 2010, was used for the study. We used the differenced generalized methods of moments' estimation to test our dynamic model for the dataset controlling for leader specific effects. We observed that democratic leaders were able to attract foreign direct investment and impact on economic growth positively. With regard to regime transitions, we observed that business cycle existed, and it reduced the rate of growth in democratic governance in election periods as compared to autocratic governance. Overall, our results show that there is a limit to which an ageing leader can stay in office. Moreover, the overall results point to a democratic governance having an advantage over an autocratic one in the process of promoting increased and sustainable growth and investments.