Reviewerly/exHarmony huggingface dataset:
A Real-Time Negative Obstacle Detection Method for Autonomous Trucks in Open-Pit Mines

Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the characteristics of road negative obstacles in open-pit mines, a real-time target detection model based on the Yolov4 network was built. It uses RepVGG as the backbone feature extraction network, applying SimAM space and a channel attention mechanism to negative obstacle multiscale feature fusion. In addition, the classification and prediction modules of the network are optimized to improve the accuracy with which it detects negative obstacle targets. A non-maximum suppression optimization algorithm (CIoU Soft Non-Maximum Suppression, CS-NMS) is proposed in the post-processing stage of negative obstacle detection. The CS-NMS calculates the confidence of each detection frame with weighted optimization to solve the problems of encountering obscure negative obstacles or poor positioning accuracy of the detection boxes. The experimental results show that this research method achieves 96.35% mAP for detecting negative obstacles on mining roads with a real-time detection speed of 69.3 fps, and that it can effectively identify negative obstacles on unstructured roads in open-pit mines with complex backgrounds.
CNN-based Deep Transfer Learning Approach for Detecting Breast Cancer in Mammogram Images

Researchers are motivated to investigate deep learning (DL) techniques for mammogram images because of the limitations of conventional systems-based computer-aided detection (CAD) for mammography, the extreme significance of early breast cancer identification, and the high impact of false diagnosis on patients. This study develops a model for breast cancer detection from mammogram images that employ Convolutional Neural Network (CNN) based Transfer Learning (TL). The developed structure is comprised of several stages: breast region extraction is performed to extract the Region of Interest (ROI) from the background and artifacts, a Gaussian filter is employed for noise reduction, and data augmentation is carried out to increase the size of the original images for better learning of CNN. Then, a pre-trained model-based CNN using an augmented dataset is presented. Deep features are extracted from CNN and they are trained based on using TL. A mini-MIAS dataset was conducted as the testing ground for this experiment, and the best accuracy achieved was 95.71%. The developed framework performs significantly better than other current methods when compared to them.
MHKD-MVQA: Multimodal Hierarchical Knowledge Distillation for Medical Visual Question Answering

Medical Visual Question Answering (VQA) has emerged as a promising solution to enhance clinic-decision making and patient interactions. Given a medical image and a corresponding question, medical VQA aims to predict an informative answer by reasoning the visual and textual information. However, datasets with limited samples circumscribe the generalization of medical VQA, reducing its accuracy when applied to unseen medical samples. Existing works tried to solve this problem with meta-learning or self-supervised learning but still failed to achieve satisfactory performance on medical VQA with insufficient samples. To address this problem, we propose multimodal hierarchical knowledge distillation for medical VQA (MHKD-MVQA). In the primary novelty of MHKD-MVQA, we distill knowledge from not only the output but also the intermediate layers, which leverages the knowledge from limited samples to a greater extent. Meanwhile, medical images and questions are embedded in a shared latent space, enabling our model to tackle multimodal samples. We evaluate our model on two medical VQA datasets, VQA-MED 2019 and VQA-RAD, where MHKD-MVQA achieves state-of-the-art performance and outperforms baselines by 3.6% and 1.6%, respectively. The extensive experiments also highlight the generalization of knowledge distillation by analyzing the class activation maps on medical images concerning specific questions.
Local approximation for perfect discrimination of quantum states

Quantum state discrimination involves identifying a given state out of a set of possible states. When the states are mutually orthogonal, perfect state discrimination is always possible using a global measurement. In the case of multipartite systems when the parties are constrained to use multiple rounds of local operations and classical communication (LOCC), perfect state discrimination is often impossible even with the use of asymptotic LOCC, wherein an error is allowed but must vanish in the limit of an infinite number of rounds. Utilizing our recent results on asymptotic LOCC, we derive a lower bound on the error probability for LOCC discrimination of any given set of mutually orthogonal pure states. Informed by the insights gained from this lower bound, we are able to prove necessary conditions for perfect state discrimination by asymptotic LOCC. We then illustrate by example the power of these necessary conditions in significantly simplifying the determination of whether perfect discrimination of a given set of states can be accomplished arbitrarily closely using LOCC. The latter examples include a proof that perfect discrimination by asymptotic LOCC is impossible for any minimal unextendible product basis (UPB), where minimal means that for the given multipartite system no UPB with a smaller number of states can exist. We also give a simple proof that what has been called strong nonlocality without entanglement is considerably stronger than had previously been demonstrated.
Fast Guided Median Filter

Faster computation of a weighted median (WM) filter is impeded by the construction of a weighted histogram for every local window of data. Since the calculated weights vary for each local window, it is difficult, using a sliding window approach, to construct the weighted histogram efficiently. In this paper, we propose a novel WM filter that overcomes the difficulty of histogram construction. Our proposed method achieves real-time processing for higher resolution images and can be applied to multidimensional, multichannel, and high precision data. The weight kernel used in our WM filter is the pointwise guided filter, which is derived from the guided filter. The use of kernels based on the guided filter avoids gradient reversal artifacts and shows a higher denoising performance than the Gaussian kernel based on the color/intensity distance. The core idea of the proposed method is a formulation that allows the use of histogram updates with a sliding window approach to find the weighted median. For high precision data we propose an algorithm based on a linked list that can reduce the memory requirements of storing histograms and the computational cost of updating them. We present implementations of the proposed method that are suitable for both CPU and GPU. Experimental results show that the proposed method indeed realizes faster computation than conventional WM filters and is capable of filtering multidimensional, multichannel, and high precision data. This is an approach which is difficult to achieve with conventional methods.
HEDF: A Method for Early Forecasting Software Defects Based on Human Error Mechanisms

As the primary cause of software defects, human error is the key to understanding, and perhaps to forecasting and avoiding defects. Little research has been done to forecast defects on the basis of the cognitive errors that cause them. The existing “defect prediction” models are applied to code once it has been produced: therefore, their “predictions” have little implications for preventing the defects. This paper proposes an approach, “Human-Error-based Defect Forecast” (HEDF), to forecasting the exact defects at early stages of software development, before the code is produced, through knowledge about the cognitive mechanisms that cause developers’ errors. This approach is based on a model of human error mechanisms underlying software defects: a defect is caused by an error-prone scenario triggering human error modes, which psychologists have observed to recur across diverse activities. Software defects can then be forecast by identifying such error-prone scenarios the in requirements and/or design documents. We assessed this approach empirically, with 55 programmers in a programming competition and four representative analysts serving as the users of the approach. Impressively, the approach was able to forecast, at the requirement phase, 75.7% of the defects later committed by all of the programmers. When considering just the defect forms, which may manifest as distinct defects even in the same program, the proposed method predicted 31.8% of them. This approach substantially improved the defect forecasting performances for analysts of various expertise, with a minimum of 100% improvement, compared to forecasts without the approach. If the forecast had been used to prevent the defects, it could have saved an estimated 46.2% of the debugging effort and increased the fraction of programmers delivering an acceptable program by 32.6%. The observed excellent performance of HEDF in forecasting (early at requirement stage) the exact forms and locations of defects that may be later introduced by developers into code makes it a promising candidate for preventing the defects, worthy of further study.
Studying and Understanding the Tradeoffs Between Generality and Reduction in Software Debloating

Existing approaches for program debloating often use a usage profile, typically provided as a set of inputs, for identifying the features of a program to be preserved. Specifically, given a program and a set of inputs, these techniques produce a reduced program that behaves correctly for these inputs. Focusing only on reduction, however, would typically result in programs that are overfitted to the inputs used for debloating. For this reason, another important factor to consider in the context of debloating is generality, which measures the extent to which a debloated program behaves correctly also for inputs that were not in the initial usage profile. Unfortunately, most evaluations of existing debloating approaches only consider reduction, thus providing partial information on the effectiveness of these approaches. To address this limitation, we perform an empirical evaluation of the reduction and generality of 4 debloating techniques, 3 state-of-the-art ones, and a baseline, on a set of 25 programs and different sets of inputs for these programs. Our results show that these approaches can indeed produce programs that are overfitted to the inputs used and have low generality. Based on these results, we also propose two new augmentation approaches and evaluate their effectiveness. The results of this additional evaluation show that these two approaches can help improve program generality without significantly affecting size reduction. Finally, because different approaches have different strengths and weaknesses, we also provide guidelines to help users choose the most suitable approach based on their specific needs and context.
Transformer-Based Recognition of Activities of Daily Living from Wearable Sensor Data

Smart support systems for the recognition of Activities of Daily Living (ADLs) can help elderly people live independently for longer improving their standard of living. Many machine learning approaches have been proposed lately for Human Activity Recognition (HAR), including elaborated networks that contain convolutional, recurrent, and attentive layers. The ubiquity of wearable devices has provided an increasing amount of time-series data that can be used for such applications in an unobtrusive manner. But there are not many studies on the performance of the attention-based Transformer model in HAR, especially not for complex activities such as ADLs. This work implements and evaluates the novel self-attention Transformer model for the classification of ADLs and compares it to the already well-established approach of recurrent Long-Short Term Memory (LSTM) networks. The proposed method is a two-level hierarchical model, in which atomic activities are initially recognized in the first step and their probability scores are extracted and utilized for the Transformer-based classification of seven more complex ADLs in the second step. The Transformer is used at the second step to classify seven ADLs. Our results show that the Transformer model reaches the same performance and even outperforms LSTM networks cleary in the subject-dependent configuration (73.36 % and 69.09 %), while relying only on attention-mechanism to depict global dependencies between input and output without the need to use any recurrence. The proposed model was tested using two different segment lengths, indicating its effectiveness in learning long-range dependencies of shorter actions in complex activities.
Heterogeneous Reinforcement Learning Network for Aspect-Based Sentiment Classification With External Knowledge

Aspect-based sentiment classification aims to automatically predict the sentiment polarity of the specific aspect in a text. However, it is challenging to confirm the mapping between the aspect and the core context since a number of existing methods concentrate on building the global relations of the full context rather than the partial connections based on the aspects. Motivated by the fundamental insights of reinforcement learning, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>H</b></u> eterogeneous <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>R</b></u> einforcement <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>L</b></u> earning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>N</b></u> etwork for aspect-based sentiment analysis (HRLN) to alleviate these issues, which contains two primary components, a heterogeneous network module, and a knowledge graph-based reinforcement learning module consistent with common-sense knowledge and emotional knowledge. To evaluate the effectiveness of HRLN, we conduct extensive experiments on five benchmark datasets, which indicate that HRLN achieves competitive performance and yields state-of-the-art results on all datasets. Additionally, we present an intuitive comprehension of why our HRLN model is more robust for aspect-based sentiment classification via case studies.
Label-Only Model Inversion Attacks: Attack With the Least Information

In a model inversion attack, an adversary attempts to reconstruct the training data records of a target model using only the model’s output. In launching a contemporary model inversion attack, the strategies discussed are generally based on either predicted confidence score vectors, i.e., black-box attacks, or the parameters of a target model, i.e., white-box attacks. However, in the real world, model owners usually only give out the predicted labels; the confidence score vectors and model parameters are hidden as a defense mechanism to prevent such attacks. Unfortunately, we have found a model inversion method that can reconstruct representative samples of the target model’s training data based only on the output labels. We believe this attack requires the least information to succeed and, therefore, has the best applicability. The key idea is to exploit the error rate of the target model to compute the median distance from a set of data records to the decision boundary of the target model. The distance is then used to generate confidence score vectors which are adopted to train an attack model to reconstruct the representative samples. The experimental results show that highly recognizable representative samples can be reconstructed with far less information than existing methods.
YOLO-GG: a slight object detection model for empty-dish recycling robot

Empty-dish recycling robot is an effective product to solve the labor shortage problem in food service. A key research point is to design a fast and accurate empty dish detection model for the robot. With the development of deep learning techniques, researchers have presented several deep learning-based models for empty-dish detection with excellent accuracy. However, to implement the empty dish detection model on an embedded device in the robot, the model should be optimized with a compact size and less calculation. To realize the optimized empty-dish detection model, this paper proposes a novel YOLO-GG model which implements the GPU-efficient Ghost module on the YOLO. In detail, the proposal adopts the G-Ghost module to replace the backbone of YOLOV4 in CSPDarknet. Meanwhile, we reduce the convolution layer in the Neck of the YOLOV4 to decrease the model parameters. The proposed model is trained on the Dish-20 dataset. Experimental results show that YOLO-GG achieves 23.4G floating point operations, 99.34% mean average precision, and 71.2 frames per second. The lower parameters and high accuracy of YOLO-GG prove the effectiveness.
How Global Observation Works in Federated Learning: Integrating Vertical Training Into Horizontal Federated Learning

Federated learning (FL) has recently emerged as an innovative paradigm to train models among distributed agents. Conventional FL considers the center as an aggregator and trains from distributed data, while the collected global information at the center is not effectively utilized. Thus, the restricted information from local observations may limit the model accuracy. If FL can introduce data sets from the network server, the distributed models may be largely improved by the extra global information. Since network agents may not be completely trusted, the center cannot directly broadcast its raw data for security concern. Then, how to combine the central sets with FL? In this article, we propose to add a learning model at the center, which obtains the central sets as input. The outputs can be transmitted to network agents and integrated into local models instead of the raw data. The central and local models could be trained to form an integration for intelligent inference. Then, what is the integrated performance gain comparing with the original horizontal FL (HFL) and how to implement it? To figure out these two problems, we propose the vertical-HFL (VHFL) scheme, where models of the center and agents are trained collaboratively. We further analyze its convergence and the related communication channel, proposing the theoretical bounds to guide the network implementation of VHFL. Some simulation results will demonstrate the effectiveness of our proposed VHFL scheme. It is expected that VHFL will be an important block for the next generation of smart services.
Video Based Mobility Monitoring of Elderly People Using Deep Learning Models

In recent years, the number of older people living alone has increased rapidly. Innovative vision systems to remotely assess people’s mobility can help healthy, active, and happy aging. In the related literature, the mobility assessment of older people is not yet widespread in clinical practice. In addition, the poor availability of data typically forces the analyses to binary classification, e.g. normal/anomalous behavior, instead of processing exhaustive medical protocols. In this paper, real videos of elderly people performing three mobility tests of a clinical protocol are automatically categorized, emulating the complex evaluation process of expert physiotherapists. Videos acquired using low-cost cameras are initially processed to obtain skeletal information. A proper data augmentation technique is then used to enlarge the dataset variability. Thus, significant features are extracted to generate a set of inputs in the form of time series. Four deep neural network architectures with feedback connections, even aided by a preliminary convolutional layer, are proposed to label the input features in discrete classes or to estimate a continuous mobility score as the result of a regression task. The best results are achieved by the proposed Conv-BiLSTM classifier, which achieves the best accuracy, ranging between 88.12% and 90%. Further comparisons with shallow learning classifiers still prove the superiority of the deep Conv-BiLSTM classifier in assessing people’s mobility, since deep networks can evaluate the quality of test executions.
Point Cloud Adversarial Perturbation Generation for Adversarial Attacks

In recent years, 3D model analysis has made a revolutionary development.Point cloud contains rich 3D object geometry information, which is an important 3D object data format widely used in many applications.However, the irregularity and disorder of the point cloud also cause its vulnerability to environmental impact, which may bring security risks to safety-critical 3D applications such as self-driving tasks.Recently, there are only a few methods engaged to attack the point cloud models to improve the robustness of point cloud analysis models.Most of them only focus on the attack by adjusting the points but ignore learning the perturbation's distribution characteristics.In this work, we propose a novel framework to attack point cloud models.By introducing the GAN structure, we train a generator to produce slight pointto-point perturbations according to the sample's raw classification, which can effectively boost the attack performance.Meanwhile, we propose an outlier removal module to constrain the magnitude of the generated perturbation.The goal is to guarantee the visual quality of generation samples to improve the difficulty of training and further improve the robustness of 3D analysis models.Finally, we carry out extensive attack experiments, and the related results demonstrate the effectiveness of our proposed method.
SA-PatchCore: Anomaly Detection in Dataset With Co-Occurrence Relationships Using Self-Attention

Various unsupervised anomaly detection methods using deep learning have recently been proposed, and the accuracy of the anomaly detection technique for local anomalies has been improved.However, no anomaly detection dataset includes co-occurrence-related anomalies, which are combinationrelated.Thus, the accuracy of anomaly detection for co-occurrence-related anomalies has not progressed.Therefore, we propose SA-PatchCore, which introduces self-attention to the state-of-the-art local anomaly detection model, PatchCore.It detects anomalies in co-occurrence relationships and anomalies in local areas with the benefit of the self-attention module, which can consider contexts between separated words introduced first in the natural language processing field.As no anomaly detection dataset includes anomalies in co-occurrence relation, we prepared a new dataset called the Co-occurrence Anomaly Detection Screw Dataset (CAD-SD).Furthermore, we performed experiments on anomaly detection using the new dataset.SA-PatchCore achieves high anomaly detection performance compared with PatchCore in the CAD-SD.Moreover, our proposed model shows almost the same anomaly detection performance as PatchCore in an MVTec Anomaly Detection dataset, which is composed of anomalies in a local area.As a contribution to the anomaly detection task, we have released the CAD-SD to the public.The code and dataset are publicly available at https://github.com/IshidaKengo/SA