Reviewerly/exHarmony huggingface dataset:
Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning

Learning a risk-aware policy is essential but rather challenging in unstructured robotic tasks. Safe reinforcement learning methods open up new possibilities to tackle this problem. However, the conservative policy updates make it intractable to achieve sufficient exploration and desirable performance in complex, sample-expensive environments. In this paper, we propose a dual-agent safe reinforcement learning strategy consisting of a baseline and a safe agent. Such a decoupled framework enables high flexibility, data efficiency and risk-awareness for RL-based control. Concretely, the baseline agent is responsible for maximizing rewards under standard RL settings. Thus, it is compatible with off-the-shelf training techniques of unconstrained optimization, exploration and exploitation. On the other hand, the safe agent mimics the baseline agent for policy improvement and learns to fulfill safety constraints via off-policy RL tuning. In contrast to training from scratch, safe policy correction requires significantly fewer interactions to obtain a near-optimal policy. The dual policies can be optimized synchronously via a shared replay buffer, or leveraging the pre-trained model or the non-learning-based controller as a fixed baseline agent. Experimental results show that our approach can learn feasible skills without prior knowledge as well as deriving risk-averse counterparts from pre-trained unsafe policies. The proposed method outperforms the state-of-the-art safe RL algorithms on difficult robot locomotion and manipulation tasks with respect to both safety constraint satisfaction and sample efficiency.
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks

Recently, the vulnerability of deep image classification models to adversarial attacks has been investigated. However, such an issue has not been thoroughly studied for image-to-image tasks that take an input image and generate an output image (e.g., colorization, denoising, deblurring, etc.) This paper presents comprehensive investigations into the vulnerability of deep image-to-image models to adversarial attacks. For five popular image-to-image tasks, 16 deep models are analyzed from various standpoints such as output quality degradation due to attacks, transferability of adversarial examples across different tasks, and characteristics of perturbations. We show that unlike image classification tasks, the performance degradation on image-to-image tasks largely differs depending on various factors, e.g., attack methods and task objectives. In addition, we analyze the effectiveness of conventional defense methods used for classification models in improving the robustness of the image-to-image models.
Composition-Guided Neural Network for Image Cropping Aesthetic Assessment

How to explore the interaction between image aesthetic rules and crops is the key to finding views with good composition. Besides, it is subjective to evaluate candidate crops, which mainly depends on aesthetic knowledge, but it is not an easy task for people without extensive photography experience. However, existing methods mostly find good views by extracting general aesthetic features of crops without fully exploring the aesthetic rules. Motivated by this, we innovatively propose a composition-guided image cropping aesthetic assessment network (CGICAANet) for efficiently finding good crops and optimizing the cropping operation. Specifically, we adopt a direct and comprehensive composition pattern module, which adaptively mines suitable compositions for the images and emphasizes the dominant position of visual elements to contribute to optimizing the best crops in an interpretable way. Moreover, we designed a multi-task loss function to train the model. Particularly, to explore the commonality between predicted crops and labels, the complete intersection-over-union loss is adopted thoroughly considering the overlap area, central point distance and the consistency of aspect ratios for crops concurrently. Therefore, the predicted best crop can preserve the visual elements and have better composition. Experimental results with lightweight MobileNetV2 and ShuffleNetV2 as backbone networks demonstrate that our method can obtain comparable or better performance in terms of efficiency and accuracy.
Population Initialization Factor in Binary Multi-Objective Grey Wolf Optimization for Features Selection

Features selection methods not only reduce the dimensionality, but also improve significantly the classification results.In this study, the effect of the initialization population using the population factor has been explored.There are twenty wolves obtained by the population initialization method in binary multiobjective grey wolf optimization for features selection.There are two objectives function that will be minimized i.e. number of features and error rate.The proposed method has been compared with the previous study Binary Multi-Objective Grey Wolf Optimization (BMOGWO-S) using UCI datasets, oil and gas datasets.The results reflect that the proposed method outperforms all existence methods in terms of reducing feature numbers and error rates.
I Still Know What You Did Last Summer: Inferring Sensitive User Activities on Messaging Applications Through Traffic Analysis

Instant Messaging (IM) applications such as Signal, Telegram, and WhatsApp have become tremendously popular in recent years. Unfortunately, such IM services have been targets of governmental surveillance and censorship, as these services are home to public and private communications on socially and politically sensitive topics. To protect their clients, popular IM services deploy state-of-the-art encryption. Despite the use of advanced encryption, we show that popular IM applications leak sensitive information about their clients to adversaries merely monitoring their encrypted IM traffic, with no need for leveraging any software vulnerabilities of IM applications. Specifically, we devise traffic analysis attacks enabling an adversary to identify participants of target IM communications (e.g., forums) with high accuracies. We believe that our study demonstrates a significant, real-world threat to the users of such services. We demonstrate the practicality of our attacks through extensive experiments on real-world IM communications. We show that standard countermeasure techniques can degrade the effectiveness of these attacks. We hope our study will encourage IM providers to integrate effective traffic obfuscation into their software. In the meantime, we have designed a countermeasure system, called IMProxy that can be used by IM clients with no need for any support from IM providers. We demonstrate the effectiveness of IMProxy through simulation and experiments.
Detect Lane Line Based on Bi-directional Feature Pyramid Network

The efficient detection and accurate identification of traffic signs is an important guarantee for the safe driving of intelligent vehicles, which has attracted the attention of many researchers. For the realistic traffic signs with small size and large scale changes, the performance of existing detection methods can not meet the application requirement of traffic signs detection. In this paper, based on the Yolov5 method, this paper propose a traffic signs detection algorithm based on the Yolov5 and weighted bi-directional feature pyramid network (BI-FPN). The BI-FPN helps combine the high semantic information at the top layer with high resolution information at the bottom layer to output a semantically rich feature embedding with clear localization information. Qualitative and quantitative results verify the effectiveness of this work, which can achieve effective detection of traffic signs in complex scenes. yolov5 is a one stage object detection tool with good detection accuracy for real-time detection tasks, however, it difficult to meet the requirements of multi-scale and low FLOPs in the process of traffic sign recognition.
A Simple Method for Synchronising Multiple IMUs using the Magnetometer

This paper presents a novel method to synchronise multiple IMU (inertial measurement units) devices using their onboard magnetometers. The method described uses an external electromagnetic pulse to create a known event measured by the magnetometer of multiple IMUs and in turn used to synchronise these devices. The method is applied to 4 IMU devices decreasing their de-synchronisation from 270ms when using only the RTC (real time clock) to 40ms over a 1 hour recording. It is proposed that this can be further improved to approximately 3ms by increasing the magnetometer's sample frequency from 25Hz to 300Hz.
Visual Cross-View Metric Localization with Dense Uncertainty Estimates

This work addresses visual cross-view metric localization for outdoor robotics. Given a ground-level color image and a satellite patch that contains the local surroundings, the task is to identify the location of the ground camera within the satellite patch. Related work addressed this task for range-sensors (LiDAR, Radar), but for vision, only as a secondary regression step after an initial cross-view image retrieval step. Since the local satellite patch could also be retrieved through any rough localization prior (e.g. from GPS/GNSS, temporal filtering), we drop the image retrieval objective and focus on the metric localization only. We devise a novel network architecture with denser satellite descriptors, similarity matching at the bottleneck (rather than at the output as in image retrieval), and a dense spatial distribution as output to capture multi-modal localization ambiguities. We compare against a state-of-the-art regression baseline that uses global image descriptors. Quantitative and qualitative experimental results on the recently proposed VIGOR and the Oxford RobotCar datasets validate our design. The produced probabilities are correlated with localization accuracy, and can even be used to roughly estimate the ground camera's heading when its orientation is unknown. Overall, our method reduces the median metric localization error by 51%, 37%, and 28% compared to the state-of-the-art when generalizing respectively in the same area, across areas, and across time.
Toward Accurate Online Multi-target Multi-camera Tracking in Real-time

Multi-target multi-camera tracking is the task of determining the trajectories of objects within a network of cameras. Besides many others, it is a crucial task, e.g., in traffic analysis or law enforcement. Current research mainly focuses on offline algorithms for cross-camera association, which require all video data at once and often lack real-time capability. Thus, such approaches are unsuitable for tracking a criminal's escape route and enabling immediate intervention and arrest. To close this gap, we introduce a novel online multi-target multi-camera tracking method in this work which integrates spatial-temporal information and applies a new probability-based association metric. Moreover, we prove the real-time capability and significantly outperform the baseline on two datasets. In addition, the performance gap to more complex and slower offline methods is discussed to indicate the current state of research.
Emotion Cause Pair Extraction Based on Multitask

Emotion cause pair extraction is a sub-task of sentiment analysis tasks, which aims to extract all emotion clauses in a given document and the cause clauses corresponding to the emotion. At present, only the method of sharing parameters at the bottom layer is used to build the connection between tasks, and the shared word encoder is used in word encoding, the phenomenon that the model pays different attention to emotional words and cause words is ignored, and the rich interactive relationship information between the three tasks cannot be fully utilized. This paper proposes a multi-task emotion cause pair extraction model based on feature fusion. The model learns the multi-information at the word level through the feature fusion module, and uses the two tasks of emotion clause extraction and cause clause extraction as auxiliary tasks. The extracted result is transformed into label information, and the method of label embedding is used to integrate into the generation of emotion cause pair representation, thereby improving the effectiveness of the emotion cause pair extraction.The experimental results of the model in the paper on the ECPE data set prove that the Mul-ECPE model has improved in the evaluation indicator of the three tasks compared to the previous series of models.
Novel Applications for VAE-based Anomaly Detection Systems

Deep generative modeling (DGM) is an increasingly popular approach that can create novel and unseen data, starting from a given data set. As the technology shows promising applications, many ethical issues also arise. For example, their misuse can enable disinformation campaigns and powerful phishing attempts. Research also shows different biases affect deep learning models, leading to social issues such as misrepresentation. In this work, we formulate a novel setting to deal with similar problems, showing that a repurposed anomaly detection system effectively generates novel data, avoiding generating specified unwanted data. We propose Variational Auto-encoding Binary Classifiers (V-ABC): a novel model that repurposes and extends the Auto-encoding Binary Classifier (ABC) anomaly detector using the Variational Auto-encoder (VAE). We survey the limitations of existing approaches and explore many tools to show the model's inner workings in an interpretable way. This proposal has excellent potential for generative applications: models that rely on user-generated data could automatically filter out unwanted content, such as offensive language, obscene images, and misleading information.
Automated implementation of test scenarios by UML combinational diagrams via uniformed algorithm

The software testing process is not only important but also a complex stage SDLC (software development life cycle) process. For estimating the activities of the system in the software testing stage testers implement test scenarios as input for the system under test (SUT). For improving the superiority of the system and also finding faults implementing test-cases shows an energetic role. Our research work target towards implementation of test cases automatically by combinational Unified Modeling Language (UML) diagram so that it can display higher effectiveness in starting phase of the system development procedure of model-based testing. This research uses UML diagrams like Use Case Diagram (UCD), Activity Diagram (AD), and a combination of both generates System Testing Graph (STG). The combination of these two diagrams has been proved as the well-suited diagrams to implement test cases from the earlier combination graphs. Our technique suggested in this research paper is Uniformed Graph Traversing (UGT) Algorithm that is combined with Level Order Traversal (LOT) Algorithm to implement predictable test scenarios. In this research paper, we prove that the UGT algorithm is useful to implement test scenarios, and also these test scenarios are providing precise outcomes; each vertex existing in the test scenarios includes some situation. In this proposed approach we also disclose faults for example decision fault, fault of dependency, interaction fault, and loop fault which results in assessment effort reduction. The implemented test scenarios are directed to one requests controlling tool which can be in contradiction of the requests. It also protects time period and work and also, raises the value of implemented test scenarios, thus enhances the complete routine in a testing phase.
ClusterVote: Automatic Summarization Dataset Construction with Document Clusters

Creating a summarization dataset is a costly task due to the amount of expertise and human work required to compose quality summaries. To alleviate the issue, several pseudo-summary approaches were developed, but due to a lack of domain adaptation mechanism, they were not applied beyond language model pretraining. We find that this shortcoming can be overcome by leveraging document clusters. We propose ClusterVote, a pseudo-summarization approach that accounts for domain summarization patterns by studying links between related documents. The method can be configured for different levels of granularity and produce both extractive and abstractive summaries. We evaluate the approach by collecting Telegram news summarization dataset and testing state-of-the-art models. The experimental results show that the most refined variant of ClusterVote has similar extractive properties to CNN/Daily Mail dataset and proves to be challenging for summarization systems.
Open-Set Face Identification with Sequential Analysis and Out-of-Distribution Data Detection

One of the main issues in face identification is to create a real-time application with high accuracy. Images are presented by high-dimensional feature vectors that are produced by convolutional neural networks. In order to effectively process such vectors, the hierarchical algorithm was proposed in this paper that applies sequential analysis to search the nearest neighbors among the reference images of the most reliable classes selected at a certain algorithm level. Principal component analysis was also applied to select the most significant part of feature vectors. Moreover, another issue of face recognition was investigated: the lack of training data of specific types (bad quality image, different scale or illumination, children/old people, etc.), The recognition accuracy may be low for input images that are not similar to the majority of images in the dataset used to train the feature extractors. Therefore, we propose the level of preprocessing input images by detection of rare data. In this paper datasets such as VGGFace2, MS-Celeb-1M, All ages faces, Large age gap face, and different facial descriptors were used for the testing goal. Also, we provide a procedure to collect a special dataset for a given training set by using different transformations and automatic detection of anomalies.
Exploration of Method to Improve the Teaching Effect of Course “Operating System”

Operating system is a strategic technology and a foundational software in information technology field, but it is really not easy to teach and learn this course well. To solve the problem, a method of online and offline hybrid teaching is adopted, while giving full play to the role of probing into technical topics and students' sharing in class. By the four stages of this process, before, during and after class and at ordinary times, students change gradually from knowing to being able to express, then to mastering, and finally to broadening their cognition and creative application. This makes a beneficial practical exploration on the improvement of the teaching effect of Operating System course.
Quantum-Inspired Solvers on Mixed-Integer Linear Programming Problem

Mixed-integer linear programming (MILP) plays a crucial role in artificial intelligence, biochemistry, finance, cryp-tography, etc. Notwithstanding popular for decades, the researches of MILP solvers are still limited by the resource consumption caused by complexity and failure of Moore's Law. Quantum-inspired Ising machines, as a new computing paradigm, can be used to solve integer programming problems by reducing them into Ising models. Therefore, it is necessary to understand the technical evolution of quantum inspired solvers to break the bottleneck. In this paper, the concept and traditional algorithms for MILP are introduced. Then, focused on Ising model, the principle and implementations of annealers and coherent Ising machines are summarized. Finally, the paper discusses the challenges and opportunities of miniaturized solvers in the future.
Date Recognition in Historical Parish Records

In Northern Europe, parish records provide centuries of lineage information, useful not only for settling inheritance disputes, but also for studying hereditary diseases, social mobility, etc. The key information to extract from scans of parish records to obtain lineage information is dates: birth dates (of children and their parents) and dates of baptisms. We present a new dataset of birth dates from Danish parish records and use it to benchmark different approaches to handwritten date recognition, some based on classification and some based on transduction. We evaluate these approaches across several experimental protocols and different segmentation strategies. A state-of-the-art transformer-based transduction model exhibits lower error rates than image classifiers in most scenarios. The image classifiers can nevertheless offer a compelling trade-off in terms of accuracy and computational resource requirements.
Brain Region-Based Vigilance Assessment Using Electroencephalography and Eye Tracking Data Fusion

Vigilance is the capacity to remain alert for an extended time while performing a task. Staying alert is obligatory in many jobs, particularly those that involve monitoring, such as surveillance tasks, security monitoring, and air traffic control. These monitoring tasks require a specific level of arousal to maintain an adequate level of cognitive efficiency. In this study, we investigate the possibility of assessing the vigilance levels using a fusion of electroencephalography (EEG) and eye tracking data. Vigilance levels were established by performing a modified version of the Stroop color word task (SCWT) for 30 minutes. Feature-level fusion based on the canonical correlation analysis (CCA) was employed to each brain region to improve the classification accuracy of vigilance level assessment. Results obtained using support vector machines (SVM) classifier show that fusion of EEG+eye tracking modalities has improved the classification accuracy compared to individual modality. The EEG+Eye tracking fusion on the right central brain region achieved the highest classification accuracy of 97.4 ± 1.3%, compared to the individual Beta EEG with 92.0±7.3% and Eye tracking with 76.8±8.4%, respectively. Likewise, EEG and Eye tracking fusion on the right frontal region showed classification accuracy of 96.9 ± 1.1% for both the Alpha and Beta bands. Meanwhile, when all brain regions were utilized, the highest classification accuracy of EEG+Eye tracking was 96.8 ± 0.6% using Delta band compared to the EEG alone with 88.18 ± 8.5% and eye tracking alone with 76.8 ± 8.4 %, respectively. The overall results showed that vigilance is a brain region specific and the fusion of EEG+ and Eye tracking data using CCA has significantly improved the classification accuracy of vigilance levels assessment.
Interactive Visualization for Data Science Scripts

As the field of data science continues to grow, so does the need for adequate tools to understand and debug data science scripts. Current debugging practices fall short when applied to a data science setting, due to the exploratory and iterative nature of analysis scripts. Additionally, computational notebooks, the preferred scripting environment of many data scientists, present additional challenges to understanding and debugging workflows, including the non-linear execution of code snippets. This paper presents Anteater, a trace-based visual debugging method for data science scripts. Anteater automatically traces and visualizes execution data with minimal analyst input. The visualizations illustrate execution and value behaviors that aid in understanding the results of analysis scripts. To maximize the number of workflows supported, we present prototype implementations in both Python and Jupyter. Last, to demonstrate Anteater’s support for analysis understanding tasks, we provide two usage scenarios on real world analysis scripts.