Reviewerly/exHarmony huggingface dataset:
Towards Interpretable Defense Against Adversarial Attacks via Causal Inference

Deep learning-based models are vulnerable to adversarial attacks. Defense against adversarial attacks is essential for sensitive and safety-critical scenarios. However, deep learning methods still lack effective and efficient defense mechanisms against adversarial attacks. Most of the existing methods are just stopgaps for specific adversarial samples. The main obstacle is that how adversarial samples fool the deep learning models is still unclear. The underlying working mechanism of adversarial samples has not been well explored, and it is the bottleneck of adversarial attack defense. In this paper, we build a causal model to interpret the generation and performance of adversarial samples. The self-attention/transformer is adopted as a powerful tool in this causal model. Compared to existing methods, causality enables us to analyze adversarial samples more naturally and intrinsically. Based on this causal model, the working mechanism of adversarial samples is revealed, and instructive analysis is provided. Then, we propose simple and effective adversarial sample detection and recognition methods according to the revealed working mechanism. The causal insights enable us to detect and recognize adversarial samples without any extra model or training. Extensive experiments are conducted to demonstrate the effectiveness of the proposed methods. Our methods outperform the state-of-the-art defense methods under various adversarial attacks.
Semantically supervised appearance decomposition for virtual staging from a single panorama

We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatically generated semantic maps (with floor, wall, ceiling, lamp, window and door labels) that have shown success on perspective views and are trained for panoramas using transfer learning without any further annotations. A GAN-based approach supervised by coarse information obtained from the semantic map extracts specular reflection and direct sunlight regions on the floor and walls. These lighting effects are removed via a similar GAN-based approach and a semantic-aware inpainting step. The appearance decomposition enables multiple applications including sun direction estimation, virtual furniture insertion, floor material replacement, and sun direction change, providing an effective tool for virtual home staging. We demonstrate the effectiveness of our approach on a large and recently released dataset of panoramas of empty homes.
Optimization of physical quantities in the autoencoder latent space

We propose a strategy for optimizing physical quantities based on exploring in the latent space of a variational autoencoder (VAE). We train a VAE model using various spin configurations formed on a two-dimensional chiral magnetic system. Three optimization algorithms are used to explore the latent space of the trained VAE. The first algorithm, the single-code modification algorithm, is designed for improving the local energetic stability of spin configurations to generate physically plausible spin states. The other two algorithms, the genetic algorithm and the stochastic algorithm, aim to optimize the global physical quantities, such as topological index, magnetization, energy, and directional correlation. The advantage of our method is that various optimization algorithms can be applied in the latent space containing the abstracted representation constructed by the trained VAE model. Our method based on latent space exploration is utilized for efficient physical quantity optimization.
An Empirical Evaluation of Document Embeddings and Similarity Metrics for Scientific Articles

The comparison of documents—such as articles or patents search, bibliography recommendations systems, visualization of document collections, etc.—has a wide range of applications in several fields. One of the key tasks that such problems have in common is the evaluation of a similarity metric. Many such metrics have been proposed in the literature. Lately, deep learning techniques have gained a lot of popularity. However, it is difficult to analyze how those metrics perform against each other. In this paper, we present a systematic empirical evaluation of several of the most popular similarity metrics when applied to research articles. We analyze the results of those metrics in two ways, with a synthetic test that uses scientific papers and Ph.D. theses, and in a real-world scenario where we evaluate their ability to cluster papers from different areas of research.
The Innovation of Blended Teaching Mode of College English in Mobile Network Environment

With the rapid development of economic globalization, a new type of mobile learning mode has emerged. Currently, the life of contemporary college students is inseparable from cell phones and computers, and such electronic devices are considered the important medium of mobile learning in the second classroom. In the process of education reform and “Internet +” integration, the blended teaching mode relying on the mobile network environment is favored by college English teachers. Furthermore, to improve the quality of English teaching, English teachers use the flipped classroom format to fully integrate the traditional classroom with online courses. In such format, a comprehensive, diversified assessment and evaluation system is built effectively. Specifically, starting from the current situation of college English teaching, the article analyzes and researches the flipped classroom hybrid teaching mode in four aspects to provide reference values for related scholars. Considering mentioned characteristics and advantages, the necessity and feasibility of adopting mobile learning mode in the second classroom teaching of college English are analyzed in this paper. Meanwhile, the application mode of specific devices for better learning is considered for better learning.
Analyzing sales proposal rejections via machine learning

The sales profession is fraught with customer rejection and defections. Understanding why customers say "no" to a sales proposal is complex given that factors at the organizational-, individual-, and interactional-level are synthesized in the customer's decision-making process. Academics and practitioners alike therefore stand to benefit from greater understanding of this phenomenon. The current study leverages text-based machine learning on postmortem interview transcripts from 113 business-to-business sales failures, spanning over 1,500 pages of text, to provide exploratory insights into the reasons for sales proposal rejections. Results reveal several thematic facets of sales proposal failures from the perspective of the customer, along with insights that variance in topic salience—i.e., buyer focus on a few topics or a spread of dimensions—is contingent on supplier incumbency status. Specifically, using topic modeling, findings show that buyers converge on a distributed (concentrated) range of sales proposal rejection topics for in- (out-) supplier proposals. Additionally, the authors show how the text-based machine learning approach can highlight key areas of concern for firms, enabling them to effectively enact changes that will improve future outcomes. Collectively, this research contributes to efforts to bridge the chasm between theoretical, managerial, and technical aspects of machine learning in sales.
Taxonomy of bug tracking process smells: Perceptions of practitioners and an empirical analysis

While there is no consensus on a formally specified bug tracking process, some certain rules and best practices for an optimal bug tracking process are accepted by many companies and open-source software (OSS) projects. Despite slight variations between different platforms, the primary aim of all these rules and practices is to perform a more efficient bug tracking process. Practitioners’ non-compliance with the best practices not only impedes the benefits of the bug tracking process but also negatively affects the other phases of software development life cycle. The goal of this study is to gain a better knowledge of the bad practices that occur during the bug tracking process (bug tracking process smells) and to perform quantitative analysis to show that these process smells exist in bug tracking systems. Moreover, we want to know the perception of software practitioners related to these process smells and also observe the impact of process smells on the bug tracking process. Based on the results of a multivocal literature review, we analyzed 60 sources in academic and gray literature and propose a taxonomy of 12 bad practices in the bug tracking process. To quantitatively analyze these process smells, we inspected bug reports collected from eight projects which use Jira, Bugzilla, and GitHub Issues. To get an idea about the perception of practitioners about the taxonomy of bug tracking process smells, we conducted a targeted survey with 30 software practitioners. Moreover, we statistically analyzed the impact of bug tracking process smells on the resolution time and reopening count of bugs. We observed from our empirical results that a considerable amount of bug tracking process smells exist in all projects and some of the process smell categories have statistically significant impacts on quality and speed. Survey results shows that the majority of software practitioners agree with the proposed taxonomy of BT process smells. The statistical analysis reveals that bug tracking process smells have an impact on OSS projects. The proposed taxonomy may serve as a foundation for best practices and tool support for detecting and avoiding bug tracking process smells.
It's All Relative: Regression Analysis with Compositional Predictors

Compositional data reside in a simplex and measure fractions or proportions of parts to a whole. Most existing regression methods for such data rely on log-ratio transformations that are inadequate or inappropriate in modeling high-dimensional data with excessive zeros and hierarchical structures. Moreover, such models usually lack a straightforward interpretation due to the interrelation between parts of a composition. We develop a novel relative-shift regression framework that directly uses proportions as predictors. The new framework provides a paradigm shift for regression analysis with compositional predictors and offers a superior interpretation of how shifting concentration between parts affects the response. New equi-sparsity and tree-guided regularization methods and an efficient smoothing proximal gradient algorithm are developed to facilitate feature aggregation and dimension reduction in regression. A unified finite-sample prediction error bound is derived for the proposed regularized estimators. We demonstrate the efficacy of the proposed methods in extensive simulation studies and a real gut microbiome study. Guided by the taxonomy of the microbiome data, the framework identifies important taxa at different taxonomic levels associated with the neurodevelopment of preterm infants.
An Empirical Analysis of Different Techniques for Spam Detection

Digital shopping has been increasingly popular over the years, as a result of the increased demand for internet access. This is due to the fact that a large number of items and services are readily available online. Thus, customer and organisational feedback on many of these goods & services is extremely valuable to both parties involved. Unfortunately, in order to generate profit or marketing, scammers frequently fabricate bogus reviews. Customers and organisations are unable to form an informed perspective about the merchandise because of the phoney reviews produced by scam artists. As a result, it is necessary to recognise and eradicate bogus reviews or spam reviews in order to avoid misleading potential customers in the future. Spam has emerged as a significant concern on the Internet and in electronic communication in recent years. There have been numerous strategies suggested to combat them. This study presents a summary of the current approach of revising spam filtering, as well as its advantages and disadvantages. The classification of traditional and learning methods, as well as their evaluation and correlation, are all included in this section. Spam articles are tested on some personal enemies and compared to each other. The planning application spam detection technique has been taken into consideration.
On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers

Over the last decade, advances in machine learning and sensing technology have paved the way for the belief that safe, accessible, and convenient autonomous vehicles may be realized in the near future. Despite the prolific competencies of machine learning models for learning the nuances of sensing, actuation, and control, they are notoriously difficult to assure. The challenge here is that some models, such as neural networks, are “black box” in nature, making verification and validation difficult, and sometimes infeasible. Moreover, these models are often tasked with operating in uncertain and dynamic environments where design time assurance may only be partially transferable. Thus, it is critical to monitor these components at runtime. One approach for providing runtime assurance of systems with unverified components is the simplex architecture, where an unverified component is wrapped with a safety controller and a switching logic designed to prevent dangerous behavior. In this paper, we propose the use of a real-time reachability algorithm for the implementation of such an architecture for the safety assurance of a 1/10 scale open source autonomous vehicle platform known as F1/10. The reachability algorithm (a) provides provable guarantees of safety, and (b) is used to detect potentially unsafe scenarios. In our approach, the need to analyze the underlying controller is abstracted away, instead focusing on the effects of the controller’s decisions on the system’s future states. We demonstrate the efficacy of our architecture through experiments conducted both in simulation and on an embedded hardware platform.
UNIPD-BPE: Synchronized RGB-D and Inertial Data for Multimodal Body Pose Estimation and Tracking

The ability to estimate human motion without requiring any external on-body sensor or marker is of paramount importance in a variety of fields, ranging from human–robot interaction, Industry 4.0, surveillance, and telerehabilitation. The recent development of portable, low-cost RGB-D cameras pushed forward the accuracy of markerless motion capture systems. However, despite the widespread use of such sensors, a dataset including complex scenes with multiple interacting people, recorded with a calibrated network of RGB-D cameras and an external system for assessing the pose estimation accuracy, is still missing. This paper presents the University of Padova Body Pose Estimation dataset (UNIPD-BPE), an extensive dataset for multi-sensor body pose estimation containing both single-person and multi-person sequences with up to 4 interacting people. A network with 5 Microsoft Azure Kinect RGB-D cameras is exploited to record synchronized high-definition RGB and depth data of the scene from multiple viewpoints, as well as to estimate the subjects’ poses using the Azure Kinect Body Tracking SDK. Simultaneously, full-body Xsens MVN Awinda inertial suits allow obtaining accurate poses and anatomical joint angles, while also providing raw data from the 17 IMUs required by each suit. This dataset aims to push forward the development and validation of multi-camera markerless body pose estimation and tracking algorithms, as well as multimodal approaches focused on merging visual and inertial data.
A Computational Framework for Cyber Threats in Medical IoT Systems

Smart social systems are ones where a number of individuals share and interact with each other via various networking devices. There exist a number of benefits to including smart-based systems in networks such as religions, economy, medicine, and other networks. However, the involvement of several cyber threats leads to adverse effects on society in terms of finance, business, liability, economy, psychology etc. The aim of this paper is to present a secure and efficient medical Internet of Things communication mechanism by preventing various cyber threats. The proposed framework uses Artificial Intelligence-based techniques such as Levenberg–Marquardt (LM) and Viterbi algorithms to prevent various social cyber threats during interaction and sharing of messages. The proposed mechanism is simulated and validated with various performance metrics compared with the traditional mechanism.
Unsupervised Anomaly Detection with an Enhanced Teacher for Student-Teacher Feature Pyramid Matching

Anomaly detection or outlier is one of the challenging subjects in unsupervised learning. This paper is introduced a student-teacher framework for anomaly detection that its teacher network is enhanced for achieving high-performance metrics. For this purpose, we first pretrain the ResNet-18 network on the ImageNet and then finetune it on the MVTech-AD dataset. Experiment results on the image-level and pixel-level demonstrate that this idea has achieved better metrics than the previous methods. Our model, Enhanced Teacher for Student-Teacher Feature Pyramid (ET-STPM), achieved 0.971 mean accuracy on the image-level and 0.977 mean accuracy on the pixel-level for anomaly detection.
Leveraging network structure for efficient dynamic negative sampling in network embedding

Unsupervised network embedding learns low-dimensional vector representations of nodes based on the network structure. However, typical graphs only contain positive edges. Hence, most network embedding models take sampled negative edges as input to avoid trivial solutions. However, conventional negative sampling methods follow fixed distributions to generate negative samples. Such a static sampling strategy could dramatically hurt the performance of node embeddings in downstream tasks. Researchers have proposed several adaptive sampling strategies to improve the quality of negative samples. However, existing methods do not sufficiently use the network structure to explore high-quality samples, resulting in complex and inefficient negative sampling. This paper develops a novel dynamic negative sampling scheme, which maintains a candidate sample population for each node. We update the populations dynamically and retain high-quality samples in each iteration. We develop a novel network embedding algorithm based on the proposed model, which selects high-quality negative samples adaptively from the population. We conduct extensive experiments to evaluate its effectiveness based on several benchmark datasets. The experimental results verify the superiority of the proposed methods over state-of-the-art approaches.
Boosting RGB-D salient object detection with adaptively cooperative dynamic fusion network

The suitable employment of RGB and depth data shows great significance in promoting the development of computer vision tasks and robot-environment interactions. However, there are different advantages and disadvantages in the early and late fusion of the two types of data. In addition, due to the diversity of the object information, using a single type of data in a specific scenario results in being semantically misleading. Based on the above considerations, we propose a transformer-based adaptively cooperative dynamic fusion network (ACDNet) with a dynamic composite structure (DCS) for salient object detection. This structure is designed to flexibly utilize the advantages of feature fusion in different stages. Second, an adaptively cooperative semantic guidance (ACG) scheme is designed to suppress inaccurate features in multilevel multimodal feature fusion. Furthermore, we proposed a perceptual aggregation module (PAM) to optimize the network from the perspectives of spatial perception and scale perception, which strengthens the network's ability to perceive multiscale objects. Extensive experiments conducted on 8 RGB-D SOD datasets illustrate that the proposed network outperforms 24 state-of-the-art algorithms.
Proportion constrained weakly supervised histopathology image classification

Multiple instance learning (MIL) deals with data grouped into bags of instances, of which only the global information is known. In recent years, this weakly supervised learning paradigm has become very popular in histological image analysis because it alleviates the burden of labeling all cancerous regions of large Whole Slide Images (WSIs) in detail. However, these methods require large datasets to perform properly, and many approaches only focus on simple binary classification. This often does not match the real-world problems where multi-label settings are frequent and possible constraints must be taken into account. In this work, we propose a novel multi-label MIL formulation based on inequality constraints that is able to incorporate prior knowledge about instance proportions. Our method has a theoretical foundation in optimization with log-barrier extensions, applied to bag-level class proportions. This encourages the model to respect the proportion ordering during training. Extensive experiments on a new public dataset of prostate cancer WSIs analysis, SICAP-MIL, demonstrate that using the prior proportion information we can achieve instance-level results similar to supervised methods on datasets of similar size. In comparison with prior MIL settings, our method allows for ∼13% improvements in instance-level accuracy, and ∼3% in the multi-label mean area under the ROC curve at the bag-level.
Investigating the Determinants of the Growth of the New Energy Industry: Using Quantile Regression Approach

ABSTRACT Expanding the supplies of new energy can not only reduce CO2 emissions, but also alleviate energy shortage. This paper applies the quantile regression to investigate the new energy industry in China. The results show that economic growth exerts the greatest effect on the new energy industry in the lower 10th quantile province. This is because these provinces have the developed economies, demand for a higher ecological environment and new energy resources. Foreign energy dependence has a minimal impact on the new energy industry in the 25th–50th quantile province, due to their minimal oil importation. The contribution of technological progress to the upper 90th quantile province is the lowest, because their R&amp;D capabilities are the weakest. The impact of energy consumption structure decreases in steps from the lower 10th quantile provinces to the upper 90th quantile provinces. The agricultural sector promotes the new energy industry in most provinces.