Reviewerly/exHarmony huggingface dataset:
How is your Wi-Fi connection today? DoS attacks on WPA3-SAE

WPA3-Personal renders the Simultaneous Authentication of Equals (SAE) password-authenticated key agreement method mandatory. The scheme achieves forward secrecy and is highly resistant to offline brute-force dictionary attacks. Given that SAE is based on the Dragonfly handshake, essentially a simple password exponential key exchange, it remains susceptible to clogging type of attacks at the Access Point side. To resist such attacks, SAE includes an anti-clogging scheme. To shed light on this contemporary and high-stakes issue, this work offers a full-fledged empirical study on Denial of Service (DoS) against SAE. By utilizing both real-life modern Wi-Fi 6 certified and non-certified equipment and the OpenBSD’s hostapd, we expose a significant number of novel DoS assaults affecting virtually any AP. No less important, more than a dozen of vendor-depended and severe zero-day DoS assaults are manifested, showing that the implementation of the protocol by vendors is not yet mature enough. The fallout of the introduced attacks to the associated stations ranges from a temporary loss of Internet connectivity to outright disconnection. To our knowledge, this work provides the first wholemeal appraisal of SAE’s mechanism endurance against DoS, and it is therefore anticipated to serve as a basis for further research in this timely and intriguing area.
ResDepth: A deep residual prior for 3D reconstruction from high-resolution satellite images

Modern optical satellite sensors enable high-resolution stereo reconstruction from space. But the challenging imaging conditions when observing the Earth from space push stereo matching to its limits. In practice, the resulting digital surface models (DSMs) are fairly noisy and often do not attain the accuracy needed for high-resolution applications such as 3D city modeling. Arguably, stereo correspondence based on low-level image similarity is insufficient and should be complemented with a priori knowledge about the expected surface geometry beyond basic local smoothness. To that end, we introduce ResDepth, a convolutional neural network that learns such an expressive geometric prior from example data. ResDepth refines an initial, raw stereo DSM while conditioning the refinement on the images. I.e., it acts as a smart, learned post-processing filter and can seamlessly complement any stereo matching pipeline. In a series of experiments, we find that the proposed method consistently improves stereo DSMs both quantitatively and qualitatively. We show that the prior encoded in the network weights captures meaningful geometric characteristics of urban design, which also generalize across different districts and even from one city to another. Moreover, we demonstrate that, by training on a variety of stereo pairs, ResDepth can acquire a sufficient degree of invariance against variations in imaging conditions and acquisition geometry.
Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy

Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms.
The impact of blockchain on internal audit

This paper presents the main issues on competencies and knowledge, risks and challenges in performing internal audit when adopting blockchains. The main purpose is to propose a framework for stages, procedures and elements for an internal audit plan in organizations that implement blockchains in smart crop production. It is analyzed basic elements of blockchain technology, characteristics and functions of internal audit and internal control, the impacts of blockchains implementation on organization and focus areas affecting human, financial and technical resources, risk identification, blockchain control procedures, risk management and mitigation. It is presented example of suggested framework for internal audit plan.
Recurrent synchronization network for emotion-cause pair extraction

Emotion-cause pair extraction is a fundamental task in emotion analysis, which aims to extract emotions and their corresponding causes from documents. Recent studies have employed two auxiliary tasks, namely emotion extraction and cause extraction, to facilitate the detection of emotion-cause pairs and their joint resolution in a multi-task learning framework. However, the implicit information-sharing mechanism in existing methods fails to fully utilize the rich interactive relations among these three tasks. In this study, we propose a recurrent synchronization network that explicitly models the interaction among different tasks. Our model performs multiple rounds of inference to detect emotions, causes, and emotion-cause pairs iteratively. Following each round of inference, the information from different modules is synchronized through explicit information transmission, allowing the three tasks to collaborate effectively. Extensive experiments demonstrate that our model can extract emotion-cause pairs more accurately, while significantly outperforming existing methods.
Better Than Reference in Low-Light Image Enhancement: Conditional Re-Enhancement Network

Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.
Exploration of DevOps testing process capabilities: An ISM and fuzzy TOPSIS analysis

DevOps is an emerging paradigm that refer to a collaborative culture of development and operation teams aiming to develop the high quality software product. Software organizations are adopting DevOps culture for software development and easy maintenance instead of using traditional SDLC mechanism. To enter the production stage, in DevOps process, the software product have to pass through quality gates were the software are tested during development phase to meet the established targeted criteria. This indicates that the mechanism of testing in DevOps process is not straightforward, and to establish strong DevOps testing platform there is a need to explore more automated testing practices. Thus, using multivocal literature review approach, we have selected 39 studies and identify the 20 testing capabilities. Finally, the interpretive structure modeling (ISM) and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) were applied. The results shows that (C2, CCi=0.808; C6, CCi=0.720; and C3, CCi=0.705) are top ranked testing capabilities. Using analysis results, we develop a holistic structure of testing capabilities to show their inter-relationship with each other and their priorities to select the best testing capabilities for DevOps process.
A Web service clustering method based on topic enhanced Gibbs sampling algorithm for the Dirichlet Multinomial Mixture model and service collaboration graph

A method to enhance Web service clustering is proposed in this paper. Since current service clustering methods usually face low quality of service representation vectors and lack consideration of service collaboration, we try to provide an improved topic model to generate high-quality service representation vectors and design a service clustering method to integrate function similarity and collaboration similarity. First, by introducing feature word extraction and probability distribution correction into GSDMM, we present a model called TE-GSDMM (topic enhanced Gibbs sampling algorithm for the Dirichlet Multinomial Mixture model). Then, a service collaboration graph is put forward to model cooperation relationships and generate service collaboration vectors. Collaboration similarity is assessed by the similarity of service collaboration vectors. Finally, the K-means++ algorithm is employed to cluster Web services by evaluating service function similarity and collaboration similarity. Experiments show that TE-GSDMM outperforms other topic models in generating high-quality service representation vectors for service clustering. Moreover, service clustering performance is further improved by integrating collaboration similarity. Thus, the proposed method effectively enhances Web service clustering by improving the quality of service representation vectors and integrating service collaboration similarity.
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models

Deep learning models, especially those large-scale and high-performance ones, can be very costly to train, demanding a considerable amount of data and computational resources. As a result, deep learning models have become one of the most valuable assets in modern artificial intelligence. Unauthorized duplication or reproduction of deep learning models can lead to copyright infringement and cause huge economic losses to model owners, calling for effective copyright protection techniques. Existing protection techniques are mostly based on watermarking, which embeds an owner-specified watermark into the model. While being able to provide exact ownership verification, these techniques are 1) invasive, i.e., they need to tamper with the training process, which may affect the model utility or introduce new security risks into the model; 2) prone to adaptive attacks that attempt to remove/replace the watermark or adversarially block the retrieval of the watermark; and 3) not robust to the emerging model extraction attacks. Latest fingerprinting work on deep learning models, though being non-invasive, also falls short when facing the diverse and ever-growing attack scenarios.In this paper, we propose a novel testing framework for deep learning copyright protection: DEEPJUDGE. DEEPJUDGE quantitatively tests the similarities between two deep learning models: a victim model and a suspect model. It leverages a diverse set of testing metrics and efficient test case generation algorithms to produce a chain of supporting evidence to help determine whether a suspect model is a copy of the victim model. Advantages of DEEPJUDGE include: 1) non-invasive, as it works directly on the model and does not tamper with the training process; 2) efficient, as it only needs a small set of seed test cases and a quick scan of the two models; 3) flexible, i.e., it can easily incorporate new testing metrics or test case generation methods to obtain more confident and robust judgement; and 4) fairly robust to model extraction attacks and adaptive attacks. We verify the effectiveness of DEEPJUDGE under three typical copyright infringement scenarios, including model finetuning, pruning and extraction, via extensive experiments on both image classification and speech recognition datasets with a variety of model architectures.
Decentralized Cross-Blockchain Asset Transfers

Today, several solutions for cross-blockchain asset transfers exist. However, these solutions are either tailored to specific assets or neglect finality guarantees that prevent assets from getting lost in transit. In this paper, we present a cross-blockchain asset transfer protocol that supports arbitrary assets and adheres to finality requirements. The ability to freely transfer assets between blockchains may increase transaction throughput and provide developers with more flexibility by allowing them to design digital assets that leverage the capacities and capabilities of multiple blockchains.
A Survey on Machine Reading Comprehension Systems

Abstract Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural Language Processing. The goal of this field is to develop systems for answering the questions regarding a given context. In this paper, we present a comprehensive survey on diverse aspects of MRC systems, including their approaches, structures, input/outputs, and research novelties. We illustrate the recent trends in this field based on a review of 241 papers published during 2016–2020. Our investigation demonstrated that the focus of research has changed in recent years from answer extraction to answer generation, from single- to multi-document reading comprehension, and from learning from scratch to using pre-trained word vectors. Moreover, we discuss the popular datasets and the evaluation metrics in this field. The paper ends with an investigation of the most-cited papers and their contributions.
Real-Time Object Detection Algorithm of Autonomous Vehicles Based on Improved YOLOv5s

Object detection is one of the basic task of autonomous vehicles. Object detection of high accuracy and fast detection speed are conducive to improve the safety of autonomous vehicles. In view of the large number of small objects in actual road traffic, this paper proposes a real-time object detection algorithm based on improved YOLOv5s. By adding shallow high-resolution features and changing the size of output feature map, the detection ability of the algorithm for small objects is significantly improved. The mean Average Precision of the improved YOLOv5s algorithm on BDD100K dataset increased by 3.2 percentage points, and the average detection speed is 74.6 FPS. The experimental results show that the improved YOLOv5s algorithm enhances the detection ability of small objects and proves its feasibility in various complex road scenes.
Introducing the Gab Hate Corpus: defining and applying hate-based rhetoric to social media posts at scale

We present the Gab Hate Corpus (GHC), consisting of 27,665 posts from the social network service gab.com, each annotated for the presence of “hate-based rhetoric” by a minimum of three annotators. Posts were labeled according to a coding typology derived from a synthesis of hate speech definitions across legal precedent, previous hate speech coding typologies, and definitions from psychology and sociology, comprising hierarchical labels indicating dehumanizing and violent speech as well as indicators of targeted groups and rhetorical framing. We provide inter-annotator agreement statistics and perform a classification analysis in order to validate the corpus and establish performance baselines. The GHC complements existing hate speech datasets in its theoretical grounding and by providing a large, representative sample of richly annotated social media posts.
Time Series Anomaly Detection With Adversarial Reconstruction Networks

Time series data naturally exist in many domains including medical data analysis, infrastructure sensor monitoring, and motion tracking. However, a very small portion of anomalous time series can be observed, comparing to the whole data. Most existing approaches are based on the supervised classification model requiring representative labels for anomaly class(es), which is challenging in real-world problems. So can we learn how to detect anomalous time ticks in an effective yet efficient way, given mostly normal time series data? Therefore, we propose an unsupervised reconstruction model named BeatGAN which learns to detect anomalies based on normal data, or data which majority of samples are normal. BeatGAN provides a framework to adversarially learn to reconstruct, which can cooperate with both 1-d CNN and RNN. Rarely observed anomalies can result in larger reconstruction errors, which are then detected based on extreme value theory. Moreover, data augmentation with dynamic time warping regularizes reconstruction and provides robustness. In the experiments, effectiveness and sensitivity are studied in both synthetic data and various real-world time series. BeatGAN achieves better accuracy and fast inference.
Young and old users prefer immersive virtual reality over a social robot for short-term cognitive training

Socially Assistive Robots (SARs) and immersive Virtual Reality (iVR) are interactive platforms that promote user engagement, which can motivate users to adhere to therapeutic frameworks. SARs use social presence to create affective relationships with users, leveraging the human tendency to be driven by social interactions. iVR uses spatial presence to provide an intense multisensory experience that submerges users in a virtual world. We adapted two such platforms – a SAR and an iVR – to deliver cognitive training (CT), by integrating established cognitive tasks in gamified environments that convey a strong sense of presence. Sixty-four participants underwent CT with both platforms. We tested: (1) their perception of both platforms; (2) whether they preferred one over the other in the short term; (3) their projected preferences for long-term training; and (4) whether their preferences correlated with personal characteristics. They preferred the virtual experience in the short term across age and gender. For long-term CT, there was equal projected preference for both platforms. It may be that a combination of social and spatial presence might yield engagement in long-term training.
Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method

Computer vision-based techniques for structural displacement measurement are rapidly becoming popular in civil structural engineering. However, most existing computer vision-based displacement measurement methods require man-made targets for object matching or tracking, besides usually the measurement accuracies are seriously sensitive to the ambient illumination variations. A computer vision-based illumination robust and multi-point simultaneous measuring method is proposed for structural displacement measurements. The method consists of two part, one is for segmenting the beam body from its background, the segmentation is perfectly carried out by fully convolutional network (FCN) and conditional random field (CRF); another is digital image correlation (DIC)-based displacement measurement. A simply supported beam is built in laboratory. The accuracy and illumination robustness are verified through three groups of elaborately designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation, numbers of locations along with the segmented beam body can be chosen and measured simultaneously. It is verified that the method is illumination robust since the displacement measurements are with the smallest fluctuations to the illumination variations. The proposed method does not require any man-made targets attached on the structure, but because of the exploitation of DIC in displacement measurement, the regions centered on the measuring points need to have texture feature.
Learning Safe, Generalizable Perception-Based Hybrid Control With Certificates

Many robotic tasks require high-dimensional sensors such as cameras and Lidar to navigate complex environments, but developing certifiably safe feedback controllers around these sensors remains a challenging open problem, particularly when learning is involved. Previous works have proved the safety of perception-feedback controllers by separating the perception and control subsystems and making strong assumptions on the abilities of the perception subsystem. In this work, we introduce a novel learning-enabled perception-feedback hybrid controller, where we use Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) to show the safety and liveness of a full-stack perception-feedback controller. We use neural networks to learn a CBF and CLF for the full-stack system directly in the observation space of the robot, without the need to assume a separate perception-based state estimator. Our hybrid controller, called LOCUS (Learning-enabled Observation-feedback Control Using Switching), can safely navigate unknown environments, consistently reach its goal, and generalizes safely to environments outside of the training dataset. We demonstrate LOCUS in experiments both in simulation and in hardware, where it successfully navigates a changing environment using feedback from a Lidar sensor.