Reviewerly/exHarmony huggingface dataset:
Full-Stack Hierarchical Fusion of Static Features for Smart Contracts Vulnerability Detection

The security of smart contracts has drawn attention in recent years due to their immutability and ability to hold assets. Existing machine learning and deep learning methods addressing vulnerabilities in smart contracts often partially combine pooled features from first the contract source code, second, the build based approach made of features extracted during source code compilation, and third, the bytecode approach relying on features obtained from the Ethereum virtual machine bytecode analysis. Together those three approaches form the full-stack, and they are usually being conducted under static analysis thanks to its speed of execution. However, to the best of our knowledge, no single work has yet simultaneously undertaken a full-stack intralayer and cross-layer features fusion for smart contracts vulnerability assessment under static analysis, without making use of expert-based patterns nor without manually fusing the various features extracted from shuffled partial combinations of layers in the full-stack. This paper introduces a full-stack hierarchical fusion of static features for smart contracts vulnerability detection. In our construction, we associate each layer of the full-stack to a modality and leverage automatic intramodality and crossmodality pooled features fusion from state-of-the-art artificial neural networks and deep neural networks. Additionally, our models are applied to the hierarchy of power set layers in the full-stack, without any expert-based rule. Furthermore, our work aims to assess the increase in vulnerability detection performance and provide guidance for future research on smart contracts vulnerability detection.
Learning Delicate Pixel-Level Representations for Bottom-Up Human Pose Estimation

The Gaussian heatmap regression, which is the mainstream technique, has already occupied a pivotal position in locating keypoints of the human body appearing in images or videos. Many researchers have conducted thorough and comprehensive studies, but we find that they rarely devote their mind to the impact of different pixel types on keypoint detection. Inspired by this discovery, this paper mainly studies the process of Gaussian heatmap construction and adopt a new technique in the bottom-up method to mitigate the issue of pixel imbalance. Additionally, AF1-measure, which is considered false positives and false positives in the evaluation process to balance AP and AR, is proposed to deal with scenarios where there is a huge gap between AP and AR during the experiment and researchers cannot evaluate the performance of the network model more accurately. Large experimental results show that our technique outstrips the advanced approaches in various evaluation metrics.
Knocking on Tangle's Doors: Security Analysis of IOTA Ports

Blockchains are currently deployed in a large number of different scenarios, such as cryptocurrencies, communications, factory automation, and vehicular networks. Both academia's and industry's interest in this technology is motivated by its distributed nature and its inherent security in managing information. However, traditional blockchains are limited in terms of the transaction rate they can approve. To overcome this limitation, IOTA proposed a novel blockchain based on a Directed Acyclic Graph (DAG) structure (the Tangle). It handles a larger number of transactions to target high-traffic scenarios such as Internet of Things (IoT). However, the security of the IOTA's blockchain depends on the design of its underlying communication protocols. In this paper, we analyze the security of the IOTA Tangle from a transport layer perspective. In particular, we show that well-known port-based attacks can jeopardize the integrity and availability of the IOTA services. We focus on the IOTA's TCP-based ports, and provide a discussion of all the possible attacks and their effects. We assess their validity via experimental evaluation showing their effect on the network operations. Results show that SYN flooding affects the gossip protocol by fully disrupting the service. We also find that 16% of the nodes are unable to create a neighbours list, leading to a disruption of the information exchange process. On the other side, we also show that some components are not affected by these attacks. To the best of our knowledge, we are the first to identify and validate these vulnerabilities in IOTA. Lastly, we discuss some possible countermeasures.
Virtual and Augmented Reality in Art During the Pandemic

COVID-19 pandemic brought the whole world to a stand still. One of the biggest issues was a restriction in travel to reduce the spread of the virus. Everybody was forced to stay home and as a result visits to art galleries, museums were all prohibited. Virtual Reality (VR) and Augmented Reality (AR) were used to conduct virtual tours to art galleries and museums even before the pandemic but not to the extent it is being used today. If these immersive technologies were not put into use then the virtual art exhibition nor the virtual tour of museums would have never been a reality. This chapter highlights the use of these technologies to promote art during this pandemic. This situation provided us lots of learning experiences to exploit the maximum capabilities of VR and AR to enhance the virtual tour of Art lovers. Though some people felt that the joy and happiness of visiting such galleries cannot be replaced by virtual tours, there was no other choice to reduce the spread of the virus as well as make them available artifact visible to the public.
SOMC:A Object-Level Data Augmentation for Sea Surface Object Detection

Abstract The deep learning model is a data-driven model and more high-quality data will bring it better results. In the task of Unmanned Surface Vessel’s object detection based on optical images or videos, the object is sparser than the target in the natural scene. The current datasets of sea scenes often have some disadvantages such as high image acquisition costs, wide range of changes in object size, imbalance in the number of different objects and so on, which limit the generalization of the model for the detection of sea surface objects. In order to solve problems of insufficient scene and poor effect in current sea surface object detection, an object-level data augmentation for sea surface objects called SOMC is proposed. According to the different scenarios faced by the USV when performing autonomous obstacle avoidance, patrol and other tasks, SOMC generates suitable scenarios by mixing and copying targets conveniently, providing the possibility of unlimited expansion of the sea surface object. The experiment selected images in the video taken by the camera on top of the USV. A sufficient amount of comparative experiment prove that the SOMC integrates with existing excellent data augmentations and achieved an improvement in the detection effect, which proves the effectiveness and practicability of the SOMC in the perception task of the USV.
Self-learn to Explain Siamese Networks Robustly

Learning to compare two objects are essential in applications, especially when labeled data are scarce and imbalanced. As these applications can involve humans and make high-stake decisions, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradient-based explanations due to the additional compared object in SN, in contrast to architectures with a single input instance. We optimize for global invariance based on unlabeled data using self-learning to promote the stability of local explanations for individual input. The invariance leads to constrained optimization problems that can be solved using gradient descent-ascent (GDA), or KL-divergence regularized unconstrained optimization solved by SGD. We provide convergence proofs when the objective functions are nonconvex due to the Siamese architecture. Results on tabular and graph data from neuroscience and chemical engineering show that our local explanations robustly respects the self-learned invariance while optimizing the explanation faithfulness and simplicity. We further demonstrate the convergence of GDA experimentally.
A TLA+ Formal Proof of a Cross-Chain Swap

Blockchains are a specific type of distributed ledgers structured by a sequence of blocks of transactional data linked to each other. The use of blockchains has increased over time, and several new blockchains are emerging. It is therefore essential to enhance the interoperability between blockchain implementations to allow decentralised trading. One way to achieve this is with Cross-Chain Swap protocols. These protocols are critical systems as they handle assets. Therefore, it must be sure that the system does not contain errors. In this paper, we describe the Cross-Chain Swap problem in a formal way. We define safety and weak-liveness properties that guarantee no correct participant will be worse-off in an asynchronous system. Moreover, we provide a formally proved Byzantine fault-tolerant protocol that satisfies the swap specification. The protocol abstracts the blockchain enough to suit various distributed ledger frameworks aiming to perform a cross-chain swap. In addition, we illustrate how the described abstract protocol can be instantiated in a blockchain system.
An Innovative Career Management Platform Empowered by AI, Big Data, and Blockchain Technologies: Focusing on Female Engineers

With the advent of the fourth industrial revolution, professional resource management in the engineering sector has been gaining importance. And countries around the world are paying special attention to realizing and using the potential of female engineering talent that has been on the rise. Nevertheless, there is still a leaky pipeline of female engineering talent. As such, this study aims to provide a new platform that incorporates the latest technologies to promote female engineering talent. First, it introduces the existing career management platforms developed for female engineering students, along with their limitations to become a lifelong career management platform. In addition, the study proposes a customized career management platform for female engineering talents in line with their life cycles, empowered by artificial intelligence, big data and blockchain technologies which are characteristic of the fourth industrial revolution. With the help of a career management platform reinforced by such latest technologies, career interruption and a loss of female engineering talent will be prevented, and through sustainable career management of talent, the overall national competitiveness will be enhanced.
Rhizomatic Learning and Use of Mobile Instant Messaging Platforms

Rapid development and adoption of mobile technologies including Mobile Instant Messaging (MIM) platforms have transformed teaching and learning practices over the past decade. This paper presents an analysis of how students used WhatsApp as an example of a MIM platform to organise their learning activities. Drawing on Rhizomatic Learning Theory (RLT), the study showed that WhatsApp supported students to navigate through a complex network of learning where knowledge was generated beyond the confines of the formal learning context. WhatsApp supported learners during the transition to remote learning that was introduced during the lockdown due to the Covid-19 pandemic. Further, WhatsApp use supported learners to cope well with new learning experiences and practices. The study offers insights into the perspective of learners on the use of technology during challenging and uncertain times. The insights can be useful for lecturers when developing technology strategies and teaching best practices during uncertain times.
Tradeoffs with physical delay feedback reservoir computing

The Reservoir Computing (RC) paradigm is a supervised machine learning scheme utilizing natural computational ability of dynamical systems. Delayed chaotic systems provide rich dynamics for information processing by using the system's transient response to an external input, so have been identified as suitable systems for reservoir computing. In delay-feedback reservoir computing a virtual network is achieved through time-multiplexing, trading off input frequency and reservoir complexity. Here we investigate several features and tradeoffs that affect physical delay-dynamical reservoirs, based on the Mackey-Glass model, including the role of input mask, and a ‘devirtualisation’ of the output. We evaluate performance using the NARMA-10 benchmark. We find that random binary masks with no offset outperform other masking schemes, but require a careful choice of model parameters. We find that reading the output of a subset of ‘virtual’ nodes, the ‘devirtualised network’, does not affect computational power, which enables a tradeoff between output design complexity and readout frequency.
Semi-supervised incremental learning with few examples for discovering medical association rules

Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time-consuming. The purpose of this research is to design a new semi-supervised algorithm that performs like supervised algorithms but uses an affordable amount of training data.In this work we propose a new semi-supervised data mining model that combines unsupervised techniques (Fisher's exact test) with limited supervision. Starting with a small seed of annotated data, the model improves results (F-measure) obtained, using a fully supervised system (standard supervised ML algorithms). The idea is based on utilising the agreement between the predictions of the supervised system and those of the unsupervised techniques in a series of iterative steps.The new semi-supervised ML algorithm improves the results of supervised algorithms computed using the F-measure in the task of mining medical association rules, but training with an affordable amount of manually annotated data.Using a small amount of annotated data (which is easily achievable) leads to results similar to those of a supervised system. The proposal may be an important step for the practical development of techniques for mining association rules and generating new valuable scientific medical knowledge.
Motion parameters measurement of user-defined key points using 3D pose estimation

Motion parameters measurement is essential for understanding animal behavior, exploring the laws of object motion, and studying control methods. Nowadays, advanced computer vision based on machine learning technology supports markerless object tracking in 2D videos. However, due to the fact that all objects move in three-dimensional space, this paper introduces a method of measuring motion parameters using 3D pose estimation. First, an enhanced iterative bundle adjustment algorithm is proposed for multi-camera calibration in a multi-camera vision system by adding two control parameters, which dramatically reduces the reprojection error of multi-camera calibration and lays the foundation for high-precision triangulation. Then, a new spatiotemporal loss function is proposed, which considers the relationship between key points that do not constitute limbs, thereby improving triangulation accuracy. The new multi-camera calibration algorithm is evaluated on ChArUco and 3D pose estimation for metronome, planet pendulum, human hand, Koi, and cheetah. The experimental results show that: (1) the two hyper-parameters in the enhanced iterative bundle adjustment algorithm effectively suppress the influence of noise and play a good role in reducing the reprojection error of multi-camera calibration; (2) the spatiotemporal loss function has a strong constraining ability, the time loss can stabilize high frame rate video triangulation to maintain accuracy, while the space loss can improve the accuracy of triangulation for more complex structures; (3) multi-view data fusion is also conducive to improving the accuracy of triangulation. Moreover, the method was successfully applied to some actual measurement scenes: (1) the accurate measurement of the frequency of a metronome; and (2) the success measurement of the movement of a Koi, which conforms to the basic model of fish swimming. Some dynamic measurement results are displayed at https://github.com/wux024/AdamPose.
Safe-Error Attacks on SIKE and CSIDH

The isogeny-based post-quantum schemes SIKE (NIST PQC round 3 alternate candidate) and CSIDH (Asiacrypt 2018) have received only little attention with respect to their fault attack resilience so far. We aim to fill this gap and provide a better understanding of their vulnerability by analyzing their resistance towards safe-error attacks. We present four safe-error attacks, two against SIKE and two against a constant-time implementation of CSIDH that uses dummy isogenies. The attacks use targeted bitflips during the respective isogeny-graph traversals. All four attacks lead to full key recovery. By using voltage and clock glitching, we physically carried out two of the attacks - one against each scheme -, thus demonstrate that full key recovery is also possible in practice.
A novel switching Gaussian-heavy-tailed distribution based robust fixed-interval smoother

In nonlinear systems, the stochastic process and measurement noises may be non-stationary heavy-tailed distribution due to the dynamic outliers induced by unreliable sensors and complicated environments. The main purpose of this paper is to address the problem by establishing a new switching Gaussian-heavy-tailed (SGHT) distribution. We model the noise with the help of switching between the Gaussian and the newly designed heavy-tailed distribution. Then, utilizing two auxiliary parameters satisfying categorical and Bernoulli distributions respectively, we construct the SGHT distribution as a hierarchical Gaussian presentation. Furthermore, applying variational Bayesian inference, a novel SGHT distribution based robust fixed-interval smoother is derived. The experiment results of the synthetic data and real vehicle localization dataset demonstrate the superior performance of the proposed smoother as compared with cutting-edge smoother.
Feature Attention Parallel Aggregation Network for Single Image Haze Removal

Images captured in hazy weather often suffer from color distortion and texture blur due to turbid media suspended in the atmosphere.In this paper, we propose a Feature Attention Parallel Aggregation Network (FAPANet) to restore a clear image directly from the corresponding hazy input.It adopts the encoder-decoder structure while incorporating residual learning and attention mechanism.FAPANet consists of two key modules: a novel feature attention aggregation module (FAAM) and an adaptive feature fusion module (AFFM).FAAM recalibrates features by integrating channel attention and pixel attention in parallel to stimulate useful information and suppress redundant features.The shallow and deep layers of neural networks tend to characterize the low-level and high-level semantic features of images, respectively, so we introduce AFFM to fuse these two features adaptively.Meanwhile, a joint loss function, composed of L1 loss, perceptual loss, and structural similarity (SSIM) loss, is employed in the training stage for better results with more vivid colors and richer details.Comprehensive experiments on both synthetic and real-world images demonstrate the impressive performance of the proposed approach.
SI-Net: Multi-Scale Context-Aware Convolutional Block for Speaker Verification

Utilizing multi-scale information adequately is essential for building a high-performance speaker verification (SV) system. Biological research shows that the human auditory system employs a multi-timescale processing mode to extract information and has a mechanism of integrating multi-scale information to encode sound information. Inspired by this, we propose a novel block, named Split-Integration (SI), to explore multi-scale context-aware feature learning at a granular level for speaker verification. Our model involves a pair of operations, (i) multi-scale split, which is designed to imitate the multi-timescale processing mode, extracting multi-scale features by grouping and stacking different sizes of filters, and (ii) dynamic integration, which aims at reflecting analogy with the fusion mechanism, introducing KL divergence to measure the complementarity between multi-scale features such that the model fully integrates multi-scale features and produces better speaker-discriminative representation. Experiments are conducted on Voxceleb and Speakers in the Wild(SITW) datasets. Results demonstrate that our approach achieves a relative 10%–20% improvement on equal error rate (EER) over a strong baseline in the SV task.
Uncalibrated Visual Servo System Based on Kalman Filter Optimized by Improved STOA

The traditional visual servoing of robot is difficult to calibrate the parameters accurately. At present, Kalman filter is used to estimate the image Jacobian matrix, but the noise parameter setting is not appropriate, so the servoing is not accurate. The traditional STOA(Sooty Tern Optimization Algorithm) algorithm is improved by using the adaptive search radius strategy to improve its local convergence ability, and the improved STOA algorithm is used to optimize the noise parameters of Kalman filter, and the penalty term coefficient of the objective function is improved to find the optimal noise parameters under the current system model. Based on the optimal noise parameters obtained in this paper, the noise parameters are applied to the robot eye-in-hand structure, and the image Jacobian matrix is estimated online at each moment, so as to realize the calibration free visual servoing of the robot.
Identifying NAT Devices to Detect Shadow IT: A Machine Learning Approach

Network Address Translation (NAT) is an address remapping technique placed at the borders of stub domains. It is present in almost all routers and CPEs. Most NAT devices implement Port Address Translation (PAT), which allows the mapping of multiple private IP addresses to one public IP address. Based on port number information, PAT matches the incoming traffic to the corresponding "hidden" client. In an enterprise context, and with the proliferation of unauthorized wired and wireless NAT routers, NAT can be used for re-distributing an Intranet or Internet connection or for deploying hidden devices that are not visible to the enterprise IT or under its oversight, thus causing a problem known as shadow IT. Thus, it is important to detect NAT devices in an intranet to prevent this particular problem. Previous methods in identifying NAT behavior were based on features extracted from traffic traces per flow. In this paper, we propose a method to identify NAT devices using a machine learning approach from aggregated flow features. The approach uses multiple statistical features in addition to source and destination IPs and port numbers, extracted from passively collected traffic data. We also use aggregated features extracted within multiple window sizes and feed them to a machine learning classifier to study the effect of timing on NAT detection. Our approach works completely passively and achieves an accuracy of 96.9% when all features are utilized.