Reviewerly/exHarmony huggingface dataset:
Code Will Speak: Early detection of Ponzi Smart Contracts on Ethereum

The prevalence of Bitcoin has attracted a mass of investors into the blockchain ecosystem. Unfortunately, benefiting from its anonymity and immutability, scammers deploy various traps in smart contracts to exploit other participants and seize illegal proceeds. To identify smart Ponzi contracts-a classic fraud widely popular on Ethereum, previous studies present several machine learning-based models with considerable accuracy. However, the performance of their models relies on the behavioral features of smart contracts to a large margin, which are extracted from the transaction records only after a contract has been running for some time. In this paper, we borrow ideas from text feature extraction from Natural Language Processing (NLP) to build a classification model based on an improved CatBoost algorithm. A novel feature extraction pattern is applied in our model to deeply mine the logic of smart contract code. This approach can be used to detect Ponzi schemes at deployment time with improved performance, and thus can avoid the loss of investors originally.
D<sup>2</sup>Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos

Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the efficacy of dilated and deformable convolutions for various image-level segmentation tasks. This gives reason to believe that 3D extensions of such convolutions should also yield performance improvements for video-level segmentation tasks. However, this aspect has not yet been explored thoroughly in existing literature. In this paper, we propose Dynamic Dilated Convolutions (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Conv3D): a novel type of convolution which draws inspiration from dilated and deformable convolutions and extends them to the 3D (spatio-temporal) domain. We experimentally show that D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Conv3D can be used to improve the performance of multiple 3D CNN architectures across multiple video segmentation related benchmarks by simply employing D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Conv3D as a drop-in replacement for standard convolutions. We further show that D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D. Lastly, we set a new state-of-the-art on the DAVIS 2016 Unsupervised Video Object Segmentation benchmark. Code is made publicly available at https://github.com/Schmiddo/d2conv3d.
Obstacle avoidance for orchard vehicle trinocular vision system based on coupling of geometric constraint and virtual force field method

Nowadays the use of visual navigation in orchard is rapidly growing, while current research mainly focuses on orchard row detection or obstacle detection separately. The economic, fast and reliable navigation vision system that completing orchard row following and obstacle avoidance simultaneously and the obstacle avoidance algorithm for vision system without complex multiple navigation systems combining are needed further research. In this paper, a trinocular vision system for orchard vehicle is set up and the obstacle avoidance algorithm based on coupling of geometric constraint and virtual force field is designed for its vision system. First of all, based on analyzing the different vision system characteristics and the detection demand of orchard obstacle avoidance the trinocular vision system for orchard vehicle is set up based on a wide-angle camera and binocular stereo vision system. Then, the image processing algorithm of orchard row and obstacle detection is designed. For orchard row detection, the trunk regions enhancement algorithm is designed based on grayscale morphology filtering the trunk regions prediction algorithm is designed based on optical flow method to improve the trunk regions detection speed and accuracy. For obstacle detection, the background equalization algorithm based on H-channel image characteristics and the interference weaken algorithm with G-channel image characteristics are designed. Based on the row and obstacle detection results the algorithm for obstacle avoidance in orchard based on coupling of geometric constraint and virtual force field is designed. In the obstacle avoidance process, the virtual force field method is used as the mainly control of the vehicle obstacle avoidance process, and the geometric constraint between visual model of trinocular vision system and obstacle position is coupling with it to fulfill the avoidance demand of the obstacle with special shape and other condition that influence the virtual force field calculation. The experimental results show that the trinocular vision system for orchard vehicle built in this paper has strong adaptability to the actual environment which is accurate and stable for orchard row detection and obstacle detection simultaneously. The average deviation is 4.76 cm and 7.05 cm at 0.5 m/s and 1.0 m/s respectively, the average deviation of obstacle distance detection in the Z-axis direction is 3.18 cm, and in the X-axis direction is 0.45 cm. And the obstacle avoidance algorithm designed in this paper is effective in the orchard which can meet the actual production requirements. The research results will lay a foundation for the development of intelligent equipment and unmanned management in orchard.
A framework for visual question answering with the integration of scene-text using PHOCs and fisher vectors

Text contained in an image gives useful information about that image. Consider a warning signboard with text “high voltage”; it indicates the hazard or risk involved in the image. Thus, this semantic textual information can be very useful for better understanding of images, which is not utilized by the existing visual question answering (VQA) models. However, the presence of this textual information in images can strongly guide the VQA task. This work deal with the task of visual question answering by exploiting these textual cues together with the visual content to boost the accuracy of VQA models. In the work, a novel VQA model is proposed based on the PHOC and fisher vector based representation. Based on the PHOCs of the scene-text, we have constructed a powerful descriptor by using a Fisher Vectors. Also, the proposed model uses transformer model together with dynamic pointer networks for answer decoding process. Thus, the proposed model uses a sequence of decoding steps for answer generation instead of just assuming answer prediction as a classification problem as considered by previous works. We have shown the qualitative and quantitative results on three popular datasets: VQA 2.0, TextVQA and ST-VQA. The results show the effectiveness of the proposed model over the existing models.
Re-identification of individuals in genomic datasets using public face images

DNA sequencing is becoming increasingly commonplace, both in medical and direct-to-consumer settings. To promote discovery, collected genomic data is often de-identified and shared, either in public repositories, such as OpenSNP, or with researchers through access-controlled repositories. However, recent studies have suggested that genomic data can be effectively matched to high-resolution three-dimensional face images, which raises a concern that the increasingly ubiquitous public face images can be linked to shared genomic data, thereby re-identifying individuals in the genomic data. While these investigations illustrate the possibility of such an attack, they assume that those performing the linkage have access to extremely well-curated data. Given that this is unlikely to be the case in practice, it calls into question the pragmatic nature of the attack. As such, we systematically study this re-identification risk from two perspectives: first, we investigate how successful such linkage attacks can be when real face images are used, and second, we consider how we can empower individuals to have better control over the associated re-identification risk. We observe that the true risk of re-identification is likely substantially smaller for most individuals than prior literature suggests. In addition, we demonstrate that the addition of a small amount of carefully crafted noise to images can enable a controlled trade-off between re-identification success and the quality of shared images, with risk typically significantly lowered even with noise that is imperceptible to humans.
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation

Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynamics is crucial for sequential recommendation, most of the existing studies concentrate solely on the user side while overlooking the sequential patterns existing in the counterpart, i.e., the item side. Although a few studies investigate the dynamics involved in the dual sides, the complex user-item interactions are not fully exploited from a global perspective to derive dynamic user and item representations. In this paper, we devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe). To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice and exploit time-sliced graph neural networks to learn user and item representations. Moreover, to enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices based on temporal point process. Comprehensive experiments on three public real-world datasets demonstrate DRL-SRe outperforms the state-of-the-art sequential recommendation models with a large margin.
Regularized Least Squares Twin SVM for Multiclass Classification

Support vector machines (SVMs) have been successfully used in classification and regression problems. However, SVM suffers from high computational complexity which limits its applicability. Twin SVM (TWSVM) reduces the complexity of SVM, however, it still suffers due to the optimization of quadratic programming problems (QPPs). To make TWSVM model more efficient, least squares twin SVM (LSTSVM) solves a pair of linear equations for generating the optimal hyperplanes. LSTSVM is useful for solving multiclass classification problems due to less computational cost and good generalization performance. Multiclass classification problems require high computational cost and thus need efficient algorithms to reduce the training time. A new regularization based method for multiclass classification problems for different multiclass classification methods, namely “One-versus-All (OVA)”, “One-versus One (OVO)”, “All-versus-One (AVO)” and “Direct Acyclic Graph (DAG)” is proposed in this work. It is named as multiclass regularized least squares twin support vector machine (MRLSTSVM). The standard LSTSVM algorithm gives emphasis on reducing the empirical risk only, however, the proposed MRLSTSVM implements structural risk minimization (SRM) principle to reduce overfitting. Our regularization based approach leads to positive definite matrices in the formulation of MRLSTSVM. For each classifier, the computational complexity is analyzed and discussed their advantages and disadvantages. The performance analysis is tested by conducting experiments on a wide range of benchmark UCI datasets. In comparison to other baseline multiclass classifiers in terms of accuracy, the proposed approach MRLSTSVM (OVO) shows better generalization performance.
Aion: Enabling Open Systems through Strong Availability Guarantees for Enclaves

Embedded Trusted Execution Environments (TEEs) can provide strong security for software in the IoT or in critical control systems. Approaches to combine this security with real-time and availability guarantees are currently missing. In this paper we present Aion, a configurable security architecture that provides a notion of guaranteed real-time execution for dynamically loaded enclaves. We implement preemptive multitasking and restricted atomicity on top of strong enclave software isolation and attestation. Our approach allows the hardware to enforce confidentiality and integrity protections, while a decoupled small enclaved scheduler software component can enforce availability and guarantee strict deadlines of a bounded number of protected applications, without necessarily introducing a notion of priorities amongst these applications. We implement a prototype on a light-weight TEE processor and provide a case study. Our implementation can guarantee that protected applications can handle interrupts and make progress with deterministic activation latencies, even in the presence of a strong adversary with arbitrary code execution capabilities.
Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic

In this research paper, we are going to see the profound scientific use of computer technology applied in the fields of AI and Machine Learning primarily focused on Image Processing and Pattern recognition. Techniques such as ours are widely used to recognize real life objects including human faces etc. Thus, using such techniques, we can recognize a person from pictures. Using face recognition modules from python's huge collection of libraries, we are able to train the model to recognize people while wearing masks. Since when masks are worn, half of the facial features are lost, therefore developing a technique to recognize faces in such way is crucial. This specific technology of face detection is used in biometrics, video surveillance, etc. Therefore it's at utmost importance to increase the security as well as efficiency whilst making the recognition faster.
A Graph Federated Architecture with Privacy Preserving Learning

Federated learning involves a central processor that interacts with multiple agents to determine a global model. The process consists of repeatedly exchanging estimates, which may end up divulging some private information from the local agents. This scheme can be inconvenient when dealing with sensitive data, and therefore, there is a need for the privatization of the algorithm. Furthermore, the current architecture of a server connected to multiple clients is highly sensitive to communication failures and computational overload at the server. In this work, we develop a private multi-server federated learning scheme, which we call graph federated learning. We use cryptographic and differential privacy concepts to privatize the federated learning algorithm over a graph structure. We further show under convexity and Lipschitz conditions, that the privatized process matches the performance of the non-private algorithm.
Interpretable Mamdani neuro-fuzzy model through context awareness and linguistic adaptation

Interpretable machine learning is trending as it aims to build a human-understandable decision process. There are two main types of machine learning systems: white-box and black-box models. White-box models are inherently interpretable but commonly suffer from under-fitting phenomena; on the other hand, black-box models perform quite well in a wide range of application domain problems, but their reasoning behind a decision is hard or even impossible to understand. In the soft-computing area, fuzzy inference systems are rule-based systems that use fuzzy reasoning, bringing human perception modeling and computing with word capability. These rule-based systems are designed either manually or automatically but are commonly optimized to fit better some phenomena' data (in a supervised learning task). After the optimization process, the initial semantic meaning of fuzzy sets is modified (slightly, in the best cases), creating a gray-box model. The principal objective of the proposed methodology in this paper is to extract a high-quality rule in terms of comprehensibility, accuracy and fidelity. This is accomplished by using a fuzzy linguistic interpretable model from an optimized neuro-fuzzy model, considering the initial knowledge context with which it was built. A grammar-guided genetic algorithm is used as the optimization process to find the interpretable description of the model. A collection of 16 datasets for classification tasks were used to evaluate our proposal, obtaining an f1-score of 0.814 with 0.026 standard deviation in the optimized model; the obtained fidelity, in terms of similarity from the interpretable model to the optimized one, was 0.93 of mean with 0.018 standard deviation. Obtained results show that neuro-fuzzy systems could play an important role in interpretable machine learning, providing natural language explanations from previous knowledge.
Secure Multi-party Computation of Differentially Private Heavy Hitters

Private learning of top-k, i.e., the k most frequent values also called heavy hitters, is a common industry scenario: Companies want to privately learn, e.g., frequently typed new words to improve suggestions on mobile devices, often used browser settings, telemetry data of frequent crashes, heavily shared articles, etc.
A Universal Law of Robustness via Isoperimetry

Classically, data interpolation with a parametrized model class is possible as long as the number of parameters is larger than the number of equations to be satisfied. A puzzling phenomenon in deep learning is that models are trained with many more parameters than what this classical theory would suggest. We propose a partial theoretical explanation for this phenomenon. We prove that for a broad class of data distributions and model classes, overparametrization is necessary if one wants to interpolate the data smoothly . Namely we show that smooth interpolation requires d times more parameters than mere interpolation, where d is the ambient data dimension. We prove this universal law of robustness for any smoothly parametrized function class with polynomial size weights, and any covariate distribution verifying isoperimetry (or a mixture thereof). In the case of two-layer neural networks and Gaussian covariates, this law was conjectured in prior work by Bubeck, Li, and Nagaraj. We also give an interpretation of our result as an improved generalization bound for model classes consisting of smooth functions.
Radio Frequency Spectrum Sensing by Automatic Modulation Classification in Cognitive Radio System Using Multiscale Deep CNN

Automatic modulation categorization (AMC) is used in many applications such as cognitive radio, adaptive communication, electronic reconnaissance, and non-cooperative communications. Predicting the modulation class of an unknown radio signal without having any prior information of the signal parameters is challenging. This paper proposes a novel multiscale deep-learning-based approach for the automatic modulation classification using radio signals. The approach considered the fixed boundary range-based Empirical wavelet transform (FBREWT) based multiscale analysis technique to decompose the radio signal into sub-band signals or modes. The sub-band signals computed from the radio signal combined with the deep convolutional neural network (CNN) are used to classify modulation types. The approach is tested using the radio signals of different signal-to-noise ratio (SNR) values and four different channel types such as additive white Gaussian noise (AWGN) combination of Rayleigh fading and AWGN channels, the combination of Rician flat fading and AWGN channels, and combination of Nakagami-m fading and AWGN channels for AMC. The results show that the proposed FBREWT based deep-learning approach achieves an overall classification accuracy of 97% for AMC using the radio signals with 10dB SNR for the AWGN channel. Moreover, the proposed approach has obtained the accuracy’s as 94.56%, 95%, and 97.33% using radio signals with 10 dB SNR values for Rayleigh fading, Rician flat fading, and Nakagami-m fading channels combined through AWGN link. The comparison of the proposed multiscale deep learning-based approach with existing methods is shown for AMC.
Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework

Semantic communications will play a critical role in enabling goal-oriented services over next-generation wireless systems. However, most prior art in this domain is restricted to specific applications (e.g., text or image), and it does not enable goal-oriented communications in which the effectiveness of the transmitted information must be considered along with the semantics so as to execute a certain task. In this paper, a comprehensive semantic communications framework is proposed for enabling goal-oriented task execution. To capture the semantics between a speaker and a listener, a common language is defined using the concept of beliefs to enable the speaker to describe the environment observations to the listener. Then, an optimization problem is posed to choose the minimum set of beliefs that perfectly describes the observation while minimizing the task execution time and transmission cost. A novel top-down framework that combines curriculum learning (CL) and reinforcement learning (RL) is proposed to solve this problem. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution time, and transmission cost during training.
Relational Graph Neural Network with Neighbor Interactions for Bundle Recommendation Service

Bundle recommendation plays a crucial role in the service ecosystem. However, most existing bundle recommendation methods are limited in several critical aspects such as the lack of injecting different relations into the representations of bundles and items, and the ignorance of neighbor interactions. To address these limitations, in this paper, we propose a relational graph neural network with neighbor interactions for bundle recommendation. Specifically, we firstly construct two relational graphs, e.g., user-bundle-item interaction graph and bundle-item affiliation graph. We utilize a relational graph neural network to inject different relations into representations of bundles and items. Secondly, we consider neighbor interactions to highlight common properties of neighbors. Finally, a multi-task learning framework is also exploited to capture users' preferences at the item level to further enhance bundle recommendation performance. Comprehensive experiments on two real-world public datasets demonstrate that our proposed method can outperform various representative bundle recommendation methods.