Reviewerly/exHarmony huggingface dataset:
STAN: Towards Describing Bytecodes of Smart Contract

More than eight million smart contracts have been deployed into Ethereum, which is the most popular blockchain that supports smart contract. However, less than 1% of deployed smart contracts are open-source, and it is difficult for users to understand the functionality and internal mechanism of those closed-source contracts. Although a few decompilers for smart contracts have been recently proposed, it is still not easy for users to grasp the semantic information of the contract, not to mention the potential misleading due to decompilation errors. In this paper, we propose the first system named Stan to generate descriptions for the bytecodes of smart contracts to help users comprehend them. In particular, for each interface in a smart contract, Stan can generate four categories of descriptions, including functionality description, usage description, behavior description, and payment description, by leveraging symbolic execution and NLP (Natural Language Processing) techniques. Extensive experiments show that Stan can generate adequate, accurate and readable descriptions for contract's bytecodes, which have practical value for users.
Two in One Image Secret Sharing Scheme (TiOISSS) for extended progressive visual cryptography using simple modular arithmetic operations

Existing Extended Progressive Visual Cryptography scheme (EPVCS) suffers from the problem of pixel expansion, poor quality of reconstructed image and residual trace of cover images in the reconstructed image. Hence in this paper, Two in One Image secret sharing scheme for EPVCS is proposed which decodes the encrypted image in two stages. The proposed scheme provides k,n threshold construction using meaningful shares without pixel expansion and demonstrates that the reconstructed image has improved quality compared to the existing schemes. To reduce the computational complexity, the proposed scheme uses simple Modular Arithmetic operations instead of Galois field. The proposed scheme has the additional advantages of supporting any value of k and n, no overhead in resizing the secret image and no residual trace of cover image. Simulation results and performance analysis show the effectiveness of proposed scheme with improved contrast, 99% Structural Similarity of the reconstructed image and good progressive reconstruction.
Texture-aware and structure-preserving superpixel segmentation

Superpixel segmentation is an important pre-processing step in many computer vision applications. However, existing methods are mainly constructed on the basis of pixel-level features, which are generally vulnerable to the interference of strong gradient textures and fail to make the generated superpixels adhere to the object boundary well. Therefore, we bring forward a texture-aware and structure-preserving superpixel segmentation algorithm. First, we propose a cluster centroid oriented quarter-circular mask, which prevents the fine-scale structure pixels from sampling inconsistent texture pixels when gathering neighboring information from the local image patch. Next, we put forward a texture-suppressed and structure-preserving hybrid gradient. Its magnitude can well represent the probability that a pixel belongs to the structure one, and its direction can embody the main direction of image structures. Then, we design a color distance with texture-structure tradeoff strategy, which is based on the quarter-circular mask and the hybrid gradient direction. Finally, we devise an integrated structure-avoiding clustering distance, which can be obtained by seeking the maximum hybrid gradient magnitude along the linear path between a pixel and the superpixel centroid. It can effectively prevent the superpixel from crossing the object boundaries. Experimental results show that the superpixels generated by our algorithm not only adhere to object boundaries closely but also preserve regular shape. Our algorithm outperforms state-of-the-art methods, especially for the images with strong gradient textures.
Local Domain Adaptation for Cross-Domain Activity Recognition

Sensor-based human activity recognition (HAR) aims to recognize a human's physical actions by using sensors attached to different body parts. As a user-specific application, HAR often suffers poor generalization from training on an individual to testing on another individual, or from one body part to another body part. To tackle this cross-domain HAR problem, this article proposes a domain adaptation (DA) method called local domain adaptation (LDA), whose core is to align cluster-to-cluster distributions between the source domain and the target domain. On the one hand, LDA differs from existing set-to-set alignment by reducing the distribution discrepancy at a finer granularity. On the other hand, LDA is superior to the class-to-class alignment because it can provide more accurate soft labels for the target domain. Specifically, LDA contains three main steps: 1) groups the activity class into several high-level abstract clusters; 2) maps the original data of each cluster in both domains into the same low-dimension subspace to align the intracluster data distribution; 3) predicts the class labels for target domain in the low-dimension subspace. Experimental results on two public HAR benchmark datasets show that LDA outperforms state-of-the-art DA methods for the cross-domain HAR.
Edge Computing Assisted Adaptive Streaming Scheme for Mobile Networks

Various video streaming platforms are responsible for continuously generating a majority of Internet traffic. The promising solution for the smooth video streaming services is to use the HTTP adaptive streaming (HAS). To guarantee a high quality of experience (QoE), the clients using the HAS dedicate the bitrate adaptation for the upcoming video segments. However, the client-driven approach causes degradation in QoE, inefficient resource utilization, and unfair bitrate allocation when multiple clients stream a video over the same access network. Edge computing-assisted adaptive streaming gives an opportunity to jointly optimize the QoE of clients, resource utilization, and fairness among clients by shifting the adaptation intelligence from the clients to the edge cloud. In this paper, we first present an adaptive streaming framework taking advantage of the capabilities of multi-access edge computing (MEC). Next, we design an optimization model that jointly considers the main influencing factors in QoE and fairness among clients. The proposed scheme formulates the joint optimization problem with the various constraints by considering the correlation between QoE and fairness. To efficiently solve the joint optimization problem, we propose a greedy-based bitrate allocation algorithm for multiple clients. The results from the performance evaluation show that the proposed scheme can improve the QoE of clients and resource utilization compared with the existing schemes and minimize the loss in fairness.
Variable-Rate Multi-Frequency Image Compression using Modulated Generalized Octave Convolution

In this proposal, we design a learned multi-frequency image compression approach that uses generalized octave convolutions to factorize the latent representations into high-frequency (HF) and low-frequency (LF) components, and the LF components have lower resolution than HF components, which can improve the rate-distortion performance, similar to wavelet transform. Moreover, compared to the original octave convolution, the proposed generalized octave convolution (GoConv) and octave transposed-convolution (GoTConv) with internal activation layers preserve more spatial structure of the information, and enable more effective filtering between the HF and LF components, which further improve the performance. In addition, we develop a variable-rate scheme using the Lagrangian parameter to modulate all the internal feature maps in the autoencoder, which allows the scheme to achieve the large bitrate range of the JPEG AI with only three models. Experiments show that the proposed scheme achieves much better Y MS-SSIM than VVC. In terms of YUV PSNR, our scheme is very similar to HEVC.
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interpretation of the situations, and often fail to handle more sophisticated situations. To address such limits of neural solvers, we introduce the concept of a situation model, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose SMART, which adopts attributed grammar as the representation of situation models for algebra story problems. Specifically, we first train an information extraction module to extract nodes, attributes and relations from problem texts and then generate a parse graph based on a pre-defined attributed grammar. An iterative learning strategy is also proposed to further improve the performance of SMART. To study this task more rigorously, we carefully curate a new dataset named ASP6.6k. Experimental results on ASP6.6k show that the proposed model outperforms all previous neural solvers by a large margin, while preserving much better interpretability. To test these models' generalization capability, we also design an out-of-distribution (OOD) evaluation, in which problems are more complex than those in the training set. Our model exceeds state-of-the-art models by 17% in the OOD evaluation, demonstrating its superior generalization ability.
Neural Hair Rendering

In this paper, we propose a generic neural-based hair rendering pipeline that can synthesize photo-realistic images from virtual 3D hair models. Unlike existing supervised translation methods that require model-level similarity to preserve consistent structure representation for both real images and fake renderings, our method adopts an unsupervised solution to work on arbitrary hair models. The key component of our method is a shared latent space to encode appearance-invariant structure information of both domains, which generates realistic renderings conditioned by extra appearance inputs. This is achieved by domain-specific pre-disentangled structure representation, partially shared domain encoder layers and a structure discriminator. We also propose a simple yet effective temporal conditioning method to enforce consistency for video sequence generation. We demonstrate the superiority of our method by testing it on a large number of portraits and comparing it with alternative baselines and state-of-the-art unsupervised image translation methods.
AutoMeTS: The Autocomplete for Medical Text Simplification

The goal of text simplification (TS) is to transform difficult text into a version that is easier to understand and more broadly accessible to a wide variety of readers.In some domains, such as healthcare, fully automated approaches cannot be used since information must be accurately preserved.Instead, semi-automated approaches can be used that assist a human writer in simplifying text faster and at a higher quality.In this paper, we examine the application of autocomplete to text simplification in the medical domain.We introduce a new parallel medical data set consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset.We compare four PNLMs (BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of the sentence to be simplified can be incorporated to achieve better results (6.17% absolute improvement over the best individual model).We also introduce an ensemble model that combines the four PNLMs and outperforms the best individual model by 2.1%, resulting in an overall word prediction accuracy of 64.52%.
End-to-end Semantic Role Labeling with Neural Transition-based Model

End-to-end semantic role labeling (SRL) has been received increasing interest. It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly. Recent work is mostly focused on graph-based neural models, while the transition-based framework with neural networks which has been widely used in a number of closely-related tasks, has not been studied for the joint task yet. In this paper, we present the first work of transition-based neural models for end-to-end SRL. Our transition model incrementally discovers all sentential predicates as well as their arguments by a set of transition actions. The actions of the two subtasks are executed mutually for full interactions. Besides, we suggest high-order compositions to extract non-local features, which can enhance the proposed transition model further. Experimental results on CoNLL09 and Universal Proposition Bank show that our final model can produce state-of-the-art performance, and meanwhile keeps highly efficient in decoding. We also conduct detailed experimental analysis for a deep understanding of our proposed model.
Development and Evaluation of an Outdoor Multisensory AR System for Cultural Heritage

Enhancing tourist visits to cultural heritage sites by making use of mobile augmented reality has been a tendency in the last few years, presenting mainly audiovisual experiences.However, these explorations using only visuals and sounds, or narratives, do not allow users to be presented with, for example, a particular smell that can be important to feel engaged or to better understand the history of the site.This article pursues the goal of creating an experience that puts the user in a scene planned to evoke several stimuli with SensiMAR prototype -a Multisensory Augmented Reality system that aims to be used in cultural heritage outdoors.When using SensiMAR, the user will be involved with visual reconstructions, surrounded by the soundscape of ancient times, and is exposed to a particular smell very common that time.Given the novelty of this proposal, ascertaining the usability of such a system was raised as a foremost demand.Thus, in addition to its development and implementation specifications, an experimental study was conducted to evaluate the usability of the system in end-users' perspective.The results obtained from random visitors of an archaeological site were analysed according to their sex, age, previous experience with augmented reality technology, and provided condition -audiovisual condition, and multisensory condition, with visual, audio, and smell stimuli.Results were collected from a total of 67 participants and show that this multisensory prototype achieved good usability results across all groups.No statistically differences were found, demonstrating good usability of the SensiMAR system regardless of their sex, age, previous experience with the technology or provided condition.
Efficient Secure Building Blocks With Application to Privacy Preserving Machine Learning Algorithms

Nowadays different entities (such as hospitals, cyber security companies, banks, etc.) collect data of the same nature but often with different statistical properties.It has been shown that if these entities combine their privately collected datasets to train a machine learning model, they would end up with a trained model that often outperforms the human experts of the corresponding field(s) in terms of classification accuracy.However, due to judicial, privacy and cost reasons, no entity is willing to share their data with others.We have the same problem during the classification (inference) stage.Namely, the user doesn't want to reveal any information about his query or its' final classification, while the owner of the trained model wants to keep this model private.In this article we overcome these drawbacks by firstly introducing novel efficient secure building blocks for general purpose, which can also be used to build privacy preserving machine learning algorithms for both training and classification (inference) purposes under strict privacy and security requirements.Our theoretical analysis and experimentation results show that our building blocks (hence also our privacy preserving algorithms which are built on top of them) are more efficient than most (if not all) of the state-of-the-art schemes in terms of computation and communication cost, as well as security characteristics in the semi-honest model.Furthermore, and to the best of our knowledge, for the Naïve Bayes model we extend this efficiency for the first time to also deal with active malicious users, which arbitrarily deviate from the protocol.
Contrast Enhancement of Multiple Tissues in MR Brain Images With Reversibility

Contrast enhancement (CE) of magnetic resonance (MR) brain images is an important technique to bring out the tissue details for clinical diagnosis. Recently, a new form of image enhancement has been proposed to complete the task without any information loss. Specifically, information required to restore the original image is reversibly hidden into the enhanced image. Moreover, several image segmentation based algorithms have been proposed so that the region of interest can be exclusively enhanced. However, with the reversible algorithms, it is hard to properly enhance the tissues in MR brain images when they are relatively small or connected with each other. To address this issue, a hierarchical CE scheme is proposed for MR brain images with reversibility in this letter. Firstly, a deep convolutional neural network is used to segment multiple tissue classes automatically. Then, the segmented tissues are individually utilized to guide the CE procedure so that individual-tissue-enhanced images are generated. Compared with using the background information to guide the CE procedure, better tissue enhancement effects and visual quality are both obtained by our proposed hierarchical scheme. The evaluation results obtained over MR brain test images demonstrate the reversibility and adaptability of the proposed scheme for the enhancement of interested tissues.
REF-Net: Robust, Efficient, and Fast Network for Semantic Segmentation Applications Using Devices With Limited Computational Resources

Considering importance of the autonomous driving applications for mobile devices, it is imperative to develop both fast and accurate semantic segmentation models.Thanks to emergence of Deep Learning (DL) techniques, the segmentation models enhanced their accuracy.However, this improved performance of currently popular DL models for self-driving car applications come at the cost of time and computational efficiency.Moreover, networks with efficient model architecture experience lack of accuracy.Therefore, in this study, we propose robust, efficient, and fast network (REF-Net) that combines carefully formulated encoding and decoding paths.Specifically, the contraction path uses mixture of dilated and asymmetric convolution layers with skip connections and bottleneck layers, while the decoding path benefits from nearest neighbor interpolation method that demands no trainable parameters to restore original image size.This model architecture considerably reduces the number of trainable parameters, required memory space, training, and inference time.In fact, the proposed model required nearly 90 times fewer trainable parameters and approximately 4 times less memory space that allowed 3-fold faster training runtime and 2-fold inference speedup in the conducted experiments using Cambridge-driving Labeled Video Database (CamVid) and Cityscapes datasets.Moreover, despite its notable efficiency in terms of memory and time, the REF-Net attained superior results in several segmentation evaluation metrics that showed roughly 2%, 4%, and 3% increase in pixel accuracy, Dice coefficient, and Jaccard Index, respectively.
Machine Learning for Securing SDN based 5G Network

The fifth-generation (5G) network supports many systems such as reliable communication in potential applications that require maximum security.Advancement in Software-Defined Networking (SDN) is growing with the emerging network architectures targeted from many servers with the various types of Distributed Denial of Service (DDoS) attackers.When malicious users send DDoS attacks, the SDN based 5G networks face security problems and challenges.Despite the security solutions for preventing DDoS attacks in SDN, securing the SDN controller is one of the challenging problems.The purpose of this research is to analyze the suitable machine learning (ML) for securing the SDN controller targeted by DDoS attacks.This paper proposes a security scheme that includes the ML algorithm, adaptive bandwidth mechanism, and dynamic threshold technique.Therefore, the main focus is on the mitigation scheme of DDoS attacks considered in SDN controller through the ML trained model.In this scheme, the proposed approach uses the best ML as a method for finding security solutions that enhance the security of the SDN controller and network performance.In this method, the Extreme Gradient Boosting (XGBoost) and other ML algorithms were used, which not only enhance the accuracy of the security solutions but also improve the overall network performance.
A Survey on Image Encryption using Chaos-based Techniques

Encryption methods such as AES (Advanced Encryption Standard), DES (Data Encryption Standard), etc. cannot be used for image encryption as images contain a huge amount of redundant data, a high correlation between neighboring pixels and size of the image is very large. Chaos- based techniques have suitable properties that are required for image encryption. The properties include sensitivity to initial conditions, pseudorandom number, ergodicity, and density of periodic orbits. In this paper, a survey of image encryption using chaos-maps such as a logistic map, piecewise linear chaotic map (PWLCM), tent map, etc. is done in order to choose best map for image encryption. Comparison of image encryption using different chaotic maps is done by considering parameters such as key-space and correlation analysis.
Label propagation with multi-stage inference for visual domain adaptation

Domain adaptation aims at leveraging rich label information in source domain and predicting on a related but different target domain, which makes significant progress in visual recognition. Most existing methods attempt to align domains but have limited considerations on the preservation of data structure, especially for unlabeled target data. In this paper, we present a novel solution for unsupervised domain adaptation based on label propagation. Specifically, we construct the affinity graph separately to capture both within-domain and cross-domain property. Inspired by the overlap exists between two domains, we propose multi-stage inference. Samples shared by two domains are expected to have more reliable labels, then they are picked to assist further predictions. Extensive experiments on three real-world datasets demonstrate that our method is comparable or superior to existing methods, we also provide some discussion about the interpretation of multi-stage inference.