Reviewerly/exHarmony huggingface dataset:
A Critical-Path-Coverage-Based Vulnerability Detection Method for Smart Contracts

The second generation of blockchain represented by smart contracts has been developing vigorously in recent years. However, frequent smart contract vulnerability incidents pose a serious risk to blockchain ecosystem security. Since current symbol execution tools often fall into path explosion and thus lead to inefficient detection, this paper expands Mythril's framework to optimize its performance. Firstly, it finds out potential vulnerable code regions using static analysis and identifies critical paths that may have security defects. Then, aiming at the problem that traditional search algorithms cannot actively locate and explore critical paths, this paper presents a multi-objective oriented path search (MOPS) strategy based on path priority. This strategy guides dynamic symbolic execution to cover critical paths quickly, avoiding blind traversal of program execution paths. Finally, it describes security rules and proposes corresponding detection logics for different vulnerability categories. This paper analyzes over 1000 smart contracts extracted from Etherscan. Compared with existing tools based on symbolic execution, the proposed method can reduce time consumption by around 35% while ensuring the accuracy of vulnerability detection. Moreover, existing tools often issue warnings that do not actually cause financial losses. But the proposed method only concentrates on code regions related to transfer of funds, so it can reduce the false alarm rate to some extent.
Taming Anycast in the Wild Internet

Anycast is a popular tool for deploying global, widely available systems, including DNS infrastructure and content delivery networks (CDNs). The optimization of these networks often focuses on the deployment and management of anycast sites. However, such approaches fail to consider one of the primary configurations of a large anycast network: the set of networks that receive anycast announcements at each site (i.e., an announcement configuration). Altering these configurations, even without the deployment of additional sites, can have profound impacts on both anycast site selection and round-trip times.
Recognizing Part Attributes With Insufficient Data

Recognizing the attributes of objects and their parts is central to many computer vision applications. Although great progress has been made to apply object-level recognition, recognizing the attributes of parts remains less applicable since the training data for part attributes recognition is usually scarce especially for internet-scale applications. Furthermore, most existing part attribute recognition methods rely on the part annotations which are more expensive to obtain. In order to solve the data insufficiency problem and get rid of dependence on the part annotation, we introduce a novel Concept Sharing Network (CSN) for part attribute recognition. A great advantage of CSN is its capability of recognizing the part attribute (a combination of part location and appearance pattern) that has insufficient or zero training data, by learning the part location and appearance pattern respectively from the training data that usually mix them in a single label. Extensive experiments on CUB, Celeb A, and a newly proposed human attribute dataset demonstrate the effectiveness of CSN and its advantages over other methods, especially for the attributes with few training samples. Further experiments show that CSN can also perform zero-shot part attribute recognition.
Model for Classification of Poems in Hindi Language Based on Ras

The developed model will classify poem into Shringar, Hasya, Adbhuta, Shanta, Raudra, Veera, Karuna, Bhayanaka, Vibhasta rasas, which will use mix of part-of-speech-based feature and emotional features to classify the poem. Emotional features are features, which are responsible for particular emotion and it is represented in 9 categories. We have 9 classes each class-containing feature for one class, overlapping of feature is possible and is dealt with term frequency in the document. The classifiers used are support vector machine and naive Bayes. The model has used 55 poems as a dataset of all 9 genres consisting of 10531 words.
Cross-Reference Stitching Quality Assessment for 360° Omnidirectional Images

Along with the development of virtual reality (VR), omnidirectional images play an important role in producing multimedia content with an immersive experience. However, despite various existing approaches for omnidirectional image stitching, how to quantitatively assess the quality of stitched images is still insufficiently explored. To address this problem, we first establish a novel omnidirectional image dataset containing stitched images as well as dual-fisheye images captured from standard quarters of 0$^\circ$, 90$^\circ$, 180$^\circ$, and 270$^\circ$. In this manner, when evaluating the quality of an image stitched from a pair of fisheye images (\eg, 0$^\circ$ and 180$^\circ$), the other pair of fisheye images (\eg, 90$^\circ$ and 270$^\circ$) can be used as the cross-reference to provide ground-truth observations of the stitching regions. Based on this dataset, we propose a set of Omnidirectional Stitching Image Quality Assessment (OS-IQA) metrics. In these metrics, the stitching regions are assessed by exploring the local relationships between the stitched image and its cross-reference with histogram statistics, perceptual hash and sparse reconstruction, while the whole stitched images are assessed by the global indicators of color difference and fitness of blind zones.Qualitative and quantitative experiments show our method outperforms the classic IQA metrics and is highly consistent with human subjective evaluations. To the best of our knowledge, it is the first attempt that assesses the stitching quality of omnidirectional images by using cross-references.
Hybrid neural conditional random fields for multi-view sequence labeling

In traditional machine learning, conditional random fields (CRF) is the mainstream probability model for sequence labeling problems. CRF considers the relation between adjacent labels other than decoding each label independently, and better performance is expected to achieve. However, there are few multi-view learning methods involving CRF which can be directly used for sequence labeling tasks. In this paper, we propose a novel multi-view CRF model to label sequential data, called MVCRF, which well exploits two principles for multi-view learning: consensus and complementary. We first use different neural networks to extract features from multiple views. Then, considering the consistency among the different views, we introduce a joint representation space for the extracted features and minimize the distance between the two views for regularization. Meanwhile, following the complementary principle, the features of multiple views are integrated into the framework of CRF. We train MVCRF in an end-to-end fashion and evaluate it on two benchmark data sets. The experimental results illustrate that MVCRF obtains state-of-the-art performance: F1 score 95.44% for chunking on CoNLL-2000, 95.06% for chunking and 96.99% for named entity recognition (NER) on CoNLL-2003.
Annotation Efficient Cross-Modal Retrieval with Adversarial Attentive Alignment

Visual-semantic embeddings are central to many multimedia applications such as cross-modal retrieval between visual data and natural language descriptions. Conventionally, learning a joint embedding space relies on large parallel multimodal corpora. Since massive human annotation is expensive to obtain, there is a strong motivation in developing versatile algorithms to learn from large corpora with fewer annotations. In this paper, we propose a novel framework to leverage automatically extracted regional semantics from un-annotated images as additional weak supervision to learn visual-semantic embeddings. The proposed model employs adversarial attentive alignments to close the inherent heterogeneous gaps between annotated and un-annotated portions of visual and textual domains. To demonstrate its superiority, we conduct extensive experiments on sparsely annotated multimodal corpora. The experimental results show that the proposed model outperforms state-of-the-art visual-semantic embedding models by a significant margin for cross-modal retrieval tasks on the sparse Flickr30k and MS-COCO datasets. It is also worth noting that, despite using only 20% of the annotations, the proposed model can achieve competitive performance (Recall at 10 > 80.0% for 1K and > 70.0% for 5K text-to-image retrieval) compared to the benchmarks trained with the complete annotations.
SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition

Understanding the spatial relations between objects in images is a surprisingly challenging task. A chair may be "behind" a person even if it appears to the left of the person in the image (depending on which way the person is facing). Two students that appear close to each other in the image may not in fact be "next to" each other if there is a third student between them. We introduce SpatialSense, a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques. SpatialSense is constructed through adversarial crowdsourcing, in which human annotators are tasked with finding spatial relations that are difficult to predict using simple cues such as 2D spatial configuration or language priors. Adversarial crowdsourcing significantly reduces dataset bias and samples more interesting relations in the long tail compared to existing datasets. On SpatialSense, state-of-the-art recognition models perform comparably to simple baselines, suggesting that they rely on straightforward cues instead of fully reasoning about this complex task. The SpatialSense benchmark provides a path forward to advancing the spatial reasoning capabilities of computer vision systems. The dataset and code are available at https://github.com/princeton-vl/SpatialSense.
BoR: Toward High-Performance Permissioned Blockchain in RDMA-Enabled Network

Known as a distributed ledger, blockchain is becoming prevalent due to its decentralization, traceability and tamper resistance. Particularly, permissioned blockchain such as Hyperledger Fabric shows great application prospects as the infrastructure of IoT security, credit management, etc. Many cloud platforms like AWS, Azure, Oracle and IBM cloud currently provide blockchain as a service, in which tenants can quickly build permissioned blockchain and run smart contract based applications. However, the transactions throughput and scalability in the permissioned blockchain are not ideal, despite many optimization efforts in consensus protocol and parallel chain. Existing solutions still reveals some limitations like excessive CPU scheduling, inefficient block broadcast and high latency of initial blocks synchronization when new nodes join blockchain network. Inspired by the emerging RDMA (Remote Direct Memory Access) network, we propose BoR, an RDMA-based permissioned blockchain framework. By offloading the block transfer transaction into RDMA NICs, it can increase block broadcast speed and reduce block sync delay. We exploit the RDMA primitives to redesign the block synchronization protocol and accelerate DPoS (Delegated Proof of Stake) consensus process for higher throughput and lower latency in kernel-bypass manner. As demonstrated in our evaluation with different workloads, BoR with lower CPU utilization significantly outperforms the state-of-the-art EoS blockchain.
Generating UML Use Case Models from Software Requirements Using Natural Language Processing

Modeling the system's specifications from the functionality perspective is an important step in analyzing the software requirements. UML use case diagram is one of the most used functional modeling techniques in the software development process. This paper provides an approach to generate the UML use case diagrams from the requirements text using natural language processing. The approach consists of several steps to process the requirements text. Starting with filtering the text from the mistakes and going through natural language processes till generating the use case diagrams. Experimental evaluations have been done on several public software projects to demonstrate the accuracy of the proposed approach.
Perceptual Visual Reasoning with Knowledge Propagation

Visual Question Answering (VQA) aims to answer natural language questions given images, where great challenges lie in comprehensive understanding and reasoning based on the rich contents provided by both questions and images. Most existing literature on VQA fuses the image and question features together with attention mechanism to answer the questions. In order to obtain a more human-like inferential ability, there have been some preliminary module-based approaches which decompose the whole problem into modular sub-problems. However, these methods still suffer from unsolved challenges such as lacking sufficient explainability and logical inference --- no doubt the gap between these preliminary studies and the real human reasoning behaviors is still extremely large. To tackle the challenges, we propose a Perceptual Visual Reasoning (PVR) model which advances one important step towards the more explainable VQA in this paper. Our proposed PVR model is a module-based approach which incorporates the concept of logical and/or for logic inference, introduces a richer group of perceptual modules for better logic generalization and utilizes the supervised information on each sub-module for more explainability. Knowledge propagation is therefore enabled by resorting to the modular design and supervision on sub-modules. We carry out extensive experiments with various evaluation metrics to demonstrate the superiority of the proposed PVR model against other state-of-the-art methods.
PuppetGAN: Cross-Domain Image Manipulation by Demonstration

In this work we propose a model that can manipulate individual visual attributes of objects in a real scene using examples of how respective attribute manipulations affect the output of a simulation. As an example, we train our model to manipulate the expression of a human face using nonphotorealistic 3D renders of a face with varied expression. Our model manages to preserve all other visual attributes of a real face, such as head orientation, even though this and other attributes are not labeled in either real or synthetic domain. Since our model learns to manipulate a specific property in isolation using only "synthetic demonstrations" of such manipulations without explicitly provided labels, it can be applied to shape, texture, lighting, and other properties that are difficult to measure or represent as real-valued vectors. We measure the degree to which our model preserves other attributes of a real image when a single specific attribute is manipulated. We use digit datasets to analyze how discrepancy in attribute distributions affects the performance of our model, and demonstrate results in a far more difficult setting: learning to manipulate real human faces using nonphotorealistic 3D renders.
Sentiment Analysis Based on Gated Recurrent Unit

Sentiment analysis is a term which is used for classification one text is positive, negative or neutral. Sentiment analysis is used many applications like e-commercial in order to make pricing of things, customer churn analysis, brand strategy and advertisement. These goals can be executed as automatic process thanks to machine learning. Sentiment analysis is handled as supervised learning process in machine learning. In order to classification of text as a positive, negative or neutral is used user comments and votes. There are many machine learning supervised algorithms can be used for this purpose such as LSTM, CNN, GRU. In this study, a sentiment analysis was implemented by using Gated Recurrent Unit using 243 thousand line of the Hepsiburada, Turkish e-commercial platform, datasets and obtained 0.95 accuracy.
Active learning through label error statistical methods

Clustering-based active learning splits data into a number of blocks and queries the labels of the most critical instances. An active learner must decide how to choose these critical instances and how to split the blocks. In this paper, we present theoretical and practical statistical methods for analyzing the relationship between the label error and the neighbor radius, and design new split and selection strategies to handle these two issues. First, we define statistical functions for the label error based on a single instance and instance pairs. Second, we build practical statistical models, calculate empirical label errors, and guide the block splitting process. Third, using these practical models, we develop a center-and-edge instance selection strategy for choosing critical instances. Fourth, we design a new algorithm called active learning through label error statistical methods (ALSE). Learning experiments were performed with 20 datasets from various domains. The results of significance tests verify the effectiveness of ALSE and its superiority over state-of-the-art active learning algorithms.
Adaptive Feature Fusion via Graph Neural Network for Person Re-identification

Person Re-identification (ReID) targets to identify a probe person appeared under multiple camera views. Existing methods focus on proposing a robust model to capture the discriminative information. However, they all generate a representation by mining useful clues from a given single image, and ignore the intercommunication with other images. To address this issue, we propose a novel network named Feature-Fusing Graph Neural Network (FFGNN), which fully utilizes the relationships among the nearest neighbors of the given image, and allows message propagation to update the feature of the node during representation learning. Given an anchor image, the FFGNN firstly obtains its Top-K nearest images based on the feature generated by the trained Feature-Extracting Network(FEN). We then construct a graph G based on the obtained K+1 images, in which each node represents the feature of an image. The edge of the graph G is obtained by combing the visual similarity and Jaccard similarity between nodes. Within the constructed graph G, FFGNN conducts message propagation and adaptive feature fusion between nodes by iteratively performing graph convolutional operation on the input features. Finally, the FFGNN outputs a robust and discriminative representation which contains the information from its similar images. Extensive experiments on three public person ReID datasets including Market-1501, DukeMTMC-ReID, and CUHK03 demonstrate that the proposed model can achieve significant improvement against state-of-the-art methods.
Optimization of Artificial Bee Colony Algorithm Based Load Balancing in Smart Grid Cloud

Cloud computing is a computing paradigm that allocates computing resources by using virtualization technology. Load balancing algorithm is a vital part of the cloud data center to guarantee effective resource utilization and energy consumption management. Current load balance algorithms in cloud focus on the specific systems or application requirements which lack scalable adaptivity. In this paper, the optimization problem of Artificial Bee Colony (ABC) based on load balance algorithm is proposed to improve the overall load balance performance and achieve better adaptivity. The smart grid cloud sources characteristics to cluster virtual machine (VM) is used and the ABC algorithm is optimized. Simulation analysis validates the effectiveness of the proposed method.
Comprehensive Contemplation of Probabilistic Aspects in Intelligent Analytics

In Big Data analysis, the application of machine learning has proven to be a revolutionary. The systematic review of literature shows that research has been carried out on the domain of big data analytics particularly text analytics with the inclusion of machine learning approaches. This extensive survey deals with the data at hand that provides different ways and issues while combining the machine learning approaches with the text. During the course of the survey, various publications in the field of synchronous application of machine learning in text analytics were searched and studied. Classification framework is proposed as the contribution of machine learning in text analytics. A classification framework represented the various application areas to motivate researchers for future research on the application of two emerging technologies.
Object detection with Raspberry Pi3 and Movidius Neural Network Stick

Object detection and classification is an increasingly important field of research in machine learning. Currently, powerful GPUs (Graphics Processing Units) are used to perform the computation-intensive operations in the shortest possible computing time. However, these systems are associated with high costs. In this paper a system for object detection and classification is developed, which gets by with less resources. This should minimize the costs while keeping the performance acceptable for the target application. To keep the costs low, a Raspberry Pi3 is used as development platform in connection with a Movidius stick for the outsourcing of the ANN. After explaining the theoretical basics of object detection and ANNs, this paper shows the implementation process of the selected hardware and software. For the evaluation of this system the algorithms YOLO and MobileNet are used and pre-trained models are used as basis. Based on the MSCOCO data set, both the quality of the object classification and the computing time are evaluated.
Feature Construction for Posts and Users Combined with LightGBM for Social Media Popularity Prediction

In this paper, we propose to address the Social Media Prediction (SMP) Challenge by using regression model with multiple features extracted from various aspects of posts. More specifically, we extract textual features, numeric features, and construct user-related features to this end. For textual features, the rich texts possessed by the posts are integrated to build a corpus, based on which we train a language model to learn the vector representation of semantic information. For numeric features, we construct several new features, including the length and the word numbers of title. For the user-related features, we design a "user id count" based on the number of times each user posted in the entire dataset to show the activity of the user. Finally, the multiple features are feed into LightGBM to predict popularity scores. Extensive experiments conducted on the Social Media Prediction Dataset show the superiority of our method. Our approach achieves the 3rd place in the SMP Challenge.