Reviewerly/exHarmony huggingface dataset:
A Novel Joint Model with Second-Order Features and Matching Attention for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of the specific aspect for a given sentence. Attention-based models are widely used in this task because they can extract semantic information between context words to make up for the deficiency of sequence models in semantic encoding. In order to enhance the extraction of high-quality semantic information, we propose a novel joint model with Second-Order Features and Matching Attention (SOMA) for aspect-based sentiment analysis. Firstly, we introduce the second-order statistics to extract vital information and interact with the first-order features to generate the interaction representation. Secondly, we adopt Euclidean distance to replace the traditional matrix transformation to capture the semantic similarity between aspect terms and context words. Finally, we form a joint representation to focus on the meaningful words in the sentence. We conduct extensive experiments and comparisons on SemEval 2014, SemEval 2016, and Twitter datasets. Experimental results demonstrate the effectiveness of our model.
Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits

It has been recently shown that general policies for many classical planning domains can be expressed and learned in terms of a pool of features defined from the domain predicates using a description logic grammar. At the same time, most description logics correspond to a fragment of k-variable counting logic (C_k) for k=2, that has been shown to provide a tight characterization of the expressive power of graph neural networks. In this work, we make use of these results to understand the power and limits of using graph neural networks (GNNs) for learning optimal general policies over a number of tractable planning domains where such policies are known to exist. For this, we train a simple GNN in a supervised manner to approximate the optimal value function V*(s) of a number of sample states s. As predicted by the theory, it is observed that general optimal policies are obtained in domains where general optimal value functions can be defined with C_2 features but not in those requiring more expressive C_3 features. In addition, it is observed that the features learned are in close correspondence with the features needed to express V* in closed form. The theory and the analysis of the domains let us understand the features that are actually learned as well as those that cannot be learned in this way, and let us move in a principled manner from a combinatorial optimization approach to learning general policies to a potentially, more robust and scalable approach based on deep learning.
Cloud Security Best Practices Derived from Mission Thread Analysis

This report presents four important security practices that are practical and effective for improving the cybersecurity posture of cloud-deployed information technology (IT) systems. These practices help to address the risks, threats, and vulnerabilities that organizations face in deploying or moving applications and systems to a cloud service provider (CSP). The practices address cloud security issues that consumers are experiencing, illustrated by several recent cloud security incidents. The report demonstrates the practices through examples using cloud services available from Amazon Web Service (AWS), Microsoft, and Google.The presented practices are geared toward small and medium-sized organizations; however, all organizations, independent of size, can use these practices to improve the security of their cloud usage. The focus here is on hybrid deployments where some IT applications deploy or move to a CSP while other IT applications remain in the organization’s data center. Small and medium-sized organizations likely have limited resources; where possible, these practices describe implementation approaches that may be effective in limited-resource situations.
Designated-Verifier Anonymous Credential for Identity Management in Decentralized Systems

Most of the existing identity management is the centralized architecture that has to validate, certify, and manage identity in a centralized approach by trusted authorities. Decentralized identity is causing widespread public concern because it enables to give back control of identity to clients, and the client then has the ability to control when, where, and with whom they share their credentials. A decentralized solution atop on blockchain will bypass the centralized architecture and address the single point of the failure problem. To our knowledge, blockchain is an inherited pseudonym but it cannot achieve anonymity and auditability directly. In this paper, we approach the problem of decentralized identity management starting from the designated-verifier anonymous credential (DVAC in short). DVAC would assist to build a new practical decentralized identity management with anonymity and auditability. Apart from the advantages of the conventional anonymous credential, the main advantage of the proposed DVAC atop blockchain is that the issued cryptographic token will be divided into shares at the issue phase and will be combined at the showing credential phase. Further, the smooth projective hash function ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="sans-serif">SPHF</mi> </math> in short) is regarded as a designated-verifier zero-knowledge proof system. Thus, we introduce the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi mathvariant="sans-serif">SPHF</mi> </math> to achieve the designated verifiability without compromising the privacy of clients. Finally, the security of the proposed DVAC is proved along with theoretical and experimental evaluations.
Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion

Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD-DataSet and NWPU-DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.
Efficient Task Offloading for MEC-Enabled Vehicular Networks: A Non-Cooperative Game Theoretic Approach

Vehicular Edge Computing (VEC) is a new leading technology to enhance the vehicular performance through task offloading where resource-confined vehicles offload their computing task to the vehicular multi-access edge computing (MEC) networks in proximity. However, the environment of vehicular task offloading is extremely dynamic and faces some challenges to determine the location of processing the offloaded task. As a result, to achieve optimal performance by using traditional VEC system is difficult because in advance we don't know the demand of vehicles. Therefore, a non-cooperative game theory-based efficient task offloading (NGTO) scheme is proposed in this study where the offloading decisions are taken either the MEC server or remote cloud server through the game-theoretic approach. To reduce the processing latency of the vehicles' computation tasks and assure the maximum utility of each vehicle, we used a distributed best response offloading strategy. Our proposed strategy accommodates its offloading probability to achieve a unique equilibrium under certain conditions. Detailed performance evaluation affirms that our proposed NGTO scheme can outperform in all scenarios. It can minimize the response time at almost 41.2 % and average task failure rate at approximately 56.3% when compared with a local roadside unit computing (LRC) scheme. The reduced response time and task failure rates are approximately 25.2% and 20.4%, respectively, when compared with a collaborative (LRC with cloud via roadside unit) offloading scheme.
An Extended Fuzzy C-Means Segmentation for an Efficient BTD With the Region of Interest of SCP

Magnetic resonance imaging (MRI) is the primary source to diagnose a brain tumor or masses in the medical sciences. It is emerging to detect the tumors from the scanned MRI brain images at early stages for the best treatments. Existing image segmentation techniques, morphological, fuzzy c-means are wildly successful in the extraction region of interest (ROI) in brain image segmentation. Proper extraction of ROIs is useful for regularizing the regions of tumors from the brain image with effective binarization in the segmentation. However, the existing techniques are limiting the irregular boundaries or shapes in tumor segmentation. Thus, this paper presents the proposed work extending the FCM with the spatial correlated pixel (RSCP), known as FCM-RSCP. It overcomes the problem of irregular boundaries by assessing correlated spatial information during segmentation. Benchmarked MRI brain images are used in the experiment for demonstrating the efficiency of the proposed methodology.
Fisher-regularized Support Vector Machine with Pinball Loss Function

Fisher-regularized support vector machine (Fish-erSVM) can approximatively fulfill the Fisher criterion and obtain good statistical separability, which is a combined method of the support vector machine and Fisher discriminant analysis. However, the hinge loss function is related to the shortest distance between two-class sets, and FisherSVM may be hence sensitive to noise. To remedy it, the pinball loss function, which is related to the quantile distance, is introduced into FisherSVM and then a Fisher-regularized support vector machine with pinball loss function (Pin-FisherSVM) is proposed, which combines the noise insensitivity of the pinball loss function with the statistical separability of FisherSVM well. Pin-FisherSVM can be cast as a quadratic programming, which results a globally optimal solution. Experimental results on artificial and real-world datasets demonstrate that our proposed method is insensitive to label noise or feature noise. Compared to FisherSVM, the proposed Pin-FisherSVM has the same computational complexity and exhibits superior noise insensitivity.
Dr. Phish: Phishing Website Detector

Phishing is a common attack on credulous people by making them disclose their unique information. It is a type of cyber-crime where false sites allure exploited people to give delicate data. This paper deals with methods for detecting phishing websites by analyzing various features of URLs by Machine learning techniques. This experimentation discusses the methods used for detection of phishing websites based on lexical features, host properties and page importance properties. We consider various data mining algorithms for evaluation of the features in order to get a better understanding of the structure of URLs that spread phishing. To protect end users from visiting these sites, we can try to identify the phishing URLs by analyzing their lexical and host-based features.A particular challenge in this domain is that criminals are constantly making new strategies to counter our defense measures. To succeed in this contest, we need Machine Learning algorithms that continually adapt to new examples and features of phishing URLs.
Assembling Blockchain and IoT for Smart Food-Supply Chain

Globalization and extreme competition in the market have made the food supply chain more complex than before. To address the complexities in the food supply chain, blockchain has become more evident than the traditional ways. Use of blockchain technologies can ensure the efficiency, transparency and reliability of the food supply chain starting from the farms to forks. This study focuses on the applicability of blockchain systems to the agriculture field and provides comparative analysis among the existing methodologies that are dedicated to uplift the security, effectiveness and reliability of blockchain based food supply chains. This research has also proposed a novel model that couples blockchain and IoT based sensor modules, which can be applicable in real-life scenarios. The proposed method essentially follows a multi-layer architecture where each layer is presented by means of a blockchain technology. Each layer is fed by the ambient conditions which may impact better quality of crops. The IoT module will ensure better farming as well as the blockchain system will ensure the quality of the end product. If the packaged food degrades in quality, the reason behind the degradation can be located by the model presented in this paper. The presented framework may reach promising efficiency in practical life but the framework implementation cost may create a burden for the investors.
Weakly Supervised Semantic Segmentation with Patch-Based Metric Learning Enhancement

Weakly supervised semantic segmentation (WSSS) methods are more flexible and less costly than supervised ones since no pixel-level annotation is required. Class activation maps (CAMs) are commonly used in existing WSSS methods with image-level annotations to identify seed localization cues. However, as CAMs are obtained from a classification network that mainly focuses on the most discriminative parts of an object, less discriminative parts may be ignored and not identified. This study aims to improve the local visual understanding on objects of the classification network by considering an additional metric learning task on patches sampled from each CAM-based object proposal. As the patches contain different object parts and surrounding backgrounds, not only the most discriminative object parts but the entire objects are learned through leveraging the patch similarity. After the joint training process with the proposed patch-based metric learning and classification tasks, we expect more discriminative local features can be learned by the backbone network. As a result, more complete class-specific regions of an object can be identified. Extensive experiments on the PASCAL VOC 2012 dataset validate the superiority of our method. Our proposed model achieves improvement compared with the state-of-the-art methods.
Emotion Extraction and Classification from Twitter Text

Human beings are treated specially for their emotions. Emotions define a person's perspective and feelings towards the stated topic. In the present digital era, social media is given topmost priority.People share their ideas, opinions, and information through various social media platforms like Facebook, Twitter, Instagram, etc. Twitter is such a powerful social platform where people can connect and share their feeling in the form of tweets. In our system, we have picked out the basic six human emotions (Happy, Sad, Anger, Surprise, Love, Disgust). Our system aims to identify and extract the emotion from the text that is tweeted by the users.
An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls

A recent innovation in the trusted execution environment (TEE) technologies enables the delegation of privacy-preserving computation to the cloud system. In particular, Intel SGX, an extension of x86 instruction set architecture (ISA), accelerates this trend by offering hardware-protected isolation with near-native performance. However, SGX inherently suffers from performance degradation depending on the workload characteristics due to the hardware restriction and design decisions that primarily concern the security guarantee. The system-level optimizations on SGX runtime and kernel module have been proposed to resolve this, but they cannot effectively reflect application-specific characteristics that largely impact the performance of legacy SGX applications. This work presents an optimization strategy to achieve application-level optimization by utilizing asynchronous switchless calls to reduce enclave transition, one of the dominant overheads of using SGX. Based on the systematic analysis, our methodology examines the performance benefit for each enclave transition wrapper and selectively applies switchless calls without modifying the legacy codebases. The evaluation shows that our optimization strategy successfully improves the end-to-end performance of our showcasing application, an SGX-enabled network middlebox.
An enhanced and interpretable feature representation approach to support shape classification from binary images

Shape classification from binary images is a challenging task within the computer vision community. Commonly, contour and structural features are computed to describe the objects and code patterns robust against rotation, scaling, and shape deformation. However, current techniques get a high-dimensional feature space decreasing the system performance and the attribute interpretability. Here, we introduce an enhanced and interpretable feature representation approach to support shape classification from binary images. Our method, named EIFR, employs a bag of contour fragments-based feature estimation, intrinsically robust to occlusion and shape deformation. Then, a ReliefF-based feature selection is applied to filter non-discriminative attributes. In turn, a kernel-alignment-based projection is used to measure the feature relevance enhancing the data representation through the matching between a similarity matrix computed from filtered attributes and a kernel matrix built from the shape labels. Attained results on benchmark datasets prove that EIFR improves the curvature-based features’ interpretability and favors the classification performance.
Visual Sensor-Based Image Analysis of the Relationship between Nutritional Diet and Athletic Ability of Sports Dance Athletes

Sports dance originated from the international standard national standard dance. Since China formally established the “China International Ballroom Dance Association” in 1986, it has developed rapidly. At the same time, related research on sports dancers has become a hot spot. However, most of the current researches are limited to athletes’ physical training and competitive skills, and the research on athletes’ dietary nutrition and athletic ability is still blank. In response to this situation, this paper presents a study on the relationship between nutritional diet and athletic ability of sports dancers based on image analysis of visual sensors. This article is mainly divided into three parts. The first part is the basic theories and core concepts of related research. This part introduces the modes and algorithms of visual sensors, analyzes the specific problems of current sports dance athletes in my country in terms of dietary nutrition, and continues with the adverse effects. It affects the athlete’s athletic ability and even threatens the athlete’s health. Improving the diet of athletes and establishing healthy dietary standards are of great significance to the development of this field. The second part is the establishment method of the nutritional intervention experiment model, which gives the principle and specific operation steps of the experiment design in detail. The third part is a comparative test. To further confirm the influence of nutrition on dance performance in athletes, this article conducted a number of comparative studies such as immunoglobulin conversion after intervention, changes in the indicators of lipid metabolism after intervention, and dietary mineral intake. Through the analysis of experimental data, it can be seen that reasonable dietary supplementation has a positive effect on the recovery of athletes’ physical fitness, which can enhance the body’s immunity while improving athletic ability.
Spam Detection Using Genetic Algorithm Optimized LSTM Model

Sinhmar, Abhinav Malhotra, Vinamra Yadav, Raghav Kumar, ManojThe advancement in technology over the years has resulted in the increased usage of SMS which in turn has provided certain groups a chance to exploit this service by spreading spam messages to consumers making it difficult for people to receive important information and also possessing a threat to their privacy. There are numerous machine learning and deep learning techniques that have been used for spam detection and have proved to be effective. But in deep learning techniques, it is essential to fine-tune the hyperparameters which requires excessive computational power and time, making the process less feasible. The proposed work aims at reducing this computational barrier and time by using Genetic Algorithm in order to select the key hyperparameters. A randomly generated population of LSTM models was created and further generations were produced following the different stages of the genetic algorithm multiple times until the terminal condition was met, and the performance of each candidate solution was evaluated using a chosen fitness function. The most optimal configuration was obtained from the final generation which is used to classify the messages. Four metrics, namely the accuracy, precision, recall and f1-score were used to analyze the model’s performance. The experimental results demonstrate that the Genetic Algorithm optimized LSTM model was able to outperform the other machine learning models.
On Feature Decorrelation in Self-Supervised Learning

In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentations. A potential issue of this idea is the existence of completely collapsed solutions (i.e., constant features), which are typically avoided implicitly by carefully chosen implementation details. In this work, we study a relatively concise framework containing the most common components from recent approaches. We verify the existence of complete collapse and discover another reachable collapse pattern that is usually overlooked, namely dimensional collapse. We connect dimensional collapse with strong correlations between axes and consider such connection as a strong motivation for feature decorrelation (i.e., standardizing the covariance matrix). The capability of correlation as an unsupervised metric and the gains from feature decorrelation are verified empirically to highlight the importance and the potential of this insight.