Reviewerly/exHarmony huggingface dataset:
A Novel Outlier-Tolerable and Predictive Approach to Web Service Composition

The Quality-of-Service (QoS) aspects of Web service has gained popularity in the field of service computing. QoS-oriented Web service composition is a distributed model to construct new web service on top of existing primitive or other composite web services with QoS guarantees. A major challenge in this field is that the QoS data of candidate services are with run-time fluctuations and thus difficult to predict. Traditional approaches in this direction tended to address the challenge by statistics, prediction and neural network-based models. A major limitation of these methods lies in that they ignore outliers data in the historical QoS data, in terms of inconsistencies, errors, shifts, corruptions, etc. In this work, instead, we consider outliers in QoS series to be non-neglectable, and propose an outlier-tolerable and predictive approach to service composition through leveraging a joint estimation-based outlier detection method and a niched genetic algorithm. To validate the effectiveness of our proposed method, we conduct extensive case studies based on different outlier conditions, and the experimental results show that our method is superior to existing ones.
e-seller Web Application for Vegetables using STS Framework

Now-a-Days most of the people prefer to shop inonline. In manual system customer and seller have to face someissues like time, environmental changes, bargaining, quality etc.Most common online shopping refers to dresses, cosmetics, homeequipment’s, where people can sit in their house, buy items with ease and save their time. In our project, we are going to build such application where people can easily check the required vegetables and buy them according to their requirements. For developing any application, we need front end and backend part. In our application for developing the backend part, spring boot with rest controller, JPA hibernate and oracle for database managementare used. To develop the business logic in application, core JAVAand JPA with hibernate are used. Swagger also known as open API intiative, is a framework used to import our API format and convert it into Json format. It can perform actions related to APIprocess. Swagger is also used for the documentation purpose. JDBC is used to connect our application to the oracle databse and manage its operations.
Session-based Recommendation via Memory Network and Dwell-time Attention

The session-based recommendation task predicts a user’s next interaction based on ongoing anonymous sessions. Previous studies mainly focused on improving click-through rates, while few studies have yielded further investigation on how to increase purchase conversion rates in session-based recommendations. However, the purchase rate improvement is a crucial metric to measure the performance of recommendation systems in e-commerce scenarios, which means that the prediction of users’ purchase behavior is of significant value. To this end, we build a novel model to predict what items people will be more likely to buy in the current session. The basic intuition of this paper is that within a session, the user’s dwell-time on each item reflects the interest level, and collaborative information from neighbor buy-sessions ((sessions that end with a purchased item) can be leveraged to boost the recommendation accuracy. We propose a new hybrid framework that leverages dwell-time information to model users’ actual interests. In addition, a memory network is employed to store and retrieve history buy-sessions as collaborative information. Finally, the representation of the current session and its neighbor sessions are combined to predict what items users are more likely to buy. Experimental results on two real e-commerce datasets show that the proposed model locates the actual demands (items to buy) more precisely than the state-of-art baselines.
Analyzing the Effectiveness of the Gaussian Mixture Model Clustering Algorithm in Software Enhancement Effort Estimation

Background: The influence of data clustering on the effort estimating process has been studied extensively. Studies focus on partitioning and density-based clustering, and some use hierarchical clustering, but most focus on software development effort estimation. Aim: We focus on the Gaussian Mixture Model algorithm’s effectiveness in the software enhancement effort estimation. Method: We used the Gaussian Mixture Model clustering algorithm to cluster the dataset into clusters and then applied the IFPUG FPA method for effort estimation on these clusters. The ISBSG dataset was used in this study. The number of clusters is determined using the Elbow method with the Distortion score. Besides, the k-means algorithm was also used as the comparative algorithm. The baseline model was determined by using the FPA method on the entire dataset without clustering. Result: With the number of clusters selected as 4, on six evaluation criteria, MAE, MAPE, RMSE, MBRE, and MIBRE, the experimental results show the estimated accuracy using the FPA method on clustered data significantly better when compared with no clustering. Conclusion: the software enhancement effort estimation can be significantly improved when using the Gaussian Mixture Model clustering algorithm.
Joint Geometrical and Statistical Alignment Using Triplet Loss for Deep Domain Adaptation

Although the primitive and deep learning methods have made significant progress, problems can arise if there are large differences or distribution gaps between the training and test images. To overcome this problem, shallow and deep domain adaptation (DA) approaches have been developed. However, none of the existing deep DA approaches reduce the disparity in distribution among domains statistically and geometrically and never attempt to reduce the distance among congruent images and maximize the distance between incongruous images. Therefore, in this paper, we introduce a joint geometrical and statistical alignment using the triplet loss (JGSAT) method for deep domain adaptation. More specifically, the JGSAT reduces domain shift between the domains statistically and geometrically simultaneously by incorporating maximum mean discrepancy (MMD), CORrelation ALignment (CORAL), and triplet loss in a unified framework. Ubiquitous evaluations have affirmed that the suggested method JGSAT remarkably vanquishes cutting-edge shallow and deep domain adaptation techniques on the PIE face recognition dataset.
Meta Auxiliary Learning for Top-K Recommendation

Recommender systems are playing a significant role in modern society to alleviate the information/choice overload problem, since Internet users may feel hard to identify the most favorite items or products from millions of candidates. Thanks to the recent successes in computer vision, auxiliary learning has become a powerful means to improve the performance of a target (primary) task. Even though helpful, the auxiliary learning scheme is still less explored in recommendation models. To integrate the auxiliary learning scheme, we propose a novel meta auxiliary learning framework to facilitate the recommendation model training, i.e., user and item latent representations. Specifically, we construct two self-supervised learning tasks, regarding both users and items, as auxiliary tasks to enhance the representation effectiveness of users and items. Then the auxiliary and primary tasks are further modeled as a meta learning paradigm to adaptively control the contribution of auxiliary tasks for improving the primary recommendation task. This is achieved by an implicit gradient method guaranteeing less time complexity compared with conventional meta learning methods. Via a comparison using four real-world datasets with a number of state-of-the-art methods, we show that the proposed model outperforms the best existing models on the Top-K recommendation by 3% to 23%.
Dual-VIE: Dual-Level Graph Attention Network for Visual Information Extraction

Visual Information Extraction (VIE) is a task to extract key information from document images such as waybills and receipts. Existing methods typically combine multi-modal information including textual, visual, layout features and achieve promising results on datasets in various domains. However, previous methods treat the VIE task as a token-level sequence labelling problem and have not explicitly modelled the relationship between bounding boxes. VIE heavily depends on the context, especially the relationship between key-value pairs. To address this problem, in this paper, we propose a dual-level graph attention model that combines coarse-grained and fine-grained information. At the fine-grained token level, we force the graph attention network to focus on its local token neighbours within a bounding box. At the coarse-grained bounding box level, we encourage further information interaction between bounding boxes and pay more attention to the potential key-value pairs. To the best of our knowledge, our method may be the first attempt to jointly model the correlation between bounding boxes and tokens under a unified fine-tuning framework. Experimental results show that the proposed method significantly surpasses previous methods. Compared to the strong baseline LayoutLM, our method improves the F1-score by about 3% on both datasets. Our method is an important complement to existing VIE methods.
Planning Autonomous Marine Inspection Tasks using SMT Encoding of Timelines

A key challenge in autonomous vehicles is the high-level decision making required to be reactive to significant changes in the environment. Timeline-based planning is a paradigm for temporal (time-aware) task planning that can provide such high-level decisions. We present an algorithm for incrementally translating a timeline-based planning problem into satisfability modulo theories (SMT). Off-the-shelf SMT solvers have become very efficient, and using such a solver keeps our planner implementation relatively small, simple, and extensible. Compared to existing SMT translations, our approach typically produces smaller SMT problems, and our system shows promising performance on several benchmark problems. Our planning software was developed in the context of research projects on inspection operations for UUVs on subsea installations and for mobile robots on marine installations.
Learning to Ask Specific Questions Naturally in Chat-Oriented Dialogue Systems

In order for a dialogue system to provide information tailored to the user, it is important to ask users questions and obtain the necessary information. However, asking questions abruptly or in a self-centered manner would be undesirable because it may disrupt the flow of conversation and decrease the user’s satisfaction. In this work, we propose a response generation model for a chat-oriented dialogue system that can ask specific questions naturally. Specifically, we train a response generation model that generates utterances on the basis of both the dialogue context and the question to be asked. The results of simulations and human evaluations demonstrate that the proposed model can make it easier for a system to ask specific questions while maintaining the naturalness of the dialogue.
A Distributed Ledger Based Ecosystem as an Approach to Reduce Greenhouse Gas Emissions for Shared Mobility by Incentivizing Users

Mobility as a Service (MaaS) is a rapidly growing market with a large potential for the transition of the mobility sector. Business models based on sharing economy in the mobility sector open many opportunities for providers and users to benefit from new types of transportation modes such as light electric vehicles (LEVs). However, there are deficits in the sustainability of sharing systems with such vehicles, which result primarily from the origin of the energy for charging, as well as from the charging processes and the infrastructure used for this purpose. The electricity must be generated from renewable energies and vehicles should ideally be put on charge by the user on-site in the operation area to avoid service rides by mobility service providers. Distributed Ledger Technology (DLT) can provide solutions for these issues: on the one hand, it can provide proof of origin (PoO) for the electricity; in addition, tokens that can be used to pay for vehicle usage can be generated and assigned to users to incentivize behaviors like charging the vehicles. In this paper a crypto token-based scheme for a PoO for renewable energy, as well as for user incentives to charge the vehicles to make shared mobility services more sustainable is described. The proposed solution includes a concept for a network backend, implemented as a private blockchain network using the open source blockchain solution Hyperledger Fabric. The backend will store data about the usage of LEVs and batteries as well as relevant data of charging and swapping stations. The user incentive tokens and smart contracts for transaction automatization are stored in the backend. Furthermore, a front end consisting of two applications: one for users and stakeholders respectively, is part of the solution.
Semantic Similarity Detection from text document using XLNet with a DKM-Clustered Bi-LSTM Model

Semantic similarity prediction in text documents to be an open challenge in Natural language processing. Traditional models designed for similarity identification in text documents based on the words count and position based. But these models do not concentrate much on semantic and syntactic information about the content. These limitations are going to be overcome using XLNet with a DKM-clustered Bi-LSTM model. The core contribution of this proposed work is the generalized autoregressive model used to generate the featured vectors from preprocessed data. Deep siamese Bi-LSTM model used for generate trained vectors with output labels, we proposed C-Isomap Dimensionality-based Dynamic K – Modes which cluster the documents with relevant content. After the cluster operation, the trained model will predict on the clustered documents which reduce the time for calculating the similarity scores. The performance evaluation of the proposed model evaluated with respect to accuracy rate, precision and recall parameters compared with existing models.
Deep Learning Based Image Classification Using Small VGG Net Architecture

One of the most interesting & important areas of computer vision is classification. Deep learning is important in image classification because it allows for the grouping of similar images. Certain adjustments to the images must be made before feeding them to the training model, such as normalization, image resizing, grey-scaling, and so on. Following image pre-processing, the next task is to extract features from the images, draw boxes around the objects in the image, and finally classify the images based on the detection of objects in the images. In a nutshell, classification is a simple task for humans but a difficult task for machines. In this case, we'll use the Small VGGNet (VGG– Visual Geometry Group) Architecture, which is a type of CNN, to classify images using deep learning. The qualified model is validated using three random images from Google. We experimented with scaling the image to different sizes. The deep learning model, which uses Small VGGNet architecture to focus on image classification, performed exceptionally well, correctly categorizing the images. Google Photos has already implemented image classification in their app, but uploading 1000s of images to the cloud requires a strong internet connection because computation is done on Google's server. The issue here is that if we are in a remote location, accessing the internet becomes difficult. Here, we will deploy a trained model with the application to make it easier to classify animals
Sharma-Mittal Entropy and Whale Optimization Algorithm Based Multilevel Thresholding Approach for Image Segmentation

Image thresholding is one of the most effective segmentation approaches. Multilevel thresholding approach is widely applied for segmentation in cases of complex images. In multilevel thresholding, the main goal is to search optimal threshold values for segmenting the given image into appropriate regions. In this paper, Sharma-Mittal entropy-based objective function is designed to formulate the problem of searching optimal threshold values in multi-thresholding. Here, the main issue is that the time complexity of the searching procedure increases exponentially as levels of thresholding increase. Thus, this is a NP-hard combinatorial optimization problem. To solve this problem, whale optimization algorithm (WOA) is applied for searching optimal thresholds in multilevel thresholding. WOA belongs to evolutionary computing techniques category that mimics the social behavior of humpback whales and is inspired by the bubble-net hunting strategy. Thus, in this paper, a new Sharma-Mittal entropy-based multilevel thresholding approach using WOA has been proposed. This approach is called WOA-based SMEMT approach. In the proposed approach, the optimum threshold values at different levels are searched by the minimization of objective function based on Sharma-Mittal entropy using WOA. The proposed approach is evaluated on benchmark images of standard databases, and experiment results are compared with gray wolf optimizer (GWO), teaching–learning-based optimization (TLBO), and genetic algorithm (GA) algorithms using segmented image quality evaluation parameters like peak signal to noise ratio (PSNR), uniformity, structure similarity (SSIM) index, and mean structure similarity (MSSIM) index. Results show that the proposed approach is performing better than the other approaches.
Depth Map Information from Stereo Image Pairs using Deep Learning and Bilateral Filter for Machine Vision Application

Stereo matching algorithm is a subset of machine vision study, and it is effective for creating accurate depth maps that are utilized in a variety of applications. It is found that the most difficult challenge for the stereo matching algorithm is obtaining an exact corresponding point in low texture regions. This study offers an approach that uses deep learning (DL) and bilateral filter (BF) to improve depth map precision in this area. The application of an edge-preserving filter such as this is capable of refining and removing excessive noise on the resulting image, as the filter is resistant to high contrast and brightness. Based on experimental research utilizing a common benchmarking dataset from Middlebury, the suggested method in this study provides good accuracy and is competitive with other published methods.
A Forensic Framework for Webmail Threat Detection Using Log Analysis

Today, webmail is being used in a number of organizations for all kinds of important communications as they move to cloud-based services. Several cyber threats involving phishing, malicious insider, unauthorized access to data and ransomware attacks are primarily carried out through webmail. This poses challenges and limitations for forensic investigators in the incident investigations involving emails, because actual email evidence is store at cloud service provider. The majority of work on email forensics and detection works on email information stored by email clients on desktop physical disk space. In order to gather artifacts from webmail used in browsers, volatile memory forensics is gaining popularity. However, few recent work utilizing memory forensics are focused on detecting email spoofing attempts from outside work by taking memory dumps of emails received by user from outside. This leaves the opportunity for malicious insiders to covertly send confidential data through webmail and remain undetected. In this work a novel framework is proposed, which models the malicious insider scenario and create the activity logs using volatile memory forensics. To implement and test the framework, a small tool was created using python. The framework is equally applicable for both public and private browsing. Our proposed method counters the limitation in previous schemes in terms of analyzing new email messages more efficiently using browser parent process ID. Our proposed method provides forensics investigators with a novel webmail tool that can be used to detect malicious email activity generated from with in the organisation.
An Improved Interaction Estimation and Optimization Method for Surveillance Video Synopsis

Videos synopsis is an efficient technique for condensing long-duration videos into short videos. The interactions between moving objects in the original video need to be preserved during video condensation. However, identifying objects with strong spatio-temporal proximity from a monocular video frame is a challenge. Further, the process of tube rearrangement optimization is also vital for the reduction of collision rates among moving objects. Taking the aforementioned aspects into consideration, we present a comprehensive video synopsis framework. First, we propose an interaction detection method to estimate distortion less spatio-temporal interactions between moving objects by generating the top view of a scene using a perspective transformation. Second, we propose an optimization method to reduce collisions and preserve object interactions by shrinking the search space. The experimental results demonstrate that the proposed framework provides a better estimate for object interactions from surveillance videos and generates synopsis videos with fewer collisions while preserving original interactions.
Evaluating Robustness of 3d Face Reconstruction Against Adversarial Attacks

Since deep neural networks (DNNs) have been widely used in various domains, their robustness issues have caused widespread concern. Nowadays, most researches focus on the adversarial attack in the 2D domain, and the related study in the 3D scene is limited. In this paper, we study the adversarial robustness of 3D face reconstruction, which is widely used in real-world applications. Since the 3D face reconstruction mainly relies on predicting 3D Morphable Models (3DMM) parameters, a simple way to implement adversarial attacks is manually setting the weights for different parameters to generate adversarial perturbations, denoted as Parameter-Oriented Attack (POA). This method has advantages of high practicality and disadvantages of inconvenience and uncertainty. Due to the dissimilar structures and training strategies of various 3D face reconstruction networks, POA has a low generalizability between different models. So we further propose an adaptive parameter-oriented attack (APOA), whose weights of parameters are driven by the loss themselves and can automatically search for the optimal solution in both image-specific and universal attacks. Extensive experiments conducted on four popular 3D face reconstruction models prove the effectiveness of our method and the vulnerability of 3D face reconstruction under adversarial attacks.
Occlusion-Robustness of Convolutional Neural Networks via Inverted Cutout

Convolutional Neural Networks (CNNs) are able to reliably classify objects in images if they are clearly visible and only slightly affected by small occlusions. However, heavy occlusions can strongly deteriorate the performance of CNNs, which is critical for tasks where correct identification is paramount. For many real-world applications, images are taken in unconstrained environments under suboptimal conditions, where occluded objects are inevitable. We propose a novel data augmentation method called Inverted Cutout, which can be used for training a CNN by showing only small patches of the images. Together with this augmentation method, we present several ways of making the network robust against occlusion. On the one hand, we utilize a spatial aggregation module without modifying the base network and on the other hand, we achieve occlusion-robustness with appropriate fine-tuning in conjunction with Inverted Cutout. In our experiments, we compare two different aggregation modules and two loss functions on the Occluded-Vehicles and Occluded-COCO-Vehicles datasets, showing that our approach outperforms existing state-of-the-art methods for object categorization under varying levels of occlusion.