Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: Detection of tampered images using watermarking approach through linear interpolation

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Towards Dynamic User Intention in Sequential Recommendation

User intention is an important factor to be considered for recommender systems. Different from inherent user preference addressed in traditional recommendation algorithms, which is generally static and consistent, user intention always changes dynamically in different contexts. Recent studies (represented by sequential recommendation) begin to focus on predicting what users want beyond what users like, which can better capture dynamic user intention and have attracted a surge of interest. However, user intention modeling is non-trivial because it is generally influenced by various factors, such as repeat consumption behavior, item relation, temporal dynamics, etc. To better capture dynamic user intention in sequential recommendation, we plan to investigate the influential factors and construct corresponding models to improve the performance. We also want to develop an adaptive way to model temporal evolutions of the effects caused by different factors. Based on the above investigations, we further plan to integrate these factors to deal with extremely long history sequences, where long-term user preference and short-term user demand should be carefully balanced.
An Efficient Social Spider Optimization Algorithm Based Multi-Document Summarization Model

At present times, text summarization is considered an effective technique used for the extraction of useful data from the massive quantity of documents. Depending upon the document count involved in the summarization process, it is classified into single or multi-document summarization. Compared to single document summarization, the multi-document summarization process remains a difficult task of finding a precise summary from many documents. A new Social Spider Optimization (SSO) algorithm based multi-document summarization model were proposed. This model comprises of involves preprocessing, representation of inputs, and representation of summary. The intention of the summary representation process is the generation of the summary of the documents comprising meaning data. By the optimum sentence selection procedure using the SSO algorithm, the essential sentences representing the summary are chosen. A detailed experimental validation process is carried out using the DUC 2006 and 2007 dataset. The experimental results verified the effectual outcome of the SSO algorithm compared to other optimization algorithms.
Research on Segmentation Algorithm of Gray Inhomogeneous Image Based on Cauchy Distribution

Image segmentation has a constructive position in image engineering and other fields. Among them, the research on the segmentation of uneven grayscale images is particularly important. This is due to the fact that uneven grayscale images widely exist in the real world, such as medical images, remote sensing images, and video surveillance. However, the traditional image segmentation algorithm ignores the unevenness of the gray level of the image, and the effect of such image segmentation is poor. Therefore, this paper proposes a gray-scale uneven image segmentation algorithm based on Cauchy distribution. Based on the RSF (region-scalable fitting) active contour model, this algorithm creates a new kernel function based on the Cauchy distribution, which is the absolute value of the difference between the two Cauchy distributions. On this basis, the energy functional is re-established to fit the gray value of the image inside and outside the contour, and the contour penalty item is added. Finally, the level set theory is used to convert the energy functional into a level set form and add a level set regularization term, and use the gradient descent method to minimize the energy functional. The experimental results show that using the method in this paper to segment the gray-scale uneven image has higher segmentation accuracy and segmentation efficiency, and the segmentation speed is increased by nearly 50%.
A Real-time e-Health Data Collection System from On-board Vehicle Drivers in Smart City

Most of the works in the literature related to driver distraction monitoring system focused on the design and development of sensor devices and their calibration. However, the problem of collecting data related to urgent or critical events in a prioritized manner has got a little attention for this specialized application. In this paper, assuming the availability of an Intra-vehicular Sensor Network (IVSN) consisting of heterogeneous sensor devices for monitoring an on-board driver, we have developed a medium access control protocol, namely V-MAC, that enables faster delivery of high priority events related to driver distraction. Our traffic classification algorithm and CSMA/CA driven prioritized data transmission scheduling using different superframes helps it to achieve higher data delivery performance, as depicted through evaluations carried out in MATLAB.
The Case for Scrutable, Personal, Long-Term User Models for Information Retrieval

Information retrieval systems are a ubiquitous part of people's daily lives. The personalised services of such systems are driven by user models that are typically built from data collected from large numbers of people. There is a growing demand that we build such "intelligent systems" in ways that ensure the user can understand and control them. This is the spirit of legislation across many countries, such as the European Union General Data protection Regulation (GDPR). Essentially, this reflects people's demand to control their own data and its use.
협동학습 기반 다시쓰기 활동이 대학생들의 쓰기와 말하기에 미치는 영향

The aim of this study is to investigate the effects of cooperative learning-based rewriting on college students’ writing and speaking skills. For this study, students carried out two types of rewriting activities over 16 weeks. The experimental group (N=25), implemented with cooperative learning, rewrote summaries and dialogues after reading, while the control group (N=17) rewrote summaries and dialogues by themselves. The methods employed for data collection were pre- and post-writing samples, dialogues, summaries, and speaking tests. Both groups’ writing and speaking skills improved significantly over the instructional period. However, the experimental group outperformed the control group both in summary/dialogue writing and speaking.The findings of this study suggest that instructors find ways for low-proficiency students to improve productive skills as well as receptive skills in reading classes. A variety of rewriting activities are suggested to help these students become well-rounded communicators. It is also shown that the effect of cooperative learning can be maximized when students are given opportunities to carry out tasks related to their own interests and proficiency levels.
Generating Synthetic Sequential Data for Enhanced Model Training: A Generative Adversarial Net Framework

Financial data is highly noisy with often drastic and rapid distribution changes. This causes financial models with high complexity overfit the data easily and render the model unproductive when deployed in the real-life applications. To solve this problem, we present a framework, attention generative adversarial net (A-Gan), a novel architecture to better learn the distribution of the sequence data and generate realistic sequence data. A-Gan utilizes various attention-based and residual net-based as well as combined architecture as the generator and discriminator. When used for data augmentation, we find that it increases the generalizing ability and model robustness. For the framework, we introduce a number of different architectures, including transformer-based GAN, ResNet GAN, Transformer-FCN GAN as well as Transformer-ResNet GAN. We find that, out of the 50 UCR sequence datasets, 35 show performance improvements, some quite significant. The results indicate that the model was able to learn from the original data and generate new data of similar distributions which can be used to strengthen the model. We also introduce a new metric to examine the generated data and gauge the synthetic data quality, which can further enhance.
Analysis of Security Audit Solutions Based on Association Rule Technology for Architecting in a Cloud Environment

The continuous development of modern cloud computing technology has generated a large amount of data and information for various industries and fields, which are important intangible assets for enterprises. At the same time, it can also provide high quality and flexible services, giving enterprises an advantageous position in the fierce market competition. It should be noted that cloud computing platforms will face corresponding network security issues, so it is crucial to strengthen network security audit work, this study will also explore the cloud environment based on the association rules technology security audit design scheme.
Improved Parallel Bat Algorithm Based on a Communication Strategy

Parallel processing is an effective measure of inefficient and effective computations of function optimization. Now we propose a new communication strategy for the parallel Bat Algorithm to solve numerical optimization problems. Firstly, the population of bats is divided into several independent groups, and they are independent. With every fixed number of iterations, different groups will exchange information and update. We use benchmark functions to test accuracy convergence behavior. From the analysis and summary of the experimental results, we get the following conclusions. The communication strategy improves the accuracy of BA in finding the best solution. The algorithm has improved significantly.
Development Research on Vertical Depth-Fixing Control of Small-Size ROV

Based on the mechanical structure design of the vehicle system and the dynamics generalized six degrees of freedom equation, the vertical depth-fixing control model is established under multiple constraints, which is designed to solve the posture and position control problem of remotely operated vehicle, And the transfer functions of vertical depth-fixing controller, motor and propeller are obtained. Depth-fixing PID controller and fuzzy PID controller are founded through the study of PID control and fuzzy PID control theory in SIMULINK library, and the final experiment validation, simulation and experimental results show that fuzzy PID control performs better than PID control. The former can regulate smaller overshoot amount with shorter adjust time, while obtain faster response convergence speed. And depth control in vertical has good stability and ability against interference.
The Connotation and Characteristics of Internet New Media

With the rapid development of new network media, it has changed from the past research to commercial application, which has gradually become a means of competition for many enterprises. The network new media not only spreads the form diversification, the dissemination speed is also extremely fast, the market scale also exceeded 20 billion dollars, has the universality and the global. This paper studies the characteristics of new network media and the current situation in China, finds out the existing problems, and promotes the further development of new network media.
Combined Auxiliary Networks and Bird’s Eye View Method for Real-Time Multicategory Object Recognition

Object recognition based on LIDAR data is crucial in automotive driving and is the subject of extensive research. However, the lack of accuracy and stability in complex environments obstructs the practical application of real-time recognition algorithms. In this study, we proposed a new real-time network for multicategory object recognition. The manually extracted bird’s eye view (BEV) features were adopted to replace the resource-consuming 3D convolutional operation. Besides the subject network, we designed two auxiliary networks to help the network learn the pointwise features and boxwise features, aiming to improve the category and bounding boxes’ accuracy. The KITTI dataset was adopted to train and validate the proposed network. Experimental results showed that, for hard mode, the total average precision (AP) of the category reached 97.4%. For an intersection over a union threshold of 0.5 and 0.7, the total AP of regression reached 93.2% and 85.5%; especially, the AP of car’s regression reached 95.7% and 92.2%. The proposed network also showed consistent performance in the Apollo dataset with a processing duration of 37 ms. The proposed network exhibits stable and robust object recognition performance in complex environments (multiobject, unordered objects, and multicategory). And it shows sensitivity to occlusion of the LIDAR system and insensitivity to close large objects. The proposed multifunction method simultaneously achieves real-time operation, high accuracy, and stable performance, indicating its great potential value in practical application.
Mend the Learning Approach, Not the Data: Insights for Ranking E-Commerce Products

Improved search quality enhances users' satisfaction, which directly impacts sales growth of an E-Commerce (E-Com) platform. Traditional Learning to Rank (LTR) algorithms require relevance judgments on products. In E-Com, getting such judgments poses an immense challenge. In the literature, it is proposed to employ user feedback (such as clicks, add-to-basket (AtB) clicks and orders) to generate relevance judgments. It is done in two steps: first, query-product pair data are aggregated from the logs and then order rate etc. are calculated for each pair in the logs. In this paper, we advocate counterfactual risk minimization (CRM) approach which circumvents the need of relevance judgements, data aggregation and is better suited for learning from logged data, i.e. contextual bandit feedback. Due to unavailability of public E-Com LTR dataset, we provide Mercateo dataset from our platform. It contains more than 10 million AtB click logs and 1 million order logs from a catalogue of about 3.5 million products associated with 3060 queries. To the best of our knowledge, this is the first work which examines effectiveness of CRM approach in learning ranking model from real-world logged data. Our empirical evaluation shows that our CRM approach learns effectively from logged data and beats a strong baseline ranker (\(\lambda \)-MART) by a huge margin. Our method outperforms full-information loss (e.g. cross-entropy) on various deep neural network models. These findings demonstrate that by adopting CRM approach, E-Com platforms can get better product search quality compared to full-information approach.
Billing System in Distributed Computing Environment

Distributed computing provides user with reliable, flexible and dynamic computing and storage services by organizing many computer resources. It is also popular for high performance and resource sharing. The significance of billing for distributed computing is to correctly measure resource utilization and charge fees for the task on distributed computing. Many companies use distributed computing to build cloud computing platforms. To provide customers with more attractive products and better services, this paper proposes a billing system for distributed cloud computing. This system mainly includes two parts, information collection and billing calculation. In the information collection, the process on each node periodically collects resource utilization of the assigned task and transmits the information to other part. In the billing calculation, this part calculates the cost based on the information collected and stores the records. This system is helpful for services providers to get more reasonable billing and for customer to control cost.
Target-Style-Aware Unsupervised Domain Adaptation for Object Detection

Vision modules running on mobility platforms, such as robots and cars, often face challenging situations such as a domain shift where the distributions of training (source) data and test (target) data are different. The domain shift is caused by several variation factors, such as style, camera viewpoint, object appearance, object size, backgrounds, and scene layout. In this work, we propose an object detection training framework for unsupervised domain-style adaptation. The proposed training framework transfers target-style information to source samples and simultaneously trains the detection network with these target-stylized source samples in an end-to-end manner. The detection network can learn the target domain from the target-stylized source samples. The style is extracted from object areas obtained by using pseudo-labels to reflect the style of the object areas more than that of the irrelevant backgrounds. We empirically verified that the proposed methods improve detection accuracy in diverse domain shift scenarios using the Cityscapes, FoggyCityscapes, Sim10k, BDD100k, PASCAL, and Watercolor datasets.
Opinion Mining: Legitimate vs Spurious Reviews

E-Shops reviews have become a valuable source of opinions, feeling, and experience by existing users which marks the failure and success of products and services. A set of favorable reviews proves that the product or service has vended well and users are pleased with them, and opposing reviews effects adversely. These reviews provide worthwhile information that might be used by prospective users to find sentiments of previously preferred users before making any decision or transaction to buy right product or service from E-shops. Moreover, service providers, vendors or manufacturers also make use of these reviews to discover public opinion, as well as limitations of their products or services. But, the basic fact about underlying online reviews shows something else. These reviews might be spurious, posted or written with hidden purposes, they often involve helpful or favorable opinion in order to boost, promote, and publicize their products and services, or with pessimistic intention to harm opponent prestige and business as well. So, the exploration and investigation of such reviews before opinion mining is significant. In this paper, a methodology is proposed which includes reviews acquisition using the Tag path clustering approach about mobile devices of different make along with metadata from Flipkart, spurious reviews detection based on identical and nearly identical reviews using semantic similarity with review length. Further, an opinion mining process is carried out by using the lexical database (SentiWordNet) approach for the computation of sentimental degree and orientation of both spurious and legitimate reviews.
Quantum Control and Its Application: A Brief Introduction

Abstract The development of quantum information science has brought great changes to the fields of computer and communication, and deepened people’s understanding of basic physical problems. The core problems of overcoming decoherence, preparing and manipulating quantum states are quantum control problems in essence, so the research on quantum control will attract more and more attention and be important in the development of quantum information. This paper studies several applications of quantum Lyapunov control and quantum feedback control in quantum information processing by using Schrodinger equation, and some principal equations as tools for finite-dimensional systems. In this paper, a brief introduction of quantum control and its applications is presented to review the recent development and its prospect.
Human Temporomandibular Joint Motion: A Synthesis Approach for Designing a Six-Bar Kinematic Simulator

Abstract The human earcanal can accommodate several types of in-ear devices including hearing aids, earphones, hearing protectors, and earplugs. This canal-type home has a neighbor called the temporomandibular joint (TMJ) whose movements slightly deform the shape of the earcanal. While these cyclic deformations can influence the positioning, comfort, and functioning of ear-fitted devices, they can also provide a significant amount of energy to harvest. Given their importance, the TMJ movements and earcanal deformations have been well studied. However, their mutual actions are still not fully understood. This paper presents the development of a six-bar kinematic TMJ simulator capable of replicating the complicated motion of the jaw. The development relies on a two-phase mechanism design algorithm to numerically optimize and analytically synthesize linkage mechanisms for which the classical optimization approaches cannot return a converged solution. The proposed algorithm enables the design of a kinematic simulator to generate the TMJ path with an average error as low as 1.65% while respecting all the hinge-axis parameters of the jaw. This algorithm can be subsequently used to solve nonlinear complex linkage synthesis problems, and ultimately, the developed kinematic simulator can be used to further investigate TMJ–earcanal interactions.
AI-driven Approach for Automatic Synthetic Patient Status Corpus Generation

Medical data for patients is sensitive personal information and therefore to be used in the original form is unacceptable. On the other hand, in order to be able to do various studies and analysis, we need such data. In many cases, such data even anonymized, by removing the personal identifiers, which are not suitable to be shared. Therefore we decided to create a corpus of synthetic statuses of patients that GPs place when performing a general examination. Each status consists of several sentences, each sentence describing the condition of an organ, system or part of the patient's body. We divided the status into its constituent sentences and then each sentence was classified based on the organ it refers to. We build a gold standard of manually classified sentences into list of human body organs and systems. Then we use it to train a neural network classifier of sentences that reaches almost 99% accuracy. Finally, from the all classified sentences we generate synthetic statuses, composed according to statistics in the available real statuses and medical domain constrains. The proposed approach can be easily adapted to other languages.