Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: Study on image processing using deep learning techniques

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21).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 The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Hybrid conditional planning for robotic applications

Robots who have partial observability of and incomplete knowledge about their environments may have to consider contingencies while planning, and thus necessitate cognitive abilities beyond classical planning. Moreover, during planning, they need to consider continuous feasibility checks for executability of the plans in the real world. Conditional planning is concerned with reaching goals from an initial state, in the presence of incomplete knowledge and partial observability, by considering all contingencies and by utilizing sensing actions to gather relevant knowledge when needed. A conditional plan is essentially a tree of actions where each branch of the tree represents a possible execution of actuation actions and sensing actions to reach a goal state. Hybrid conditional planning extends conditional planning by integrating feasibility checks into executability conditions of actions. We introduce a parallel offline algorithm, called HCPlan, for computing hybrid conditional plans. HCPlan relies on modeling deterministic effects of actuation actions and non-deterministic effects of sensing actions in the causality-based action language [Formula: see text]. Branches of a hybrid conditional plan are computed in parallel using a SAT solver, where continuous feasibility checks are performed as needed. We develop a comprehensive benchmark suite and introduce new evaluation metrics for hybrid conditional planning. We evaluate HCPlan with extensive experiments in terms of computational efficiency and plan quality. We perform experiments to compare HCPlan with other related conditional planners and approaches to deal with contingencies due to incomplete knowledge. We further demonstrate the applicability and usefulness of HCPlan in service robotics applications, through dynamic simulations and physical implementations.
FAQ-Based Question Answering via Knowledge Anchors

Question answering (QA) aims to understand questions and find appropriate answers. In real-world QA systems, Frequently Asked Question (FAQ) based QA is usually a practical and effective solution, especially for some complicated questions (e.g., How and Why). Recent years have witnessed the great successes of knowledge graphs (KGs) in KBQA systems, while there are still few works focusing on making full use of KGs in FAQ-based QA. In this paper, we propose a novel Knowledge Anchor based Question Answering (KAQA) framework for FAQ-based QA to better understand questions and retrieve more appropriate answers. More specifically, KAQA mainly consists of three modules: knowledge graph construction, query anchoring and query-document matching. We consider entities and triples of KGs in texts as knowledge anchors to precisely capture the core semantics, which brings in higher precision and better interpretability. The multi-channel matching strategy also enables most sentence matching models to be flexibly plugged in our KAQA framework to fit different real-world computation limitations. In experiments, we evaluate our models on both offline and online query-document matching tasks on a real-world FAQ-based QA system in WeChat Search, with detailed analysis, ablation tests and case studies. The significant improvements confirm the effectiveness and robustness of the KAQA framework in real-world FAQ-based QA.
WITHDRAWN: Fashion compatibility using convolutional neural networks

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.
Online Semisupervised Active Classification for Multiview PolSAR Data

Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and have multiple views obtained from different feature extractors or multiple frequency bands. The fast and accurate classification of PolSAR data in dynamically changing environments is a critical and challenging task. Online learning can handle this task by learning a classifier incrementally from a stream of samples. In this article, we propose an online semisupervised active learning framework for multiview PolSAR data classification, called OSAM. First, a novel online active learning strategy is designed based on the relationships among multiple views and a randomized rule, which allows to only query the labels of some informative incoming samples. Then, in order to utilize both the incoming labeled and unlabeled samples to update the classifiers, a novel online semisupervised learning model is proposed based on co-regularized multiview learning and graph regularization. In addition, the proposed method can deal with the dynamic large-scale multifeature or multifrequency PolSAR data where not only the amount of data but also the number of classes gradually increases in the learning process. Moreover, the mistake bound of the proposed method is derived rigorously. Extensive experiments are conducted on real PolSAR data to evaluate the performance of our algorithm, and the results demonstrate the effectiveness of the proposed method.
Sentiment Analysis of Fast Food Companies With Deep Learning Models

Abstract In the modern era, Internet usage has become a basic necessity in the lives of people. Nowadays, people can perform online shopping and check the customer’s views about products that purchased online. Social networking services enable users to post opinions on public platforms. Analyzing people’s opinions helps corporations to improve the quality of products and provide better customer service. However, analyzing this content manually is a daunting task. Therefore, we implemented sentiment analysis to make the process automatically. The entire process includes data collection, pre-processing, word embedding, sentiment detection and classification using deep learning techniques. Twitter was chosen as the source of data collection and tweets collected automatically by using Tweepy. In this paper, three deep learning techniques were implemented, which are CNN, Bi-LSTM and CNN-Bi-LSTM. Each of the models trained on three datasets consists of 50K, 100K and 200K tweets. The experimental result revealed that, with the increasing amount of training data size, the performance of the models improved, especially the performance of the Bi-LSTM model. When the model trained on the 200K dataset, it achieved about 3% higher accuracy than the 100K dataset and achieved about 7% higher accuracy than the 50K dataset. Finally, the Bi-LSTM model scored the highest performance in all metrics and achieved an accuracy of 95.35%.
Robust local metric learning via least square regression regularization for scene recognition

Metric learning plays an important role in various machine learning tasks. Particularly, local metric learning is prevailing since it can learn more flexible metrics on complex datasets. However it may not be robust for scene recognition because of intra-class diversity and inter-class similarity in the scene images. To address this issue, we propose a novel method, called robust local metric learning via least square regression regularization (RLML-LSR), to learn a more robust distance metric for scene recognition. We first formulate a local discriminative metric function, aimed to pull same class neighbors closer and push different classes ones farther away simultaneously. Then taking advantage of the least square regression, we minimize the regression errors of same class neighbors, such that the local geometry structure can be preserved as much as possible. Finally, the local discriminative metric function and least square regression regularization are integrated into a unified framework, which jointly promotes the robustness of local metric learning and enhances the recognition performance of scene images. Extensive experiments on both natural scene and remote sensing scene datasets demonstrate the effectiveness and robustness of the proposed RLML-LSR method for scene recognition.
Learning Better Representations for Neural Information Retrieval with Graph Information

Neural ranking models have recently gained much attention in Information Retrieval community and obtain good ranking performance. However, most of these retrieval models focus on capturing the textual matching signals between query and document but do not consider user behavior information that may be helpful for the retrieval task. Specifically, users' click and query reformulation behavior can be represented by a click-through bipartite graph and a session-flow graph, respectively. Such graph representations contain rich user behavior information and may help us better understand users' search intent beyond the textual information. In this study, we aim to incorporate this rich information encoded in these two graphs into existing neural ranking models.
Cellular Neural Network-Based Medical Image Encryption

This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic operations. In this paper, cellular neural network crumb coding transform (CNN-CCT) is proposed to perform confusion operation. It is used to shuffle the pixel values randomly. The diffusion operation is achieved by employing cipher block chain (CBC) mode of XOR operation which provides greater efficiency in hardware platforms. The diffusion operation is used to change the pixel values, thereby achieving the higher security. Simulation and comparison results infer that the proposed cryptosystem is robust against various cryptographic attacks and competitive with the state-of-the-art encryption schemes.
n-ary Isolation Forest: An Experimental Comparative Analysis

One of the most challenging problems of modern data mining and Computational Intelligence society has been the task of anomaly detection in large datasets, particularly containing mixed data, namely categorical, spatial, or spatio-temporal. In this study, we discuss various versions of the well-known Isolation Forest method as a efficient tool for finding outliers or anomalies. The versions are based on binary, ternary, etc. search trees. Traditional Isolation Forest is based on searching binary search trees. We build and investigate n-ary search trees and analyze their efficiency in the context of anomaly detection.
Micro-expression Recognition Using Enriched Two Stream 3D Convolutional Network

Face micro-expression recognition in video is an active field in computer vision research and its difficulty lies in transience in mic-expression motion and limited mic-expression databases. Particularly, the unevenness of the data set samples caused by the difficulty of capturing some types of micro-expressions will result categories with a small sample size contain too few features and it will be hard to improve this kind of category recognition rate. This can be proved by the low accuracy shown by the most advanced methods. In this paper, for spatial and temporal feature enrichment, we amplify the action amplitude of micro expressions by phase - based video amplification and use optical flows to describe the action characteristics of micro expressions. We propose a two-stream 3D convolutional network to extract both amplified optical flows and original frames. The experiment was carried out on CAS (ME)2 microexpression dataset. The proposed model is superior to existing methods especially in small sample categories.
WITHDRAWN: Experimental performance analysis of load balancing of tasks using honey bee inspired algorithm for resource allocation in cloud environment

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.
Analysis of the implementation of Blockchain as a mechanism for digital and transparent food traceability in Peruvian social programs

This paper studies the problems of the traditional food industry in Peruvian social programs, and how the Blockchain would serve as a solution that includes the immutable registration of information allowing the tracking of food by each responsible party in the process. It was observed that millions of people become ill or die from eating infected, spoiled, or intoxicated food and that these problems are directly linked to the registration and communication areas of the participants in the supply chain. It was concluded that combining technologies such as the Internet of Things (IoT) and Blockchain is an extremely attractive option for the transparent tracking of food from its production to its sale and consumption; likewise, using smart contracts ensures compliance with all the conditions necessary for its consumption. Finally, it should be mentioned that, by being able to track in seconds, the reduction of time and operating costs is imminent.
Robust Blockchain-Based Cross-Platform Audio Copyright Protection System Using Content-Based Fingerprint

Copyright infringement is a serious problem in the digital media industry. The traditional watermarking methods can neither protect copyright against attacks, especially the de-synchronization attacks, nor achieve cross-platform copyright protection. The existing blockchain-based methods have the potential to protect copyright across multiple online platforms. However, they are vulnerable to attacks. In this paper, we propose a novel Ethereum blockchain-based robust copyright protection system (RobustCPS) for audio signals, which can not only resist attacks but also achieve cross-platform copyright protection. In the proposed RobustCPS, the host audio signal is first segmented into blocks. Then the singular value decomposition (SVD) is performed on each block to generate the content-based fingerprint. After that, a database query is executed to search whether a similar fingerprint has existed in the Ethereum blockchain. If so, the rightsholders will be informed that their copyright might have been violated. Otherwise, the generated fingerprint will be stored in the Ethereum blockchain for future copyright protection actions. Simulation results demonstrate the superiority of the proposed RobustCPS.
Survey and challenges of story generation models - A multimodal perspective with five steps: Data embedding, topic modeling, storyline generation, draft story generation, and story evaluation

The story is the description of events in chronological order that have occurred between people. By delivering facts to the people reading the story, it enables them to feel emotions. Such a story is composed using the following method: each event is analyzed and a storyline is composed, which becomes a skeleton text by linking relationships between major events. As the content of users becomes more diverse, multimodal story composition has become more essential than unimodal text-based story composition. This paper discusses modality integration based on multimodal data types and type conversion for multimodal story composition. We propose a story-graph model to create a story based on the integrated analysis of various modal data. In terms of architecture, the proposed multimodal storytelling model consists of modal data and a topic modeling module that performs clustering based on cross-modal similarities and extracts a topic of clustered modalities. From the perspective of utilization, to visualize a story-graph, the proposed model summarizes nodes with a representative image. Furthermore, the latest techniques are discussed with respect to five main modules and twelve sub-modules for story composition. Lastly, problems that can become issues when composing multimodal story modules are explained.
Towards a Semantic Representation for Functional Software Requirements

Requirements are core elements in any software project. Therefore, understanding and representing the meaning of requirements play an essential role in automating any requirement engineering task. In this paper, we propose a semantic representation, called ReqVec, for functional software requirements. ReqVec is calculated based on three main phases: First, a set of lexical and syntactic steps are performed to analyze textual requirements. Then, semantic dimensions for requirements are calculated based on a words classifier and the well-known word embedding model Word2vec. Finally, ReqVec is constructed based on the representations of these dimensions. Two experiments conducted to evaluate how the proposed ReqVec can capture meaningful semantic information to solve two well-known Requirements Engineering tasks: detecting semantic relation between requirements, and requirements categorization. The proposed representation was efficient enough to detect related requirements with 0.92 F-measure and to categorize requirements with 0.88 F-measure.
Classification Acceleration via Merging Decision Trees

We study the problem of merging decision trees: Given k decision trees $T_1,T_2,T_3...,T_k$, we merge these trees into one super tree T with (often) much smaller size. The resultant super tree T, which is an integration of k decision trees with each leaf having a major label, can also be considered as a (lossless) compression of a random forest. For any testing instance, it is guaranteed that the tree T gives the same prediction as the random forest consisting of $T_1,T_2,T_3...,T_k$ but it saves the computational effort needed for traversing multiple trees. The proposed method is suitable for classification problems with time constraints, for example, the online classification task such that it needs to predict a label for a new instance before the next instance arrives. Experiments on five datasets confirm that the super tree T runs significantly faster than the random forest with k trees. The merging procedure also saves space needed storing those k trees, and it makes the forest model more interpretable, since naturally one tree is easier to be interpreted than k trees.
Circle-based Group Recommendation in Social Networks

A large amount of data available on Web has proven to be an immense resource for innovative recommender system (RS) techniques and concepts. The traditional recommender system intended to provide recommendations for a single user. However, in certain domains the recommendation is required for a group of users. As to provide better recommendations for a group of users, we leverage the concept of circles in a network. In this work, we use the genetic algorithm (GA) K_Means clustering algorithm to generate social circles in a network. Then, we compute the status of each user in these overlapping circles. Finally, a circle-based group recommendation approach is used to generate the final group recommendation. The results obtained on the Epinions dataset validate the eminence of the proposed model over traditional approaches of group recommendation.
BSCF: Learning background suppressed correlation filter tracker for wireless multimedia sensor networks

Recently, the Internet of Things (IoT) devices have been widely used in all sectors of society, especially in the field of wireless multimedia sensor networks (WMSNs). However, massive IoT video data requires efficient back-end processing to detect and track prominent objects for performing interconnection feedback. Correlation filter (CF)-based trackers present favorable properties for our back-end processing needs, such as fast computation speed, robustness to photometric and geometric variations. In this paper, we propose a novel background suppressed correlation filter (BSCF)-based target tracking method for wireless multimedia sensor networks, this method can incorporate all global background patches to enhance the tracking performance. Firstly, we reformulate the original ridge regression objective by introducing a convolution suppression term so that all real background patches will limit the generation of the filter through the circular shift operator and cropping operator. We then provide the closed form solutions of BSCF for multi-channel features via the alternating direction method of multipliers (ADMM). Further, we suggest a decomposition strategy to apply the background suppression framework as a general module to other CF-based trackers. The extensive experiments demonstrate that the proposed method performs favorably on multiple datasets against recent state-of-the-art methods. In particular, our BSCF ranks the first place on OTB-2013 with an AUC of 0.688 and an average precision of 0.904, exceeding the advanced CF tracker STRCF.