Reviewerly/exHarmony huggingface dataset:
Exploring Significant Characteristics and Models for Classification of Structure Function of Academic Documents

With the increasing abundance of literature resources, how to acquire knowledge elements efficiently and accurately is the key to achieving accurate literature retrieval and utilization of available literature resources. The identification of the structure function of academic documents is a fundamental work to meet the above requirements. In this study, the proceedings of the Association for Computational Linguistics (ACL) conferences are used as the primitive corpus, and the training corpus of chapter category is obtained by manual annotation. Based on the chapter titles and the in-chapter texts, traditional machine learning and deep learning models are both used for classifier training. Our results show that the title of a chapter is more beneficial to the identification of the structure function of academic documents than the in-chapter texts. The highest F1 value in our experiments is 0.9249, which is obtained on the traditional logistic regression (LR) and support vector machine (SVM) models (slightly higher than on the convolutional neural network [CNN]). And through the experiment of adding other chapter characteristics based on the traditional model, we find that combining the relative position of chapters can effectively improve the classification performance. Finally, this study compares the results of experimental groups with different methods, analyzes the misclassification of the structure function of academic documents, and points out the main direction to improve the classification performance in the future.
Bit2CV: A Novel Bitcoin Anti-Fraud Deposit Scheme for Connected Vehicles

Connected vehicles (CVs) are getting increasing attention in intelligent transportation systems (ITSs). In terms of the stringent security and anonymity issues, a novel anonymous and decentralized payment platform is demanded. Bitcoin is regarded as a potential solution. But the existing Bitcoin and its various improvements lack a beforehand anti-fraud Bitcoin deposit scheme, which is necessary to prevent potential fraud risks from Bitcoin to CVs. This paper aims to build a novel anti-fraud deposit scheme as a seamless bridge between CV networks and the Bitcoin payment platform. We propose a Bitcoin-to-Connected-Vehicle deposit scheme (Bit2CV) with an outsourcing endorsement in order to build an anti-fraud deposit transaction (dtr). Firstly, Bit2CV leverages a special Bitcoin-opcode-OP_RETURN based method to record the dtr, the CV's request, and the endorsement together on Blockchain ledger. This method can achieve security features, including non-repudiation, privacy-friendly endorsement, future audit, and anti-fraud. Meanwhile, Bit2CV is fully decentralized without any involvement of centralized authorities and fully compatible with the current Bitcoin network. We conduct the security analysis and also simulations for performance evaluation. In a typical scenario of our simulation, Bit2CV total time cost is less than 477.03 ms, and the size of the endorsement is 1,732 bytes, which is much less and smaller than transaction confirmation time and the average block size, respectively. These designs and results demonstrate that Bit2CV is feasible and practical.
Secure IoT Model Based on PRESENT Lightweight Modified and Chaotic Key Generator

The rapid development of the Internet of Things (IoT) technologies has increased the number of connected devices with constrained resources. Therefore, there is an urgent need to develop secure, lightweight cryptography algorithms that are suitable for the properties of these devices and secure it. The key is an important part that must be considered in any cryptography algorithm because it determines the strength of its security. In this paper, a new hybrid method of key generation depends on chaos theory is presented. The proposed method used a 2-D chaos system (a hybrid from Cat and Henon chaotic maps) combination with a lightweight PRESENT algorithm to improve its security. It provided a high level of chaos encryption in the Internet of Things systems that transmit data. This paper compares the performance of the Present Algorithm after implementing the proposed chaotic keys generator (PCKG) method in terms of throughput, processing time, memory consumption, and storage space consumption. The PRESENT Algorithm with a (PCKG) method shows better performance and successfully passed the NIST tests with higher randomness results.
WITHDRAWN: Nature-inspired cloud processing theory of optimization for adaptive task schedule

Cloud computing is a model to permit access for resources that providing computing resources as innovative check for researchers. Critical Resource management plays a major role in cloud computing which is mainly adaptable. Handling resources in the cloud computing plays a major role depends on organized action the cloud. Provoke a lot of interest in the use of resources, thereby enhancing the efficiency of the private cloud. Main intention of this research work is to review various cloud decision technologies and to arrive with energy cognizant cloud resolution strategy to optimize resources in cloud computing. Workloads balance between virtual machines because of distinct features. Proposed Cloud scheduling algorithm scale down energy consumption in the cloud strategy. Energy-conscious cloud scheduling algorithm implemented by Adaptive Fruit Fly Optimization (AFO) technique called metaphorical approach. Balance workload and enhance results for all mentioned tasks using certain Time period of execution, Response Time of Resources (RTR), Energy Utilization criterion Adaptive Fruit Fly Optimization technique improves comprehensive process flow, Resource Response time and optimizes adequate time. Proposed algorithm improves speed and resources by adopting hybrid technology. Implemented model works entirely by increase in utilization there by lower costs. Results from simulation stats that rate of utilization is 20 percent greater while compared to current model, thus overall shows an performance improvement.
Deep Cross-Species Feature Learning for Animal Face Recognition via Residual Interspecies Equivariant Network

Although human face recognition has achieved exceptional success driven by deep learning, animal face recognition (AFR) is still a research field that received less attention. Due to the big challenge in collecting large-scale animal face datasets, it is difficult to train a high-precision AFR model from scratch. In this work, we propose a novel Residual InterSpecies Equivariant Network (RiseNet) to deal with the animal face recognition task with limited training samples. First, we formulate a module called residual inter-species feature equivariant to make the feature distribution of animals face closer to the human. Second, according to the structural characteristic of animal face, the features of the upper and lower half faces are learned separately. We present an animal facial feature fusion module to treat the features of the lower half face as additional information, which improves the proposed RiseNet performance. Besides, an animal face alignment strategy is designed for the preprocessing of the proposed network, which further aligns with the human face image. Extensive experiments on two benchmarks show that our method is effective and outperforms the state-of-the-arts.
An Improved Ultrasound Tumor Segmentation Using CNN Activation Map Clustering and Active Contours

Ultrasound (US) imaging has become a primary tool for diagnosis of tumors mainly owing to its non-ionizing procedure but effective analysis is challenging due to the presence of inhomogeneties, speckle, low contrast and complex echogenic patterns. Manually defined predicates for feature extraction will not suffice, where deep learning can play a pivotal role. In this work, Convolutional Neural Networks (CNN) have been used for US breast tumor feature extraction and classification as benign/ malignant. To make the network robust to noise variations in the image and as an improvement over existing methodologies, neutrosophic preprocessing and enhancement were performed and the enhanced images were appended to the dataset and the original image during training. Using neutrosophic preprocessing alone had increased the validation accuracy of the CNN from 0.84 to 0.92, while using neutrosophic preprocessing and augmentation had increased the accuracy to 0.99. GoogLeNet with pretrained parameters were chosen as the preferred architecture for the work, with the predictions being validated using class activation maps that showed that the CNN was able to learn the features related to the tumor accurately. The final layers' activation maps are clustered using fuzzy c-means clustering, which act as an initial contour for active contour segmentation using localized energies. The clustered outputs also qualify as a validation method for the prediction accuracy of the CNN. Using the proposed segmentation, time and computational complexity can be reduced which otherwise is high for traditional encoding cum decoding based CNN segmentation architectures.
Autonomous mobile robot using distance and priority as logistics task cost

Mobile robots must deal with dynamic environments (e.g., people, animals, and objects) to move, making activities execution much more complex than in a controlled environment like from manipulators. The navigation of a mobile robot in an environment involves performing tasks that, many times, have priorities. Thus, the system that plans the route to be traveled by the robot must take into account not just the smallest path, but also the one that allows attending the priority on task execution. The hospital environment offers a high level of dynamism, which increases navigation complexity. Besides that, its tasks may have a priority associated. Following these characteristics, the developed system operates in a dynamic environment and attends logistic tasks based on their priorities and distances, in a previously mapped environment, using just wireless network from IT infrastructure, which allows health-care workers to deal with tasks more complicated and also decrease contact with patients with infectious diseases, like Coronavirus.
Malicious Mail Filtering and Tracing System Based on KNN and Improved LSTM Algorithm

Spam and phishing emails are very troublesome problems for mailbox users. Many enterprises, departments and individuals are harmed by them. Moreover, the senders of these malicious emails are in a hidden position and occupy an initiative position. The existing mailbox services can only filter and shield some malicious mails, which is difficult to reverse the disadvantage of users. To solve these problems, we propose a secure mail system using k-nearest neighbor(KNN) algorithm and improved long short-term memory(LSTM) algorithm(Bi-LSTM-Attention algorithm). KNN classifier can effectively distinguish normal emails, spam and phishing emails, and has a high accuracy. Bi-LSTM-Attention classifier classifies phishing emails according to the similarity of the malicious mail text from the same attacker to some extent. By classifying and identifying the source of malicious emails, we can grasp the characteristics of the attacker, provide materials for further research, and improve the passive status of users. Experiments show that the classification results of attack sources reach 90%, which indicate the value of further research and promotion.
Edge Curve Estimation by the Nonparametric Parzen Kernel Method

The article concerns the problem of finding the spatial curve which is the line of the abrupt or jump change in the 3d-shape, namely: the edge curve. There are many real applications where such a problems play a significant role. For instance, in computer vision in detection of edges in monochromatic pictures used in e.g. medicine diagnostics, biology and physics; in geology in analysis of satellite photographs of the earth surface for maps and/or determination of borders of forest areas, water resources, rivers, rock cliffs etc. In architecture the curves arising as a result of intersecting surfaces often are also objects of interest. The main focus of this paper is detection of abrupt changes in patterns defined by multidimensional functions. Our approach is based on the nonparametric Parzen kernel estimation of functions and their derivatives. An appropriate use of nonparametric methodology allows to establish the shape of an interesting edge curve.
Efficient Face Super-Resolution Based on Separable Convolution Projection Networks

Current super-resolution convolutional neural networks for application to face images generally use a feed-forward architecture. The network frames, however, tend to use deeper structures and larger quantities of parameters. These networks do not readily apply to restricted devices such as home robots or embedded devices, which limits the wide application of face super-resolution algorithms in real-world scenarios. In an effort to remedy this problem, a simple initial feature extraction is established in this study for shallow features of images; an intermediate convolutional layer with a novel deep separable convolution projection block is then used to reduce computational complexity while preserving accuracy. The proposed model can learn various up- and down- sampling information to retain high resolution details and generate deep, effective features. Integrating a dense connection operation allows reconstruction to further improve these super-resolution results. The proposed model is evaluated on the CASIA-WebFace database, to show that it outperforms other state-of-the-art algorithms.
Stochastic trust region inexact Newton method for large-scale machine learning

Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems. From stochastic first order methods, now the focus is shifting to stochastic second order methods due to their faster convergence and availability of computing resources. In this paper, we have proposed a novel stochastic trust region inexact Newton method, called as STRON, to solve large-scale learning problems which uses conjugate gradient to inexactly solve trust region subproblem. The method uses progressive subsampling in the calculation of gradient and Hessian values to take the advantage of both, stochastic and full-batch regimes. We have extended STRON using existing variance reduction techniques to deal with the noisy gradients and using preconditioned conjugate gradient as subproblem solver, and empirically proved that they do not work as expected, for the large-scale learning problems. Finally, our empirical results prove efficacy of the proposed method against existing methods with bench marked datasets.
Analysis of Texture Representation in Convolution Neural Network Using Wavelet Based Joint Statistics

We analyze the texture representation ability in a deep convolution neural network called VGG. For analysis, we introduce a kind of wavelet-based joint statistics called minPS that applied to the visual neuron analysis. The minPS consists of 30 dimension features, which come from several types of statistics and correlations. We apply LASSO regression to the VGG representation in order to explain the minPS features. We find that the different scale type cross-correlation does not appear in the VGG representation from the regression weight analysis. Moreover, we synthesize the texture image from the VGG in the context of the style-transfer; we confirm the lack of different scale correlations influences the periodic texture to synthesize.
Business and economics librarians’ insights on data literacy instruction in practice: An exploration of themes

Based on an applied thematic analysis, this study identifies data literacy education practices in the business and economics librarian profession. Five themes emerged from the interviews: Roles and Responsibilities; Expertise; Pedagogy and Teaching; Professional Development; and, Institutional Support. This article is for librarians who find themselves working with data and those who want to do so. The insights shared from participants offer trajectories for administrators to support the work of data literacy education. The article’s recommended future research directions section provides areas to build community and develop inquiry for interested researchers’ ongoing work in data literacy instruction and academic librarianship.
Global optimization with one-class classification-assisted selection

Selection in evolutionary algorithms (EAs) selects promising solutions from a set of candidates. Most selection strategies are fitness-driven, where each solution is selected based on its fitness value. This fitness-based strategy leads to a waste of fitness evaluations, since some unpromising solutions are thrown away without providing valuable search information while still being evaluated. Our intention is to reduce the number of fitness evaluations during the selection procedure. We treat selection as a one-class classification procedure so that the offspring solutions similar to the current population, which has the best solutions up to that point, are more likely to be selected. We use the classifier to predict the categories of newly generated solutions. Only those predicted 'promising' solutions can be selected for evaluation purposes. The efficiency of EAs can be greatly improved, since the procedure is based on the decision variables (features) before the fitness evaluations. Based on this consideration, we propose a One-class Classification-assisted Selection (OCAS) strategy for EAs. We apply the OCAS strategy to two EAs and study them on three test suites. Our experimental results reveal that the number of fitness evaluations can be clearly decreased by OCAS.
Proxemic Reasoning for Group Approach

How should a robot approach a group of people? To answer this, we examine a large real-world data set of approximately 1500 individuals and find that, contrary to previous models, people do not simply approach an individual within the group. They use features of the group to determine aspects of their approach. To explain and test these features, we formalize an Approach Problem and develop several strategies of determining approach to a group. These strategies are then compared to the empirical data. One strategy performed consistent with the empirical data, which we then demonstrate on a robot platform.
The Complexity of Verifying Loop-Free Programs as Differentially Private

We study the problem of verifying differential privacy for loop-free programs with probabilistic choice. Programs in this class can be seen as randomized Boolean circuits, which we will use as a formal model to answer two different questions: first, deciding whether a program satisfies a prescribed level of privacy; second, approximating the privacy parameters a program realizes. We show that the problem of deciding whether a program satisfies e-differential privacy is coNP^#P-complete. In fact, this is the case when either the input domain or the output range of the program is large. Further, we show that deciding whether a program is (e,δ)-differentially private is coNP^#P-hard, and in coNP^#P for small output domains, but always in coNP^{#P^#P}. Finally, we show that the problem of approximating the level of differential privacy is both NP-hard and coNP-hard. These results complement previous results by Murtagh and Vadhan [Jack Murtagh and Salil P. Vadhan, 2016] showing that deciding the optimal composition of differentially private components is #P-complete, and that approximating the optimal composition of differentially private components is in P.
A Part Fusion Model for Action Recognition in Still Images

Recognizing actions from still images is very challenging due to the lack of human movement information and the variance of background in action categories. The existing methods in action recognition focus on extracting scene context information, modeling the human-object pair and its interactions or using human body information, of which part-based methods are one of the successful methods, which extract rich semantic information from the human body parts or pose. However, most of part-based models need to rely on expensive part annotations to improve the recognition accuracy. Different from these methods, in this paper, we propose a part fusion model which can effectively combine the discriminative parts information without extra annotations for action recognition. In our model, a guided attention module is used to further extract the more discriminative information in the image. And the part-level features are trained with the different weighted loss, which is mainly based on different object and background parts' characteristics. In order to further enhance the model performance, part-level features are fused to form new image-level features in the global supervision learning. This method achieves state-of-the-art result on the PPMI dataset and significant competitive performance on the Stanford-40 dataset, which demonstrates the effectiveness of our method for characterizing actions.
Mirrored conditional random field model for object recognition in indoor environments

Traditional object recognition algorithms are based on a commonly adopted closed-set hypothesis, assuming that the knowledge given in training is complete. However, real situations are often open and nonstatic, in which case the models only obtain incomplete knowledge during the training phase. This paper proposes a new type of conditional random field (CRF) model to solve a special case of incomplete knowledge, in which the visual appearance of certain objects changes significantly between training and testing, and as a result, certain unary features (features of individual objects) extracted from red green blue-depth (RGB-D) images are no longer reliable. Mirror nodes are introduced into the architecture based on the standard CRF model to build the mirrored conditional random field (Mirror-CRF) model, which integrates two types of object nodes: original nodes and mirror nodes. The mirror nodes have no unary features, only pairwise features, which describe relationships between two objects and are more reliable than unary features for object recognition in the case of appearance variation. The experimental results show that the Mirror-CRF model reduces the influence of significant changes in the appearance of certain objects and improves the object recognition ability under the condition of incomplete knowledge.
Query-based Single Document Summarization using Hybrid Semantic and Graph-based Approach

In this era of digitization, a plethora of information, spanning a multitude of themes, is available worldwide. The demand for query focused text summarization is growing rapidly due to the presence of irrelevant and repetitive information. Many researchers have immensely studied various unsupervised and supervised techniques for generic text summarization. However, the field of query-based summarization still demands significant research and development. This paper presents a novel hybrid methodology to extract various sentences that are relevant to a user-given question or query. The proposed model uses unsupervised algorithms to first detect the topics in the data by generating an intuitive semantic structure. It then employs a conditional semantic similarity measure to retrieve data containing keywords. An enhanced graph-based approach has been used to include additional query-relevant data as well as discard the redundant information to generate an efficient output summary using a weighted graph. The performance of the model is measured using two evaluation metrics, namely ROUGE-N/L scores and a peer-review survey. The efficiency of the semantic model is evaluated through coherence measure. DUC-2005 and an email dataset have been used for evaluation. The results showcase an improved performance compared to other traditional methods and encourage future work in this field.