Reviewerly/exHarmony huggingface dataset:
Interdisciplinary approach in research and teaching: advantages and problems of application

The article deals with the interdisciplinary approach as an interdependent system of knowledge integration, expressed in research and teaching. The interpretation of the term “interdisciplinary approach in research and training” by E.M. Mirsky is taken as a basis, he interdisciplinary interaction is considered as a relationship between the systems of disciplinary knowledge in the process of integration and differentiation of sciences and collective forms of work of scientists in different fields of knowledge in the study of the same object. The article is based on an interdisciplinary approach the scientific base and system of education as a complex of fundamental and taxonomic disciplines are considered. The interdisciplinary connections with the focus on the effectiveness of training are considered. It contains the consistency of educational programs, determined by didactic goals and content. Interdisciplinary connections with the competence approach allow transferring knowledge, skills and abilities from one sphere of science and professional activity to another. Interdisciplinary approach is aimed at linking academic disciplines. There are several formats of implementation of an interdisciplinary approach to teaching at universities in the specialty “Economics”, based on an interdisciplinary approach offers to form the competence in the study of economic disciplines. The contradictions in the assimilation of knowledge, ideas and researching methods between the Sciences are smoothed out, and there is a complex application of the theory and practice obtained on the basis of the study of disciplines in professional activities. Both significant advantages and problems arising from the use of an interdisciplinary approach are noted.
Comparative Object Similarity Learning-Based Robust Visual Tracking

Tracking-by-detection for visual object tracking is the most popular and successful framework at present. It treats the tracking problem as a classification task and learns information about the target from each tracking result online. Accurate model learning of the classifier requires numerous positive samples. However, it is difficult to obtain numerous positive training samples at the beginning of visual tracking. In this paper, we propose a novel comparative object similarity learning method to strengthen the training samples set. The core of our approach is that the comparative object similarity information between the candidate objects is taken into account when training classifiers. In addition, the classifier model is updated with the image information of the target to be predicted by further exploring the temporal context between successive image frames. According to the Bayesian inference theorem, the tracking results, which are estimated from the posterior probability distribution of target, are more accurate. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks-based features to validate the effectiveness of the algorithm. The quantitative and qualitative experimental results demonstrate that the proposed method performs superiorly against several state-of-the-art algorithms on large-scale challenging benchmark datasets.
Method for Detecting Chinese Texts in Natural Scenes Based on Improved Faster R-CNN

In this study, a natural scene text detection method based on the improved faster region-based convolutional neural network (R-CNN) is proposed. This method extracts image features with the Inception-ResNet architecture, adopts a region proposal network to generate region proposals for the extracted features, merges the fine-tuned features with the region proposals, and finally, uses Fast R-CNN to classify and locate text. The proposed method solves the problems of varying text sizes and the text being obscured in the image. Compared with the original Faster R-CNN, the multilevel Inception-ResNet network model presented in this study can extract deeper text features. The extracted feature map is further sparsely represented by Reduction B, Inception ResNet C and Avg Pool, and then is fused with text regions obtained by the text feature mapping lower layer network to acquire the exact text regions. The text detection method presented in this study is tested on the 2017 dataset of ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17), which contains a large number of distorted, blurry, different scale and size texts. An accuracy of 76.4% is achieved in this platform, thereby proving the efficiency of the proposed method.
COBOL Systems Migration to SOA: Assessing Antipatterns and Complexity

we assess antipatterns of Web Services' WSDL documents generated upon the three migration approaches.In addition, generated Web Services' interfaces are measured in complexity to attend both comprehension and interoperability.We apply a metric suite (by Baski & Misra) to measure complexity on services interfaces -i.e., WSDL documents.Migrations of two real COBOL systems upon the three approaches were assessed on antipatterns evidences and the complexity level of the generated SOA frontiers -a total of 431 WSDL documents.
Collaborative Approaches to Archaeology Programming and the Increase of Digital Literacy Among Archaeology Students

Abstract Digital methods provide archaeologists with ever-increasing opportunities to collect more data about the past in new formats. These larger evidentiary datasets, in turn, help us to address questions about the human past with increasing precision. To take full advantage of these opportunities, archaeologists must develop digital literacy skills and learn how to lead digital projects. Here, we describe seven digitally-based projects we have undertaken at the University of Pennsylvania in order to create new tools for archaeological data collection and sharing, as well as to test collaborative models for the digital humanities programming process. In these projects, archaeology students work directly with engineering students. Through this interface, the students from both areas gain valuable transdisciplinary experience while experimenting with new ways to accomplish programming goals and to collect archaeological data. The learning potential for these students was a key motivation for our initiative. Our projects have already led to several websites and digital applications that are available as open source downloads. We present our impressions of this collaborative process with the goal of encouraging other archaeologists to form similar digital humanities partnerships.
CSDK: A Chi-square distribution-Kernel method for image de-noising under the Internet of things big data environment

Nowadays, Internet of things not only brings promising opportunities but also faces a lot of challenges. It attracts a lot of researchers’ attention and has important economic and social values. Internet of things plays a key role in the big data processing, especially in image field. Image de-noising still is a key problem in image pre-processing. Considering a given noisy image, the selection of thresholds should significantly affect the quality of the de-noising image. Although the state-of-the-art wavelet image de-noising methods perform better than other de-noising methods, they are not very effective for de-noising with different noises and with redundancy convergence time, sometimes. To mitigate the poor effect of traditional de-noising methods, this article proposes a new wavelet soft threshold based on the Chi-square distribution-Kernel method under the Internet of things big data environment. The new method alternates three minimization steps. First, the Chi-square distribution-Kernel model is constructed to find the customized threshold that corresponds to the de-noised image. Second, a freedom degree is considered, which is related to the customized wavelet coefficient of the Chi-square distribution-Kernel to be thresholded for image de-noising. Here, noisy image is first decomposed into many levels to obtain different frequency bands and the soft thresholding method based on Chi-square distribution-Kernel method is used to remove the noisy coefficients, by fixing the optimum threshold value using the proposed method. Third, the wavelet soft thresholding based on Chi-square distribution-Kernel method is adopted to handle the image de-noising, and a significant improvement is obtained by a specially developed Chi-square distribution-Kernel method. Finally, the experimental results illustrate that this computationally scalable algorithm achieves state-of-the-art de-noising performance in terms of peak signal-to-noise ratio, normalized mean square error, structural similarity, and subjective visual quality. It also shows a consistent accuracy, edge preservation, and detailed retention improvement compared to the classic de-noising algorithms.
Robustifying Scholia: paving the way for knowledge discovery and research assessment through Wikidata

Knowledge workers like researchers, students, journalists, research evaluators or funders need tools to explore what is known, how it was discovered, who made which contributions, and where the scholarly record has gaps. Existing tools and services of this kind are not available as Linked Open Data, but Wikidata is. It has the technology, active contributor base, and content to build a large-scale knowledge graph for scholarship, also known as WikiCite. Scholia visualizes this graph in an exploratory interface with profiles and links to the literature. However, it is just a working prototype. This project aims to "robustify Scholia" with back-end development and testing based on pilot corpora. The main objective at this stage is to attain stability in challenging cases such as server throttling and handling of large or incomplete datasets. Further goals include integrating Scholia with data curation and manuscript writing workflows, serving more languages, generating usage stats, and documentation.
Research on a Trustworthiness Measurement Method of Cloud Service Construction Processes Based on Information Entropy

The popularity of cloud computing has made cloud services gradually become the leading computing model nowadays. The trustworthiness of cloud services depends mainly on construction processes. The trustworthiness measurement of cloud service construction processes (CSCPs) is crucial for cloud service developers. It can help to find out the causes of failures and to improve the development process, thereby ensuring the quality of cloud service. Herein, firstly, a trustworthiness hierarchy model of CSCP was proposed, and the influential factors of the processes were identified following the international standard ISO/IEC 12207 of the software development process.Further, a method was developed combined with the theory of information entropy and the concept of trustworthiness. It aimed to calculate the risk uncertainty and risk loss expectation affecting trustworthiness. Also, the trustworthiness of cloud service and its main construction processes were calculated. Finally, the feasibility of the measurement method were verified through a case study, and through comparing with AHP and CMM/CMMI methods, the advantages of this method were embodied.
Path-based Attribute-aware Representation Learning for Relation Prediction

Knowledge graphs (KGs) have been applied to many semantic-driven applications, including knowledge interchange and semantic inference. However, most KGs are far from complete and are growing rapidly. Although significant progress has been made in the symbolic representation learning of KGs with structural information, the textual knowledge that plays a crucial role in relation prediction is underutilized, and the issues of redundancy and noise path remain to be settled. In this paper, a Path-based Attribute-aware Representation Learning model (PARL) has been proposed to perform path denoising and path representation learning for the relation prediction task. We develop a novel text-enhanced relation prediction architecture, which interactively learns KG structural and textual representations to vary the sparsity and reliability of KG. Moreover, a path denoising algorithm is presented to emphasize paths with rich information and reduce the impact of redundancy and noise path. Experiments on a public dataset demonstrate that PARL consistently outperforms state-of-the-art methods on relation prediction and KG completion tasks.
Efficient trajectory of a car-like mobile robot

Purpose The purpose is to create an algorithm that optimizes the trajectories that an autonomous vehicle must follow to reduce its energy consumption and reduce the emission of greenhouse gases. Design/methodology/approach An algorithm is presented that respects the dynamic constraints of the robot, including the characteristics of power delivery by the motor, the behaviour of the tires and the basic inertial parameters. Using quadratic sequential programming with distributed and non-monotonous search direction (Quadratic Programming Algorithm with Distributed and Non-Monotone Line Search), an optimization algorithm proposed and developed by Professor K. Schittkowski is implemented. Findings Relations between important operating variables have been obtained, such as the evolution of the autonomous vehicle’s velocity, the driving torque supplied by the engine and the forces acting on the tires. In a subsequent analysis, the aim is to analyse the relationship between trajectory made and energy consumed and calculate the reduction of greenhouse gas emissions. Also this method has been checked against another different methodology commented on in the references. Research limitations/implications The main limitation comes from the modelling that has been done. As greater is the mechanical systems analysed, more simplifying hypotheses should be introduced to solve the corresponding equations with the current computers. However, the solutions are obtained and they can be used qualitatively to draw conclusions. Practical implications One main objective is to obtain guidelines to reduce greenhouse gas emissions by reducing energy consumption in the realization of autonomous vehicles’ trajectories. The first step to achieve that is to obtain a good model of the autonomous vehicle that takes into account not only its kinematics but also its dynamic properties, and to propose an optimization process that allows to minimize the energy consumed. In this paper, important relationships between work variables have been obtained. Social implications The idea is to be friendly with nature and the environment. This algorithm can help by reducing an instance of greenhouse gases. Originality/value Originality comes from the fact that we not only look for the autonomous vehicle’s modelling, the simulation of its motion and the analysis of its working parameters, but also try to obtain from its working those guidelines that are useful to reduce the energy consumed and the contamination capability of these autonomous vehicles or car-like robots.
Image Spam Detection on Instagram Using Convolutional Neural Network

Instagram is a social media to share moments in the form of photos and videos that is currently in great demand. However, the popularity of Instagram also widely used by certain people to spread spam for their personal interests such as advertising. Therefore, it requires a system for detecting spam on Instagram to obtain useful information and expected by users. The previous researches on image spam detection have been done to filter out inappropriate content on email using conventional classification methods. Recently, the Convolutional Neural Network (CNN) is a method that obtains higher accuracy than conventional classification methods for image classification problems without prior feature extraction process. We propose image spam detection using CNN on social media Instagram. The four architectures of CNN are used to compare the performance of each architecture, i.e., 3-layer, 5-layer, AlexNet, and VGG16. The performance of the system is evaluated by 8000 images taken from Instagram using web crawler. The results show that the highest accuracy achieves 0.842 by using a VGG16 architecture.
Authorized Client-Side Deduplication Using CP-ABE in Cloud Storage

Since deduplication inevitably implies data sharing, control over access permissions in an encrypted deduplication storage is more important than a traditional encrypted storage. Therefore, in terms of flexibility, data deduplication should be combined with data access control techniques. In this paper, we propose an authorized deduplication scheme using CP-ABE to solve this problem. The proposed scheme provides client-side deduplication while providing confidentiality through client-side encryption to prevent exposure of users’ sensitive data on untrusted cloud servers. Also, unlike existing convergent encryption schemes, it provides authorized convergent encryption by using CP-ABE to allow only authorized users to access critical data. The proposed authorized deduplication scheme provides an adequate trade-off between storage space efficiency and security in cloud environment and is very suitable for the hybrid cloud model considering both the data security and the storage efficiency in a business environment.
Using patterns as objectives for general video game level generation

Automatically generating multiple levels and creating infinite game-play for video games is a complicated task. In this paper, we introduce a pattern-based level generator for general video games. Our generator uses a genetic algorithm to automatically generate levels. The fitness function promotes contiguous patterns and penalizes levels with single or isolated elements. The generator generates levels using two different types of fitness functions; α and β. α levels are generated using the original fitness function and β levels through the inverse of the fitness function. In our experiments, Relative performance profiles indicate that controllers using more advanced techniques achieve better overall performance on α generated levels. The generator finished at the third spot in the 2018 general video game level generation competition.
Efficient hierarchical temporal segmentation method for facial expressionsequences

Temporal segmentation of facial expression sequences is important to understand and analyze human facial expressions. It is, however, challenging to deal with the complexity of facial muscle movements by finding a suitable metric to distinguish among different expressions and to deal with the uncontrolled environmental factors in the real world. This paper presents a two-step unsupervised segmentation method composed of rough segmentation and fine segmentation stages to compute the optimal segmentation positions in video sequences to facilitate the segmentation of different facial expressions. The proposed method performs localization of facial expression patches to aid in recognition and extraction of specific features. In the rough segmentation stage, facial sequences are segmented into distinct facial behaviors based on the similarity between sequence frames, while similarity between segments is computed to obtain optimal segmentation positions in the fine segmentation stage. The proposed method has been evaluated in experiments using the MMI dataset and real videos. Experiment results compared to other state-of-the-art methods indicate better performance of the proposed method.
Label Embedding with Partial Heterogeneous Contexts

Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, these contexts are heterogeneous and often partially observed in practical tasks, imposing significant challenges to capture the overall relatedness among labels. In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges. Categorizing heterogeneous contexts into two groups, relational context and descriptive context, we design tailor-made matrix factorization formula to effectively exploit the label relatedness in each context. With a shared embedding principle across heterogeneous contexts, the label relatedness is selectively aligned in a shared space. Due to our elegant formulation, PHCLE overcomes the partial context problem and can nicely incorporate more contexts, which both cannot be tackled with existing multi-context label embedding methods. An effective alternative optimization algorithm is further derived to solve the sparse matrix factorization problem. Experimental results demonstrate that the label embeddings obtained with PHCLE achieve superb performance in image classification task and exhibit good interpretability in the downstream label similarity analysis and image understanding task.
SocialAuth: Designing Touch Behavioral Smartphone User Authentication Based on Social Networking Applications

Modern smartphones expressed an exponential growth and have become a personal assistant in people's daily lives, i.e., keeping connected with peers. Users are willing to store their personal data even sensitive information on the phones, making these devices an attractive target for cyber-criminals. Due to the limitations of traditional authentication methods like Personal Identification Number (PIN), research has been moved to the design of touch behavioral authentication on smartphones. However, how to design a robust behavioral authentication in a long-term period remains a challenge due to behavioral inconsistency. In this work, we advocate that touch gestures could become more consistent when users interact with specific applications. In this work, we focus on social networking applications and design a touch behavioral authentication scheme called SocialAuth. In the evaluation, we conduct a user study with 50 participants and demonstrate that touch behavioral deviation under our scheme could be significantly decreased and kept relatively stable even after a long-term period, i.e., a single SVM classifier could achieve an average error rate of about 3.1% and 3.7% before and after two weeks, respectively.
A MPPT speed optimization strategy for photovolatic system using VWP interval based on weather forecast

For most of existing maximum power point tracking (MPPT) methods, it is very difficult to find a common optimization strategy to improve their tracking speed. To solve this question, in this text, a variable-weather-parameter (VWP) interval based on weather forecast (WF-VWP interval), whose lower and upper bounds can change with varying irradiance, temperature and load in real time, is proposed. By the use of it, the irradiance and temperature sensors can be omitted to ensure the low hardware cost. Based on WF-VWP interval, a MPPT optimization strategy, which can implement the complementary advantages between optimized MPPT method and VWP technique, is proposed. Here, the control signal of photovoltaic (PV) system can be always compressed within the WF-VWP interval to guarantee the MPPT rapidity. Finally, some simulation experiments verify that the proposed WF-VWP interval is feasible, available and reasonable regardless of varying irradiance or temperature, illustrate that there is a reasonable width of the WF-VWP interval regardless of varying irradiance, temperature or error band, and show the MPPT rapidity of the conventional P&O method can be greatly improved by the proposed optimization strategy.