Reviewerly/exHarmony huggingface dataset:
Peer-to-Peer Lending Platform Selection Using Intuitionistic Fuzzy Soft Set and D-S Theory of Evidence

Peer-to-Peer (P2P) lending brings many benefits for both lenders and borrowers, as well as risk for them, especially for the lenders. To help the lenders select a reliable P2P lending platform with high return and low risk, a more comprehensive selection method is necessary. However, vast information with fluctuant, fuzzy and subjective data on the P2P lending platform make it more difficult for lenders to make the right choice. This paper intends to propose a selection method by using intuitionistic fuzzy soft set to organize the data, and using D-S theory of evidence to integrate the intuitionistic fuzzy values. Besides, the paper analyses the critical factors affecting the lenders’ decision, and presents some indicators for evaluating. Finally, an empirical experiment was given by choosing several P2P lending platforms with real data, and the results are compared with the intuitionistic fuzzy weighted average and Development Index from Wangdaizhijia, that verify the validity and superiority of the proposed method.
Fractal approach for determining the optimal number of topics in the field of topic modeling.

In this paper we apply multifractal formalism to the analysis of statistical behaviour of topic models under condition of varying number of topics. Our analysis reveals the existence of two self-similar regions and one transition region in the function of density-of-states depending on the number of topics. As earlier a function that can be expressed through density-of-states was successfully used to determine the optimal number of topics, we test the applicability of the density-of-states function for the same purpose. We provide numerical results for three topic models (PLSA, ARTM, and LDA Gibbs sampling) on two marked-up collections containing texts in two different languages. Our experiments show that the "true" number of topics, as determined by the human mark-up, occurs in the transition region.
The Knowledge and Language Gap in Medical Information Seeking

Interest in medical information retrieval has risen significantly in the last few years. The Internet has become a primary source for consumers looking for health information and advice; however, their lack of expertise causes a language and knowledge gap that affects their ability to properly formulate their information needs. Health experts also struggle to efficiently search the large amount of medical literature available to them, which impacts their ability of integrating the latest research findings in clinical practice. In this dissertation, I propose several methods to overcome these challenges, thus improving search outcomes.
Integral Attacks on Round-Reduced Bel-T-256

Bel-T is the national block cipher encryption standard of the Republic of Belarus. It has a 128-bit block size and a variable key length of 128, 192 or 256 bits. Bel-T combines a Feistel network with a Lai-Massey scheme to build a complex round function with 7 S-box layers per round then iterate this round function 8 times to construct the whole cipher. In this paper, we present integral attacks against Bel-T-256 using the propagation of the bit-based division property. Firstly, we propose two 2-round integral characteristics by employing a Mixed Integer Linear Programming (MILP) (Our open source code to generate the MILP model can be downloaded from https://github.com/mhgharieb/Bel-T-256 ) approach to propagate the division property through the round function. Then, we utilize these integral characteristics to attack 3 $$\frac{2}{7}$$ rounds (out of 8) Bel-T-256 with data and time complexities of $$2^{13}$$ chosen plaintexts and $$2^{199.33}$$ encryption operations, respectively. We also present an attack against 3 $$\frac{6}{7}$$ rounds with data and time complexities of $$2^{33}$$ chosen plaintexts and $$2^{254.61}$$ encryption operations, respectively. To the best of our knowledge, these attacks are the first published theoretical attacks against the cipher in the single-key model.
Analysis of distributed database access path prediction based on recurrent neural network in internet of things

Summary For avoiding the phenomenon of congestion, delay, and jitter in thedatabase access path, it is necessary to study the prediction method of database access path. Predicted database access path has high delay and jitter when using existing database access path prediction method to predict the path. Therefore, a prediction method of database access path is proposed based on the recurrent neural network. A decision matrix is constructed and normalized based on the analytic hierarchy process. The evaluation value of the alternative transit data center is calculated by the arithmetic weighted average operator, and the transit data center is selected according to the evaluation result. The matter‐element analysis model is used to establish the mapping relationship between the user experience quality and the network service quality parameters. Moreover, the user experience quality evaluation level objective function of the database access path prediction method is constructed. Through the objective function to obtain the optimal database access path, the database access path prediction is completed. The experimental results show that the delay of the database access path predicted by the proposed method is much lower than other methods. The jitter is less than 30 ms and the jitter is small, which verifies the effectiveness of the path prediction method.
Dual-Domain Audio Watermarking Algorithm Based on Flexible Segmentation and Adaptive Embedding

This paper proposes a novel dual-domain audio watermarking approach based on flexible segmentation and adaptive embedding aimed to improve robustness and imperceptibility. Compared with conventional watermarking strategies, the proposed approach has two advantages. First, a novel audio beat detection approach is designed to flexibly segment the audio, which provides stronger robustness to synchronization attacks. The audio is decomposed by the discrete wavelet packet transform. Then, the covariance relationships of the decomposition coefficients at different time instants are calculated to determine the locations of the beats and to establish a flexible segmentation model. Second, a dual-domain embedding approach is proposed to realize better robustness to compression attacks while maintaining imperceptibility. In each segment, the psychoacoustic model is used to calculate the audio masking threshold, which divides the signals into the masking signal domain and masked signal domain. The signals in the masking signal domain are robust to compression attacks, and the signals in the masked signal domain have better imperceptibility. To combine these advantages, we embed the watermark into the two domains simultaneously by using the distortion-compensated dither modulation quantization approach. To reduce the impact of the watermark on the original audio, the frequency band with the lowest mask-to-noise ratio is selected as the embedding position for each domain. Moreover, the adaptive quantization steps are calculated to control the embedding strength according to the masking effect. The adaptive embedding will improve the robustness to compression attacks without significantly affecting the original audio quality. The effectiveness of our approach is verified through simulation experiments.
Hands-On Data Publishing with Researchers: Five Experiments with Metadata in Multiple Domains

The current requirements for open data in the EU are increasing the awareness of researchers with respect to data management and data publication. Metadata is essential in research data management, namely on data discovery and reuse. Current practices tend to either leave metadata definition to researchers, or to assign their creation to curators. The former typically results in ad-hoc descriptors, while the latter follows standards but lacks specificity. In this exploratory study, we adopt a researcher-curator collaborative approach in five data publication cases, involving researchers in data description and discussing the use of both generic and domain-oriented metadata. The study shows that researchers working on familiar datasets can contribute effectively to the definition of metadata models, in addition to the actual metadata creation. The cases also provide preliminary evidence of cross-disciplinary descriptor use. Moreover, the interaction with curators highlights the advantages of data management, making researchers more open to participate in the corresponding tasks.
Monitoring the growth of Polycystic Ovary Syndrome using Mono-modal Image Registration Technique

In this paper, a unique approach to determine poly cystic ovary syndrome (PCOS) in female at her early stages caused due to an endocrine abnormality is proposed; ultrasound abdomen scan image is an efficient tool to determine PCOS. Along with it, the growth of the cyst is monitored at different intervals by image registration technique which affects females during their reproductive cycle. Initially, cyst detection is carried out by preprocessing technique and further the growth is monitored by image registration technique. To pre-process the scan image for speckle reduction, the existing approaches such as Gabor, Gaussian filter, adaptive filter, wavelet filter were used. These approaches have its own limitations and fail to detect subtle parts of speckles which contain the minute information of cysts. To overcome this, wavelet filter db2 is adopted in this paper. The growth of the cyst is being monitored at regular intervals by applying image registration technique with correlation coefficient similarity metrics and affine transformation. The system is implemented in Matlab. The proposed work will detect PCOS at the earlier stage with an accuracy of 93%. The advantage of this system is, to help the experts in the diagnosis of PCOS and deciding the necessary therapies regarding the patient's condition in its early stages.
CNN-based Context Sensitive Lemmatization

Morphological analysis is always considered as an important task in natural language processing (NLP). Lemmatization is a major morphological operation that finds the dictionary headword/root of a surface word. In context sensitive languages, the context of a surface word plays a key role to find its lemma. So far, a limited number of neural lemmatizers have been developed for in-context lemmatization and these models account the entire sentence as context. In this research, we hypothesize that a limited context is sufficient for lemmatization and thus, make use of the convolutional neural network to accomplish the task. Our proposed BLSTM-CNN lemmatizer is evaluated on four languages, two Indic (Hindi and Marathi) and two European (French and Spanish) ones. The experimental results show that consideration of smaller context indeed achieves better performance over the approaches considering whole sentence as context in determining lemmas of surface words.
More scalable solution for multi-robot–multi-target assignment problem

During multi-robot exploration, the calculation cost of the target assignment increases significantly with an increase in the map size and number of robots. In this study, a target elimination method is proposed to overcome this challenge. The benefit of the proposed method increases as the map size and number of robots increase, which is demonstrated both empirically and theoretically. Moreover, it is established that the proposed method does not rely on a specific map representation or robot–target assignment method.
An integrated approach towards service composition life cycle: A transportation process case study

In Web Service research, providing methods and tools to cater for automatic composition of services on the Web is still the object of ongoing research activity. Despite the proposed approaches, this issue remains open. Our current contribution deals with this topic and presents a Dynamic Web Service Composition Framework (DWSC). The DWSC behavior is based mainly on two components: service dynamic designer (WSDD) and service selection based on popularity (WSSP) to propose a composite service. The DWSC architecture, designed to automatically compose services based on popularity, has the advantage of proposing a list of suggested web services according the user request from a service repository and presenting a dynamic orchestration based on the selected web services. Further, we present a practical case study according the transportation process on supply chain, examples and experimental results that demonstrate the feasibility and effectiveness of our work.
Continuance Intention in Blockchain-Enabled Supply Chain Applications: Modelling the Moderating Effect of Supply Chain Stakeholders Trust

If blockchain technologies are emerging as important game changers in the supply chain, this is largely attributed to their high operational and strategic business value. While this high potential is acknowledged by the practitioner’s literature, very few empirical studies have been conducted on the factors explaining the adoption, use and continuance of blockchain-enabled supply chain applications. To fill this knowledge gap, this study extended the expectation-confirmation model (ECM) by integrating the supply chain stakeholders trust to analyze to the continuance intention in blockchain-enabled supply chain applications. The proposed model was tested and supported by the data collected among 344 supply chain professionals in India. The paper ends up with the formulation of important implications for practice and research.
Virtual View Generation Based on 3D-Dense-Attentive GAN Networks

A binocular vision system is a common perception component of an intelligent vehicle. Benefiting from the biomimetic structure, the system is simple and effective. Which are extremely snesitive on external factors, especially missing vision signals. In this paper, a virtual view-generation algorithm based on generative adversarial networks (GAN) is proposed to enhance the robustness of binocular vision systems. The proposed model consists of two parts: generative network and discriminator network. To improve the quality of a virtual view, a generative network structure based on 3D convolutional neural networks (3D-CNN) and attentive mechanisms is introduced to extract the time-series features from image sequences. To avoid gradient vanish during training, the dense block structure is utilized to improve the discriminator network. Meanwhile, three kinds of image features, including image edge, depth map and optical flow are extracted to constrain the supervised training of model. The final results on KITTI and Cityscapes datasets demonstrate that our algorithm outperforms conventional methods, and the missing vision signal can be replaced by a generated virtual view.
Design and Development of a Stencil for Mobile User Interface (UI) Design

The paper prototyping is a quick method of UI design and concept presentation. However, hand-drawn UI prototypes (paper prototypes) are not always visually good. Sometimes rough hand sketched prototypes create confusions during UI concept validation with the target users or clients. Therefore, aim of this paper is to design and develop the UI stencil to help mobile UI designers to crate quick, easy and visually pleasing paper prototypes. A total of three UI stencil prototypes were designed and developed for usability testing to ensure the user acceptance of UI stencil. It was observed that UI designers were willing to use the ‘Stencil-3’ for mobile UI design as the level of perceived usability and perceived ease of use were higher in case of ‘Stencil-3’ than the other stencils. Moreover, it was observed that users were taken comparatively less time for UI screens using ‘Stencil-3’ than the time taken for hand drawings of the same UI screens. Hence, this UI stencil might be beneficial for UI designers for quick, consistent and pleasant paper prototype design.
A general non-parametric active learning framework for classification on multiple manifolds

Active learning is an important paradigm for investigating learners’ behavior and reducing costs on labeling. We propose a novel non-parametric active learning framework which utilizes label propagation to sense the potential data clusters/manifolds in the feature space and minimizes global uncertainty to investigate the unexplored clusters/manifolds for querying examples. Based on this framework, it is convenient to design new active learning algorithms for targeted problems. Furthermore, we analyze the sample selection mechanism of our proposed method and provide a formal proof. While selecting informative examples, our method has the following characteristics: (1) in each iteration, examples are primarily chosen from the cluster which contains unlabeled samples; (2) if there is more than one cluster with unlabeled samples, it will choose from the one containing the most samples; (3) the example which has the closest connection with the others will be preferentially selected for the same cluster. The designed algorithms achieve empirical success in multi-class classification and dramatically reduce the label costs on several real world datasets.
Parameter Estimation of Chaotic Systems Using Density Estimation of Strange Attractors in the State Space

In this chapter, the focus will be on parameter estimation methods of chaotic systems. A so-called density estimation approach will be considered, and its application in a chaotic system identification problem will be described. The estimation method is based on the attractor distribution modeling in the state space using a Gaussian mixture model (GMM). The purpose of the modeling in the state space is to overcome some problems that can be caused in the traditional parameter estimation methods of chaotic systems. Here, the learning phase and evaluation phase of the parameter estimation method will be considered exactly, and some information criteria will be reported to get a proper model of the attractor. Some experimental results will be then shown to verify the success of the parameter estimation procedure.
Ambient Monitoring in Smart Home for Independent Living

Ambient monitoring is a much discussed area in the domain of smart home research. Ambient monitoring system supports and encourages the elders to live independently. In this paper, we deliberate upon the framework of an ambient monitoring system for elders. The necessity of the smart home system for elders, the role of activity recognition in a smart home system and influence of the segmentation method in activity recognition are discussed. In this work, a new segmentation method called area-based segmentation using optimal change point detection is proposed. This segmentation method is implemented and results are analysed by using real sensor data which is collected from smart home test bed. Set of features are extracted from the segmented data, and the activities are classified using Naive Bayes, kNN and SVM classifiers. This research work gives an insight to the researchers into the application of activity recognition in smart homes.
Combining binary classifiers in different dichotomy spaces for text categorization

Several supervised machine learning applications are commonly represented as multi-class problems, but it is harder to distinguish several classes rather than just two classes. In contrast to the approaches one-against-all and all-pairs that transform a multi-class problem into a set of binary problems, Dichotomy Transformation (DT) converts a multi-class problem into a different problem where the goal is to verify if a pair of documents belongs to the same class or not. To perform this task, DT generates a dichotomy set obtained by combining a pair of documents, each belongs to either a positive class (documents in the pair that have the same class) or a negative class (documents in the pair that come from different classes). The definition of this dichotomy set plays an important role in the overall accuracy of the system. So, an alternative to avoid searching for the best dichotomy set is using multiple classifier systems because we can have many different sets where each one is used to train one binary classifier instead of having only one dichotomy set. Herein we propose Combined Dichotomy Transformations (CoDiT), a Text Categorization system that combines binary classifiers that are trained with different dichotomy sets using DT. By using DT, the number of training examples increases exponentially when compared with the original training set. This is a desirable property because each classifier can be trained with different data without reducing the number of examples or features. Therefore, it is possible to compose an ensemble with diverse and strong classifiers. Experiments using 14 databases show that CoDiT achieves statistically better results in comparison to SVM, Bagging, Random Subspace, BoosTexter, and Random Forest.
Multi Modal Aptitude Detection System for Smart Office

Smart offices are places that aim to provide a favorable, interactive, and healthy environment for the employees. As a result, their working efficiency is enhanced. In order to achieve these numerous systems such as lie detector, emotion detection, and recognition, automation of office equipment has been proposed and implemented. To contribute toward one of the goals of smart offices, a novel ”multi-modal aptitude detection system” is being proposed. The proposed system helps in analyzing the working aptitude of employees in order to help them in providing the more conducive and healthy working environment. In this paper, a detailed layered framework, implementation details, and achieved outcomes related to the proposed novel system are presented. The results achieved show that this novel system after successfully becoming a part of smart office can bring significant enhancements in the efficiency of employees. The proposed system helps in learning and identifying job preferences of employees based on their working aptitude. The system is validated and showed an accuracy of 96% in determining an employee's aptitude. The results are further verified by computing the F1 scores, and more details regarding testing and validation are provided.
Repair and Restoration of Corrupted LZSS Files

Data compression and decompression have been widely used in modern communication and data transmission. But how to decompress the corrupted lossless compressed files remains a challenge. Aiming at the Lempel-Ziv-Storer-Szymanski (LZSS), a lossless data compression algorithm widely used in the field of general coding, this paper proposes an effective method to repair the errors and decompress and restore the corrupted LZSS files, and provides the theoretical basis for the method. By using the residual redundancy left by the LZSS encoder to carry the check information, the method can repair the errors in LZSS compressed data without any loss of compression performance. The proposed method neither requires additional bits nor changes coding rules or data formats. It is fully compatible with standard algorithms. That is, the data compressed by LZSS with error repair capability can still be decompressed by the standard LZSS decoder. The experimental results verify the validity and practicability of the proposed method.