Reviewerly/exHarmony huggingface dataset:
New Approach to Recommend Banking Products Through a Hybrid Recommender System

This research aims to add value in the private banking sector by increasing the sale of financial products to the customer by personalising the recommendation. It proposes a conceptual definition of a new process for private banking when recommending banking products. To this end, a hybrid method of recommending financial products is presented: collaborative filtering combined with content-based filtering. This task involves exploring intelligent algorithms to create the right recommendation for each client. The expected results are the prediction with a high degree of accuracy by integrating a hybrid method that ensures the personalization of products suggested by a bank.
The Characteristics of Virtual Reality Usage in Educational Systems

The purpose of the paper is to provide a brief insight into the characteristics of virtual reality technology and its current educational potential. As this technology is becoming widespread in consumer electronic market, its effect on student (digital) lives is starting to gain importance, and as such becomes interesting alternate educational technology. Even though there still are many obstacles to its everyday use, its advantages and increasing availability puts it in the focus of many educational researchers.
Visual comparison of statistical feature aggregation methods for video-based similarity applications

Video data is increasingly being provided by multiple sources. These sources are used to obtain reliable feature information within the context of training data shortages. As such it becomes crucial to interpret and refine such information. Recent research on video content analysis often use deep-learning features due to their outstanding performance in different domains. These features are aggregated over time to create a video-level descriptor. In this research, we explore the potential of statistical feature aggregation methods in combining deep-learning features that represent the visual content of videos. In particular, the contributions of this paper are two-fold: Firstly, we compared statistical feature aggregation methods by selecting movie sequels, calculating their in-sequel-mean-distance and out-of-sequel-mean-distance as well as the Bhattacharyya distance between them and using Principal Component Analysis (PCA) and T-distributed Stochastic Neighbour Embedding (t-SNE) to visualise their distribution in the feature space. This is important to easily comprehend the video content without the need to watch them and understand how well the statistical feature aggregation methods represent the videos. Secondly, the performance of these aggregation methods is explored in the context of a content-based video retrieval task which is evaluated in terms of relevance. The observed results show that the statistical feature aggregation method based on variance outperforms the methods based on maximum, mean, median, median absolute deviation and interquartile range. This outcome is supported when interpreting the t-SNE visualisation method as well as Bhattacharyya distance calculations. Overall statistical feature aggregation methods which measure the spread of a distribution achieve better performance compared to methods that are a measure of location.
Multi-UAVs Target Attack Based on Improved Genetic Algorithm

Task assignment is the main conundrum of multiple unmanned aerial vehicles (multi-UAVs) cooperative task execution, which has great research value. To solve the problem of task scheduling for multi-UAVs, a method based on improved genetic algorithm is proposed in this paper. Firstly, a mathematical model of task assignment is established based on the application environment of multi-UAVs attacking targets. Secondly, aiming at the problem of slow convergence speed and easy to fall into local extremum of genetic algorithm, the improved Metropolis criterion of simulated annealing algorithm is introduced into the genetic algorithm. Finally, the simulation results show that the improved genetic algorithm can reduce the amount of calculation, improve the quality of the results, and solve the task assignment problem of multi-UAVs more effectively.
Session Types Without Sophistry

Whereas ordinary types approximate the results, session types approximate communication among computations. As a form of typestate, they describe not only what is communicated now but also what is to be communicated next. Writing session-typed programs in an ordinary programming language such an OCaml requires inordinary cleverness to simulate type-level computations and linear typing – meaning the implementation and the error messages are very hard to understand. One is constantly reminded of template metaprogramming in C++. We present a system exploring a very different approach to session typing: lowering type-level sophistry to ordinary programming, while maintaining the static assurances. Error messages are detailed and customizable, and one can use an ordinary debugger to investigate session-type problems. Our system is a binary-session–typed DSL for service-oriented programming in OCaml, supporting multiple communication channels, internal and external choices, recursion, and also channel delegation. The key idea is staging: ordinary run-time checks in the generator play the role of "type-checks" from the point of view of the generated program. What is a fancy type to the latter is ordinary data to the generator.
MultiSec: A Bionics Security Architecture of Ad-hoc Network

Advanced in IoT (Internet-of-Things) technology recently, it is more popular to make things connect with each other, like wearable device, home appliance, transportation, etc. There’s no doubt that internet will finally connect everything together. An ad-hoc network is suitable for extending the coverage of the Internet. However, the security of the ad-hoc network is the main problem to solve. It is hard to engage an anti-virus software on a power-limited device or a low-cost device with limited computing power. Even it is hard to defend the attacks by one device, it is possible to defend attacks by the entire ad-hoc network. In this paper, we came up with a new architecture that combined software and hardware called MultiSec, inspired by the method of how animal immune pathogens. With a new protocol stack called SecPath and a special anti-virus engine called SecGuard working on the ad-hoc network. It is easier to reduce the power consumption in this way but with a powerful anti-virus software working on it. Then with the control of SecGuard, the network will only accept the trusted devices and make it take part in the defense work. SecGuard will move randomly in the ad-hoc network, gathering working feature of the entire ad-hoc network. It will detect the suspicious data transport in network and abnormal device status then block data or ban device to keep the network safe. SecPath is independent of the TCP/IP protocol stack and can work together with it. We evaluated some basic features of MultiSec on STM32 platform and it works well. The evaluate system can show as a hardware demo. And we think the architecture is worth to share.
The Democratization of Machine Learning Features

In the Machine Age, Machine learning (ML) becomes a secret sauce to success for any business. Machine learning applications are not limited to autonomous cars or robotics but are widely used in almost all sectors including finance, healthcare, entertainment, government systems, telecommunications, and many others. Due to a lack of enterprise ML strategy, many enterprises still repeat the tedious steps and spend most of the time massaging the required data. It is easier to access a variety of data because of big data lakes and data democratization. Despite it and decent advances in ML, engineers still spend significant time in data cleansing and feature engineering. Most of the steps are often repeated in this exercise. As a result, it generates identical features with variations that lead to inconsistent results in testing and training ML applications. It often stretches the time to go-live and increases the number of iterations to ship a final ML application. Sharing the best practices and best features are not only time-savers but they also help to jumpstart ML application development. The democratization of ML features is a powerful way to share useful features, to reduce time go-live, and to enable rapid ML application development. It is one of the emerging trends in enterprise ML application development and this paper presents details about a way to achieve ML feature democratization.
Computerised Segmentation of Medical Images using Neural Networks and GLCM

This article proposes a novel method combining the Neural Networks (NN) and the features of Gray Level Co-Occurrence Matrix (GLCM) for segmenting Region of Interest (ROI) of multiple medical images. The proposed methodology combines the texture of different pixels of medical images with the Radial Bias Function Neural Networks (RBFNN) in order to increase the performance of ROI segmentation and to obtain an optimal segmented region. The proposed approach works in two steps. Initially, the image borders are detected in order to separate the background skin and the ROI. This starts by extracting the GLCM features by the process of texture analysis which represents the ROI border clearly. GLCM features such as energy, homogeneity, contrast and the correlation are extracted. Secondly, the extracted features are passed towards the RBFNN for generating the ROI as segmented area. 870 scans of multiple medical images stored in a database are used for analysing the accuracy of the proposed methodology. Analysis of accuracy shows that the proposed methodology segments the ROIs of multiple medical images more accurately.
Orthogonal Learning Firefly Algorithm

Abstract The primary aim of this original work is to provide a more in-depth insight into the relations between control parameters adjustments, learning techniques, inner swarm dynamics and possible hybridization strategies for popular swarm metaheuristic Firefly Algorithm (FA). In this paper, a proven method, orthogonal learning, is fused with FA, specifically with its hybrid modification Firefly Particle Swarm Optimization (FFPSO). The parameters of the proposed Orthogonal Learning Firefly Algorithm are also initially thoroughly explored and tuned. The performance of the developed algorithm is examined and compared with canonical FA and above-mentioned FFPSO. Comparisons have been conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using the Friedman rank test.
Algorithmic performance-data classification of condensing heat exchangers

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
On Franklin wavelets

The Franklin wavelet (piecewise linear wavelet) is a wavelet function [Formula: see text] with dilation factor 2 which is continuous on [Formula: see text], linear on [Formula: see text] and [Formula: see text] for all [Formula: see text]; which is constructed from a multiresolution analysis(MRA) with a scaling function [Formula: see text] which is continuous on [Formula: see text], linear on [Formula: see text] and [Formula: see text] for every [Formula: see text]. Wavelets with composite dilation factor have the ability to produce long and narrow window functions, with varied orientations, well-suited to applications of image processing; also it is more effective and flexible for the construction of multiscale image representation. This motivates us to consider the following problem to construct some composite dilation Franklin wavelets. Given a natural number [Formula: see text], we ask, is there a continuous [Formula: see text] that is linear on [Formula: see text] and [Formula: see text], for [Formula: see text], [Formula: see text] and for all [Formula: see text], which generates an MRA with dilation factor [Formula: see text] to construct an orthonormal [Formula: see text]- wavelet, i.e. a wavelet with dilation factor [Formula: see text]? For [Formula: see text] and [Formula: see text] we have proved that [Formula: see text] generates an MRA with dilation factor [Formula: see text] only if [Formula: see text]. Next for [Formula: see text] and [Formula: see text], we have proved that, if [Formula: see text] generates an MRA with dilation factor [Formula: see text], then [Formula: see text]. Also, for [Formula: see text], we have proved existence of a continuous [Formula: see text] which generates an MRA with dilation factor [Formula: see text]. We have also constructed the corresponding orthonormal [Formula: see text]-wavelet.
An Empirical Study on Critical Blocking Bugs

Blocking bugs are a severe type of bugs that prevent other bugs from being fixed. As software becomes increasingly complex and large, blocking bugs occur in many large-scale software, especially in software ecosystems. Blocking bugs may have a high negative impact on software development and maintenance. Usually, blocking bugs preventing more bugs should be more concerned. In this paper, we focus on a special type of blocking bugs that block at least two bugs, which we call Critical Blocking Bugs (CBBs).
Nonclassical effects for a qubit coupled to a coherent two-mode cavity with intrinsic decoherence

In this paper, we use the su(1,1)-algebraic treatment to explore the effects of the intrinsic decoherence in a two-mode cavity containing a two-level atom. Each field is resonant with the qubit through a four-photon process. The role of the intrinsic decoherence and the superposition of the initial generalized Barut-Girardello coherent state on the quantum effects is investigated via different quantifiers as: atomic inversion, linear entropy and negativity. It is found that the non-linearity of the four-photon process leads to generate non-classical effects with a high oscillatory behavior. The superposition of the Barut-Girardello coherent state controls the dynamics of the purity loss and the entanglement. It is found that the nonclassical effects are very sensitive to the nonlinear interactions between the qubit and the two-mode cavity.
B-SPARQL: A typed language for Querying the Big Knowledge

We introduce B-SPARQL as an extension of the classical SPARQL to deal with the problem of querying big knowledge. B-SPARQL extends SPARQL mainly in two directions. The first one is to query big knowledge in a global way such as `how much of the knowledge items of the knowledge base are confidential?' The second one is to query big knowledge in its translated digital form instead of in its original symbolic form. The former is necessary when evaluating and maintaining an immense knowledge base. The latter is useful when it is very inefficient or even impossible to deal with an immense knowledge base. In accordance, B-SPARQL not only deals with basic knowledge elements such as subjects, predicates and objects of RDF triples. It also deals with triples and knowledge bases and knowledge areas (parts of knowledge bases), including SPARQL endpoints, immediately by considering them as basic knowledge elements as well.
Nurse care activity recognition based on machine learning techniques using accelerometer data

Sensor-based human activity recognition has become one of the challenging and emerging research areas. Several machine learning algorithm with appropriate feature extraction has been used to solve human activity recognition task. However, recent research mainly focused on various deep learning algorithms, our focus of this study is measuring the performance of traditional machine learning algorithms with the incorporation of frequency-domain features. Because deep learning methods require a high computational cost. In this paper, we used Naive Bayes, K-Nearest Neighbour, SVM, Random Forest and Multilayer Perceptron with necessary feature extraction for our experimentation. We achieved best performance for K-Nearest Neighbour. Our experiment was a part of "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data" followed by the team MoonShot_BD. We concluded that with proper feature extraction, machine learning techniques may be useful to solve activity recognition with a low computational cost.
Multi Secret Image Sharing Scheme of General Access Structure with Meaningful Shares

A Multi Secret Image sharing scheme can share several secret images among certain participators securely. Boolean-based secret sharing schemes are one kind of secret sharing method with light-weighted computation compared to the previous complex algebraic-based methods, which can realize the sharing of multi secret images. However, the existing Boolean-based multi secret sharing schemes are mostly restricted to the particular case of (2, n) and (n, n), only few Boolean-based multi secret sharing schemes study the general access structure, and the shares are mostly meaningless. In this paper, a new Boolean-based multi secret sharing scheme with the general access structure is proposed. All the shares are meaningful, which can avoid attracting the attention of adversaries, and the secret images can be recovered in a lossless manner. The feasibility of the scheme is proven, the performance is validated by the experiments on the gray images, and the analysis of the comparison with other methods is also given out.
DBFCM: a Density-Based Fuzzy C-Means with Self-Regulated Fuzzy Clustering Parameters

As an interdisciplinary of fuzzy theory and clustering, Fuzzy C-Means (FCM) is widely applied for identifying categories with unlabeled data. However, its application to data which is hard to visualize rises the difficulty for users to determine the input parameters, especially for the number of clusters. In this paper, a kind of fuzzy clustering algorithm with self-regulated parameters named Density-Based Fuzzy C-Means (DBFCM) is proposed by integrating the idea of Density-Based Spatial Clustering of Application with Noise (DBSCAN) into FCM. Its advantage is using the inherit density characteristic of input data to self-determine the parameters of fuzzy clustering. The experimental results demonstrate that the proposed DBFCM can not only self-determine the proper parameters, but also accelerate the convergence process compared to the original FCM.
Image Enhancement and Translation for RGB-D Indoor Scene Recognition

Most existing methods for RGB-D indoor scene recognition adopt the backbone networks designed for image recognition. They overly focus on global features but largely ignore the local features, resulting in unsatisfactory accuracy in practice. This paper proposes a T-like network called T-Net to comprehensively exploit both global and local features by multi-scale supervision. In detail, we add an image translation branch and introduce pixel-level semantic segmentation annotations along with the image-level labels, to jointly supervise the model to excavate more regions of objects. In addition, low-quality source images without image enhancement cause difficulty in extracting representative features. To address this issue, the Multi-Scale Retinex with Color Restoration (MSRCR) is introduced to enhance the brightness and contrast of the RGB images. We demonstrate that the proposed method achieves superior performance to the state-of-the-art methods on SUN RGB-D and NYU Depth v2 Datasets.
M-Learning: Are We Ready to Go Mobile?

Mobile devices, which have been incorporated to everyday life all over the world – call a cab, order food, pay bills, search the Internet, listen to music, communicate with friends etc. – are heavily supported by cell phones. This technology has also become a part of the learning process. It can happen in an informal way since a lot of knowledge is available on the Internet. In this scenario, higher Education Institutions must be ready to fulfill the needs of their students. This work addresses the steps in the preparation for an institution to introduce Mobile Learning in its courses in Electrical Engineering and related areas. The starting point is an experience of over two decades in Information and Communication Technology supported learning which includes courses in Blended Learning with Flipped Classroom. A large collection of learning objects of different natures is also available. The Information and Communication Technology supported education is hosted on a platform with the unique characteristic of managing contents, users, remote labs and courses in an integrated way. The steps discussed in this work address the different characteristics of Mobile Learning and also key issues that have been under discussion in the University and in the Engineering Education community in the country. This paper reports a work in progress.
Conceptual Reasoning for Generating Automated Psychotherapeutic Responses

The need for software applications that can assist with mental disorders has never been greater. Individuals suffering from mental illnesses often avoid consultation with a psychotherapist, because they do not realize the need, or because they cannot or will not face the social and economic consequences, which can be severe. Between ideal treatment by a human therapist and self-help websites lies the possibility of a helpful interaction with a language-using computer. A practical model of empathic response planning for sentence generation in a forthcoming automated psychotherapist is described here. The model combines emotional state tracking, contextual information from the patient’s history and continuously updated therapeutic goals to form suitable conceptual graphs that may then be realized as suitable textual sentences.
State estimation for the system with unknown sensor inputs, asynchronous correlated noises and packet loss compensation

To settle the unknown sensor inputs, correlated noises and packet loss compensation issues for linear system, an optimal estimation algorithm is studied considering linear unbiased minimum variance criterion. By describing packet losses and the compensation values with a series of Bernoulli distribution random variables, a new measurement equation is constructed. Subsequently, considering there is no prior information about unknown inputs, based on the projection theorem and auxiliary equation, the optimal estimation algorithm is derived. Finally, the convergence and robustness are shown by the simulation.
Outlier Detection via Kernel Preserving Embedding and Random Walk

Since outlier detection has a wide range of potential applications, it has become a fundamental and hot research topic in data mining. Recently, the technique of self-representation has attracted extensive attention and many low-rank representation based outlier detection algorithms have been witnessed. However, most of them pay more attention to minimize the reconstruction error of the data, without involving the manifold structure. Meanwhile, as a low-rank constraint, the single nuclear norm often leads to suboptimal solution. To alleviate these problems, in this paper, we propose a novel outlier detection method, which adopts kernel-based distance to retain the overall relations. Moreover, the double nuclear norm is exploited to address the suboptimal problem. Further, a tailored random walk is used to identify outliers, after the similarity relations of the data available. The extensive simulation experiments on five public datasets demonstrate the superiority of the proposed method in comparing to the state-of-the-art outlier detection algorithms.