Reviewerly/exHarmony huggingface dataset:
A Model Selection Method for Machine Learning by Differential Evolution

While the application of big data attracts more and more attention, machine learning algorithms are developing with each passing day, and the models produced by machine learning are increasingly diversified. The focus of big data applications has gradually shifted from model training to the prediction and inference. Choosing the most suitable model for enterprise application scenarios among many machine learning models has become a topic of research that has attracted much attention. Though ensemble methods have been proposed to discover best model by multiple training phase, studies of finding best combination within multiple modes are still few. Configuring different machine learning models with appropriate parameters and looking for parameters is an NP-hard problem, which requires an optimization algorithm. This study proposes a differential evolution algorithm to integrate multiple trained machine learning models into an appropriate model. In this paper, the regression model is taken as an example and the differential evolution algorithm is compared with the genetic algorithm. Three benchmark datasets are used to examine, and the results show that the differential evolution algorithm outperforms genetic algorithm.
Grid Authentication: A Memorability and User Sentiment Study

Despite being one of the most crucial parts of online transactions, the most used authentication system, the username and password system, has shown to be weaker than ever. With the increase of processing power within computers, offline password attacks such as dictionary attacks, rainbow tables, and hash tables have become more effective against divulging account information from stolen databases. This has led to alternative solutions being proposed, such as logging in with a social media account or password managers, which do not replace the password entirely. Graphical alternatives have previously proposed, but none of them have become widely used. In a previous paper we proposed our own alternative called “Grid Authentication”, which would allow users to authenticate using a sequence of clicks on a colored Grid, shown to be resistant against offline password attacks. Now we have implemented and tested Grid Authentication’s memorability and recorded user sentiment data. Participants logged in using a newly created password, an 8-character password randomly generated for them, as well as used Grid Authentication scheme for three days each, once per day. We found that overall, Grid Authentication’s memorability was like a user chosen password, and far superior to the randomly generated 8-character password. We also observed that user’s overall sentiment towards Grid Authentication increased significantly after three days of regular use. Despite this, while sentiment over the system was overall positive, users perceived that they remembered the password more easily, perhaps given hints as to why alternative authentication types have not become widely used.
Talker Quality in Interactive Scenarios

In passive scenarios, people just listen (and watch) stimuli, which allows the participants to concentrate well on the task, and facilitates careful preparation and manipulation of the stimuli. In contrast to this, interaction introduces several issues, first of all, it induces verbal flexibility, as the participants should not read text out, but have to produce spontaneous speech for real conversations. For the field of acoustically analyzing Talker Quality, these individual differences between utterances in content and duration require robust acoustic parameters. Even more crucial is the case for experiment that includes pre-defined conditions to be manipulated.
Designing Inclusive User Experience for Impaired People. An RtD Driven Proposal for Albanian Urban Spaces

A significant percentage of the studies regarding the user experience of urban spaces does not take into account a segment of the population characterized by people with physical and communication impairment. Even though HCI is a current major field of interest in design, disability - and consequently accessibility - is still a localized and understudied field of opportunity for contemporary research. Most of the solution proposed by architects - and designers - tend to refer to a generic type of user and, even though claiming to be inclusive, they primarily engage able-bodied people. In the last decades, design and HCI have moved beyond the workplace and have begun to tackle increasingly wicked and particular problems affecting the everyday lives of people. The result of this significant shift materialized into a playful, bottom-up and human-centered way to design the urban space. The research for this contribution is rooted in a Research Through Design (RtD) approach (Zimmerman et al. 2007, 2010), where design is used to raise awareness and to generate design knowledge on disability and accessibility issues. This paper aims for proposing catalytic interventions for public space where interactive technologies are used to create a bridge between different typologies of users. Moreover, this paper is speculative also for the authors themselves. The identified operative categories will set the sparkle for the deployment of a design cell where to gather quantitative/qualitative data to understand better the spatial needs of impaired people, and to offer them a better and more inclusive design experience.
Combining Collaborative Filtering and Semantic-Based Techniques to Recommend Components for Mashup Design

Mashup editors enable end-users to mix the functionalities of several applications to derive a new one. However, when the end-user faces the development of a new mashup application s/he has to cope with the abundance of services and information sources available on the Web, and with complex operations like filtering and joining. Thus, even a simple to use mashup editor is not capable of providing adequate support, unless it embeds intelligent methods to process the semantics of available mashups and rank them based on how much they meet user needs. Most existing mashup editors process either semantic or statistical information to derive recommendations for the mashups considered suitable to user needs. However, none of them uses both strategies in a synergistic way. In this paper we present a new mashup advisory approach and a system that combines the statistical and semantic based approaches, by using collaborative filtering techniques and semantic tagging, in order to rank mashups based on user goals. We have proven the validity of the proposed approach through experimental sessions based on data from the ProgrammableWeb repository.
Image Super-Resolution Using Aggregated Residual Transformation Networks With Spatial Attention

For a long time, previous studies focused on convolutional neural network depth and width to improve accuracy in image super-resolution tasks. Although overall performance has grown as going deeper and going wider over time, until recently, these two elements start to show saturating accuracy and we meet a marginal utility. To address this problem, model efficiency is improved successfully by introducing two effective strategies, multi-cardinality, and spatial attention, to image super-resolution from high-level vision tasks. We propose a novel and efficient architecture aggregated residual attention network (ARAN) and set a new state-of-the-art model efficiency. According to the multi-cardinality strategy, we use group convolutions in each basic module. Moreover, we apply spatial attention blocks as gate units to capture detailed information in input images, which can be the counterparts of their basic modules. Extra representation ability is demonstrated compared with both the same sized enhanced deep super-resolution network (EDSR) baseline model and currently the-state-of-the-art cascading residual network (CARN). The experiments suggest the effectiveness of these two missing strategies previously. Especially, in the aspect of model efficiency, ARAN exceeds almost all the medium sized models currently. Code and pre-trained models are publicly available on the github: https://github.com/Xingrun-Xing/ARAN.
Mass Diffusion Recommendation Algorithm Based on Multi-subnet Composited Complex Network Model

Social recommendation algorithm that integrates social networks is widely used in big data information recommendation. However, there are many relationships among users of social networks, and the influence of each relationship on recommendation is different. Simply introducing a certain social relationship will inevitably affect the accuracy of recommendation algorithm. Based on multi-subnet composited complex network model, the multi-relationship composite network is constructed by loading multi-relationship social network on the user-commodity bipartite graph, and a mass diffusion recommendation algorithm based on multi-relationship composite network is proposed. The experimental results on real datasets Epioions and FilmTrust show that the proposed recommendation algorithm with two kinds of social relations has a significant improvement in recommendation accuracy and diversity compared with the recommendation algorithm with one kind of social relations and the traditional mass diffusion algorithm.
EPCI: An Embedding Method for Post-Correction of Inconsistency in the RDF Knowledge Bases

There are many methods for the RDF knowledge base correction in which errors are detected and then deleted or repaired if necessary. Although most of them do only detection and deletion of the errors and do not pay attention to repair. There are only a few methods to repair these errors that their accuracy is not acceptable. To solve this problem, a method will be proposed with a suitable accuracy that is called EPCI. The focus of EPCI is on inconsistency errors correction because previous correction methods have paid less attention to these kinds of errors. Most of its existing studies avoid these errors before knowledge base creation in the form of pre-correction. Although in some cases, it is needed to correct these errors after its creation as post-correction. For this reason, in EPCI a post-correction method is proposed for this objective. In the studied post-correction methods so far it has not been mentioned that inconsistency can be solved with the embedding methods and most of them solved only numeric inconsistencies. Nowadays, embedding methods are rapidly growing with high precision. So in EPCI, this technique will be used to solve also the problem of non-numeric inconsistent triples.
Registration of Breast MRI and 3D Scan Data Based on Surface Matching

The creation of 3D complete models of the woman breast that aggregate radiological and surface information is a crucial step for the development of surgery planning tools in the context of breast cancer. This requires the registration of interior and surface data of the breast, which has to recover large breast deformations caused by the different poses of the patient during data acquisition and has to deal with the lack of landmarks between both modalities, apart from the nipple. In this paper, the registration of Magnetic Resonance Imaging exams and 3D surface data reconstructed from Kinect <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> acquisitions is explored using a biomechanical modelling of breast pose transformations combined with a free form deformation to finely match the data. The results are promising, with an average euclidean distance between the matched data of 0. 81±0.09 mm being achieved.
Face Style Transfer and Removal with Generative Adversarial Network

In this paper, we present a method to transfer the style of a stylized face to another face without style and recover photo-realistic face from the same stylized face image simultaneously. Here style refers to the local patterns or textures of some existing paintings. Style transfer gives a new way for artistic creation while style removal can be beneficial for face verification or photo-realistic content editing. Our approach contains two components: the Style Transfer Network (STN) and the Style Removal Network (SRN). STN renders the style of the stylized image to the non-stylized image and the SRN is designed to remove the style of a stylized photo. By applying the two networks successively to an original input photo, the output should match the input photo. The experiment results in a variety of portraits and styles demonstrate our approach's effectiveness.
Mathematical modeling process of training specialists in the engineering industry at a technical university

Mathematical models are constructed to describe the functioning of the educational system in the form of Cobb-Douglas production function. As an example, it is considered the process of training specialists in the engineering industry is at flagship Samara state technical University. The constructed models allow to analyze the characteristics of the efficiency of use of basic resources in the university and to identify promising ways to improve staffing in the region.
A Joint Image Compression–Encryption Algorithm Based on SPIHT Coding and 3D Chaotic Map

In this paper, the author has proposed secure and fast joint image compression–encryption algorithm using discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) algorithm, and 3D chaotic map. The proposed approach is divided into three phases for partial encryption, full encryption, and permutation in order to elaborate the cryptographic properties. Compression part is carried out via secure SPIHT. This inclusion of security in SPIHT has no reflex on compression properties. Intensive experiments are performed to justify and validate the proposed approach.
Thyroid Diagnosis Using Multilayer Perceptron

Thyroid disease is one of main origin of serious medical issues for human subsistence. Therefore, proper diagnosis of thyroid disease is treated as an important issue to determine treatment for patients. A new approach on Multi-layer Perception (MLP) using back propagation learning algorithm to classify Thyroid disease is presented. It consists of an input layer with 4 neurons, 10 hidden layer with 3 neurons and an output layer with just 1 neuron. The relevant choice of activation objective and the number of neurons in the hidden layer and also the number of layers are achieved using MLP test and error method. The proposed method shows better performance in terms of classification accuracy. For simulation results MATLAB Tool is used.
Responsive Open Learning Environment in a Higher Education Institution

We have started to hear more and more about responsive open learning environments (ROLEs). They are the next generation of personal learning environments (PLEs). PLEs are based on the basic aggregation of existing content and services mainly using Web 2.0 technologies. ROLEs are mutating lifelong learning by introducing a new infrastructure to a world while dealing with existing learning management systems, institutions, and technologies, and these systems have test-beds. In this chapter, the authors describe first experiences utilizing ROLEs at Istanbul Technical University in Istanbul. The results of the study showed the readiness of the technology for large-scale trials and the benefits for the students leading to new insights in the design of ROLEs for more informal learning situations.
Motion Analysis of Manufacturing of Large “Echizen Washi” Japanese Traditional Paper

The Echizen Japanese paper has a long history, in the 4–5 century the paper was reported in Japan, it has been left in the ancient documents of "Shosoin" who had crowded the paper in Fukui Prefecture Echizen. Higher than 1500 years before the request is to have responded with high technology. High technology of craftsmen built by the inquisitive to the new technology and hard work that Persistent, leading to now. It is said to Echizen Japanese paper in Japan of handmade Japanese paper, to make the largest Japanese paper. Its work is carried out in a work called "Nagasisuki" and "Tamesuki". Work of this large-format sum papermaking is performed by the two people of the craftsman. Skilled artisans to indicate from the beginning of the work to determine the can of the product end. And the working process of a large-sized paper in this study, the behavior analysis of the skilled person performs, an object of the present invention is to analyze the differences in behavior of a person skilled two people.
Wheelchair Navigation: Automatically Adapting to Evolving Environments

Power wheelchairs can increase independence by supporting the mobility of their users. However, severe disabilities of users can render controlling the wheelchair difficult, if not impossible, especially over longer periods of time. This paper describes a proposal for research into techniques that would improve the experience and quality of life of wheelchair users by reducing the cognitive burden introduced by repetitive and complicated navigation tasks and manoeuvres. This will be achieved by sharing the control between the user and an autonomous controller. A number of techniques will be used to achieve this aim. Simultaneous Localization and Mapping (SLAM) and topological mapping will be used for navigation between rooms while Computer Vision techniques will allow the (semi) automatic recognition of places in the user's home, based on the detection and categorization of objects. Finally, medium to high level automation will be provided. This includes automatic and transparent assistance with tasks such as navigating through doorways but also autonomous navigation to specific locations using high level constructs ("take me to the kitchen table").
A Visual Saliency Detection Approach by Fusing Low-Level Priors With High-Level Priors

Saliency detection has always been a challenging and interesting research area for researchers. The existing methodologies either focus on foreground regions or background regions of an image by computing low-level features. However, considering only low-level features did not produce worthy results. In this paper, low-level features, which are extracted using super pixels, are embodied with high-level priors. The background features are assumed as the low-level prior due to the similarity in the background areas and boundary of an image which are interconnected and have minimum distance in between them. High-level priors such as location, color, and semantic prior are incorporated with low-level prior to spotlight the salient area in the image. The experimental results illustrate that the proposed approach outperform the sate-of-the-art methods.
Mapping anti-sexual harassment and changing social norms in Egypt

Sexual harassment is a global problem. Based on available data, Egypt has higher than average rates of sexual harassment for the Middle East and North Africa region and many other countries in the Global South. This article explores how one organization, HarassMap, has mapped sexual harassment using crowdsourced technology, engaged in anti-sexual harassment activities and sought to change social norms to move toward an environment of zero tolerance. We highlight the evolving activism since 2010, and its respective learning, within an operational environment influenced by restrictive political, religious and socio-cultural spheres. The organization has effectively contributed to changing the public discourse and nurturing an engaged community. It has also supported the development of policies in university and private sector settings, many of which are firsts for the country. This article provides insight on how anti-sexual harassment activities can occur in challenging contexts, using crowdsourcing mapping when traditional methods are illegal or could lead to violence. We draw on these experiences to reflect on more effective forms of support that external actors can provide within restrictive operational environments.
Research Challenges in Big Data Security with Hadoop Platform

Every minute in the internet, enormous volume of data is generating tuning into big data. A new paradigm of data storage and processing is essential. There is extremely no query that Hadoop is an essentially unruly technology. Latest innovation in scalability, appearance, and data dispensation competence has been beating us apiece of few months over the last few years. In this source of ecosystem is the extremely classification of novelty. Big data has distorted data analytics given that extent, presentation, and flexibility that was just not potential a few years ago, at an outlay that was evenly inconceivable. But as Hadoop turn into the new standard of information technology, developers, and security policy are playing grab awake to identify with Hadoop security. Moreover, current security hypothesis and mechanism have been established. In this paper, we discuss lays out a series of recommended security controls for Hadoop along with an access control framework, which enforces access control policies dynamically based on the sensitivity of the data and systemic security, operational security and architecture for data security. A relative study of latest advances in big data for security. A number of prospect information for big data security and privacy methods are discussed.
Dynamic Graph Link Prediction by Semantic Evolution

Dynamic graph link prediction has attracted increasing attention in various fields such as social networks, paper citation networks and knowledge graphs. Many models have been developed to predict the future graph structure. In this paper, we propose a link prediction model with semantic evolution (LISE), to predict links in a sequence of graph over time. Our approach is based on the discovery of non-random initialization dynamic word embedding which is a kind of method to study semantic evolution. It can help us train node embedding in the same space and introduce temporal context into the embedding training of nodes. Based on node embedding in the same space, LISE can unify historical behavior, graph snapshots structure information and dynamic attributes into a frame. We evaluate our proposed method and various comparing methods on two real-world datasets. The experimental results prove the effectiveness of the link prediction made by LISE model.
Predictive model for epistasis-based basis evaluation on pseudo-boolean function using deep neural networks

Complexity of a problem can be substantially reduced through basis change, however, it is not easy to find an appropriate basis in representation because of difficulty of basis evaluation. To address this issue, a method has been proposed to evaluate a basis based on the epistasis that shows the problem difficulty. However, the basis evaluation is very time-consuming. In this study, a method is proposed to evaluate a basis quickly by developing a model that estimates the epistasis from the basis by using deep neural networks. As experimental results of variant-onemax and NK-landscape problems, the epistasis has been estimated successfully by using the proposed method.