Reviewerly/exHarmony huggingface dataset:
Efficient Complex Valued Neural Network with Fourier Transform on Image Denoising

Denoising has become one of the main topics of study in the Computer Vision domain, since it is useful not only for media applications but also for other domains in Computer Vision. Recently, Complex valued neural networks have shown some capabilities when applying it to solve denoising problems with great results. To expand from this idea, we proposed a method of using Fourier transform to convert the images to the complex domain, which was used in other denoising method, and use the magnitude and phase of the transformed image to filter out noises on the complex domain with our Complex Valued Neural Network. The results of this model outperforms previous Deep Learning approaches in most cases while in other cases, our approach came close to the performance of the previous best model.
Biodigital Technologies and the Bioeconomy: The Global New Green Deal?

This chapter explores the co-incidence of Europe, US and China new green deal policies, together with the international cooperation of many nations, and argus that it seems like a promising intersection of national and global intentions. It introduces the scientific backdrop of these policies, and various biodigital convergences. Based on a range of policy documents, the chapter looks into the emergence of bioeconomy and describes its main features. The conclusion indicates that biodigital convergence and its various implications from biotechnology to bioeconomy seem to promise a Copernican shift in our current way of life. Historicizing this historical shift and comparing it to predictions of a similar magnitude were expected from other technologies, however, it advocates caution. The world is still not in the age of bioeconomy, yet the first signs of the new age of bioeconomy are all around us. Bioeconomy is of great importance for new green deal initiatives, and it is crucial to develop bioeconomy towards a new green age that at last takes sustainability seriously.
XSS2DENT, Detecting Cross-Site Scripting Attacks (XSS) Vulnerabilities: A Case Study

Based on the OWASP Top 10 Security Risk, Cross-Site Scripting (XSS) attacks rank seventh, according to the latest report released in 2017. To execute this attack, a web application is used to send malicious code, generally in the form of a script on the browser side. To verify the level of vulnerability of educational establishments in Santo Domingo de los Tsachilas city against this threat, a model has been proposed, whose objective is to obtain information by executing a controlled, combined and camouflaged attack in a security cybernetics challenge, distributed through flyers with a QR Code, to students from different areas in the city. With this information, a report was prepared in order to socialize vulnerable establishments, with information that allowed them to review the status of their security infrastructure and establish new blocking rules against this attack. The results indicate that our model was successfully executed in 3 universities. In addition, it is shown how our attack spread beyond the city limits, reaching countries such as Mexico, Argentina, Colombia, among others.
Covariance Features Improve Low-Resource Reservoir Computing Performance in Multivariate Time Series Classification

Lawrie, Sofía Moreno-Bote, Rubén Gilson, MatthieuBiological systems exhibit tremendous performance and flexibility in learning for a broad diversity of inputs, which are in general time series. Inspired by their biological counterpart, artificial neural networks used in machine learning for classification aim to extract activity patterns within input signals to transform them into stereotypical output patterns that represent categories. For the vast majority, they rely on fixed target values in output to represent probabilities or implement winner-take-all decisions, which correspond in the case of time series to first-order statistics. In other words, the basis for such classification of time series is the transformation of input high-order statistics into output first-order statistics. However, the transformation of input statistics to second- or higher-order statistics has not been much explored yet. Here, we consider a computational scheme based on a reservoir that maps information engrained in input multivariate time series statistics to second-order statistics of its own activity, before being fed to a usual classifier (logistic regression). We compare this covariance decoding with the “classical” mean decoding applied to the reservoir for classification with both synthetic and real datasets of multivariate time series. We show that covariance decoding can extract a broader diversity of second-order statistics from the input signals, yielding higher performance with smaller resources (i.e., reservoir size). Our results pave the way for the characterization of elaborate input-output mappings between statistical orders to efficiently represent and process input signals with complex spatio-temporal structures.
A Vision-Based Approach for Solid Waste Materials Feature Extraction Using Deep Learning Techniques

The present method of segregating solid waste material is hand picking method and it is prone to dangerous to waste picker’s health because of harmful substances into garbage. So to simplify this process, an automated computer vision-based system will be able to segregate the waste using object detection method. The feature extraction of different solid waste material images is an essential task before object detection. We have used Trashnet dataset along with custom dataset which contains small scale waste materials so as to classify and detect the small scale objects effectively. We have tested and analysed a well-known deep learning models like VGG16, VGG19, ResNet50 and AlexNet, as a result after experimentation we found that VGG16 has 98% training accuracy, 76% test accuracy and data augmentation was applied to increase the accuracy.
Developing Bangla Handwritten Numerals, Basic and Compound Characters Recognition System Using a Deep Convolutional Neural Network

Bangla handwritten character recognition is a crucial task in the field of digitization, building OCR, etc. There are numerous characters in the Bangla language. They have their own styles and shapes. Some of them have so many similarities too. The handwriting style is also a big factor in character recognition. There is a lot of versatility in handwriting style between people. So, recognizing handwritten characters is a very difficult job. But few works were focused on recognizing all types (basic, compound, and numerals) of Bangla individual characters. A multi-branch Deep CNN based model with data augmentation has been proposed in this paper to perform the task. The data augmentation is performed by modifying the positions of the characters in the images to make the recognition system position-independent. Multi-branch is used to explore and process multiple types of features from the same source together. The datasets used to train, validate and test the model are the CMATERdb dataset and the Ekush dataset. Different datasets have been used for this experiment to analyze the results more precisely. The model has obtained 97.82% validation accuracy and 97.3% testing accuracy on the CMATERdb dataset (231 character class) and 96.18% validation accuracy and 96.13% testing accuracy on the Ekush dataset (122 character class). From the results, it can be observed that the model achieved pretty high accuracy on different datasets. On recognizing various Bangla characters from mouse input in real-time, it took only 0.66 ms on average to recognize them. Handwritten character recognition has many real-life applications as everything is going to be in digital form. Because of high classification accuracy, our proposed model can be used and extended to do more complex tasks in the corresponding fields.
Towards Efficient Context-Sensitive Deliberation

We propose a context-sensitive deliberation framework where the decision context does not deliver an action straight away, but where rather the decision context and agent characteristics influence the type of deliberation and type of information evaluated which will affect the final decision. The framework is based on the Contextual Action Framework for Computational Agents (CAFCA). Our framework also tailors the deliberation type used to the decision context the agent finds itself in, starting from the least cognitive taxing deliberation types unless the context requires more complex deliberation types. As a proof-of-concept the paper shows how context and information relevance can be used to conceptually expand the deliberation system of an agent.
Progress of No-Reference Image Quality Assessment Based on Deep Learning

Image quality assessment is the basic problem of image processing and its cross-field. Due to No-reference Image Quality Assessment (NR-IQA) is free from the constraint of reference information Consequently, NR-IQA become a popular topic in image quality assessment. The paper reviews the typical models based on deep learning published in the recent ten years. Firstly, the commonly used image databases and algorithm performance indicators are introduced. Then, the characteristics and guiding ideology are analyzed in each model and compare model performance on common databases. Finally, analyzes the possible development trends by summarizing the existing research.
Motivations for and Benefits of Adopting the Test Maturity Model integration (TMMi)

Test Maturity Model integration (TMMi) is a popular model for maturity assessment and capability improvement of software testing practices in industry. Originally inspired by the Capability Maturity Model Integration (CMMI), and managed by the TMMi Foundation, the TMMi specification provides guidelines for assessing and improving testing capabilities of teams and organizations. In this invited paper, we discuss motivations for and benefits of adopting the TMMi. The discussion is based on an international user survey, which received data from 74 companies that have received TMMi assessments and certifications.
Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Data deduplication is the trending and flexible data compression mechanism for reducing the strong space and data in the cloud. In this mechanism, the entire data in the file is divided into a small number of chunks, and a unique hash code is generated during the file uploading process and is stored in the cloud for further assessment. Along with the file storage, providing security to the file is also an important task, which can be done by using the enhanced convergent encryption technique in this proposed paper. The traditional encryption algorithms do not encourage data encryption while outsourcing the file to data because the usage of different algorithms generates different cipher texts even though the contents of the files are identical, due to which the deduplication process is not possible to reduce the data efficiently. The proposed paper aims at the crucial cloud taxability functionality so that encrypted files from different entities can be saved by the overhead bandwidth and storage. With the advancement of communication trends, data sharing among social media is increasing day by day. To share the data with security in the cloud, one of the popular mechanisms is “attribute-based encryption”, which allows the user to share the data having the common credentials to access the files. In this paper, attributed-based encryption is performed by generating a more number of convergent keys to share the data across multiple entities like mobiles, desktops, tablets, and other cloud servers.
Towards Zero Knowledge Argument for Double Discrete Logarithm with Constant Cost

It has been an open problem whether a constant round andconstant cost protocol exists for the Double Discrete Logarithm problem(DDLOG). In this paper, we show that such a protocol exists for bilineargroups under the restriction x = poly( λ ) where x and λ are the secretexponent and the security parameter, respectively. The protocol costsO (1) exponentiations and pairings on both the prover and verifier, andit exchanges 53 (67) group (field) elements, regardless of the desiredsoundness
Paradigm Shifting of Education System During COVID-19 Pandemic: A Qualitative Study on Education Components

The COVID-19 pandemic has had a far-reaching effect on the education system, leading to some changes in the conventional education system. Educational institutions are adopting alternative methods to adapt to the changed situation. The purpose of this study is to explore the changes that have taken place in the education system due to the pandemic situation. A qualitative phenomenological approach has been used to conduct the study. To get a complete picture of the shifting paradigm of the education system, two interview methods such as In-depth Interviews (IDI), Key Informant Interviews (KII)) have been adopted. Interviews were taken from 15 respondents. A purposive sampling technique has been used to reach the target sample. An unstructured questionnaire has been developed to collect necessary data. The findings of this study showed that COVID-19 pandemic has led to changes in various components of the education system. Curriculum, syllabus, assessment method, teaching method, learning method, etc. have changed in the education system. The elements of education are shifting from offline to online. The use of online-based learning materials is increasing. The curriculum has been made more flexible with a more compressed and shortened syllabus to suit the students learning under such unforeseen circumstances. The education policy should be changed to adapt to the changed situation.
A machine learning approach for predicting human shortest path task performance

Finding a shortest path for a given pair of vertices in a graph drawing is one of the fundamental tasks for qualitative evaluation of graph drawings. In this paper, we present the first machine learning approach to predict human shortest path task performance, including accuracy, response time, and mental effort. To predict the shortest path task performance, we utilize correlated quality metrics and the ground truth data from the shortest path experiments. Specifically, we introduce path faithfulness metrics and show strong correlations with the shortest path task performance. Moreover, to mitigate the problem of insufficient ground truth training data, we use the transfer learning method to pre-train our deep model, exploiting the correlated quality metrics. Experimental results using the ground truth human shortest path experiment data show that our models can successfully predict the shortest path task performance. In particular, model MSP achieves an MSE (i.e., test mean square error) of 0.7243 (i.e., data range from −17.27 to 1.81) for prediction.
A Sentiment Detection Tool for Multiple Domains

The sentiment is opinion or thought about a particular product or a specific situation. Sentiment analysis is applied to extract the subject information from text. Sentiment is computed using feature level, entity level, sentence level, document level. This paper proposed a sentiment detection tool that detects the sentiment (positive or negative) of any input comment from the web in multidomain-based on sentence-level and document level (text analysis) by analyzing the Polarity score of each and individual word. This work will allow users to choose the type of content they want to read or focus on. It will help in analyzing what kind of impact that text is going to leave on the reader. This will help in detecting the Polarity of text. We designed a sentiment detection tool to obtain the Polarity of sentence and document. This work aims to divide or distribute the content according to their sentiments.
High-Reflective Surfaces Shape Measurement Technology Based on Adaptive Fringe Projection

Fringe projection profilometry (FPP) is limited by the pixel depth of the camera and cannot correctly reconstruct complex objects with highly reflective surfaces. In this paper, we proposed an improved adaptive fringe technique to avoid image saturation. This method only needs to project a uniform gray level image and a set of horizontal and vertical phase-shifting fringe patterns to generate adaptive fringes. The gray level of saturated regions can be fitted by the uniform image and the coordinates of the areas are obtained by calculating the horizontal and vertical fringes. To avoid the process of recursion, the concept of the grayscale correction function is introduced. Furthermore, a modified mapping formula is adopted and refined, which enhances the mapping effect and reconstruction accuracy. Compared with the original adaptive fringe projection (AFP), the modified method further decreases the number of projected fringe patterns. We analyzed the error between the fitted value and the standard value to evaluate the performance of the method. Experimental results demonstrated that the purposed method maintains the accuracy of reconstruction while improving time efficiency compared to other high dynamic range (HDR) measurement methods.
Composite Recommendations with Heterogeneous Graphs

Recommender systems (RS) help users to deal with the problem of information overload. These systems suggest items to users based on their preferences. Traditionally, RS recommend a single item to the user, such as a movie, a song, or a place to eat. In some real-life situations, the user’s preference for a particular item could be influenced by the entity (e.g., a brewery, a restaurant) that sells or produces the item (e.g., a beer, a hamburger). For example, when suggesting a hamburger, the recommendation might be enhanced by considering the user’s liking for the restaurant that serves it. Therefore, the user’s preference for the entity’s characteristics must also be considered when recommending an item. This paper presents a recommender system model for composite recommendations. We define a composite recommendation as the recommendation of items with a “Has-a” relationship. For example, a restaurant “has-a” hamburger or a brewery “has-a” beer. Therefore, our model can recommend a tuple $$\langle entity, item \rangle $$ instead of a single item as traditional RS. As a basis, we use metapath2vec to obtain node embeddings from a heterogeneous graph. We formed the heterogeneous graph using features and user-entity, user-item, and entity-item interactions. The node embeddings for entities with a composite relationship are aggregated during recommendation to account for the user’s preference for these entities. Our proposed model was tested with a real-life dataset composed of users, breweries, and beers. The results show that our proposed approach obtains better results than a baseline model, which does not consider the composite relationship.
Building a General Purpose Educational Augmented Reality Application: The Case of ARTutor

Augmented Reality (AR) technology is constantly evolving. Nowadays, there are AR platforms that allow educators to build AR experiences for their students. Most of these tools are not free and have specific domain orientation, allowing instructors to use them for specific domains and courses. The main aim of this study is to provide details on the approach, design and development frameworks that developers have to follow in order to build a general- purpose educational AR tool. The available technologies will be presented and an optimum approach and design will be proposed. In addition, this study presents the architecture and the adopted technologies of the general-purpose educational AR platform ARTutor (Version 3), a free tool for educators and their students. Compared to other AR platforms, ARTutor is a completely free and easy-to-use tool. With ARTutor, educators can create augmented books and add virtual content on top of printed material. The new version of ARTutor is based on state-of-the-art technologies such as Google’s ARCore and Apple’s ARKit. Evaluation results with pre-service teachers revealed that ARTutor is a valuable tool for education and it provides an easy way to implement AR experiences thus it can be easily adopted by various educational approaches.
Inference of Aggregate Hidden Markov Models With Continuous Observations

We consider a class of inference problems for large populations where each individual is modeled by the same hidden Markov model (HMM). We focus on aggregate inference problems in HMMs with discrete state space and continuous observation space. The continuous observations are aggregated in a way such that the individuals are indistinguishable from measurements. We propose an aggregate inference algorithm called continuous observation collective forward-backward algorithm. It extends the recently proposed collective forward-backward algorithm for aggregate inference in HMMs with discrete observations to the case of continuous observations.
Identification of Historic Urban Landscapes Using a Comparative Approach Based on Deep Learning Classification Techniques

Multiple stakeholders with different levels of perception are involved in decisions concerning historic urban landscape (HUL) . The decision-making process is often time-consuming, confusing due to the lack of precise data and human perception bias.In previous research, a quality assessment of building integration in a HUL was conducted using deep learning methodology combined with an expert panel evaluation. The results obtained reflected the expert panel judgement, therefore highlighted a bias in the computer prediction.The aims of the present study were to bring a novel approach to urban analysis, to provide a solution for the time and resources’ loss. A comparative approach was used where an algorithm compared two different HUL areas with minimal human intervention. The prediction model data were then assessed using a heatmap visualisation method to understand the relevant areas for computer predictions.It was concluded that highlighted zones can be considered as relevant elements of the HUL to define the areas’ identity and therefore the use of such methodology brings more accurate and “bias-free” results, thus, significantly reducing the analysis time frame. This novel approach also offers to urban planners and architects new reliable data to help with decision making while intervening in HUL.
A Framework for the Adoption of Agile within Software SMEs in Saudi Arabia

Agile is an established and globally adopted software development methodology for managing software projects, known for reducing time to market and costs, and improving customer satisfaction. However, in developing countries, such as those in the Middle East, Agile is still in the early stages of adoption compared to Europe and North America. This is problematic, specifically in the case of Saudi Arabia, which is increasingly looking at the software industry to diversify its economy to reduce the reliance on the oil industry. It is essential to understand the factors that influence the decision of Saudi software organisations to adopt Agile. Hence, this study aims to critically review the influential factors identified in the Agile adoption framework that may support or challenge the adoption of Agile in small and medium-sized enterprises (SMEs) in Saudi Arabia. To this end, we carried out a focus group study with five software practitioners working in different Saudi software SMEs. The main findings indicate the apparent influence of all the identified factors in the developed framework except those related to process, with a high level of importance attached to culture, people and knowledge factors. This study refines the framework and helps software organisations assess their readiness for adopting Agile during the development process.