Reviewerly/exHarmony huggingface dataset:
Cryptocurrencies in the Digital Era: The Role of Technological Trust and Its International Effects

Due to the lack of trust as a result of the 2008 global financial crisis, the leading cryptocurrency, Bitcoin, is increasingly thought of as an alternative to the prevailing financial architecture by presenting a technologically more trustworthy alternative. Because of the effect it had due to its number of users, volume, and market size, Bitcoin attracted the attention of the whole world. By using blockchain technology and mining, Bitcoin attempts to replace the services based on trust and the mediation role offered by banks in the traditional finance system. With its characteristics such as low cost, fast transaction times, and low risks, Bitcoin first emerged in 2008 and since then, due to these characteristics, has triggered an important change and transformation. The fact that Bitcoin is used for speculative investment motives, and that the energy used for its mining is made up of conventional energy sources brought a debate about the sustainability and environmental effects of this system. This article evaluates the evolution of money in the era of digital transformation and the repositioning of cryptocurrencies with a focus on Bitcoin. The study analyzes the global effects of blockchain technology and cryptocurrencies, and the risks, opportunities and environmental effects of mining.
Mobile-Based Media as the Solution in Teaching and Learning Listening Skill

Abstract Learning listening skill is more challenging for English as a foreign language (EFL) students. Based on an investigation at several high schools in Padang, the existence of listening skill in English subject is almost neglected in teaching and learning process because of many reasons such as the limited media and less opportunity to practice listening. Teachers are difficult to prepare appropriate media to teach listening while students are difficult to find out the appropriate listening materials to practice. Innovation in using media is needed in this era because people use technology such as the computer, smartphone, internet in life. This paper uses library research method that aims to describe such as solution by utilizing mobile phone in teaching and learning listening skill considering almost all students already had their own Smartphone. Based on the study, there are several benefits in using technology especially mobile-based learning in teaching and learning process; increase teachers creativity in designing teaching materials and media, increases students’ motivation in learning, mobility, accessibility, and excellent potential for providing students with the learning materials whenever and wherever. In summary, using mobile-based learning can be a solution to solve several problems in teaching and learning listening.
Smart Cabin: A Semantic-Based Framework for Indoor Comfort Customization Inside a Cruise Cabin

This paper introduces Smart Cabin, a semantic-based framework for indoor comfort metrics customization inside a cruise cabin. Smart Cabin merges Ambient Intelligence, Ambient Assisted Living and Context Awareness perspectives to provide customized comfort experience to the cruise’s passengers. Considering that passengers may be afflicted by some impairments, Smart Cabin aims at mitigating discomfort situations by providing tailored comfort settings. The framework leverages ontological representations of passengers’ health conditions, activities, cabin’s environment, and available devices to provide passengers with indoor temperature, humidity rate, CO2 concentration, and luminance suitable for their health conditions and to the activities they want to perform inside the cruise cabin. Passengers’ interactions with Smart Cabin are performed with a simple smartphone application, while the ontologies composing the knowledge base are reasoned and hosted on a semantic repository. Two use cases depict the framework’s functioning in two typical scenarios: saving energy when the passenger leaves the cabin while reestablishing customized comfort when she/he returns, and adapting indoor comfort metrics when two or more passengers decide to perform different activities inside the same cabin.
Towards a Reconciliation Between Reasoning and Learning - A Position Paper

The paper first examines the contours of artificial intelligence (AI) at its beginnings, more than sixty years ago, and points out the important place that machine learning already had at that time. The ambition of AI of making machines capable of performing any information processing task that the human mind can do, means that AI should cover the two modes of human thinking: the instinctive (reactive) one and the deliberative one. This also corresponds to the difference between mastering a skill without being able to articulate it and holding some pieces of knowledge that one can use to explain and teach. In case a function-based representation applies to a considered AI problem, the respective merits of learning a universal approximation of the function vs. a rule-based representation are discussed, with a view to better draw the contours of AI. Moreover, the paper reviews the relative positions of knowledge and data in reasoning and learning, and advocates the need for bridging the two tasks. The paper is also a plea for a unified view of the various facets of AI as a science.
Stock Trend Extraction using Rule-based and Syntactic Feature-based Relationships between Named Entities

Many research topics still debate to predict the trends of a stock in the financial markets. Trend extraction is an important part of the information retrieved from the financial data sources, such as news articles or web pages. For trend extraction on text document, named entities are identified and relations between them are extracted. These trends are extracted from finding relationships between named entities related words for stock data. The relationships of entities in stock news articles have unstructured, time dependency, different word range and length without syntactic structure. Many previous researchers didn't propose the trend extraction based on named entities and their relationships. This paper proposes rule-based and syntactic feature-based relation extraction method between named entities for the trend extraction. This proposed system extracts trends by finding the relationships between named entities for stock data. The experimental results extract trends from news using relationships within stock related named entities.
Increasing Accuracy of Medical CNN Applying Optimization Algorithms: An Image Classification Case

Convolutional Neural Networks (CNN) for medical image classification involves particular features: big images, expensive training, complex architecture with several layers and hyperparameters, etc. Thus, increasing the accuracy or adjusting medical CNN is a challenging task that requires many resources, much time, and specialized knowledge. In this work, we proposed and tested an efficient approach to increase accuracy of a biomedical CNN using optimization algorithms. Our approach starts with a known deep network architecture and tunes it, together with its hyperparameters, to generate a final adjusted one. We have reached improvements in the quality of the results of about 40% when starting from a simple architecture and 12% from a manually adjusted architecture, with only 40 tries in a biomedical image classification case.
Novelty Detection for Multi-Label Stream Classification

In Multi-Label Stream Classification (MLSC) examples arriving in a stream can be simultaneously classified into multiple classes. This is a very challenging task, especially considering that new classes can emerge during the stream (Concept Evolution), and known classes can change over time (Concept Drift). In real situations, these characteristics come together with a scenario with Infinitely Delayed Labels, where we can never access the true class labels of the examples to update classifiers. In order to overcome these issues, this paper proposes a new method called MultI-label learNing Algorithm for Data Streams with Binary Relevance transformation (MINAS-BR). Our proposal uses a new Novelty Detection (ND) procedure to detect concept evolution and concept drift, being updated in an unsupervised fashion. We also propose a new methodology to evaluate MLSC methods in scenarios with Infinitely Delayed Labels. Experiments over synthetic data sets attested the potential of MINAS-BR, which was able to adapt to different concept drift and concept evolution scenarios, obtaining superior or competitive performances in comparison to literature baselines.
Evaluation of Question Classification

The goal of this paper is to study question classification for the Arabic language using machine learning approaches. Different experiments were conducted using two types of weighting schemes and three classifiers; Multinomial Naïve Bayes, Decision Trees, and Support Vector Machine. The dataset used in the experiments is an updated version of CLEF. The best results were obtained when the dataset was preprocessed by removing punctuation, diacritics, stop-words, and performing normalization and stemming and then using the TF-IDF weighting scheme; with SVM being the best classifier among the three with an F1-score of 81%.
Prototyping Usability and User Experience

In the last 15 years, since the Agile Manifesto emerged, many researchers and professionals have attempted to associate agile methodologies with Usability and User Experience (UX). This association happens to create a model that allows for delivering valuable software. However, most of the approaches identified in the literature that combine these concepts demand a lot of knowledge, cost, time, and, resources. Therefore, it is necessary to develop a more accessible technique and help in the cost and the deadline of the project. For this reason, we created a technique that integrates the most popular agile technique, prototyping, with two of the most important software quality criteria, Usability and UX. As a result, the technique called "User Experience and Usability Guideline for Agile Project" (UXUG-AP) has guidelines focusing on Usability and UX characteristics which support the software design. The technique was designed to be used by agile teams, independently of the amount of resources, project size, and the agile methodology adopted.
Semblance: An empirical similarity kernel on probability spaces

Semblance uses empirical data distributions to improve the sensitivity and robustness in downstream machine learning algorithms.
Mixture of Deep Neural Networks for Instancewise Feature Selection

Learning relevant features is important for interpreting data in a machine learning model. Comparing with selecting a relevant feature subset for the entire data, instancewise feature selection is more flexible for model interpretation. However current instancewise feature selection approaches are complex and suffer from high computational cost. We consider instancewise feature selection under supervised learning framework. We design a compact and interpretable neural network to approach the problem. To reduce the computational cost and gain better interpretability, we group relevant features and construct a mixture of neural networks. Using softmax as activation function for sub-model selection, the model membership can be learned accurately through gradient descent. To the best of our knowledge, our model is the first interpretable deep neural network model for instancewise feature selection using end-to-end training.
The Relationship Between Cognitive Complexity and the Probability of Defects

Researchers have identified several quality metrics to predict defects, relying on different information however, these approaches lack metrics to estimate the effort of program understandability of system artifacts. In this research, novel metrics to compute the cognitive complexity of a slice are introduced. These metrics help identify code that is more likely to have defects due to being challenging to comprehension. The metrics include such measures as the total number of slices in a file, the size, the average number of identifiers, and the average spatial distance of a slice. Empirical investigation into how cognitive complexity correlates with defects in the version histories of 3 open-source systems is performed. The results show that the increase of cognitive complexity significantly increases the number of defects in 93% of the cases.
Improved HMM-Based Mixed-Language (Telugu–Hindi) Polyglot Speech Synthesis

Speech synthesis research has recently focused on developing polyglot speech synthesizers for handling mixed-language sentences. Polyglot speech synthesis systems are particularly essential in multilingual countries like India. This paper aims at enhancing the quality of hidden Markov model (HMM)-based polyglot synthesizer for the two popular Indian languages (Telugu–Hindi) by incorporating an advanced excitation modeling approach. Experimental results show that the voice quality of our mixed-language synthesizer is significantly better than the baseline systems.
Deep learning‐based research on the influence of training data size for breast cancer pathology detection

In pathological diagnosis of breast cancer, there are problems such as shortage of pathologists, difficulties in sample labeling, and huge workload of manual diagnosis. Therefore, deep learning-based computer-assisted pathology analysis systems have been developed to diagnose breast cancer and have achieved impressive results. However, it is difficult to obtain a large number of training sets due to the scarcity of pathological images and the huge labeling costs. Therefore, the size of the training set should be planned before building the pathology computer-assisted breast cancer analysis system. Here, the authors present a study to determine the optimal size of the training data set needed to achieve high classification accuracy when developing a pathology computer-assisted breast cancer analysis system. The authors trained two kind of CNNs using six different sizes of training data set and then tested the resulting system with a total of 10,000 images. All images were acquired from the Camelyon17 challenge. Here, the authors propose a scheme for determining the size of the training set and the size of the model in developing the pathology computer-assisted breast cancer analysis systems, which can be easily applied to develop systems for other different pathological images.
Improved method for correcting sample Mahalanobis distance without estimating population eigenvalues or eigenvectors of covariance matrix

Abstract The recognition performance of the sample Mahalanobis distance (SMD) deteriorates as the number of learning samples decreases. Therefore, it is important to correct the SMD for a population Mahalanobis distance (PMD) such that it becomes equivalent to the case of infinite learning samples. In order to reduce the computation time and cost for this main purpose, this paper presents a correction method that does not require the estimation of the population eigenvalues or eigenvectors of the covariance matrix. In short, this method only requires the sample eigenvalues of the covariance matrix, number of learning samples, and dimensionality to correct the SMD for the PMD. This method involves the summation of the SMD’s principal components (each of which is divided by its expectation obtained using the delta method), Lawley’s bias estimation, and the variances of the sample eigenvectors. A numerical experiment demonstrates that this method works well for various cases of learning sample number, dimensionality, population eigenvalues sequence, and non-centrality. The application of this method also shows improved performance of estimating a Gaussian mixture model using the expectation–maximization algorithm.
Approach to Managing Data From Diverse Sources

The goal of the article is to reveal the essence of the approach to managing data from various types of sources. The proposed approach as a set of methods whose operations are aimed at determining, creating, storing, supporting data, as well as ensuring access to data and processes of manipulating them in one or more sources for effective joint use of data, unlike to the known ones, is based on the concept of the dataspace taking into account the role and place of databases, built based on the schema with the universal basis of relations, in it. This approach provides an end-to-end solution of processing and understanding data expands existing capabilities for consistent processing and data management from loosely coupled diverse data sources in changing functioning conditions.
Modeling of process of regulating production, processing and marketing of milk and milk products

Abstract The article is devoted to the investigation of problems of regulating processes in dairy industry. One of such problems is the absence of effective informative technological technology of simulating these processes. To achieve the goal set, it has been supposed to apply the simulation based on the theory of Petri nets. Methodological base of the investigation includes the formal theory of Petri nets as well as the theory of its practical application. The work has demonstrated main stages of evolution of Petri nets from simple to innodiversification ones, the latter being suggested for simulating technological processes and systems of organizations of dairy industry. Specific feature of innodiversification Peri nets is the conjunction of combinations of all known kinds of Petri nets and the application of their advantages to solve particular tasks. It has been found that the instruments of innodiversification Petri nets may be effectively applied to simulate organizations of dairy industry of any level.
Using Software Metrics for Predicting Vulnerable Code-Components: A Study on Java and Python Open Source Projects

Software vulnerabilities often remain hidden until an attacker exploits the weak/insecure code. Therefore, testing the software from a vulnerability discovery perspective becomes challenging for developers if they do not inspect their code thoroughly (which is time-consuming). We propose that vulnerability prediction using certain software metrics can support the testing process by identifying vulnerable code-components (e.g., functions, classes, etc.). Once a code-component is predicted as vulnerable, the developers can focus their testing efforts on it, thereby avoiding the time/effort required for testing the entire application. The current paper presents a study that compares how software metrics perform as vulnerability predictors for software projects developed in two different languages (Java vs Python). The goal of this research is to analyze the vulnerability prediction performance of software metrics for different programming languages. We designed and conducted experiments on security vulnerabilities reported for three Java projects (Apache Tomcat 6, Tomcat 7, Apache CXF) and two Python projects (Django and Keystone). In this paper, we focus on a specific type of code component: Functions. We apply Machine Learning models for predicting vulnerable functions. Overall results show that software metrics-based vulnerability prediction is more useful for Java projects than Python projects (i.e., software metrics when used as features were able to predict Java vulnerable functions with a higher recall and precision compared to Python vulnerable functions prediction).
Promoting Smart Water Systems in Developing Countries Through Innovation Partnerships: Evidence from VIA Water-Supported Projects in Africa

Over the past decades, the potential of Information and Communication Technologies (ICTs) to improve water management has been demonstrated. However, the development and/or adoption of ICT-focused water innovations in developing countries does not seem to occur at the expected pace, which calls for suitable innovation approaches. This chapter investigates how these innovations can be fostered through partnerships. The explorative analysis of 24 ICT-focused water innovation partnerships (ICT-WIPs) implemented in Africa leads to two important findings. First, it appears that these partnerships enable effective exchange of complementary tangible and intangible resources and co-creation of ICT-focused water solutions in a cost-effective and timely manner but also pose collaboration challenges due to the heterogeneity of innovation partners. Second, the analysis demonstrates the importance of concurrent use of relational (trust-based) and structural (legally binding control-based) partnership governance mechanisms to mitigate these challenges, notably by reducing opportunistic behaviours and increasing clarity of partners’ commitments and rights. We conclude that well-designed and -implemented ICT-WIPs can contribute to enhancing the capabilities of developing countries to implement the smart water systems agenda.
Towards Effective Assessment for Social Engineering Attacks

Social engineering attacks have drawn more and more attention from both academia and industry, due to the serious threats they pose to information security via exploitation of human vulnerabilities. Unlike technology-based attacks, which have been investigated for decades, there is no efficient security requirements analysis approach for dealing with social engineering attacks. One major obstacle to this problem is the uncertainty of human behavior, making it difficult to effectively assess social engineering attacks. In this paper, we investigate the nature of social engineering attacks and identify their essential factors. Based on such findings, we formulate the problem of social engineering attack assessment, which can be quantitatively calculated using probabilistic model checking. Finally, we present a research agenda that details critical research directions and discusses corresponding challenges.
Concatenated Feature Pyramid Network for Instance Segmentation

Low level features like edges and textures play an important role in accurately localizing instances in neural networks. In this paper, we propose an architecture which improves feature pyramid networks commonly used in instance segmentation networks by incorporating low level features in all layers of the pyramid in an optimal and efficient way. Specifically, we introduce a new layer which learns new correlations from feature maps of multiple feature pyramid levels holistically and enhances the semantic information of the feature pyramid to improve accuracy. Our architecture is simple to implement in instance segmentation or object detection frameworks to boost accuracy. Using this method in Mask RCNN, our model achieves consistent improvement in precision on COCO Dataset with the computational overhead compared to the original feature pyramid network.