Reviewerly/exHarmony huggingface dataset:
Alphanumeric Character Recognition on Tiny Dataset

Alphanumeric character recognition is the ability of a computer to understand digits and alphabets image as an input. This topic has struggled to reach a wide variety of people since we need quite a lot of labeled training data. A small training sample achieves sub-standard accuracy in training data that is model is not able to learn accurately. Because of this handwritten (alphanumeric character), recognition is not widely been used. We resolve this issue by creating new labeled training data. Generated data is generated from existing samples, with a realistic data expansion method that is an actual variant of human handwriting. This is done by changing or adding more value to the parameters. Our model had just 10–11% training samples per character compared to other character-recognition methods. We achieved close to existing state-of-the-art character recognition results of all four datasets. Images reconstruction strategy was also used. Our system can be used when training data is less, and the user wants to achieve reasonable accuracy.
A Study on Determining Household Poverty Status

Poverty is the normal challenge faced by the worldwide community. The human society has never ceased to fight against poverty. This research study focuses on determining significant attributes that can be utilized to distinguish poor and non-poor household units. At least one selected community in the Philippines is utilized to validate and test the model for Classification using Support Vector Machine (SVM) algorithm. To check the accuracy and evaluate the model 80% of the total poor and non-poor households are used as a training set and the remaining 20% as a testing set to minimize the impact of disparities and determine whether the model's classifications are correct. Accuracy, Precision, Recall and F1-Score are likewise done to interpret and gauge the performance of the SVM algorithm for the binary classification model in which the outcome indicates 88.64% precise.
Imagination by Design

With the notion of the zero-level digital divide, questions of technological structure have become more pressing for matters of agency. In this, design plays a major role, as it is assumed to be of structural significance, predetermining the architecture of a given technology and, thereby, what one can actually do with it. When also considering the daily importance of digital technologies, the question arises, regarding how these designed technologies impact the way we come to realize reality and, ultimately, the self. How self-determinedly does one utilize digital technologies? As digital products, such as apps lack material elements, which could be adjusted to suit one’s personal needs, they are very much dependent on design being available to the senses. Building upon this insight, designers aim at creating user experiences. As experience is a highly personal process, design is dependent on the subject’s compliance to cognitively realize such experience. This paper will theoretically explore how design is individually constituted and how education can provide for possibilities to self-determinedly act within the designed environments of digital technologies. Starting with the notion of ‹imagineering›, it will be suggested to conceptualize design as a mental image in order to better understand how design can be cognitively adjusted once it is constituted.
A trustworthy framework for resource-aware embedded programming

Systems with non-functional requirements, such as Energy, Time and Security (ETS), are of increasing importance due to the proliferation of embedded devices with limited resources such as drones, wireless sensors, and tablet computers. Currently, however, there are little to no programmer supported methodologies or frameworks to allow them to reason about ETS properties in their source code. Drive is one such existing framework supporting the developer by lifting non-functional properties to the source-level through the Contract Specification Language (CSL), allowing non-functional properties to be first-class citizens, and supporting programmer-written code-level contracts to guarantee the non-functional specifications of the program are met. In this paper, we extend the Drive system by providing rigorous implementations of the underlying proof-engine, modeling the specification of the annotations and assertions from CSL for a representative subset of C, called Imp. We define both an improved abstract interpretation that automatically derives proofs of assertions, and define inference algorithms for the derivation of both abstract interpretations and the context over which the interpretation is indexed. We use the dependently-typed programming language, Idris, to give a formal definition, and implementation, of our abstract interpretation. Finally, we show our well-formed abstract interpretation over some representative exemplars demonstrating provable assertions of ETS.
Software to Determine the Readability of Written Documents by Implementing a Variation of the Gunning Fog Index Using the Google Linguistic Corpus

In English linguistics the Gunning Fog Index is used to determine the readability of texts. This methodology isn’t as effective in the Spanish language because the complexity of words isn’t determined by the number of syllables, unlike what happens in English. Therefore, a software was developed that allows us to estimate the readability of an academic text written in Spanish in a quantitative way. This software allows to compare the traditional methodology of the Gunning fog index and a modification to it, using the corpus linguistics for the Spanish language, based on thousands of texts digitized by Google, where the frequency of use of certain words is related. Texts produced by students from first to last semester were evaluated. Each text was subjected to the Gunning fog index assessment methodology and the corpus methodology, changing the percentage of complex words to the percentage of unknown words. In the evaluation of first semester texts it was found that the average fog index was 29.25, and an average of 37.9 complex words, for these same texts was found a modified fog index of 18.62 and 5.1 unknown words. On the other hand, for the evaluation of the texts produced in the last semester, the average fog index was 27.55 and an average of 51.4 complex words, with the modified fog index was an average of 15.08 and 7.1 unknown words. With this study, aspects related to the best use of punctuation marks and the increase of vocabulary related to the profession can be identified in a quantitative way.
Assessing the usability of the Saudi Digital Library from the perspective of Saudi scholarship students

This study aims to assess the usability of the Saudi Digital Library from the perspective of Saudi scholarship students. To this end, it uses a global tool for measuring the usability of computer application interfaces, namely the Software Usability Measurement Inventory (SUMI). It includes five criteria for measuring usability from the user's perspective: effect, efficiency, learnability, helpfulness and control. In general, the participating Saudi scholarship students were neutral about the level of usability in the Saudi Digital Library interface, there was a good level of satisfaction among them, also, there a positive relationship between the usability criteria of the interface of the Saudi Digital Library and user satisfaction. Gender played a role in the, statistical differences in the responses of the participants about the usability of the Saudi Digital Library interface based on in these criteria; these differences were in favor of the male sample. There were also statistical differences in the responses of the participants due to differences in Internet skills, these differences were in favor of participants who had Internet skills.
Heterogeneous Decomposition of Predictive Modeling Approach on Crime Dataset Using Machine Learning

Substantial increase in criminal activities has been directly affecting socio-economical development and quality of life. Predictive crime data analysis is one of the challenging tasks in the field of data analysis, where machine learning algorithm plays a significant role. In this paper, machine learning techniques are applied to a crime dataset for predicting features that affect the pattern of crime occurrence. Decomposition method has used to split the large dataset into sub-dataset in order to increase the accuracy rate. In this work, we used different supervised classification machine learning techniques to predict crime incident by resolution, depending on its feature attributes. Different classification techniques are used like Naïve Bayes, Generalized linear model, Binary Logistic Regression, Decision Tree, Random Forest, Gradient Boost. Results of different algorithms have been compared and effective approach has been discussed. The average prediction accuracy of these machine learning approaches is approximately 86%.
Online Modeling Method of Fault Diagnosis Based on CNN and OS-ELM

Because of the interrelation and restriction among the various functional units within the complex mechanical equipment, the characteristic of the equipment running state and its signal features is time-varying. The single static model established by off-line data is difficult to reflect the real-time running state of equipment in a complete and effective way. Therefore, a fault diagnosis method for convolutional neural network (CNN) and online sequential-extreme learning machine (OS-ELM) is proposed. In this method, the S-transform of the vibration signal is transformed and the S-transform spectrum is constructed firstly. Then, the characteristics of CNN adaptive extraction of the S change spectrum are used to establish the OS-ELM fault diagnosis model. OS-ELM is used to update the fault diagnosis model in real time to improve the dynamic adaptability and operational efficiency of the fault diagnosis model by gradually adding new samples. It reduces the retraining time of the model. The simulation results of bearing data sets show that the method can identify fault types quickly and effectively.
Detecting Malicious Twitter Bots Using Machine Learning

Cybercrimes and phishing scams have increased multi-folds over the past few years. Now a days, hackers are coming up with new techniques to hack accounts and gain sensitive information about people and organisations. Social networking site like Twitter is one such tool. And due to its large audience hackers use such sites to reach large number of people. They use such sites to circulate malicious URLs, phishing mails etc. which serve as the entry point into the target system. And with the introduction of Twitter Bots, this work got even easier. Twitter bots can send tweets without any human intervention after a fixed regular interval of time. Also their frequency of tweets is much more than humans and therefore they are frequently used by hackers to spread malicious URLs. And due to large number of active members, these malicious URLs are reaching out to more people, therefore increasing the phishing scams and frauds. So this paper proposes a model which will use different algorithms of machine learning, first to detect twitter bots and then find out which of them is posting malicious URLs. In the proposed model, some features have been suggested which distinguishes a twitter bot account from a benign account. Based on those statistical features, model will be trained. The model will help us to filter out the malicious bots which are harmful for legitimate users.
Exploring Blockchain-Secured Data Validation in Smart Meter Readings

Current resource consumption monitoring systems such as utility meters allow for easy manipulation to give incorrect readings and generate corrupt bills leading to huge financial losses for utility companies. In order to limit this practice, the present work proposes the use of smart meters in tandem with an Ethereum-based blockchain to secure utility (consumption) data. Electricity generation and smart metering is taken as case-study and the proposed design is benchmarked in terms of time complexity, consensus algorithms, and security to ascertain optimal blockchain parameters for practical deployments. Given the relatively nascent and un-tested existing blockchain communication protocols, a custom TCP-based protocol is also developed to address additional re-validation requirements of the metering data exchanged between the blockchain nodes. The quantitative and qualitative analysis of the proposed approach highlights a significant reduction in any human interference and errors that may lead to misappropriations in consumer metering and adds a greater degree of robustness to the billing process for the benefit of utility companies.
ANFIS based authentication performance evaluation for enhancing security in Internet of Things

In this paper, we introduce a robust authentication mechanism for identification in order to secure the wireless channel in IoT networks. Internet of Things is viewed as the most scaled down segment of nano sensor systems that have a significant potential to be embraced continuously for its large array of applications. However, when it comes to nanotechnology, smart identification technique with a secured communication channel is one of the main requirements. Operation with light computational complexity like XOR and Rotation have been used to enhance the security of the network channel. The proposed authentication mechanism has three levels of authentication. Moreover, ANFIS based authentication performance evaluation model has been developed and analyzed based on successful authentication rate and simulated with Fuzzy logic. Simulation has been conducted using MATLAB. It is being concluded after simulation that successful authentication should be 70% for any two levels and no less than 30% in any level. Finally, the proposed mechanism enhances the security in IoT networks which will indicate our mechanism as an effective one.
Geometrical Study of the Barrel as an Element of the Wine-Making Productive Process. Parameterization and Modelling

The barrel in the winemaking process is more than a recipient, it also intervenes in its maturation and fermentation. Its geometry determines the exchange of tannins and micro oxygenation, but so far, there is no research that studies the influence of geometry with the stored volume. Geometrically, the free surface according to the barrel filling level determines the alcoholic and malolactic fermentation, which influences the oxygenation of the must. The engineering of the productive process should study the optimal form of the barrel, as a resistant element and connecting it with the inner processes. The geometry influences the productive processes, the cost of the barrel and its storage and consequently, the added value of the wine. The methodology employed in this research is the following: 3D modelling of commercial barrels, measuring inner volume and surfaces with 3D-CAD, comparing results with traditional formulas, parameterizing the geometry and simulating the model with the parametric CAD tools. We conclude that the 3D model enables precise measures and delimits mistakes using the traditionally used empirical formulas. Also, the parameterization of the models allows the functional and economic optimization of the barrel, connected with the ratio of the inner exchange area and the storage volume.
The Accuracy Estimation of the Interval-Positional Characteristic in Residue Number System

Residue Number System (RNS) provides the ability to increase performance for some types of algorithms through parallel data presentation. When implementing high-performance computing algorithms on this basis, an important issue is the effective implementation of non-modular operations, including determining the sign of a number, comparing numbers, determining overflow, dividing numbers, etc. One of the mechanisms for the effective implementation of non-modular operations is the use of positional characteristics, among which one can distinguish an approximate method that has low computational complexity in comparison with other approaches. However, the use of the approximate method can lead to errors in the calculation process in order to increase productivity, which affects the correctness of the results of the calculation of a non-modular operation. For eliminate this drawback, an interval-positional characteristic is used. The article examines the question of the necessary accuracy of the calculation of the interval-positional characteristic. A sufficient condition is proved that ensures the correctness of the calculation of the interval-positional characteristic.
Towards Mature Federated Cyber Incident Management and Information Sharing Capabilities in NATO and NATO Nations

For the past decade, cyber security teams around the world have recognized the growing importance of sharing cyber security information. Data regarding recently discovered vulnerabilities, cyber threats, and cyber incident information has been recognized as vital for cyber security teams to stay resilient in the face of the ever-changing threat landscape. This paper introduces federated cyber incident management and cyber information sharing requirements for NATO and Nations and highlights the Cyber Information and Incident Coordination System (CIICS), developed in the Multinational Cyber Defence Capability Development (MN CD2) Smart Defence program, designed specifically to address such requirements.
Triplet-Center Loss Based Deep Embedding Learning Method for Speaker Verification

In this work, we introduce an effective loss function, i.e., triplet-center loss, to improve the performance of deep embedding learning methods for speaker verification (SV). The triplet-center loss is combination of triplet loss and center loss so that it shares superiorities of these two loss functions. Comparing with the widely used softmax loss, the main advantage of triplet-center loss is that it learns a center for each class, and it requires distances between samples and centers from the same class are closer than those from different classes. To evaluate the performance of triplet-center loss, we conduct extensive experiments on noisy and unconstrained dataset, i.e., Voxceleb. The results show that triplet-center loss significantly improves the performance of SV. Specifically, it reduces equal error rate (EER) from softmax loss by 11.6%, 10.4% in cosine scoring and PLDA backend, respectively.
Avoiding Chatter in an Online Co-Learning Algorithm Predicting Human Intention

Chatter can happen when an online learning algorithm is used by a robot to predict human intention while interacting with a human subject. When chatter happens, the learning algorithm continually changes its prediction, without reaching a constant prediction of human intention. Using the Rescorla-Wagner model for human learning, we analyze an expert based online learning algorithm and identify an invariant set in the state and parameter space where chatter will occur. Based on the chatter analysis, we also propose an improved expert based learning algorithm where the invariant set does not exist so that chatter can be avoided.
Step-Based Tutoring Software for Complex Procedures in Circuit Analysis

Step-based tutoring systems, in which each step of a student's work is accepted by a computer using special interfaces and provided immediate feedback, are known to be more effective in promoting learning than traditional and more common answer-based tutoring systems, in which only the final (usually numerical) answer is evaluated. Prior work showed that this approach can be highly effective in the domain of linear circuit analysis in teaching topics involving relatively simple solution procedures. Here, we demonstrate a novel application of this approach to more cognitively complex, multi-step procedures used to analyze linear circuits using the superposition and source transformation methods. Both methods require that students interactively edit a circuit diagram repeatedly, interspersed with the writing of relevant equations. Scores on post-tests and student opinions are compared using a blind classroom-based experiment where students are randomly assigned to use either the new system or a commercially published answer-based tutoring system on these topics. Post-test scores are not statistically significantly different but students prefer the step-based system by a margin of 84 to 11% for superposition and 68 to 23% for source transformations.
End-to-End Text-To-Speech synthesis for under resourced South African languages

Text-To-Speech (TTS) systems have been widely adopted around the world for various applications, such as reading to the blind and producing speech for dialog systems. There is however, a lack of TTS systems for South African languages due to limited resources. End-to-end TTS methods using Deep Learning have recently been proposed. These methods eliminate the need for time aligned TTS corpora, making them attractive for under resourced languages. In this paper we present the first reported use of an end-to-end approach for implementing TTS systems for isiXhosa and Sesotho. We train the model using the Lwazi II Sotho corpus as well as the Lwazi III Xhosa corpus. The performance of the system is compared to the Qfrency TTS system using a mean opinion score and a word error rate. The results show that the end-to-end system implemented is able to outperform the Qfrency TTS system by an MOS of 0.68 and 0.83 for intelligibility and naturalness respectively. This is one of the first reported implementations of an end-to-end TTS for any South African language.
A Novel Method for Automatic Detection of Basic Shapes on Whiteboard Images using Faster RCNN

In meetings, whiteboards are a useful tool for speakers to illustrate information, show diagrams and charts. After meetings, users always need to store the information that has been written, drawn on the board in the form of appropriate digitization as PowerPoint format. In this study, we first used pre-processing techniques to detect the frame of whiteboard from the whiteboard image and perform defatting. Next, we built the Faster RCNN model to detect three basic types of shapes, including circles, triangles and rectangles. Finally we use Line Segment Detector to detect the line segments. The research results show that our method with Faster RCNN model accurately detected the basic shapes on the whiteboard image with the Faster RCNN model, resulting in mAP@0.5 reaching 92.35% and the rate of detection of straight line with recall = 93.8%.
Dendrochronology of U.S. immigration

Abstract Immigrants are central to the identity of the United States, the population of which has grown in number and diversity as a function of new arrivals from around the globe. This article describes a visualization project that uses the visual metaphor of tree rings to explore the contribution of immigrants to the country’s population. Immigrants and native-born persons are represented and differentiated as cells in trees, with layered annual rings capturing patterns of population growth. These rings register, in their shape and color, certain environmental conditions. In order to mimic the natural process by which growth rings are formed, we devised a computational system that simulates the growth of trees as if cells were data-units. Dendrochronology involves dating certain events by analyzing patterns of growth in trees. Analogously, in our visualizations the rings can be counted and dated, showing the chronological evolution of the population. The dendrochronology theme is a poetic take on the data, yet it is also a functional and conceptual space that is used to construct language and rationales on that data. The tree-growth process not only inspires the appearance of the visualizations but also informs the rules of the computational system that creates them.