Reviewerly/exHarmony huggingface dataset:
Bengali Accent Classification from Speech Using Different Machine Learning and Deep Learning Techniques

The work starts with a question "Does human vocal folds produce different wavelength when they speak in different accent of same language?" Generally, when humans hear the language, they can easily classify the accent and region from the language. But the challenge was how we give this capability to the machine. By calculating discrete Fourier transform, Mel-spaced filter-bank and log filter-bank energies, we got Mel-frequency cepstral coefficients (MFCCs) of a voice which is the numeric representation of an analog signal. And then, we used different machine learning and deep learning algorithms to find the best possible accuracy. By detecting the region of speaker from voice, we can help security agencies and e-commerce marketing. Working with human natural language is a part of Natural Language Processing (NLP) which is branch of artificial intelligence. For feature extraction, we used MFCCs, and for classification, we used linear regression, decision tree, gradient boosting, random forest and neural network. And we got max 86% accuracy on 9303 data. The data was collected from eight different regions (Dhaka, Khulna, Barisal, Rajshahi, Sylhet, Chittagong, Mymensingh and Noakhali) of Bangladesh. We follow a simple workflow for getting the ultimate result.
Learning control coil currents from heat-flux images using convolutional neural networks at Wendelstein 7-X

Abstract An important goal of Wendelstein 7-X, the most advanced operating fusion experiment of the stellarator line, is to demonstrate the ability of stellarators to perform steady-state discharges. In this respect, the monitoring and control of the heat loads on the plasma facing components, especially of the strike-lines in the ten island divertors, will be critical during next operation phase OP2. In this paper, it is shown that deep convolutional neural networks are able to learn the relationship between the heat-flux images, obtained by the analysis of thermographic data, and the applied control coil currents in standard magnetic configuration experiments. This study is carried out in view of understanding and modeling the relationship between the heat-flux distribution in the divertor strike-lines and the actuators influencing them.
A Model for Classification of Traffic Signs Using Improved Convolutional Neural Network and Image Enhancement

In an advanced driver assistance system (ADAS), recognition of traffic signs is very important for safety driving. Recently, the convolutional neural networks (CNNs) have presented promising results. In this work, we propose a robust model based on VGG network by adding batch normalization operation. Dropout is also used to reduce the overfitting of the model. Due to the imbalance of the dataset, data augmentation is performed. Then, in order to enhance images, Contrast limited adaptive histogram equalization (CLAHE) and normalization are performed. The performance of the model is evaluated on German traffic sign recognition benchmark (GTSRB) dataset using different performance metrics namely confusion matrix, precision, recall. Experiments results show that, the proposed model reaches a state-of-art accuracy of 99.33 % and surpasses the best human performance of 98.84 %. This model can be used for real world system.
Self-registration shape measurement based on fringe projection and structure from motion

As an accurate and efficient shape measurement method, fringe-projection-based three-dimensional (3D) reconstruction has been extensively studied. However, patchwise point cloud registration without extra assistance is still a challenging task. We present a flexible and robust self-registration shape measurement method based on fringe projection and structure from motion (SfM). Other than ordinary structured-light measurement devices in which the camera and the projector are rigidly connected together, the camera and the projector in our method can be moved independently. An image-capturing scheme and underlying image-matching strategy are proposed. By selectively utilizing some sparse correspondence points across the fringe images as virtual markers, the global positions of the camera and the projector corresponding to each image are calculated and optimized under the framework of SfM. Dense global 3D points all over the object surface are finally calculated via forward intersection. Experimental results on different objects demonstrate that the proposed method can obtain a self-registered 3D point cloud with comparable accuracy to the state-of-the-art techniques by using only one camera and one projector, requiring no post-registration procedures and no assistant markers.
Visual Question Answering for Monas Tourism Object using Deep Learning

Visual Question Answering (VQA) is a machine learning task, given a pair of image and natural language visual question about the image, the task is to answer the question. It is known that there is no public VQA dataset currently available in Bahasa Indonesia. This research compiles a Monas VQA dataset that uses Bahasa Indonesia in the question and Monas, a memorial monument for Indonesian, as the image specific context to resolve the problem. This research also proposes methods to solve VQA using CNN for image embedding, techniques from the NLP field for sentence embedding e.g. Bag-of-Words, fastText, BERT, and BiLSTM, lastly multimodal machine learning to let both embedded information to interact with each other. The best performing model achieves 68.39% accuracy with architecture impact analysis presented.
Anon-ISAC: Anonymity-preserving cyber threat information sharing platform based on permissioned Blockchain

In cyber threat information sharing, secure transfer and protecting privacy are very important. In this paper we solve these issues by suggesting a platform based on private permissioned Blockchain, which provides us with access control as well. The platform is called Anon-ISAC and is built on the Enhanced Privacy ID (EPID) zero-knowledge proof scheme. It makes use of permissioned Blockchain as a way to keep identity anonymous. Organizations can share their information on incidents or other artifacts among trusted parties, while they keep their identity hidden. This will save them from unwanted consequences of exposure of sensitive security information.
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In this paper, we introduce the f-deformed photon-added-then-subtracted SU(1,1) and SU(2) displaced number states by applying both nonlinear coherent states and group theoretical approaches. In other words, we find the f-deformed formalism of photon addition/subtraction to/from a quantum state of light by considering the nonlinear coherent states associated with nonlinear oscillator algebra. In addition, we establish a connection between displaced Fock states and a particular class of Gilmore–Perelomov-type of SU(1,1) and a class of SU(2) coherent states. It is shown that various types of nonclassical states can be obtained by selecting properly the controlling parameters in both linear and nonlinear regimes. Furthermore, the nonclassicality features of the quantum states of interest are evaluated by means of photon statistics, quadrature squeezing, and the Wigner–Weyl quasi-probability distribution function. Indeed, the nonclassicality of the states is numerically examined to understand the effects of deformation functions, photons added and subtracted, and photon number occupied in the Fock state on physical properties. It is deduced that the depth as well as the domain of the nonclassicality features can appropriately be controlled by adopting the suitable parameters.
Linking therapeutic (is)landscapes, experiences of digitality and the quest for wellbeing

In this paper we examine experiences associated with tourism on Turtle Island, an exclusive privately-owned island resort in Fiji. Tropical islands have long been marketed as ‘getaways’ and places where tourists seek wellbeing. Our study considers the affective experiences and recollections of visitors as well as the views of workers at such resorts. Through mobilising the therapeutic (is)landscape idea and the concept of digitality, we advocate an extension to understandings of wellbeing into ‘well-becoming’. In this study, well-becoming occurs as a dynamic process of journeying across bluespace in search of wellness. From participatory fieldwork, online surveys, the digital record and interviews, we note that guests paradoxically seek privacy yet also transmit their wellbeing experiences to the world beyond through social media. Upon departure, they maintain affective bonds with place through social media. We show that staff also lean on social media to ‘keep in touch’ when necessarily at-a-distance from families. We identify tensions between the quest for ‘escape’ to such places and the quest for wellbeing within the gaze of digital media. We conclude that despite the disciplining influence of the ‘inverted gaze’ of digital power, visitors nonetheless experience restorative effects within this island setting.
Sentiment Polarity and Bug Introduction

Researchers have shown a growing interest in the affective states (i.e., emotions and moods) of developers while performing software engineering tasks. We investigate the association between developers’ sentiment polarity—i.e., negativity and positivity—and bug introduction. To pursue our research objective, we executed a case-control study in the Mining Software Repository (MSR) context. Our exposures are developers’ negativity and positivity captured, by using sentiment analysis, from commit comments of software repositories; while our “disease” is bug introduction—i.e., if the changes of a commit introduce bugs. We found that developers’ negativity is associated to bug introduction, as well as developers’ positivity. These findings seem to foster a continuous monitoring of developers’ affective states so as to prevent the introduction of bugs or discover bugs as early as possible.
Severity Prediction of Software Vulnerabilities Using Textual Data

Nowadays, all essential activities carried out by society are dependent on software systems. These systems with time have become more and more complex. Increase in complexity leads to introduction of vulnerabilities in the software system. Vulnerabilities are being reported every year and are increasing exponentially with time. These vulnerabilities required to be patched and fixed before getting exploited. To achieve this goal, textual data available on vulnerabilities is needed to be exploited to retrieve valuable information from it. Therefore, in this paper, we aim to develop a prediction model which will take textual description of Apache Tomcat vulnerabilities as input and will predict the severity of the vulnerabilities. Text mining techniques and machine learning algorithms are used to perform this task. The huge volume of textual data available on vulnerabilities has to be prioritized so that more severe vulnerabilities can be targeted first and limited resources can be put to its best use. Hence, it is essential to use text mining techniques for feature selection to reduce the volume of the data. The model being developed uses chi-square and information gain for feature selection, and among the machine learning algorithm, bagging technique, random forest, naïve Bayes, support vector machine are used for prediction. From results, it is observed that comparatively information gain gave better results among feature selection technique. Model based on naïve Bayes algorithm along with information gain as feature selection techniques performed good with the average values of 0.91, 90.90%, and 92.30%, respectively, for AUC, sensitivity, specificity, respectively.
Reconstructing Spatial Aspects of Motion by Image-to-Path Deep Neural Networks

The choice of an appropriate representation is important when reconstructing motion from images. In this letter we propose a new type of deep neural network (DNN) that maps images to spatial paths represented by a recently introduced motion representation called arc-length dynamic movement primitive (AL-DMP). This representation separates the spatial from temporal aspects of motion and is therefore suitable for processing data that do not contain temporal information. We propose a physically meaningful loss function for training of AL-DMPs, which improves the performance of the trained DNN, and derive its gradients, which are needed to apply the backpropagation algorithm. Moreover, the proposed DNN architecture supports the processing of variable-size input images and images with cluttered background. The developed approach was applied to the reproduction of handwritten digits from single images that do not contain temporal aspects of motion. Our experiments also show that the proposed network can be applied to input images of sizes that are different from the size of training images. Finally, the proposed approach was successfully applied for reproducing real handwritten digits with a humanoid robot.
Chinese Character Captcha Sequential Selection System Based on Convolutional Neural Network

To ensure security, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is widely used in people's online lives. This paper presents a Chinese character captcha sequential selection system based on convolutional neural network (CNN). Captchas composed of English and digits can already be identified with extremely high accuracy, but Chinese character captcha recognition is still challenging. The task we need to complete is to identify Chinese characters with different colors and different fonts that are not on a straight line with rotation and affine transformation on pictures with complex backgrounds, and then perform word order restoration on the identified Chinese characters. We divide the task into several sub-processes: Chinese character detection based on Faster R-CNN, Chinese character recognition and word order recovery based on N-Gram. In the Chinese character recognition sub-process, we have made outstanding contributions. We constructed a single Chinese character data set and built a 10-layer convolutional neural network. Eventually we achieved an accuracy of 98.43%, and completed the task perfectly.
Transferable E-Cash: A Cleaner Model and the First Practical Instantiation

Transferable e-cash is the most faithful digital analog of physical cash, as it allows users to transfer coins between them in isolation, that is, without interacting with a bank or a "ledger". Appropriate protection of user privacy and, at the same time, providing means to trace fraudulent behavior (double-spending of coins) have made instantiating the concept notoriously hard. Baldimtsi et al. (PKC'15) gave a first instantiation, but, as it relies on a powerful cryptographic primitive, the scheme is not practical. We also point out a flaw in their scheme.
Building and Evaluating Federated Models for Edge Computing

Today's state-of-the-art machine learning (ML) techniques, such as deep learning (DL) networks are typically trained using cloud platforms, leveraging elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all applications due to concerns about latency, connectivity, and privacy. For example, sharing life logging photos and videos from cellphones and wearable devices can cause privacy concerns for users, and transferring the unstructured data can burden the communication network. With the increase of such applications, federated learning (FL) is proposed as a distributed ML solution for learning on edge devices, such as cellphones and wearable devices. In FL, clients collaboratively train a model on their device without sharing their data. Each client trains a local model with their data and shares the model parameters with a FL server to aggregate and build a global model. Shifting from traditional ML techniques to federated solutions requires comparing these two approaches. Moreover, users need to study the performance of FL models to decide if federation is feasible for their learning task. In this paper, we propose an automated solution to compare centrally trained DL models with federated solutions. The tool allows users to easily analyze the accuracy of federated models for their learning task and study the effect of the federated parameters. We show the features of our tool building central and federated DL models from an input model structure for recognizing images in the MNIST benchmark dataset.
Machine Learning Pipeline for Reusing Pretrained Models

Machine learning methods have proven to be effective in analyzing vast amounts of data in various formats to obtain patterns, detect trends, gain insight, and predict outcomes based on historical data. However, training models from scratch across various real-world applications is costly in terms of both time and data consumption. Model adaptation (Domain Adaptation) is a promising methodology to tackle this problem. It can reuse the knowledge embedded in an existing model to train another model. However, model adaptation is a challenging task due to dataset bias or domain shift. In addition, data access from both the original (source) domain and the destination (target) domain is often an issue in the real world, due to data privacy and cost issues (gathering additional data may cost money). Several domain adaptation algorithms and methodologies have introduced in recent years; they reuse trained models from one source domain for a different but related target domain. Many existing domain adaptation approaches aim at modifying the trained model structure or adjusting the latent space of the target domain using data from the source domain. Domain adaptation techniques can be evaluated over several criteria, namely, accuracy, knowledge transfer, training time, and budget. In this paper, we start from the notion that in many real-world scenarios, the owner of the trained model restricts access to the model structure and the source dataset. To solve this problem, we propose a methodology to efficiently select data from the target domain (minimizing consumption of target domain data) to adapt the existing model without accessing the source domain, while still achieving acceptable accuracy. Our approach is designed for supervised and semi-supervised learning and extendable to unsupervised learning.
Weighted Complete Graphs for Condensing Data

In many real-world problems (such as industrial applications, chemistry models, social network analysis, among others), their solution can be obtained by transforming the problem in terms of vertices and edges, that is to say, using graph theory. Data Science applications are characterized by processing large volumes of data, in some cases, the data size can be higher than the resources for their processing, situation that makes prohibitive to use the traditional methods. In this way, to develop solutions based on graphs for condensing data can be a good strategy for handling big datasets. In this paper we include two methods for condensing data based on graphs, the two proposals consider a weighted complete graph by acquiring an induced subgraph or a minimum spanning tree from the whole datasets. We conducted some experiments in order to validate our proposals, using 24 benchmark real-datasets for training the 1NN, C4.5, and SVM classifiers. The results prove that our methods condensed the datasets without reducing the performance of the classifier, in terms of geometric means and the Wilcoxon's test.
Data for the industrial and municipal environmental wastes hazard contaminants assessment with integration of RES2D techniques and Oasis Montaj software

Industrial, and municipal wastes are part of the main sources of environmental hazards as well as groundwater and surface water pollutions. If not well composed, treated, and safely disposed, it could permeate through the subsurface lithologies by reaching down to the underground water aquifers, particularly in zones of unprotected aquifer units. Pollutants, most especially the landfills leachates that encompassed organic contaminants, ammonia, nitrates, total nitrogen, suspended solids, heavy metals and soluble inorganic salts, i.e., soluble nitrogen, sulphur compound, sulphate and chlorides, could posed undesirable environmental impacts due to inappropriate disposals that may give rise to gaseous fumes and leachate formations. An electrical resistivity geophysical technique utilizing the RES2D no-invasive, cost-effective and rapid method of data collection was integrated with the 3D Oasis Montaj software to approximate the volume of the generated rectangular prism model of the contaminants delineated from mixtures of the industrial, and municipal wastes plumes to be 312,000 m 3.
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild

Deep learning-based scene text detection can achieve preferable performance, powered with sufficient labeled training data. However, manual labeling is time consuming and laborious. At the extreme, the corresponding annotated data are unavailable. Exploiting synthetic data is a very promising solution except for domain distribution mismatches between synthetic datasets and real datasets. To address the severe domain distribution mismatch, we propose a synthetic-to-real domain adaptation method for scene text detection, which transfers knowledge from synthetic data (source domain) to real data (target domain). In this paper, a text self-training (TST) method and adversarial text instance alignment (ATA) for domain adaptive scene text detection are introduced. ATA helps the network learn domain-invariant features by training a domain classifier in an adversarial manner. TST diminishes the adverse effects of false positives (FPs) and false negatives (FNs) from inaccurate pseudo-labels. Two components have positive effects on improving the performance of scene text detectors when adapting from synthetic-to-real scenes. We evaluate the proposed method by transferring from SynthText, VISD to ICDAR2015, ICDAR2013. The results demonstrate the effectiveness of the proposed method with up to 10% improvement, which has important exploration significance for domain adaptive scene text detection. Code is available at https://github.com/weijiawu/SyntoReal_STD.
Collision Avoidance for Unmanned Aerial Vehicles based on Safety Radius of the Formation Geometry

In this paper, we propose a geometry-based collision avoidance method for unmanned aerial vehicles(UAVs) in formation flight. The leader-follower method was used for formation flight, and the geometry-based Closest Point of Approach (CPA) method was used for the collision avoidance. The safety radius was considered by deriving a circumscribed circle that includes the whole of the safety radius of the UAVs in the formation. The collision avoidance command was generated after calculating the closest point of approach from the intruder based on the center of the circle. The leader avoids collision as considering followers and also followers avoid collision by just following the leader. The proposed method was verified performance of formation flight and collision avoidance through numerical simulation.