Reviewerly/exHarmony huggingface dataset:
Matching ostraca fragments using a siamese neural network

As part of sociological studies, artifacts such as pottery ostraca from Upper Egypt are studied by egyptologists. These pottery pieces are covered with textual inscriptions many of which concern accounting and other economic and administrative matters. Thus, these writings contain pertinent information for understanding ancient Egyptian civilizations. The ostraca fragments retrieved from excavations are available in large quantities, but most of them are not yet analyzed, sorted, and reassembled by the egyptologists. We present a fragment matching approach based on pairwise local assemblies, using a 2D Siamese Neural Network to evaluate matching probabilities. This network is designed to predict simultaneously the existence or absence of a match, and the spatial relationship of one fragment in relation to the other (up, down, left, or right). We trained our deep learning model on a dataset of 6000 patches extracted from ostraca images, and achieved 96% accuracy on a validation dataset of 1000 patches. Then we propose a pipeline to reconstruct larger ostraca images using step by step pairwise matching, and able to produce a series of image reconstruction proposals. This method is based on the construction of a graph through iterative addition of small fragments. This work is intended as a proof of concept that archaeologists can benefit from automatic processing of their ostraca dataset.
An artificial neural network based approach for energy efficient task scheduling in cloud data centers

Energy efficiency is considered as a crucial objective in cloud data centers as it reduces cost and meets the standard set in green computing. Task scheduling an important problem becomes more complex and critical under energy efficiency consideration. Key issues in recent research on energy efficient task scheduling are execution overhead and scalability. Machine learning has been widely employed for energy efficient task scheduling problem but mostly used to predict resource consumption only instead of deciding the schedule itself. However, we used the neural network to decide which resource should be assigned to given task independently. In this paper, we proposed an energy efficient independent task scheduler using supervised neural networks with the aim to reduce makespan, energy consumption, execution overhead and number of active racks. Proposed artificial neural network-based scheduler takes incoming task and current cloud environment state as input and predict the best computing resource for given task as output which compiles our aim. We used genetic algorithm to generate a huge dataset (∼18 million training instances) and trained our neural network on this dataset using back propagation algorithm with 99.9% accuracy. We simulated experiments on heavily loaded and lightly loaded cloud environment and compared with well-known approaches: Genetic algorithm, MinMIN-MINMin heuristic and Linear regression based energy efficient task schedulers. Results clearly indicate that proposed work outperforms considered algorithms. In heavily (lightly) loaded environment, it improves makespan by 59% (64%), energy consumption by 45% (71%), execution overhead by 88% (43%) respectively and number of active racks by 70%.
Context-aware mobile learning system: Usability assessment based on a field study

Teaching-learning models supported by technology have evolved since the emergence of personal computers until the present day, where m-learning has been incorporated thanks to mobile technology. In this context, some works have proposed mobile learning systems (MLS). One of the main requirements of this kind of systems in terms of software quality is the usability. Therefore, the analysis and evaluation of the usability of MLS are especially relevant; however, few works have addressed the usability issues using field studies with students and professors in real domains. In this paper, we present a usability assessment of a Context-Aware Mobile Learning System (CAMLS) based on a field study with six professors and forty-eight high school students in a real environment. The results obtained from the usability assessment show that, on average, the services offered by the system have 82.4% acceptance by users (professors and high school students), with the learning reinforcement service through SMS messages having the highest acceptance for teachers, with a positive perception of 91.5%. Meanwhile, for the high school students, the Mobile Learning Objects (MLOs) suggestion service was highest with 81% acceptance. Based on the obtained results, the evaluated mobile learning system holds wide acceptance, satisfaction, and applicability from teachers' and students' perspectives. The usability assessment described in this study can serve as a reference for developers seeking to improve mobile learning systems development.
Point and interval forecasting of solar irradiance with an active Gaussian process

A Gaussian process regression (GPR) with active learning is proposed for developing the solar irradiance point and interval forecasting models, which consider the spatial-temporal information collected from a targeted site and a number of neighbouring sites. To enhance the performance of the GPR-based model an active learning process is developed for constructing an ad-hoc input feature set, selecting training data points, and optimising hyper-parameters of GPR models. To validate the advantages of the proposed method, a comprehensive computational study is conducted based on solar irradiance data collected from the northwest California area. In the point forecasting, the proposed method beats the state-of-the-art benchmarking methods including classical statistical models and data-driven models according to values of the normalised root mean squared error, normalised mean absolute error, normalised mean bias error, and coefficient of determination. In the interval forecasting, the proposed method outperforms the persistence model, autoregressive model with exogenous inputs, generic GPR, as well as two recently reported forecasting methods, the bootstrap-based extreme learning machine and quantile regression, in terms of the forecasting reliability. Computational results show that the proposed method is more effective than well-known existing benchmarks in the point and interval forecasting of the solar irradiance.
GEV-NN: A deep neural network architecture for class imbalance problem in binary classification

Class imbalance is a common issue in many applications such as medical diagnosis, fraud detection, web advertising, etc. Although standard deep learning method has achieved remarkably high-performance on datasets with balanced classes, its ability to classify imbalanced dataset is still limited. This paper proposes a novel end-to-end deep neural network architecture and adopts Gumbel distribution as an activation function in neural networks for class imbalance problem in the application of binary classification. Our proposed architecture, named GEV-NN, consists of three components: the first component serves to score input variables to determine a set of suitable input, the second component is an auto-encoder that learns efficient explanatory features for the minority class, and in the last component, the combination of the scored input and extracted features are then used to make the final prediction. We jointly optimize these components in an end-to-end training. Extensive experiments using real-world imbalanced datasets showed that GEV-NN significantly outperforms the state-of-the-art baselines by around 2% at most. In addition, the GEV-NN gives a beneficial advantage to interpret variable importance. We find key risk factors for hypertension, which are consistent with other scientific researches, using the first component of GEV-NN.
Coupled Attribute Learning for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) is a challenging problem in face recognition and subject to large textural and spatial structure differences of face images. Different from conventional face recognition in homogeneous environments, there exist many face images taken from different sources (including different sensors or different mechanisms) in reality. In addition, limited training samples of cross-modality pairs make HFR more challenging due to the complex generation procedure of these images. Despite the great progress that has been achieved in recent years, existing works mainly focus on HFR from only cross-modality image matching. However, it is more practical to obtain both facial images and semantic descriptions about facial attributes in real-world situations, in which the semantic description clues are nearly always obtained during the process of image generation. Motivated by human cognitive mechanisms, we naturally utilize the explicit invariant semantic description, i.e., face attributes, to help address the gap among face images of different modalities. Existing facial attributes-related face recognition methods primarily regard attributes as the high-level features used to enhance recognition performance, ignoring the inherent relationship between face attributes and identities. In this article, we propose novel coupled attribute learning for the HFR (CAL-HFR) method without labeling the attributes manually. Deep convolutional networks are employed to directly map face images in heterogeneous scenarios to a compact common space where distances are taken as dissimilarities of pairs. Coupled attribute guided triplet loss (CAGTL) is designed to train an end-to-end HFR network that can effectively eliminate defects of incorrectly estimated attributes. Extensive experiments on multiple heterogeneous scenarios demonstrate that the proposed method achieves superior performance compared with that of state-of-the-art methods. Furthermore, we make publicly available our generated pairwise annotated heterogeneous facial attribute database for evaluation and promoting related research.
Comparison of Classification Models for Early Prediction of Breast Cancer

Breast cancer is the second most leading cause of women's death in America. To create an accurate prediction model and analyze the remarkable risk factors, a data mining classification task that involves different methods has applied. Data mining has been used to extract hidden knowledge in different domains such as business, medicine, science, engineering, etc. This research aims to predict breast cancer using anthropometric data and parameters that are collected in routine blood analysis. First, we found the most important attributes in the dataset that can be selected as a Biomarker; by applying the recursive feature elimination method. We found that Age, BMI, Glucose, HOMA, and Resistin can be selected as the best Biomarker for breast cancer. We applied different classification techniques; K-NN, ANN, Decision trees, Naive Bayesian and found that artificial neural networks best classify the attribute with an accuracy of 80.00%. This study will also helps doctors and medical practitioners for early diagnosis of breast cancer.
Arbitrary-Oriented Inshore Ship Detection based on Multi-Scale Feature Fusion and Contextual Pooling on Rotation Region Proposals

Inshore ship detection plays an important role in many civilian and military applications. The complex land environment and the diversity of target sizes and distributions make it still challenging for us to obtain accurate detection results. In order to achieve precise localization and suppress false alarms, in this paper, we propose a framework which integrates a multi-scale feature fusion network, rotation region proposal network and contextual pooling together. Specifically, in order to describe ships of various sizes, different convolutional layers are fused to obtain multi-scale features based on the baseline feature extraction network. Then, for the purpose of accurate target localization and arbitrary-oriented ship detection, a rotation region proposal network and skew non-maximum suppression are employed. Finally, on account of the disadvantages that the employment of a rotation bounding box usually causes more false alarms, we implement inclined context feature pooling on rotation region proposals. A dataset including port images collected from Google Earth and a public ship dataset HRSC2016 are employed in our experiments to test the proposed method. Experimental results of model analysis validate the contribution of each module mentioned above, and contrast results show that our proposed pipeline is able to achieve state-of-the-art performance of arbitrary-oriented inshore ship detection.
Projection-Based Augmented Reality Assistance for Manual Electronic Component Assembly Processes

Personalized production is moving the progress of industrial automation forward, and demanding new tools for improving the decision-making of the operators. This paper presents a new, projection-based augmented reality system for assisting operators during electronic component assembly processes. The paper describes both the hardware and software solutions, and depicts the results obtained during a usability test with the new system.
A Time-Space Network Model for Collision-Free Routing of Planar Motions in a Multirobot Station

This article investigates a new collision-free routing problem of a multirobot system.The objective is to minimize the cycle time of operation tasks for each robot while avoiding collisions.The focus is set on the operation of the end-effector and its connected joint, and the operation is projected onto a circular area on the plane.We propose to employ a time-space network (TSN) model that maps the robot location constraints into the route planning framework, leading to a mixed integer programming (MIP) problem.A dedicated genetic algorithm is proposed for solving this MIP problem and a new encoding scheme is designed to fit the TSN formulation.Simulation experiments indicate that the proposed model can obtain the collisionfree route of the considered multirobot system.Simulation results also show that the proposed genetic algorithm can provide fast and high-quality solutions, compared to two state-of-the-art commercial solvers and a practical approach.
Toward Detecting Illegal Transactions on Bitcoin Using Machine-Learning Methods

As an emergent electronic payment system, Bitcoin has attracted attention for its desirable features such as disintermediation, decentralization, and tamper-proof recording of data. The Bitcoin network also employs public key cryptography to prevent the disclosure of information related to participating users. Although the public key cryptography ensures the privacy and hides the true identity of users in the Bitcoin network, it has recently been abused for illegal activities that have tarnished the charm of this novel technology. Detecting the illegal transactions associated with illicit activities in Bitcoin is therefore imperative. This paper proposes a machine-learning based approach that classifies Bitcoin transactions as illegal or legal. The detected illegal transactions can be excluded from the subsequent block, promoting user acceptance and adoption of the Bitcoin technology.
Age and Gender Prediction using Deep Convolutional Neural Networks

Age and gender identification have become a major part of the network, security and care. It has a common use in age specific content access for children. Social media uses it in delivering layered ads and marketing to extend it's a reach. Face recognition has developed to a great extent that we have to map it further in getting more useful results having different approaches. In this paper, we propose deep CNN to improve age and gender predication from significant results can be obtained and a significant improvement can be seen in various tasks such as face recognition. A simple convolutional network architecture is proposed to make a noticeable improvement in this field using existing methods. Using deep CNN, model is trained to an extent that accuracy of Age and Gender become 79% using HAAR Feature-based Cascade Classifiers is an effective method proposed by Paul Viola and Michael Jones. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images.
Ultrasound Bone Segmentation: A Scoping Review of Techniques and Validation Practices

<h2>Abstract</h2> Ultrasound bone segmentation is an important yet challenging task for many clinical applications. Several works have emerged attempting to improve and automate bone segmentation, which has led to a variety of computational techniques, validation practices and applied clinical scenarios. We characterize this exciting and growing body of research by reviewing published ultrasound bone segmentation techniques. We review 56 articles in detail and categorize and discuss the image analysis techniques that have been used for bone segmentation. We highlight the general trends of this field in terms of clinical motivation, image analysis techniques, ultrasound modalities and the types of validation practices used to quantify segmentation performance. Finally, we present an outlook on promising areas of research based on the unaddressed needs for solving ultrasound bone segmentation.
Efficient, Lexicon-Free OCR using Deep Learning

Contrary to popular belief, Optical Character Recognition (OCR) remains a challenging problem when text occurs in unconstrained environments, like natural scenes, due to geometrical distortions, complex backgrounds, and diverse fonts. In this paper, we present a segmentation-free OCR system that combines deep learning methods, synthetic training data generation, and data augmentation techniques. We render synthetic training data using large text corpora and over 2000 fonts. To simulate text occurring in complex natural scenes, we augment extracted samples with geometric distortions and with a proposed data augmentation technique - alpha-compositing with background textures. Our models employ a convolutional neural network encoder to extract features from text images. Inspired by the recent progress in neural machine translation and language modeling, we examine the capabilities of both recurrent and convolutional neural networks in modeling the interactions between input elements. The proposed OCR system surpasses the accuracy of leading commercial and open-source engines on distorted text samples.
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Domain adaptation studies how to build a robust model to solve pattern recognition problems when training in a source domain while testing in a related but different target domain. The existing methods focus on how to shorten the distance between the two domains, however, they have limited considerations on the preservation of data structures. In this paper, we propose a novel model for unsupervised domain adaptation. For the reduction of domain discrepancy, we propose modified A−distance, which is computationally fast and can be optimized using gradient information. Moreover, in order to capture the internal structures of target samples, within-domain normalization based sparse filtering is raised, which proved to be more powerful for domain adaptation. Extensive experiments compared to both shallow and deep methods demonstrate the effectiveness of our approach.
Domain-Adversarial Graph Neural Networks for Text Classification

Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains are often useful to improve the learning on the target domain; on the other hand, domain variance and hierarchical structure of documents from words, key phrases, sentences, paragraphs, etc. make it difficult to align domains for effective learning. To date, existing cross-domain text classification methods mainly strive to minimize feature distribution differences between domains, and they typically suffer from three major limitations - (1) difficult to capture semantics in non-consecutive phrases and long-distance word dependency because of treating texts as word sequences, (2) neglect of hierarchical coarse-grained structures of document for feature learning, and (3) narrow focus of the domains at instance levels, without using domains as supervisions to improve text classification. This paper proposes an end-to-end, domain-adversarial graph neural networks (DAGNN), for cross-domain text classification. Our motivation is to model documents as graphs and use a domain-adversarial training principle to lean features from each graph (as well as learning the separation of domains) for effective text classification. At the instance level, DAGNN uses a graph to model each document, so that it can capture non-consecutive and long-distance semantics. At the feature level, DAGNN uses graphs from different domains to jointly train hierarchical graph neural networks in order to learn good features. At the learning level, DAGNN proposes a domain-adversarial principle such that the learned features not only optimally classify documents but also separates domains. Experiments on benchmark datasets demonstrate the effectiveness of our method in cross-domain classification tasks.
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization

The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the LiDAR sensor and stereo camera for improved depth perception. Instead of directly fusing estimated depths across LiDAR and stereo modalities, we take advantages of the stereo matching network with two enhanced techniques: Input Fusion and Conditional Cost Volume Normalization (CCVNorm) on the LiDAR information. The proposed framework is generic and closely integrated with the cost volume component that is commonly utilized in stereo matching neural networks. We experimentally verify the efficacy and robustness of our method on the KITTI Stereo and Depth Completion datasets, obtaining favorable performance against various fusion strategies. Moreover, we demonstrate that, with a hierarchical extension of CCVNorm, the proposed method brings only slight overhead to the stereo matching network in terms of computation time and model size.
Developing personas &amp; use cases with user survey data: A study on the millennials’ media usage

The aim of the study is to investigate the characteristics of millennials on the usage of media (music, photo/video) and media-related devices. An online survey was conducted on 360 millennials. Based on the results, we developed representative use cases to be used in experience design projects based on the actual media usage data collected. We also classified the millennials into four specific groups by applying statistical cluster analysis techniques. The characteristics of each group were further identified. The results of this study will help designers to develop products and services that meets the needs of their target consumers.