Reviewerly/exHarmony huggingface dataset:
A Scale and Rotation Invariant Urdu Nastalique Ligature Recognition Using Cascade Forward Backpropagation Neural Network

In the emerging age of technologies, machines are becoming more and more skilled and capable just like humans. Despite the fact that machines do not have their own intelligence, but still due to advancement in Artificial Intelligence (AI), machines are rapidly advancing. The area of Pattern Recognition (PR) deals with bringing enhancements to identify obscure patterns corresponding to specific classes. Optical Character Recognition (OCR) is a subfield of PR which deals with the recognition of characters. A great work has been done for Japanese, Hindi, Arabic and Chinese scripts, but only a diminutive work has been done for Urdu script. The Urdu language is highly cursive and is written in different calligraphic styles like Naskh, Nastalique, Kofi, Devani and Riqa. The Nastalique font is very calligraphic with aesthetic beauty. The ligature segmentation of Urdu Nastalique is also more difficult as compared to other languages. Urdu Nastalique has some characteristics like stacking of ligatures and cursiveness which makes its ligature segmentation a difficult task. Cursiveness means ligatures are joined together to form a new shape. It contains connected ligatures which makes it more complicated as compared to other languages. The ligature recognition of Urdu text by an OCR is a strenuous task due to variants of scaling, rotation, orientation and font style. In this study, a scale and rotation invariant classifier for Urdu Nastalique OCR is proposed. A combination of scale and location invariant moments is used for feature extraction and the classification is performed using Cascade Forward Backpropagation Neural Network. The model is validated through independent dataset testing and 5-fold cross-validation which gave 96.474% and 96.922% accuracy. The results depict the adaptability of the proposed model due to its high accuracy for recognition of Urdu Nastalique Ligature.
Will Transfer Learning Enhance ImageNet Classification Accuracy Using ImageNet-Pretrained Models?

The huge impact of Transfer Learning (TL) techniques in many fields was achieved using several state-of-the-art ImageNet-pretrained models. These models have shown great performance improvements on this dataset over the last few years. One of the recently used TL techniques is feature extraction with the help of Feature Concatenation (FC), where the extracted features of multiple pretrained models are concatenated together before training on them, to produce more robust and discriminative feature representations on various classification tasks. However, neither TL nor FC techniques have been tested on the same dataset that initially trained the pretrained models, i.e. ImageNet. Hence, this work provides an investigative study to test the possibility of improving the ImageNet accuracy using the feature extraction approach of TL with the help of FC techniques. The results of this work show that there is no TL technique that can be used with or without FC to increase the accuracy of pretrained models on the original dataset on which they were trained. Even for FC, it cannot produce a more discriminative feature representation for the original data than what the individual models can produce.
Assistive sensor-based technology driven self-management for building resilience among people with early stage cognitive impairment

This paper reports the technologies and workplan of the AAL RESILIEN-T project. Focused on assistive technologies, RESILIEN-T aims to improve, through self-management, the autonomy, participation in social life, and skills, of older Persons with Cognitive Impairment (PwCI) who are too often considered as “objects” of research, rather than “partners”. The study investigates existing ICT solutions to improve the self-management ability of PwCl at different stages of cognitive impairment. Sensors, devices and apps to reduce the progression of the disease are analyzed. To increase sensor capability, innovative data management, i.e. Artificial Intelligence and Machine Learning algorithms, are considered to extract significant information from the data and optimize the sensor network. Moreover, approaches to involve end-users in the development are also investigated to enhance the final outputs. The study proposes a modular and integrated platform for PwCI to self-manage various activities including nutrition, physical activities, social life, cognitive training. The choice of offering an open API to integrate wearable devices and lifestyle monitoring systems from different suppliers makes available a customable and modular product. Considering that functional decline is part of the normal aging process, it might be challenging to individuate three levels of modular architecture to increase the accuracy of the monitoring with the decline of the cognitive capabilities.
Future Trends in Agile at Scale: A Summary of the 7th International Workshop on Large-Scale Agile Development

This workshop explored the main research challenges in conducting agile software development in large-scale software development. We considered multi-site companies with projects that include a large number of teams which develop sophisticated systems by adopting and using agile methods. Such topics include inter-team coordination, knowledge sharing, agile transformations, and project management models that facilitate multiple cooperating self-organising teams. The keynote talk, by Darja Ŝmite, provided empirical results on communities of practice within the music streaming service Spotify. We accepted five full research papers which are included in this volume. These five papers report empirical research studies using surveys, observational and case studies. Workshop participants also worked together in groups to establish current research topics and priorities. This workshop summary contributes a current snapshot of research along with future research agendas in the field of large-scale agile development.
Chinese Text Project: A dynamic digital library of premodern Chinese

Abstract This article presents technical approaches and innovations in digital library design developed during the design and implementation of the Chinese Text Project, a widely-used, large-scale full-text digital library of premodern Chinese writing. By leveraging a combination of domain-optimized Optical Character Recognition, a purpose-designed crowdsourcing system, and an Application Programming Interface (API), this project simultaneously provides a sustainable transcription system, search interface and reading environment, as well as an extensible platform for transcribing and working with premodern Chinese textual materials. By means of the API, intentionally loosely integrated text mining tools are used to extend the platform, while also being reusable independently with materials from other sources and in other languages.
Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent. The agent can use multiple modes of transportation in a dynamic transit vehicle network. For instance, a drone can either fly or ride on terrain vehicles for segments of their routes. DREAMR is a difficult problem of sequential decision making under uncertainty with both discrete and continuous variables. We design a novel hierarchical hybrid planning framework to solve the DREAMR problem that exploits its structural decomposability. Our framework consists of a global open-loop planning layer that invokes and monitors a local closed-loop execution layer. Additional abstractions allow efficient and seamless interleaving of planning and execution. We create a large-scale simulation for DREAMR problems, with each scenario having hundreds of transportation routes and thousands of connection points. Our algorithmic framework significantly outperforms a receding horizon control baseline, in terms of elapsed time to reach the destination and energy expended by the agent.
Deep ranking based cost-sensitive multi-label learning for distant supervision relation extraction

Knowledge base provides a potential way to improve the intelligence of information retrieval (IR) systems, for that knowledge base has numerous relations between entities which can help the IR systems to conduct inference from one entity to another entity. Relation extraction is one of the fundamental techniques to construct a knowledge base. Distant supervision is a semi-supervised learning method for relation extraction which learns with labeled and unlabeled data. However, this approach suffers the problem of relation overlapping in which one entity tuple may have multiple relation facts. We believe that relation types can have latent connections, which we call class ties, and can be exploited to enhance relation extraction. However, this property between relation classes has not been fully explored before. In this paper, to exploit class ties between relations to improve relation extraction, we propose a general ranking based multi-label learning framework combined with convolutional neural networks, in which ranking based loss functions with regularization technique are introduced to learn the latent connections between relations. Furthermore, to deal with the problem of class imbalance in distant supervision relation extraction, we further adopt cost-sensitive learning to rescale the costs from the positive and negative labels. Extensive experiments on a widely used dataset show the effectiveness of our model to exploit class ties and to relieve class imbalance problem.
Beholder-Gan: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level

"Beauty is in the eye of the beholder." This maxim, emphasizing the subjectivity of the perception of beauty, has enjoyed a wide consensus since ancient times. In the digital era, data-driven methods have been shown to be able to predict human-assigned beauty scores for facial images. In this work, we augment this ability and train a generative model that generates faces conditioned on a requested beauty score. In addition, we show how this trained generator can be used to "beautify" an input face image. By doing so, we achieve an unsupervised beautification model, in the sense that it relies on no ground truth target images. Our implementation is available on: https://github.com/beholdergan/Beholder-GAN.
Local Texture and Geometry Descriptors for Fast Block-Based Motion Estimation of Dynamic Voxelized Point Clouds

Motion estimation in dynamic point cloud analysis or compression is a computationally intensive procedure generally involving a large search space and often complex voxel matching functions. We present an extension and improvement on prior work to speed up block-based motion estimation between temporally adjacent point clouds. We introduce local, or block-based, texture descriptors as a complement to voxel geometry description. Descriptors are organized in an occupancy map which may be efficiently computed and stored. By consulting the map, a point cloud motion estimator may significantly reduce its search space while maintaining prediction distortion at similar quality levels. The proposed texture-based occupancy maps provide significant speedup, an average of 26.9% for the tested data set, with respect to prior work.
Lightweight Machine Learning Classifiers of IoT Traffic Flows

IoT traffic flows have different from traditional devices statistics and their classification become an important task because of the exponentially growing number of smart devices. Conventional Deep Packet Inspection systems that rely on inspection of open fields in TLS and DNS packets, and the trend of encrypting the open fields makes machine learning based systems the only viable option for future networks. Moreover, computational complexity of models becomes crucial for large-scale operations. In this work, we investigated whether simple models, such as Logistic Regression, SVM with linear kernel, and a Decision Tree, have suitable for real-world deployments performance of multiclass classification of IoT traces, given thoughtful features engineering. We introduced a new flow feature of categorical type that describes a set of TCP-flag fields within a flow. In addition, removal of correlated features and feature space transformation via PCA method showed their usefulness in terms of prediction complexity reduction. In order to account for online classification mode, we limited the maximal number of packets within a flow to 10. Moreover, to estimate the upper-bound performance with given features, we compared the simple algorithms with Random Forest, Gradient Boosting and a feed-forward neural network. We performed 4-fold cross-validation of models by metrics Accuracy and F1-measure. The test results demonstrated that the introduced feature increases F1-measure for logistic regression from 99.1% in the base case to 99.6%, thus closely approaching more computationally expensive models. Overall, the evaluation results demonstrated feasibility of a lightweight model for IoT flow classification task with the suitable for a practical deployment performance.
RDF-TR: Exploiting structural redundancies to boost RDF compression

The number and volume of semantic data have grown impressively over the last decade, promoting compression as an essential tool for RDF preservation, sharing and management. In contrast to universal compressors, RDF compression techniques are able to detect and exploit specific forms of redundancy in RDF data. Thus, state-of-the-art RDF compressors excel at exploiting syntactic and semantic redundancies, i.e., repetitions in the serialization format and information that can be inferred implicitly. However, little attention has been paid to the existence of structural patterns within the RDF dataset; i.e. structural redundancy. In this paper, we analyze structural regularities in real-world datasets, and show three schema-based sources of redundancies that underpin the schema-relaxed nature of RDF. Then, we propose RDF-Tr (RDF Triples Reorganizer), a preprocessing technique that discovers and removes this kind of redundancy before the RDF dataset is effectively compressed. In particular, RDF-Tr groups subjects that are described by the same predicates, and locally re-codes the objects related to these predicates. Finally, we integrate RDF-Tr with two RDF compressors, HDT and k2-triples. Our experiments show that using RDF-Tr with these compressors improves by up to 2.3 times their original effectiveness, outperforming the most prominent state-of-the-art techniques.
Social Media Cross-Source and Cross-Domain Sentiment Classification

Due to the expansion of Internet and Web 2.0 phenomenon, there is a growing interest in sentiment analysis of freely opinionated text. In this paper, we propose a novel cross-source cross-domain sentiment classification, in which cross-domain-labeled Web sources (Amazon and Tripadvisor) are used to train supervised learning models (including two deep learning algorithms) that are tested on typically nonlabeled social media reviews (Facebook and Twitter). We explored a three-step methodology, in which distinct balanced training, text preprocessing and machine learning methods were tested, using two languages: English and Italian. The best results were achieved using undersampling training and a Convolutional Neural Network. Interesting cross-source classification performances were achieved, in particular when using Amazon and Tripadvisor reviews to train a model that is tested on Facebook data for both English and Italian.
Densely convolutional attention network for image super-resolution

Recently, deep convolutional neural networks (CNNs) with skip-connections in single image super-resolution (SISR) have received great success. However, the difficulty of reconstructing high-resolution images is particularly pronounced for the limited information extracted from input low-resolution images, for which the performance bottleneck of these SR networks still exists. In this paper, we present a novel Densely Convolutional Attention Network (DCAN) for SISR to improve the quality of reconstructed images. Specifically, we propose a convolutional attention mechanism which adds an weighted skip-connection joint the original convolution. Our network can adaptively recalibrate convolutional-wise features by considering interdependencies among convolutional layers. Besides, the proposed network leverage several dense block to enhance the relationship between different convolutional layers, more information could be used sufficiently by this dense block learning method, in order that reconstructed images can obtain richer details and clearer edges. Extensive experiments show that the proposed DCAN could obtain more outstanding performance quantitatively and qualitatively against the state-of-the-art methods while cost less execution time.
An optimizer ensemble algorithm and its application to image registration

The design of effective optimization algorithms is always a hot research topic. An optimizer ensemble where any population-based optimization algorithm can be integrated is proposed in this study. First, the optimizer ensemble framework based on ensemble learning is presented. The learning table co nsisting of the population members of all optimizers is constructed to share information. The maximum number of iterations is divided into several exchange iterations. Each optimizer exchanges individuals with the learning table in exchange iterations and runs independently in the other iterations. Exchange individuals are generated by a bootstrap sample from the learning table. To maintain a balance between exchange individuals and preserved individuals, the exchange number of each optimizer is adaptively assigned according to its fitness. The output is obtained by the voting approach that selects the highest ranked solution. Second, an optimizer ensemble algorithm (OEA) which combines multiple population-based optimization algorithms is proposed. The computational complexity, convergence, and diversity of OEA are analyzed. Finally, extensive experiments on benchmark functions demonstrate that OEA outperforms several state-of-the-art algorithms. OEA is used to search the maximum mutual information in image registration. The high performance of OEA is further verified by a large number of registration results on real remote sensing images.
Improving Robustness to Adversarial Examples by Encouraging Discriminative Features

Deep neural networks (DNNs) have achieved state-of-the-art results in various pattern recognition tasks. However, they perform poorly on out-of-distribution adversarial examples i.e. inputs that are specifically crafted by an adversary to cause DNNs to misbehave, questioning the security and reliability of applications. In this paper, we hypothesize inter-class and intra-class feature variances to be one of the reasons behind the existence of adversarial examples. Additionally, learning low intra-class and high inter-class feature variance help classifiers learn decision boundaries that are more compact and leave less inter-class low-probability "pockets" in the feature space, i.e. less room for adversarial perturbations. We achieve this by imposing a center loss [1] in addition to the regular softmax cross-entropy loss while training a DNN classifier. Intuitively, the center loss encourages DNNs to simultaneously learn a center for the deep features of each class, and minimize the distances between the intra-class deep features and their corresponding class centers. Our results on state-of-the-art architectures tested on MNIST, CIFAR-10, and CIFAR-100 datasets confirm our hypothesis and highlight the importance of discriminative features in the existence of adversarial examples.
Photonic scheme of discrete quantum Fourier transform for quantum algorithms via quantum dots

Abstract We propose an optical scheme of discrete quantum Fourier transform (DQFT) via ancillary systems using quantum dots (QDs) confined in single-sided cavities (QD-cavity systems). In our DQFT scheme, the main component is a controlled-rotation k (CRk) gate, which utilizes the interactions between photons and QDs, consisting of two QD-cavity systems. Since the proposed CRk gate can be experimentally implemented with high efficiency and reliable performance, the scalability of multi-qubit DQFT scheme can also be realized through the simple composition of the proposed CRk gates via the QD-cavity systems. Subsequently, in order to demonstrate the performance of the CRk gate, we analyze the interaction between a photon and a QD-cavity system, and then indicate the condition to be efficient CRk gate with feasibility under vacuum noise and sideband leakage.
Enhanced Grassmann discriminant analysis with randomized time warping for motion recognition

This paper proposes a framework for classifying motion sequences, by extending the framework of Grassmann discriminant analysis (GDA). A problem of GDA is that its discriminant space is not necessarily optimal. This limitation becomes even more prominent when utilizing the subspace representation of randomized time warping (RTW). RTW is a sequence representation that can effectively model a motion’s temporal information by a low-dimensional subspace, simplifying the problem of comparing two sequences to that of comparing two subspaces. The key idea of the proposed enhanced GDA is projecting class subspaces onto a generalized difference subspace before mapping them on a Grassmann manifold. The GDS projection can remove overlapping components of the subspaces in the vector space, nearly orthogonalizing them. Consequently, a dictionary of orthogonalized class subspaces produces a set of more discriminant data points in the Grassmann manifold, in comparison with the original set. This set of data points can further enhance the discriminant ability of GDA. We demonstrate the validity of the proposed framework, RTW+eGDA, through experiments on motion recognition using the publicly available Cambridge gesture, KTH action, and UCF sports datasets.
Robust Shipping Label Recognition and Validation for Logistics by Using Deep Neural Networks

Shipping labels are widely used in logistics. It is important to ensure the quality of printing label and to verify contents of the shipping label on the package. We developed a verification and recognition method for various types of shipping labels by using deep neural networks. The experimental results showed 96% recognition accuracy in rotation-invariant conditions. Also, we introduce Google Maps API for validating the address which can reduce the cost of returning packages due to the invalid address. To train and evaluate the method, we have generated and collected 25 different types of shipping label dataset. We plan to release the dataset on our website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
AutoEncoders for Training Compact Deep Learning RF Classifiers for Wireless Protocols

We show that compact fully connected (FC) deep learning networks trained to classify wireless protocols using a hierarchy of multiple denoising autoencoders (AEs) outperform reference FC networks trained in a typical way, i.e., with a stochastic gradient based optimization of a given FC architecture. Not only is the complexity of such FC network, measured in number of trainable parameters and scalar multiplications, much lower than the reference FC and residual models, its accuracy also outperforms both models for nearly all tested SNR values (0 dB to 50dB). Such AE-trained networks are suited for in-situ protocol inference performed by simple mobile devices based on noisy signal measurements. Training is based on the data transmitted by real devices, and collected in a controlled environment, and systematically augmented by a policy-based data synthesis process by adding to the signal any subset of impairments commonly seen in a wireless receiver.