Reviewerly/exHarmony huggingface dataset:
Vehicle and pedestrian video-tracking with classification based on deep convolutional neural networks

In this article we propose an algorithm for the classification, tracking and counting of vehicles and pedestrians in video sequences; The algorithm is divided into two parts, a classification algorithm, which is based on convolutional neural networks, implemented using the You Only Look Once (YOLO) method; and a proposed algorithm for tracking regions of interest based in a well defined taxonomy. For the first stage of classification, We train and evaluate the performance with a set of more than 50000 labels, which we make available for their use. The tracking algorithm is evaluated against manual counts in video sequences of different scenarios captured in the management center of the Secretaria distrital de Movilidad of Bogota.
Research on fuzzy enhancement algorithms for infrared image recognition quality of power internet of things equipment based on membership function

With the development of society, the demand for power is increasing, but the inefficiency of the original power grid has gradually become an obstacle to social development, so it is urgent to reform the power grid, and the power Internet of things is an important means of power reform. Effective infrared image recognition of power Internet of Things equipment can achieve real-time monitoring of equipment status. The quality of image recognition is not only related to recognition methods, but also to the quality of the image itself. In order to improve the quality of image recognition, an image enhancement algorithm based on membership function is proposed in this paper. Firstly, piecewise linear transformation is introduced, and on the basis of it, an improved piecewise linear transformation method is proposed. Secondly, an image enhancement algorithm based on membership function is proposed by improving the traditional fuzzy enhancement method. Finally, based on the image enhancement algorithm of membership function, an improved piecewise linear transformation method is introduced to form an image blur enhancement system. Through the simulation experiment, we can find that the two image enhancement algorithms are improved in this paper, which can enhance the image enhancement effect of the original two enhancement algorithms, and the image fuzzy enhancement system composed of two improved enhancement methods can enhance the image enhancement effect again, and effectively improve the infrared image recognition quality of power Internet of things equipment.
LCD and Self-Orthogonal Group Codes in a Finite Abelian $p$ -Group Algebra

Let F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> be a finite field with q elements and p be a prime with gcd(p, q) = 1. Let G be a finite abelian p-group and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (G) be a group algebra. In this paper, we find all primitive idempotents and minimal abelian group codes in the group algebra F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (G). Furthermore, we give all LCD abelian codes (linear code with complementary dual) and self-orthogonal abelian codes of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> (G).
Anomaly detection based pronunciation verification approach using speech attribute features

Computer aided pronunciation training tools require accurate automatic pronunciation error detection algorithms to identify errors made by their users. However, the performance of these algorithms is highly dependent on the amount of mispronounced speech data used to train them and the reliability of its manual annotation. To overcome this problem, we turned the mispronunciation detection into an anomaly detection problem, which utilize algorithms trained with only correctly pronounced speech data. In this work we adopted the One-Class SVM as our anomaly detection model, with a specific model built for each phoneme. Each model was fed with a set of speech attribute features, namely the manners and places of articulation, extracted from a bank of binary DNN speech attribute detectors. We also applied multi-task learning and dropout approaches to alleviate the overfitting problem in the DNN speech attribute detectors. We trained the system using the WSJ0 and TIMIT standard data sets which contain only native English speech data and then evaluated it using three different data sets, a native English speaker corpus with artificial errors, a foreign-accented speech corpus and a children's disordered speech corpus. Finally, we compared our system with the conventional Goodness-of-Pronunciation (GOP) algorithm to demonstrate the effectiveness of our method. The results show that our method reduced the false-acceptance and false-rejection rates by 26% and 39% respectively compared to the GOP method.
Automatic Modulation Recognition Using Neural Architecture Search

Deep learning method has been proposed to deal with modulation recognition tasks instead of likelihood-based and feature-based approaches recently, but many researchers usually suffer from complex architecture engineering as designing neural networks still requires extensive expert knowledge. Neural architecture search, which uses a neural network to tune or compose other neural network architectures automatically, could be convenient for many researchers to find good deep neural networks for different data at hand. In this paper, we proposed a neural architecture search based modulation recognition framework. We use Bayesian optimization and genetic algorithm to search convolutional neural network architecture for synthetic datasets generated with GNU radio. Four existing approaches, including convolutional Neural Network (CNN), Inception Modules (Inception), Residual networks (Resnet) and Long Short-Term Memory (LSTM) based recognition algorithms, are involved for performance comparison. Simulation results indicate the effectiveness of the proposed framework, and the proposed approaches achieve significantly higher performance without manual feature engineering and architecture engineering.
Semi-Supervised Patient Similarity Clustering Algorithm Based on Electronic Medical Records

Electronic medical record (EMR) is the comprehensive description of the patients' individual health information in medical activities, which paves the way for intelligent-assisted medical decision-making research. However, due to the problems of data preprocessing, time-consuming and laborious data labeling in the Chinese electronic medical records, and the diversity of electronic medical record data storage, the research on electronic medical records poses certain challenges. Therefore, this paper intends to construct a medical domain dictionary in the word segmentation to improve the accuracy of the Chinese medical terminology recognition in electronic medical records. The Chinese text feature is extracted by using the latent Dirichl et al location (LDA) model, and the patient feature vector is constructed by feature stitching. At the same time, this paper uses the cumulative method of multi-impact indicators to construct the patient paired constraint set as the supervision information and guides the cluster learning model to optimize the patient category effect. In this paper, the comparison results show that the clustering algorithm with supervisory information is better than the simple unsupervised clustering algorithm. When constructing the supervised information set, the influence factor of the multi-dimensional attribute of the patient is better than the single-diagnosis result factor, and the clustering effect is improved as the number of paired constraint information increases.
HATS

In many complex domains, the input data are often not suited for the typical vector representations used in deep learning models. For example, in relational learning and computer vision tasks, the data are often better represented as sets (e.g., the neighborhood of a node, a cloud of points). In these cases, a key challenge is to learn an embedding function that is invariant to permutations of the input. While there has been some recent work on principled methods for learning permutation-invariant representations of sets, these approaches are limited in their applicability to set-of-sets (SoS) tasks, such as subgraph prediction and scene classification. In this work, we develop a deep neural network framework to learn inductive SoS embeddings that are invariant to SoS permutations. Specifically, we propose HATS, a hierarchical sequence model with attention mechanisms for inductive set-of-sets embeddings. We develop stochastic optimization and inference methods for learning HATS, and our experiments demonstrate that HATS achieves superior performance across a wide range of set-of-sets tasks.
Sketching Sustainability in Computing

We investigate engaging a computer science conference audience in sketching responses to the event as it occurs. In particular, we explore the response to inviting those present to engage in what is essentially an off-line, co-located, attendee-sourcing experience. Sketchnoting is a popular practice for documenting events, but these sketched records can be limited in scope at multi-track conferences, and paid professionals can be unaffordable at smaller events. Our challenges included: working with an audience with little or no experience of sketching or working with imagery; who were unaware of the possible benefits; and whose attendee engagement was variable - with individuals often working on laptops rather than actively listening during sessions. In order encourage engagement we hosted a pre-conference workshop, developed a conference-specific set of visual icons, and created prompt materials. This resulted in a remarkable visual record of the event, and also an increase in active listening and engagement.
Optimal Construction of Compressed Indexes for Highly Repetitive Texts

We propose algorithms that, given the input string of length $n$ over integer alphabet of size $\sigma$, construct the Burrows-Wheeler transform (BWT), the permuted longest-common-prefix (PLCP) array, and the LZ77 parsing in $O(n/\log_{\sigma}n+r\,{\rm polylog}\,n)$ time and working space, where $r$ is the number of runs in the BWT of the input. These are the essential components of many compressed indexes such as compressed suffix tree, FM-index, and grammar and LZ77-based indexes, but also find numerous applications in sequence analysis and data compression. The value of $r$ is a common measure of repetitiveness that is significantly smaller than $n$ if the string is highly repetitive. Since just accessing every symbol of the string requires $\Omega(n/\log_{\sigma}n)$ time, the presented algorithms are time and space optimal for inputs satisfying the assumption $n/r\in\Omega({\rm polylog}\,n)$ on the repetitiveness. For such inputs our result improves upon the currently fastest general algorithms of Belazzougui (STOC 2014) and Munro et al. (SODA 2017) which run in $O(n)$ time and use $O(n/\log_{\sigma} n)$ working space. We also show how to use our techniques to obtain optimal solutions on highly repetitive data for other fundamental string processing problems such as: Lyndon factorization, construction of run-length compressed suffix arrays, and some classical "textbook" problems such as computing the longest substring occurring at least some fixed number of times.
Citizen Dialogue Kit

In this demo we introduce our ongoing research on how to leverage the situated visualization of open and citizen science data within public space to inform and engage citizens. We developed an open-source toolkit, coined "Citizen Dialogue Kit" that is able to convey data visualizations on a set of interactive, wirelessly networked displays that can be freely positioned in urban space. The toolkit consists of a participative methodology to guide stakeholders with the choice of data and the design of its visualization, a set of off-the-shelf hardware components, and custom-made open source software that controls the whole system. We summarize the design of the toolkit and its initial deployment and conclude by discussing implications for urban visualization and future work.
Monomizo

We introduce Monomizo, a tangible daily and monthly calendar in a concrete-casted desktop object that allows users to print out their daily schedules. Monomizo was designed to integrate the benefits of digital and paper-based scheduling by providing easy conversion of schedules in digital devices to analog media (e.g., paper). To investigate users' schedule management experience with Monomizo, we conducted an in-field study of 10 participants over 6 days. The results showed that Monomizo helped users to review and reflect on the day via the screen and through attaching, writing, and physically possessing printed schedules. We also found value in encountering digital information through analog-metaphoric design that provides ambient permeation of schedules. Based on the findings, our study offers new possibilities for designing a tangible artifact that facilitates users' effective planning and use of their days.
Combining Symbolic Reasoning and Deep Learning for Human Activity Recognition

Activity recognition (AR) plays an important role in situation aware systems. Recently, deep learning approaches have shown promising results in the field of AR. However, their predictions are overconfident even in cases when the action class is incorrectly recognized. Moreover, these approaches provide information about an action class but not about the user context, such as location and manipulation of objects. To address these problems, we propose a hybrid AR architecture that combines deep learning with symbolic models to provide more realistic estimation of the classes and additional contextual information. We test the approach on a cooking dataset, describing the preparation of carrots soup. The results show that the proposed approach performs comparable to state of the art deep models inferring additional contextual properties about the current activity. The proposed approach is a first attempt to bridge the gap between deep learning and symbolic modeling for AR.
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting

We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify the network weights via a transformed- $$\ell _1$$ penalty without losing prediction accuracy in numerical experiments.
Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving

In this paper, a robust non-rigid feature matching approach for image registration with geometry constraints is proposed. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. To preserve the local geometry of these feature points, two local structure descriptors of the connectivity matrix and Laplacian coordinate are constructed. The expectation maximization (EM) algorithm is applied to solve this ML problem. Experimental results demonstrate that the proposed approach has better performance than current state-of-the-art methods.
Fault Tolerant Controller Using Interval Type-2 TSK Logic Control Systems: Application to Three Interconnected Conical Tank System

Type-2 fuzzy logic systems have recently been utilized in many control process due to their ability to model uncertainties. This paper proposes an inference mechanism for an interval type-2 Takagi-Sugeno-Kang fuzzy logic control system (IT2 TSK FLCS) when antecedents are type-2 fuzzy sets and consequent are crisp. This paper focus on fault-tolerant control application for the three inter-connected conical tank system with following cases: (1) Servo control without fault, and, (2) Regulatory control with the actuator, system component faults, and process disturbances. In both the cases, IAE and ISE are calculated. The methods presented in this paper facilitating the design of fault-tolerant controller design using IT2 TSK FLCs with significantly improved performance over type-1 FLCs approaches.
Parasitic reflection elimination using binary pattern in phase measuring deflectometry

Phase measuring deflectometry (PMD) is a high precision measurement method for optical surface shape, which has been used in the fabrication and application of high quality optical element. For the on-line surface measurement of transparent optical elements in the laser inertial confinement fusion facility, PMD is a very promising method. However, the parasitic reflection from the rear surface is an existing problem for PMD to measure transparent element. In this paper, a parasitic reflection eliminating method with the use of binary pattern is proposed, the principle of which is described in detail. The ability of the proposed method to extract the front surface information is demonstrated by the numerical simulation. And the proposed method is used to measure a transparent window glass with a thickness of about 10 mm. The surface shape result shows a good agreement with the interferometer data with a sub-wavelength level accuracy.
Improving the Accuracy of System Performance Estimation by Using Shards

We improve the measurement accuracy of retrieval system performance by better modeling the noise present in test collection scores. Our technique draws its inspiration from two approaches: one, which exploits the variable measurement accuracy of topics; the other, which randomly splits document collections into shards. We describe and theoretically analyze an ANOVA model able to capture the effects of topics, systems, and document shards as well as their interactions. Using multiple TREC collections, we empirically confirm theoretical results in terms of improved estimation accuracy and robustness of found significant differences. The improvements compared to widely used test collection measurement techniques are substantial. We speculate that our technique works because we do not assume that the topics of a test collection measure performance equally.
FG-SRGAN: A Feature-Guided Super-Resolution Generative Adversarial Network for Unpaired Image Super-Resolution

Recently, the performance of single image super-resolution has been significantly improved by convolution neural networks (CNN). However, most of these networks are trained with paired images and take the bicubic-downsampled images as inputs. It's impractical if we want to super-resolve low-resolution images in the real world, since there is no ground truth high-resolution images corresponding to the low-resolution images. To tackle this challenge, a Feature-Guided Super-Resolution Generative Adversarial Network (FG-SRGAN) for unpaired image super-resolution is proposed in this paper. A guidance module is introduced in FG-SRGAN, which is utilized to reduce the space of possible mapping functions and help to learn the correct mapping function from low-resolution domain to high-resolution domain. Furthermore, we treat the outputs of guidance module as fake examples, which can be leveraged using another adversarial loss. This is beneficial for the main task as it forces FG-SRGAN to learn valid representations for super-resolution. When applied to super-resolve low-resolution face images in the real world, FG-SRGAN is able to achieve satisfactory performance both qualitatively and quantitatively.
Tree based data fusion approach for mining temporal patterns

Discovering time profiled temporal patterns from time stamped transaction datasets is addressed in our previous research works which includes proposing new support estimation techniques, similarity measures for computing similarity between temporal patterns. This paper proposes a novel approach for discovering temporal pattern by introducing the concept of data fusion w.r.t the temporal pattern tree. The tree is generated for each timeslot and then the trees obtained for individual timeslots are merged or fused to get the overall tree for the entire dataset. The concept of tree based data fusion helps to prune elements efficiently and well ahead during pattern mining process. A pruning function is also introduced in this paper to prune invalid temporal patterns.
A framework for avoiding knowledge leakage: evidence from engineering to order firms

By adopting multiple case-studies, this paper attempts to investigate, in the engineering to order companies' context, knowledge leakage problems. It proposes a framework for managing knowledge leakage that represents the first attempt to depict together different knowledge threats, knowledge identification methods, and defensive mechanisms. Results confirm, in the context investigated, the existence of the knowledge threats and the adoption of knowledge identification and defensive mechanisms identified in the literature, even if with some exception, and recognize three "new for the literature" methods. Findings neglect the significance of knowledge management systems, thus raising doubts about their usefulness in knowledge leakage situations. Our study is valuable not only for scholars interested in the topic but also for firms' managers, as it could help them in understanding, detecting and preventing knowledge leakage situations.
Beyond GumTree: A Hybrid Approach to Generate Edit Scripts

On development using a version control system, understanding differences of source code is important. Edit scripts (in short, ES) represent differences between two versions of source code. One of the tools generating ESs is GumTree. GumTree takes two versions of source code as input and generates an ES consisting of insert, delete, update and move nodes of abstract syntax tree (in short, AST). However, the accuracy of move and update actions generated by GumTree is insufficient, which makes ESs more difficult to understand. A reason why the accuracy is insufficient is that GumTree generates ESs from only information of AST. Thus, in this research, we propose to generate easier-to-understand ESs by using not only structures of AST but also information of line differences. To evaluate our methodology, we applied it to some open source software, and we confirmed that ESs generated by our methodology are more helpful to understand the differences of source code than GumTree.
Deep Metric Learning for Identification of Mitotic Patterns of HEp-2 Cell Images

Automatic identification of mitotic type staining patterns in microscopy images is an important and challenging task, in computer-aided diagnosis (CAD) of autoimmune diseases. Such patterns are manifested on a HEp-2 based cell substrate and captured via Indirect immunoflourescence (IIF) based microscopy imaging technique. The present study proposes a deep metric learning methodology, in order to identify the mitotic staining patterns which are rather rare, among several other interphase patterns present in majority. Hence, the problem is framed as a mitotic v/s non-mitotic/interphase pattern classification problem. Here, the implemented network maps the input images into a latent space, in order to compare the distances between the samples, for class declaration, via a triplet-loss based framework. The framework yields good classification performance as max. 0.85 Matthews correlation coefficient in one case, with less false positive cases, when validated over a public dataset.