Reviewerly/exHarmony huggingface dataset:
SSN: Learning Sparse Switchable Normalization via SparsestMax

Normalization method deals with parameters training of convolution neural networks (CNNs) in which there are often multiple convolution layers. Despite the fact that layers in CNN are not homogeneous in the role they play at representing a prediction function, existing works often employ identical normalizer in different layers, making performance away from idealism. To tackle this problem and further boost performance, a recently-proposed switchable normalization (SN) provides a new perspective for deep learning: it learns to select different normalizers for different convolution layers of a ConvNet. However, SN uses softmax function to learn importance ratios to combine normalizers, not only leading to redundant computations compared to a single normalizer but also making model less interpretable. This work addresses this issue by presenting sparse switchable normalization (SSN) where the importance ratios are constrained to be sparse. Unlike $$\ell _1$$ and $$\ell _0$$ regularizations that impose difficulties in tuning layer-wise regularization coefficients, we turn this sparse-constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax. SSN has several appealing properties. (1) It inherits all benefits from SN such as applicability in various tasks and robustness to a wide range of batch sizes. (2) It is guaranteed to select only one normalizer for each normalization layer, avoiding redundant computations and improving interpretability of normalizer selection. (3) SSN can be transferred to various tasks in an end-to-end manner. Extensive experiments show that SSN outperforms its counterparts on various challenging benchmarks such as ImageNet, COCO, Cityscapes, ADE20K, Kinetics and MegaFace. Models and code are available at https://github.com/switchablenorms/Sparse_SwitchNorm .
A 4-level reference for self-adaptive processes based on SCOR and integrating Q-Learning

The supply chain operations reference (SCOR) is a model to improve, and communicate supply chain flows within an organization and with its suppliers and customers. In this paper we present L-SCOR an extension to SCOR to support the adaptive aspect of a supply chain, by adding to its six processes a seventh one sL Learn. We explain and present sL and its workflow as proposed, on the three levels of the supply chain covered by SCOR. Finally, a proposition to implement sL in the fourth level of a supply chain, using web service and Q-learning, is given.
A Robot Obstacle Avoidance Method Using Merged CNN Framework

In this paper, a merged convolution neural network (CNN) framework is proposed to automatically avoid obstacles. Although there are many methods for avoiding obstacles, previous methods mostly contain high energy-consuming and high cost. This paper aims to realize an image-based method with a monocular webcam. The experimental results illustrate that the proposed method can effectively avoid obstacles in mobile robot navigation.
A Driving Warning Method based on YOLOV3 and Neural Network

Unmanned driving warning is one of the important core issues of unmanned driving decision. This paper studies the method of prediction on the traffic warning information for unmanned driving warning, which is based on YOLOV3 and BP neural network. Firstly, weight training is carried out through self-made data set. Secondly, the trained YOLOV3 model is used to classify the image and video data: car, bicycle, bus, motorbike, person. Finally, the YOLOV3 target detection information will be classified into 5 categories by the BP neural network: safe, stop, slow, left, right. The result of the simulated experiment shows that the model works well. Based on different data set, the accuracy of the second classification is up to 0.99.
Logical Methods for Smart Contract Development

This article contains a description of the present state of business processes formalization developments. Currently existing formalized contracts automation methods have different functional limitations in application. The methodical approach to creating the tool for constructing formalized automated contracts containing a complex of hierarchically connected multilevel contracts is developed. Requirements to the tool for constructing formalized automated contracts (FACT) are defined, its structural components and mechanisms of interaction between them are described, the areas of application of the FACT and its prospects are defined.
Vision Based Deep Learning Approach for Dynamic Indian Sign Language Recognition in Healthcare

Healthcare for the deaf and dumb can be significantly improved with a Sign Language Recognition (SLR) system, which is capable of recognizing the medical terms. This paper is an effort to build such a system. SLR can be modelled as a video classification problem and here we have used a vision based deep learning approach. The dynamic nature of sign language poses additional challenge to the classification. This work explores the use of OpenPose with convolutional neural network (CNN) to recognize the sign language video sequences of 20 medical terms in Indian Sign Language (ISL) which are dynamic in nature. All the videos are recorded using common smartphone camera. The results show that even without the use of recurrent neural networks (RNN) to model the temporal information, the combined system works well with 85% accuracy. This eliminates the need for specialized camera with depth sensors or wearables. All the training and testing was done on CPU with the support of Google Colab environment.
Sport Teams Logo Detection Based On Deep Local Features

Logo detection in an unconstrained environment is a challenging task. The problem lies in the small size of logos that are to be detected and different representations of logos due to the view angle, rotation angle and perspective distortions. We provide a part of a new labelled SportLogo dataset for research. In this work we also propose a solution to the task of sport logos detection for the whole range of MLB, NBA and HNL teams. Ambiguity appears when developing the detection algorithm because of a huge number of occlusions and logo view changes due to wrinkle uniform during hockey games. Another problem is logo style changes for some teams. First, we propose an approach based on local descriptors calculation (SIFT, SURF, ORB, BRISK, FREAK, AKAZE) and provide the results of conducted experiments. Second, we propose an approach based on deep local features (DELF), which are pre-trained on Google Landmarks dataset, and compare them with classic local features. Experimental research showed that BRISK features provide the highest quality values on the labelled NHL teams dataset.
Modeling Uncertain Dynamic Plants With Interval Neural Networks by Bounded-Error Data

This paper presents a novel approach to building an interval dynamic model for an industrial plant with uncertainty by an interval neural network (INN). A new type of randomized learner model, named interval random vector functional-link network (IRVFLN), is proposed to take advantages of the inherent RVFLN in rapid modeling. The IRVFLN model is equipped with interval hidden input weights (and biases), which are randomly assigned from certain distribution/range and remain fixed, and the interval output weights can be evaluated by solving a couple of least squares problems. The comparative numerical experiments have verified the good potential of the proposed IRVFLN with the interval learner models produced by the error back-propagation algorithm. In the following modeling application, some measures for building IRVFLN with unknown but bounded (UBB) errors requirements are discussed in depth, in order to modeling an uncertain dynamic plant with IRVFLN by bounded-error data in either known or unknown error bounds. Finally, as a case study, the IRVFLN is applied to modeling a chemical interval dynamic plant with recycling, where the simulation results and generalization ability analysis demonstrate that the proposed method is suitable and effective.
Sentiment Analysis Using Residual Learning with Simplified CNN Extractor

Sentiment analysis has an important role in social media monitoring as it extracts public opinions, emotions, and feelings about certain products or services. There are several publications in building a system to identify opinions from text using rule-based approach, lexicon-based approach, or machine learning. In this paper, we propose and compare several deep learning models to solve sentiment analysis problem of the Internet Movie Database (IMDb) review sentiment dataset. The feature extractor consists of a convolutional layer, followed by a max pooling layer and a batch normalization layer. To solve the vanishing gradient problem, we use a residual connection to concatenate the input values with the extracted features before feeding the output into a recurrent layer. Our best model has an accuracy of 90.02%.
Shape descriptors - settling characteristics of irregular shaped particles

Shape descriptor is a concise yet informative representation to address challenging issues in areas as diverse as engineering, medicine and biology. The prediction of settling velocity and drag coefficient of non-spherical shaped particles as close as possible to their actual behavior has been an interest to engineering community. Often, correlations developed for predicting settling velocity do not accomplish the estimation of the drag coefficient. The solid particle shape characteristics need to be assessed with definitions of NRep and Ar suitably. Terfous et al. (2013 Terfous A, Hazzab A, Ghenaim A. 2013. Predicting the drag coefficient and settling velocity of spherical particles. Powder Technol. 239:12–20. doi:10.1016/j.powtec.2013.01.052[Crossref], [Web of Science ®] , [Google Scholar], Predicting the Drag Coefficient and Settling Velocity of Spherical Particles. Powder Technology. 239:12–20) presented a direct prediction of settling velocity for a spherical particle correlating NRep and Ar with good accuracy over a wide range of NRep. In this paper, appropriate scaling factors are suggested to represent NRep and Archimedes number and compared with data from in-house experiments and from published literature. The prediction is found to be in fair agreement with data from published work (cuboid and cylinders with various aspect ratio) and experimental data generated in-house with irregular shaped solid particles viz., sand, plastic, coal, raagi (Millets) and mustard seeds. HighlightsEstimation of settling velocity of different irregular shaped particles viz., sand, plastic, coal, mustard, raagi etc.A modified correlation to predict the settling velocity of irregular shaped particle by including shape descriptors viz, circularity, volume fraction, volume projected area diameter.The average error between the predicted and experimental data is found to be around 8.3%.
Speech recognition system based on deep neural network acoustic modeling for low resourced language-Amharic

In this paper automatic speech recognition is investigated using deep neural network (DNN) acoustic modeling method for Amharic language at syllabic acoustic units. In grapheme based database; graphemes/characters are basic units of lexicon and language model. A large portion of them represents syllables which are a combination of consonants and vowels (CV). Grapheme to phoneme (G2P) conversion was required to represent all text corpuses into CV phoneme representations via G2P conversion algorithm developed for this purpose. This algorithm used to develop syllable based pronunciation dictionary and language modeling which are vital for speech recognizer. DNN based acoustic model (AM) such as tanh-DNNs, tanh-fast-DNNs, p-norm-DNNs and p-norm-fast-DNNs are also explored with different hidden layers, hidden units and parameter settings. These DNN AMs are trained with morpheme based Amharic read speech in order to develop models. The recognition performance of our methods is evaluated in testing data and the reduced WER is achieved in p-norm-fast(p=2) DNN AMs.
Privacy-Preserving Deep Learning for Enabling Big Edge Data Analytics in Internet of Things

Big data analytics are pervasive in data-intensive systems and applications using machine learning and deep learning. IoT sensor devices are generating all types of big data, including structured (e.g., tables) and unstructured data (e.g., text and image), which are beyond the processing power of humans. However, how to conduct big data analysis on IoT data without compromising IoT privacy is still an open problem. IoT shall leverage powerful learning techniques to automatically learn patterns such as similarities, correlations and abnormalities from big sensing data in a privacy-preserving manner. To make this happen, we first examine distributed learning techniques that are suitable for IoT architectures. We then propose a privacy-preserving distributed learning framework with a novel dynamic deep learning mechanism to extract patterns and learn knowledge from IoT data. Simulations are performed to show the effectiveness and efficiency of our solution.
Spam Mail Detection and Prevention at Server Side

Spam is a genuine and irritating issue for quite a longtime. Despite the fact that a lot of arrangements have been advanced, there still remains a considerable measure to be advanced in separating spam messages all the more proficiently. These days a noteworthy issue in spam separating also as content characterization in common dialect handling is the colossal size of vector space because of the various element terms, which is normally the reason for broad figuring and moderate order. Extracting semantic implications from the substance of writings and utilizing these as highlight terms to develop the vector space, rather than utilizing words as highlight terms in convention ways, could decrease the component of vectors viably and advance the characterization in the meantime. In spite of the fact that there are a wide range of techniques to square spam messages, a large portion of program designers just mean to square spam messages from being conveyed to their customers. In this paper, we present an effective way to deal with keep spam messages from being exchanged.In this work, a Collaborative filtering approach with semantics-based text classification technology was proposed and the related feature terms were selected from the semantic meanings of the text content.
Instance-Based Video Search via Multi-Task Retrieval and Re-Ranking

With the rapid growth of video data, instance-based video search (INS), i.e., retrieving videos according to specific objects, places, actions etc., has become more and more practical and important. In this paper, a novel INS framework based on multi-task retrieval and re-ranking is proposed to retrieve particular person doing specific action. Firstly, a face matching scheme is designed to match the target persons from videos. Secondly, an object detection network and an improved two-pathway key-pose estimation network (IECO) are introduced to explore semantic depen-dences between static visual object and person's behavior. Based on the dependences, an initial INS ranklist is obtained. Thirdly, via encoding absolute and relative positions of person's poses, a new relative pose representation (RPR) method is presented. Finally, regarding RPR as the input, a light action recognition network is constructed to re-rank INS results. The experimental results on HMDB, UCF101, JHMDB and BBC Eastenders datasets demonstrate the effectiveness of the proposed INS framework.
Coverage Pattern Mining Based on MapReduce

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. However, developing fast and efficient parallel algorithms for handling large volumes of data is a challenging task. The MapReduce framework enables the distributed processing of huge amounts of data in large-scale distributed environment with robust fault-tolerance. In this paper, we propose a parallel algorithm for extracting coverage patterns. The results of our performance evaluation with real-world and synthetic datasets demonstrate that it is indeed feasible to extract coverage patterns effectively under the MapReduce framework.
Natural language interface for programming sensory-enabled scenarios for human-robot interaction

Previous research has shown that robot-mediated therapy may be effective in improving different mental or physical conditions, but this effectiveness strongly depends on how well the therapy can be translated to robot training. The goal of this study is to assist the end-users such as occupational and rehabilitation therapists to create without help of technical professional therapy-specific and sensory-enabled scenarios for the robotic assistant for use in an unstructured environment. The Cognitive Dimension of Notations framework was applied to assess the usability of the programming interface and the Cyclomatic complexity method was used to evaluate the complexity of the created robot scenarios. Eleven therapists with a mean age of 39 years working in the care for persons with visual-and-intellectual disabilities participated. The results show good usability of the interface, as measured via the CDN framework and the cyclomatic complexity analysis showed an increased complexity of the created by the occupational and rehabilitation therapist's scenarios. The participants did not request for very specifically defined behaviors for the robot, and therefore descriptions in natural text can be successfully used for robot programming.
A Jordan–Hölder type theorem for supercharacter theories

Abstract The Jordan–Hölder theorem is a general term given to a collection of theorems about maximal chains in suitably nice lattices. For example, the well-known Jordan–Hölder type theorem for chief series of finite groups has been rather useful in studying the structure of finite groups. In this paper, we present a Jordan–Hölder type theorem for supercharacter theories of finite groups, which generalizes the one for chief series of finite groups.
Calendar-Based Graphics for Visualizing People’s Daily Schedules

Calendars are broadly used in society to display temporal information and events. This article describes a new calendar display for plotting data that include a layout algorithm with many options, and faceting functionality. The functions use modular arithmetic on the date variable to restructure the data into a calendar format. The user can apply the grammar of graphics to create plots inside each calendar cell, and thus the displays synchronize neatly with ggplot2 graphics. The motivating application is studying pedestrian behavior in Melbourne, Australia, based on counts which are captured at hourly intervals by sensors scattered around the city. Faceting by the usual features such as day and month is insufficient to examine the behavior. Making displays on a monthly calendar format helps to understand pedestrian patterns relative to events such as work days, weekends, holidays, and special events. The functions for the calendar algorithm are available in the R package sugrrants. Supplementary materials for this article are available online.
VisMillion: A novel interactive visualization technique for real-time big data

The rapid increase of connected devices causes more and more data to be generated and, in some cases, this data needs to be analyzed as it is received. As such, the challenge of presenting streaming data in such way that changes in the regular flow can be detected needs to be tackled, so that timely and informed decisions can be made. This requires users to be able to perceive the information being received in the moment in detail, while maintaining the context. In this paper, we propose VisMillion, a visualization technique for large amounts of streaming data, following the concept of graceful degradation. It is comprised of several different modules positioned side by side, corresponding to different contiguous time spans, from the last few seconds to a historical view of all data received in the stream so far. Data flows through each one from right to left and, the more recent the data, the more detailed it is presented. To this end, each module uses a different technique to aggregate and process information, with special care to ensure visual continuity between modules to facilitate the analysis. VisMillion was validated through a usability evaluation with 21 participants, as well as performance tests. Results show that it fulfills its objective, successfully aiding users to detect changes, patterns and anomalies in the information being received.
The Application of LSTM Model to the Prediction of Abnormal Condition in Nuclear Power Plants

The Long Short-Term Memory (LSTM) model is investigated in this work, as a proposed prediction method for the abnormal condition in Nuclear Power Plants (NPPs). Its advantage of processing long timeline data is utilized to overcome the limitation of the traditional Recurrent Neural Network (RNN). With the assistance of the Rolling Update (RU) method, the LSTM model is trained using historical NPP operation data to obtain the capability of predicting abnormal trends. Such prediction ability is validated using simulated accident data, which demonstrates its prediction accuracy with a loss value of 3.7 × 10−6. Moreover, it is verified in this work that LSTM can predict the trends of accidents that belong to same category but differ in certain parameters.
An Improved Siamese Network for Face Sketch Recognition

Face sketch recognition identifies the face photo from a large face sketch dataset. Some traditional methods are typically used to reduce the modality gap between face photos and sketches and gain excellent recognition rate based on a pseudo image which is synthesized using the corresponded face photo. However, these methods cannot obtain better high recognition rate for all face sketch datasets, because the use of extracted features cannot lead to the elimination of the effect of different modalities' images. The feature representation of the deep convolutional neural networks as a feasible approach for identification involves wider applications than other methods. It is adapted to extract the features which eliminate the difference between face photos and sketches. The recognition rate is high for neural networks constructed by learning optimal local features, even if the input image shows geometric distortions. However, the case of overfitting leads to the unsatisfactory performance of deep learning methods on face sketch recognition tasks. Also, the sketch images are too simple to be used for extracting effective features. This paper aims to increase the matching rate using the Siamese convolution network architecture. The framework is used to extract useful features from each image pair to reduce the modality gap. Moreover, data augmentation is used to avoid overfitting. We explore the performance of three loss functions and compare the similarity between each image pair. The experimental results show that our framework is adequate for a composite sketch dataset. In addition, it reduces the influence of overfitting by using data augmentation and modifying the network structure.