Reviewerly/exHarmony huggingface dataset:
A Nonlinear Feature Fusion-Based Rating Prediction Algorithm in Heterogeneous Network

Due to the flexibility of heterogeneous information networks (HINs) in modeling heterogeneous data, researchers begin to use it to integrate the objects and relationships in recommender systems. However, how to abstract and exploit effective information and apply the information to recommender systems is a challenge. To fully mine nodes' structural features and better integrate these features simultaneously, we present a nonlinear feature fusion-based rating prediction algorithm. This algorithm first uses a meta-path-based HIN embedding model to extract the nodes' structural features. Then, the structural features are converted by a nonlinear fusion method. Finally, the fused features are input into the multilayer perceptron to achieve rating prediction. Experiments on real-life data sets, such as Movielens-100k, Yelp, Douban Book, and Douban Movie, are designed to prove the performance of the proposed model. Experimental results on the four data sets reveal that our algorithm is superior to the baselines.
Cross-Domain Identification for Thermal-to-Visible Face Recognition

Recent advances in domain adaptation, especially those applied to heterogeneous facial recognition, typically rely upon restrictive Euclidean loss functions (e.g., L2 norm) which perform best when images from two different domains (e.g., visible and thermal) are co-registered and temporally synchronized. This paper proposes a novel domain adaptation framework that combines a new feature mapping sub-network with existing deep feature models, which are based on modified network architectures (e.g., VGG16 or Resnet50). This framework is optimized by introducing new cross-domain identity and domain invariance lossfunctions for thermal-to-visible face recognition, which alleviates the requirement for precisely co-registered and synchronized imagery. We provide extensive analysis of both features and loss functions used, and compare the proposed domain adaptation framework with state-of-the-art feature based domain adaptation models on a difficult dataset containing facial imagery collected at varying ranges, poses, and expressions. Moreover, we analyze the viability of the proposed framework for more challenging tasks, such as non-frontal thermal-to-visible face recognition.
Seq2Emoji: A hybrid sequence generation model for short text emoji prediction

As a new form of visual language, emojis are widely used in social media for their vivid image and rich meaning. Predicting the most likely emojis that fit a particular short text has become an important and challenging task in both academia and industry. In this paper, we propose a hybrid sequence generation model, Seq2Emoji, to predict multiple emojis based on a short text. Seq2Emoji is an encoder–decoder model, in which we consider the correlations between emojis and take the emoji prediction task as a sequence generation problem. It extracts features through a hierarchical structure and self-attention mechanism and decodes them with a composite recurrent neural network before predicting emojis. During the prediction, Diverse Beam Search algorithm is also introduced to increase the diversity of predicted emojis. Experiments are carried out on our collected Weibo dataset (Chinese) and the results show that our proposed Seq2Emoji model is superior to the competitive models in both accuracy and diversity of emoji prediction.
Mass and Calcification Detection from Digital Mammograms Using UNets

Today Computer Aided Detection (CAD) systems are used to help radiologists in identifying and analyzing mammogram abnormalities. More recently, Convolutional Neural Networks have been proven effective in identifying abnormalities such as masse and calcification. Certain types of masse and clustered calcifications in mammograms are considered malignant growth. In this paper, we present an approach to detect the presence of these abnormalities, and segment both masses and calcifications in mammogram images. We use a fully convolutional architecture (UNet) trained to segment mass and calcification. The UNet for mass segmentation is trained on the CBIS-DDSM digitized image dataset. The InBreast dataset, which provides full field digital mammograms, has been used to train calcification segmentation.
Design of embedded dance teaching control system based on FPGA and motion recognition processing

The complex human activities, to the dance teaching, it has not been constrained, it is a difficult task for computer vision. Field-programmable gate array (FPGA) a classification of the movement of the Indian classical dance by using the powerful features of the built-in system tools. Human behavior recognition for this work, classical dance education in India from offline and live performance to the online control of material in the carried out of the guidance record of audio and dance, in. Another theme and 10 familiar dance of 200 m / bills have created, is the offline data of guidance form of a variety of classical dance in various postures. The data collection of a variety of topics. Or education attitude accounted for 60, Dance education in both cases. FPGA training and eight different sample size, will be performed by each of the multi-group object. The remaining two samples are tested, it has been trained on the FPGA. Different FPGA architecture design, and excellent recognition accuracy, because to get our test data. Compared to the same data set and of other classification model report, the recognition rate.
A Training Method for Low Rank Convolutional Neural Networks Based on Alternating Tensor Compose-Decompose Method

Over the past decade, deep learning-based computer vision methods have been shown to surpass previous state-of-the-art computer vision techniques in various fields, and have made great progress in various computer vision problems, including object detection, object segmentation, face recognition, etc. Nowadays, major IT companies are adding new deep-learning-based computer technologies to edge devices such as smartphones. However, since the computational cost of deep learning-based models is still high for edge devices, research is being actively carried out to compress deep learning-based models while not sacrificing high performance. Recently, many lightweight architectures have been proposed for deep learning-based models which are based on low-rank approximation. In this paper, we propose an alternating tensor compose-decompose (ATCD) method for the training of low-rank convolutional neural networks. The proposed training method can better train a compressed low-rank deep learning model than the conventional fixed-structure based training method, so that a compressed deep learning model with higher performance can be obtained in the end of the training. As a representative and exemplary model to which the proposed training method can be applied, we propose a rank-1 convolutional neural network (CNN) which has a structure alternatively containing 3-D rank-1 filters and 1-D filters in the training stage and a 1-D structure in the testing stage. After being trained, the 3-D rank-1 filters can be permanently decomposed into 1-D filters to achieve a fast inference in the test time. The reason that the 1-D filters are not being trained directly in 1-D form in the training stage is that the training of the 3-D rank-1 filters is easier due to the better gradient flow, which makes the training possible even in the case when the fixed structured network with fixed consecutive 1-D filters cannot be trained at all. We also show that the same training method can be applied to the well-known MobileNet architecture so that better parameters can be obtained than with the conventional fixed-structure training method. Furthermore, we show that the 1-D filters in a ResNet like structure can also be trained with the proposed method, which shows the fact that the proposed method can be applied to various structures of networks.
Predicting Hourly Solar Irradiance Using Machine Learning Methods

Accurate prediction of solar irradiance for steady supply of electrical energy has always been a difficult task both in the field of physical simulation or artificial intelligence. If solar irradiance could be predicted with very high precision, it would be easier for power grid operators to take corrective actions in advance to ensure failsafe outcomes during periods of sudden or cascading drops in solar energy. In this paper, three machine learning techniques were used to forecast hourly solar irradiance of Johannesburg city using historical meteorological data of two consecutive years. The goal was to build a model that would readily utilize historical meteorological data to predict highly accurate solar irradiance information on an hourly basis. Results obtained showed that Support Vector Regression (SVR) with normalized Root Mean Square Error (nRMSE) of 7.2% gave the best overall performance. This was followed by Artificial Neural Network (ANN) and Random forest respectively.
Neural Text Normalization in Speech-to-Text Systems with Rich Features

This paper presents the task of normalizing Vietnamese transcribed texts in Speech-to-Text (STT) systems. The main purpose is to develop a text normalizer that automatically converts proper nouns and other context-specific formatting of the transcription such as dates, time, and numbers into their appropriate expressions. To this end, we propose a solution that exploits deep neural networks with rich features followed by manually designed rules to recognize and then convert these text sequences. We also introduce a new corpus of 13 K spoken sentences to facilitate the process of the text normalization. The experimental results on this corpus are quite promising. The proposed method yields 90.67% in the F1 score in recognizing sequences of texts that need converting. We hope that this initial work will inspire other follow-up research on this important but unexplored problem.
Speculative Data Work &amp; Dashboards

This paper studies data work in an organizational context, and suggests speculative data work as a useful concept and the speculative dashboard as a design concept, to better understand and support cooperative work. Drawing on fieldwork in a Danish public sector organisation, the paper identifies and conceptualizes the speculative data work performed around processes of digitalization and the push to become data-driven. The speculative dashboard is proposed as a design concept and opportunity for design, using practices from speculative design and research to facilitate speculation about data?its sources, visualizations, practices and infrastructures. It does so by hacking the 'genre' of the business intelligence data dashboard, and using it as a framework for the juxtaposition of different kinds of data, facilitating and encouraging speculation on alternative visions for data types and use. The paper contributes an empirical study of organizational use of and attitudes towards data, informing a novel design method and concept for co-speculating on alternative visions of and for organizational data.
Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

In the age of big data, we are constantly inventing new data visualizations to consolidate massive amounts of numerical information into smaller and more digestible visual formats. These data visualizations use various visual features to convey quantitative information, such as spatial position in scatter plots, color saturation in heat maps, and area in dot maps. These data visualizations are typically composed of ensembles, or groups of related objects, that together convey information about a data set. Ensemble perception, or one's ability to perceive summary statistics from an ensemble, such as the mean, has been used as a foundation for understanding and explaining the effectiveness of certain data visualizations. However, research in data visualization has revealed some perceptual biases and conceptual difficulties people face when trying to utilize the information in these graphs. In this tutorial review, we will provide a broad overview of research conducted in ensemble perception, discuss how principles of ensemble encoding have been applied to the research in data visualization, and showcase the barriers graphs can pose to learning statistical concepts, using histograms as a specific example. The goal of this tutorial review is to highlight possible connections between three areas of research-ensemble perception, data visualization, and statistics education-and to encourage research in the practical applications of ensemble perception in solving real-world problems in statistics education.
Visual relationship detection with recurrent attention and negative sampling

Detecting relationships between objects is important for the complete understanding of visual scenes, which will be helpful for applications such as visual question answering, image search, and robotic interactions. It is however a challenging task due to the high variation of object appearance and interactions, and the often incomplete annotations. In this paper, we propose a fast method for visual relationship detection based on recurrent attention and negative sampling. First, to learn non-visual features, we use the Word2Vec model to extract semantic embedding features of object categories, and use binary masks to represent spatial location features. And we integrate the recurrent attention mechanism into the detection pipeline, enabling the network to focus on several specific parts of an image when scoring predicates for a given object pair. Then we use an undersampling technique to alleviate the influence of imbalanced annotations, particularly for zero-shot detection. The proposed method is simple but experiments prove that it is efficient and achieves state-of-the-art results on the benchmark VRD and Visual Genome (VG) datasets in most cases.
Highly accurate imaging based position measurement using holographic point replication

The experimental approach of a highly accurate imaging based stereo measurement setup with a total measurement volume of 140 mm × 100 mm × 50 mm is presented. The system can detect the position of multiple target points (LEDs) in a calibrated volume of 100 mm × 75 mm × 24 mm with an accuracy of σx=0.367μm, σy=0.373μm and σz=0.437μm. This high accuracy is reached by holographically replicating each point light source to a predefined pattern (cluster) of spots on the camera sensor and averaging the center positions of all spots. We present a detailed description of the principle, the process and results of calibration including deformation compensation and stability measurements, as well as the results of two- and three-dimensional trajectory measurements. To validate the performance and accuracy of the measurement setup, interferometers are employed as reference sensors. The residual error of a two-dimensional trajectory of distance D=277μm is σd=0.242μm and for a three-dimensional trajectory of distance D=57.2 mm it is σd = 0.674 μm.
Robust Multi-Label Relief Feature Selection Based on Fuzzy Margin Co-Optimization

Feature extraction is one of the most important tasks in multi-label learning. The performance of multi-label classification can be effectively improved by reducing the dimension of multi-label datasets. Although research on multi-label feature extraction has received extensive attention and made significant progress, further improvement is still necessary. One of the concerns is robustness, where existing multi-label feature extraction algorithms are usually sensitive to noise and outliers. To address this issue, a robust multi-label relief feature selection algorithm based on fuzzy margin co-optimization, called ML-FS-FM, is proposed in this article. Under the multi-label learning framework, the classical fuzzy relief feature weighting algorithm is introduced to ML-FS-FM, which involves the mechanisms of fuzzy feature weighting, fuzzy nearest neighbor and fuzzy instance force coefficient, so as to effectively reduce the influence of noise and outliers, and to extract feature subsets that are beneficial to the classification task. The effectiveness of the proposed algorithm is verified by a large number of experiments on multi-label datasets.
Self-Distillation for Few-Shot Image Captioning

The development of large-scale image-captioning datasets is expensive, while the abundance of unpaired images and text corpus can potentially help reduce the efforts of manual annotation. In this paper, we study the few-shot image captioning problem that only requires a small amount of annotated image-caption pairs. We propose an ensemble- based self-distillation method that allows image captioning models to be trained with unpaired images and captions. The ensemble consists of multiple base models trained with different data samples in each iteration. For learning from unpaired images, we generate multiple pseudo captions with the ensemble and allocate different weights according to their confidence levels. For learning from unpaired captions, we propose a simple yet effective pseudo feature generation method based on Gradient Descent. The pseudo captions and pseudo features from the ensemble are used to train the base models in future iterations. The proposed method is general over different image captioning models and datasets. Our experiments demonstrate significant performance improvements and meaningful captions generated with only 1% of paired training data. Source code is available at https://github.com/chenxy99/SD-FSIC.
Deep Learning Approach to DGA Classification for Effective Cyber Security

In recent years, invaders are increasing rapidly in an internet world. Generally, in order to detect the anonymous attackers algorithm needs more number of features. Many algorithms fail in the efficiency of detection malicious code. Immediately this codes will not infect the system; it will attack server after communicate later. Our research focuses on analyzing the traffic of botnets for the domain name determination to the IP address of the server. This botnet creates the domain name differently. Many domains are generated by attackers and create the huge Domain Name System (DNS) traffic. In this research paper, uses both public and real time environments datasets to detect the text features as well as knowledge based feature extraction. The classifying of Domain Generation Algorithm (DGA) generated malicious domains randomly making the efficiency down in many algorithms which were used preprocessing without proper feature extraction. Effectively, our proposed algorithm is used to detect DGA which generates malicious domains randomly. This effective detection of our proposed algorithm performs with text based label prediction and additional features for extraction to improve the efficiency of the model. Our proposed model achieved 94.9% accuracy for DGA classification with help of additional feature extraction and knowledge based extraction in the deep learning architecture.
Finding the Suitable Doppelgänger for a Face Morphing Attack

ID cards are uniquely linked to one individual via a printed or electronically provided facial image. Even though the face is treated as universal and distinctive characteristic, twins can weaken this distinctiveness because of their biological similarity. Also, humans might falsely recognise an unknown person as a friend - colloquially named a Dop-pelgänger. Recently it was demonstrated that this biological effect of similar data subjects can be purposefully established between two individuals in order to improve the vulnerability of the so-called morphing attack. This image manipulation technique creates a melted facial image which is similar to two or more data subjects. If embedded into an ID card, the manipulated reference image can be used by all participating individuals and thus the concept of a unique link is broken. This work elaborates the rather neglected part of selecting morph pairs based on a similarity score instead of a simple random assignment. It discusses the applicability of different possible algorithms. The finally developed approach considers complex real-world constraints while being executable in a reasonable amount of time and producing acceptable large morph sets. It is shown that this algorithm greatly increases the vulnerability of automated face recognition systems. Surprisingly, it also proves that an effective pre-selection of pairs questions the need of in-depth optimized morphing algorithms.
Ant Colony Optimization Using Common Social Information and Self-Memory

Ant colony optimization (ACO), which is one of the metaheuristics imitating real ant foraging behavior, is an eﬀective method to ﬁnd a solution for the traveling salesman problem (TSP). The rank-based ant system (ASrank) has been proposed as a developed version of the fundamental model AS of ACO. In the ASrank, since only ant agents that have found one of some excellent solutions are let to regulate the pheromone, the pheromone concentrates on a specific route. As a result, although the ASrank can find a relatively good solution in a short time, it has the disadvantage of being prone falling into a local solution because the pheromone concentrates on a specific route. This problem seems to come from the loss of diversity in route selection according to the rapid accumulation of pheromones to the specific routes. Some ACO models, not just the ASrank, also suffer from this problem of loss of diversity in route selection. It can be considered that the diversity of solutions as well as the selection of solutions is an important factor in the solution system by swarm intelligence such as ACO. In this paper, to solve this problem, we introduce the ant system using individual memories (ASIM) aiming to improve the ability to solve TSP while maintaining the diversity of the behavior of each ant. We apply the existing ACO algorithms and ASIM to some TSP benchmarks and compare the ability to solve TSP.
Deep Interactive Thin Object Selection

Existing deep learning based interactive segmentation methods have achieved remarkable performance with only a few user clicks, e.g. DEXTR [32] attaining 91.5% IoU on PASCAL VOC with only four extreme clicks. However, we observe even the state-of-the-art methods would often struggle in cases of objects to be segmented with elongated thin structures (e.g. bug legs and bicycle spokes). We investigate such failures, and find the critical reasons behind are two-fold: 1) lack of appropriate training dataset; and 2) extremely imbalanced distribution w.r.t. number of pixels belonging to thin and non-thin regions. Targeted at these challenges, we collect a large-scale dataset specifically for segmentation of thin elongated objects, named ThinObject-5K. Also, we present a novel integrative thin object segmentation network consisting of three streams. Among them, the high-resolution edge stream aims at preserving fine-grained details including elongated thin parts; the fixed-resolution context stream focuses on capturing semantic contexts. The two streams' outputs are then amalgamated in the fusion stream to complement each other for help producing a refined segmentation output with sharper predictions around thin parts. Extensive experimental results well demonstrate the effectiveness of our proposed solution on segmenting thin objects, surpassing the baseline by ~ 30% IoU <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">thin</sub> despite using only four clicks. Codes and dataset are available at https://github.com/liewjunhao/thin-object-selection.