Reviewerly/exHarmony huggingface dataset:
A Simple Ensemble Learning Algorithm For a Real Time High Dimensional Data

High dimension data is growing rapidly, which challenges the existing data mining and learning systems. A stable performance can be achieved precisely in high dimension data using machine learning algorithm, ensemble learning and feature selection methods. The ensemble classifiers like Decision tree, Random forest, support vector machine, Navies Bayes, Linear Discriminant Analysis are applied as a self-contained module and the output label is reliably produced by the fusion of majority voting for data analysis. At present a lot of investigations for reduction of high dimensional data set are implemented, but the study result shows only a minor improvement in accuracy. In this paper, a wrapper method is properly used to improve certain key features such as random state, tune max depth, tune number samples split, and tuned loss function. The proposed work is carried out using real-time garbage data set collected from the municipal corporation of Tiruchirappalli, India and the selected sample is tested with FGDT (Forward Gradient Boost Decision Tree) shows improvement in accuracy.
Balanced Rotation Symmetric Boolean Functions With Good Autocorrelation Properties

Rotation symmetric Boolean functions (RSBFs) are used widely in symmetric cryptography. In this paper, a systematic construction of balanced odd-variable RSBFs satisfying strict avalanche criterion is proposed. Hence, a class of (6k+3)-variable resilient RSBFs satisfying strict avalanche criterion is also presented for any k ≥ 2. Some of the obtained RSBFs have many other good cryptographic properties at the same time, that is, optimal algebraic degree, good global avalanche characteristics, high nonlinearity and nonexistence of nonzero linear structures. Moreover, we obtain some count results of RSBFs satisfying strict avalanche criterion. This is the first time that the autocorrelation properties of RSBFs are investigated systemically.
Architectural strategies for interoperability of software-intensive systems

Background: Currently, it is becoming increasingly common in the construction of more complex systems through the integration of existing and operational systems. To correctly construct these systems they must address interoperability requirements at different levels, i.e., technical, semantic, syntactic, and organizational. Several architectural strategies (i.e., architectural styles, patterns, and tactics) can be reused during such systems integration. However, there is a lack of evidence to orient which strategies and how they could be used during software integration practice. Objective: To investigate how architectural strategies have been used in practice to promote the interoperability of software-intensive systems. Method: We planned and executed an online survey with practitioners from different parts of the world, most of them with more than five years of experience in software integration. Results: We identified: (i) the main architectural strategies used in practice to promote different interoperability types in software-intensive systems; (ii) the difficulty level perceived by practitioners for using these strategies in real projects; (iii) the quality attributes more negatively impacted by these strategies; (iv) practitioners' suggestions to improve integration processes of software-intensive systems; (vi) common technologies used to integrate systems; (vii) and challenges perceived by practitioners during software-intensive systems integration. Conclusions: It is important to develop guidelines to assist practitioners in systematically selecting and applying the most suitable architectural strategies to their integration projects, analyzing the benefits and drawbacks of each strategy regarding other important quality attributes, such as security, reliability, and performance.
Persona in a form of mood boards as a part of the design process

Abstract The research aimed to design a mood board by using persona as a tool to understand consumers. Persona helps clients and designers better understand the target consumers of the organization and map consumers’ desires and needs. The research was a case study of Loewee Cipta Busana, one of Indonesia’s garment companies that planned to design a visual identity. However, to complete the final process of designing a visual identity, a visual guideline in the form of a mood board was required. The research aimed to illustrate the whole design process and to what extent a mood board could be a useful tool to communicate with clients; to minimize the waste of guessing what the client wants. The research’s result is a mood board which can help designers generate and develop ideas, keywords, and visual assets more quickly due to consumers’ understanding. Mood board helps designers to communicate with clients regarding the design concept. Mood board can also help clients have a clearer direction about what they can do to expand their network and communicate their offerings to the broader market.
Distributed Memory Graph Coloring Algorithms for Multiple GPUs

Graph coloring is often used in parallelizing scientific computations that run in distributed and multi-GPU environments; it identifies sets of independent data that can be updated in parallel. Many algorithms exist for graph coloring on a single GPU or in distributed memory, but hybrid MPI+GPU algorithms have been unexplored until this work, to the best of our knowledge. We present several MPI+GPU coloring approaches that use implementations of the distributed coloring algorithms of Gebremedhin et al. and the shared-memory algorithms of Deveci et al. The on-node parallel coloring uses implementations in KokkosKernels, which provide parallelization for both multicore CPUs and GPUs. We further extend our approaches to solve for distance-2 coloring, giving the first known distributed and multi-GPU algorithm for this problem. In addition, we propose novel methods to reduce communication in distributed graph coloring. Our experiments show that our approaches operate efficiently on inputs too large to fit on a single GPU and scale up to graphs with 76.7 billion edges running on 128 GPUs.
A Deep Learning Approach of English Vocabulary for Mobile Platform

To improve the effect of mobile learning, we should not only deepen learners' understanding of study, but also constantly improve the mobile learning mode. Therefore, from the perspective of English vocabulary deep learning, this paper investigates the main mobile vocabulary learning in the functional architecture and its advantages and disadvantages in promoting English vocabulary, and designs and develops a mobile platform oriented English vocabulary deep learning system. The development environment of the platform is J2EE development platform and the Android client uses MyEclipse ADT tool to develop the mobile client according to the Android development library. The scheme also realizes the communication between the client and the server. The system test analysis shows that the scheme can realize the intelligent evaluation of English deep learning, ensure the scientificity and objectivity of vocabulary deep learning, and improve the teaching efficiency.
Video super resolution enhancement based on two-stage 3D convolution

In recent years, computer vision has penetrated into all aspects of social production and life, especially the results achieved in image and video super-resolution processing are even more amazing. The speed and accuracy of image processing based on deep learning have reached a very good effect. Although video super-resolution is composed of multiple frames of pictures. But the difference between them is that the video has the characteristics of spatial and temporal continuity. The image processing method Processing video will cut the continuity between video frames. Because medical imaging [10], satellite imaging [11], surveillance [12] and user visual experience put forward higher requirements for video resolution. This article proposes a new solution for the video from low-quality original frames to generate high-quality super-resolution generated frames. The network structure increases the attention of neighboring HR frames, extracts basic network features, and optimizes the loss function.
High Performance Classification of Caltech-101 with a Transfer Learned Deep Convolutional Neural Network

Numerous models and working schemes have been proposed through decades for the successful recognition of the objects. Significant contributions are notable in the field of object recognition. However, near accurate recognition is still a challenge in this domain. In this research, we considered the Caltech-101 dataset having 102 diverse and imbalanced classes i.e., people, animals, landscapes, structures, furniture, etc. which made the recognition more complicated. We proposed and utilized modified InceptionV3 and modified EfficientNetB6 architectures for the recognition of objects which obtained 99.65% and 99.72% overall accuracy respectively. We further showed via experimental analysis that the softmax-averaging technique can further boost the accuracy to 99.85% and all three proposed procedures suppressed the previous studies by a notable boundary as well.
An Efficient Stereo Matching Network Using Sequential Feature Fusion

Recent stereo matching networks adopt 4D cost volumes and 3D convolutions for processing those volumes. Although these methods show good performance in terms of accuracy, they have an inherent disadvantage in that they require great deal of computing resources and memory. These requirements limit their applications for mobile environments, which are subject to inherent computing hardware constraints. Both accuracy and consumption of computing resources are important, and improving both at the same time is a non-trivial task. To deal with this problem, we propose a simple yet efficient network, called Sequential Feature Fusion Network (SFFNet) which sequentially generates and processes the cost volume using only 2D convolutions. The main building block of our network is a Sequential Feature Fusion (SFF) module which generates 3D cost volumes to cover a part of the disparity range by shifting and concatenating the target features, and processes the cost volume using 2D convolutions. A series of the SFF modules in our SFFNet are designed to gradually cover the full disparity range. Our method prevents heavy computations and allows for efficient generation of an accurate final disparity map. Various experiments show that our method has an advantage in terms of accuracy versus efficiency compared to other networks.
Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images

Most computer aided pathology detection systems rely on large volumes of quality annotated data to aid diagnostics and follow up procedures. However, quality assuring large volumes of annotated medical image data can be subjective and expensive. In this work we present a novel standardization framework that implements three few-shot learning (FSL) models that can be iteratively trained by atmost 5 images per 3D stack to generate multiple regional proposals (RPs) per test image. These FSL models include a novel parallel echo state network (ParESN) framework and an augmented U-net model. Additionally, we propose a novel target label selection algorithm (TLSA) that measures relative agreeability between RPs and the manually annotated target labels to detect the “best” quality annotation per image. Using the FSL models, our system achieves 0.28-0.64 Dice coefficient across vendor image stacks for intra-retinal cyst segmentation. Additionally, the TLSA is capable of automatically classifying high quality target labels from their noisy counterparts for 60-97% of the images while ensuring manual supervision on remaining images. Also, the proposed framework with ParESN model minimizes manual annotation checking to 12-28% of the total number of images. The TLSA metrics further provide confidence scores for the automated annotation quality assurance. Thus, the proposed framework is flexible to extensions for quality image annotation curation of other image stacks as well.
Human or Machine? It Is Not What You Write, But How You Write It

Online fraud often involves identity theft. Since most security measures are weak or can be spoofed, we investigate a more nuanced and less explored avenue: behavioral biometrics via handwriting movements. This kind of data can be used to verify whether a user is operating a device or a computer application, so it is important to distinguish between human and machine-generated movements reliably. For this purpose, we study handwritten symbols (isolated characters, digits, gestures, and signatures) produced by humans and machines, and compare and contrast several deep learning models. We find that if symbols are presented as static images, they can fool state-of-the-art classifiers (near 75% accuracy in the best case) but can be distinguished with remarkable accuracy if they are presented as temporal sequences (95% accuracy in the average case). We conclude that an accurate detection of fake movements has more to do with how users write, rather than what they write. Our work has implications for computerized systems that need to authenticate or verify legitimate human users, and provides an additional layer of security to keep attackers at bay.
On the Global Self-attention Mechanism for Graph Convolutional Networks

Applying Global Self-attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs). However, it is not clear if Graph Convolutional Networks (GCNs) can similarly benefit from such a technique. In this paper, inspired by the similarity between CNNs and GCNs, we study the impact of the Global Self-attention mechanism on GCNs. We find that consistent with the intuition, the GSA mechanism allows GCNs to capture feature-based vertex relations regardless of edge connections; As a result, the GSA mechanism can introduce extra expressive power to the GCNs. Furthermore, we analyze the impacts of the GSA mechanism on the issues of overfitting and over-smoothing. We prove that the GSA mechanism can alleviate both the overfitting and the over-smoothing issues based on some recent technical developments. Experiments on multiple benchmark datasets illustrate both superior expressive power and less significant overfitting and over-smoothing problems for the GSA-augmented GCNs, which corroborate the intuitions and the theoretical results.
Classification of Pavement Disease 3D Point Cloud Images Based on Deep Learning Network

In order to solve the problem that the linear array image of the two-dimensional road surface is easily affected by factors such as illumination changes, shadows, this thesis carried out the research on the design of the intelligent perception system of the three-dimensional information of the highway road surface. This paper first analyzes the design requirements of the intelligent perception system for 3D information on highway pavement, studies the cooperative working mechanism of various high-precision sensors, and proposes a hardware system architecture scheme for 3D information perception of pavement diseases based on multiple sensors. Then, a method of “blocking” classification of road 3D point cloud images based on deep learning network model is proposed. Based on the construction of a small block road point cloud data set, an experimental comparative study is carried out on Pointnet and Pointnet++ networks. The experimental results show that the recognition rate of pavement diseases is better than 86.7% and 90.0%.
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses

Despite the fact that the loss functions of deep neural networks are highly nonconvex, gradient-based optimization algorithms converge to approximately the same performance from many random initial points. One thread of work has focused on explaining this phenomenon by numerically characterizing the local curvature near critical points of the loss function, where the gradients are near zero. Such studies have reported that neural network losses enjoy a no-bad-local-minima property, in disagreement with more recent theoretical results. We report here that the methods used to find these putative critical points suffer from a bad local minima problem of their own: they often converge to or pass through regions where the gradient norm has a stationary point. We call these gradient-flat regions, since they arise when the gradient is approximately in the kernel of the Hessian, such that the loss is locally approximately linear, or flat, in the direction of the gradient. We describe how the presence of these regions necessitates care in both interpreting past results that claimed to find critical points of neural network losses and in designing second-order methods for optimizing neural networks.
Extracting features from app descriptions based on POS and dependency

Automatically extracting application features from their descriptions has become an efficient way to understand user requirements and accumulate related domain knowledge. Existing approaches typically extract features based on their syntactic patterns, which may lead to lots of false positives as the sentences to be processed do not contain any features. In this paper, we first propose a POS-weighted sentence classifier based on advanced word embedding techniques to filter non-feature-containing sentences before feature extraction. Specifically, we assign different POS tags with different weights according to their importance in sentences. Secondly, we defined a group of patterns with dual constraints of POS and dependency relationship, then match phrases from each feature-containing sentence to obtain features. To evaluate the performance of our classifier, we rigorously produced a dataset with corresponding annotations. The result shows that our classifier can successfully filter out 79% of non-feature-containing sentences. Applying our method to eight applications, it outperforms the state-of-the-art approach in precision, recall, and f-measure.
Efficient Resource-Aware Neural Architecture Search with Dynamic Adaptive Network Sampling

The multi-objective neural architecture search (NAS) can automatically realize the network design for high accuracy and high hardware performance for varied applications. However, the existing methods usually need to sample a large number of networks in the search process to guide the controller's search behavior. As a result, the entire search process requires a huge search time overhead. We propose a NAS framework that can perform dynamic adaptive network sampling that regulated by the latency requirements for specific devices and application scenarios. In the layer-wise network sampling process, the sampling probability for each layer is adjusted dynamically according to the current remaining available inference latency space. So that the latency of the sampled network will close to the required latency. Thereby, reducing useless network sampling and improving the search efficiency. Experimental results show that the search efficiency has a 2.35× speedup.
Color Harmonization, Deharmonization and Balancing in Augmented Reality

Color schemes play a crucial role in blending virtual objects with the real environment. Good color schemes improve user’s perception, which is of crucial importance for augmented reality. In this paper, we propose a set of novel methods based on the color harmonization methodology to recolor augmented reality content according to the real background. Three different strategies are proposed—harmonic, disharmonic, and balance—that allow for satisfying different needs in different settings depending on the application field. The first approach aims to harmonize the colors of virtual objects to make them consistent with the colors of the real background and reach a more pleasing effect to a human eye. The second approach, instead, can be adopted to generate a set of disharmonious colors with respect to real ones to be associated with the augmented virtual content to improve its distinctiveness from the real background. The third approach balances these goals by achieving a compromise between harmony and good visibility among virtual and real objects. Furthermore, the proposed re-coloring method is applied to three different case studies by adopting the three strategies to meet three different objectives, which are specific for each case study. Several parameters are calculated for each test, such as the covered area, the color distribution, and the set of generated colors. Results confirm the great potential of the proposed approaches to improve the AR visualization in different scenarios.
Compositional Generative Networks and Robustness to Perceptible Image Changes

Current Computer Vision algorithms for classifying objects, such as Deep Nets, lack robustness to image changes which, although perceptible, would not fool a human observer. We quantify this by showing how performances of Deep Nets degrades badly on images where the objects are partially occluded and degrades even worse on more challenging and adversarial situations where, for example, patches are introduced in the images to target the weak points of the algorithm. To address this problem we develop a novel architecture, called Compositional Generative Networks (Compositional Nets) which is innately robust to these types of image changes. This architecture replaces the fully connected classification head of the deep network by a generative compositional model which includes an outlier process. This enables it, for example, to localize occluders and subsequently focus on the non-occluded parts of the object. We conduct classification experiments in a variety of situations including artificially occluded images, real images of partially occluded objects from the MS-COCO dataset, and adversarial patch attacks on PASCAL3D+ and the German Traffic Sign Recognition Benchmark. Our results show that Compositional Nets are much more robust to occlusion and adversarial attacks, like patch attacks, compared to standard Deep Nets, even those which use data augmentation and adversarial training. Compositional Nets can also accurately localize these image changes, despite being trained only with class labels. We argue that testing vision algorithms in an adversarial manner which probes for the weakness of the algorithms, e.g., by patch attacks, is a more challenging way to evaluate them compared to standard methods, which simply test them on a random set of samples, and that Compositional Nets have the potential to overcome such challenges.
Oversampling using Fuzzy Rough Set Theory in Imbalanced Neural based Diabetic patient Readmission Prediction: A hybrid approach

Diabetes is a long-term illness and can lead to a variety of other complications health-wise. The growth in the counts of diabetic patients has taken a sharp hike lately which eventually resulted in an increased rate of patient admissions. Readmission rates determine the quality of the service provided by a hospital. Readmissions not only foist a massive financial burden on patients but is also responsible for an inconvenient environment for a patient. This can be avoided if high-risk patients are identified by utilizing robust machine learning approaches like Artificial Neural Network (ANN). The task becomes challenging when the Class Imbalance problem arises during the training phase of the model. The problem is very much evident in re-al-life applications with ANNs and is also prevalent in readmission prediction. In absence of any concrete study focusing on the problem in this domain, our paper proposes a Fuzzy Rough Set Theory based artificial data oversampling approach to mitigate the Class Imbalance problem while predicting high-risk diabetic patient. Two variations of the technique are tested and compared with other existing techniques. The approach is capable of reducing the effects of imbalanced classes and has resulted in enhanced performance of the ANN. The approach has also enhanced the performances of other machine learning models.