Reviewerly/exHarmony huggingface dataset:
Piecewise-linear particle swarm optimizer, basic dynamics, reference frame invariance, and search performance

Swarm intelligence (SI) algorithms have been studied in solving real-world optimization problems called black-box optimization problems. Typical features of SI algorithms are: (1) being a population-based metaheuristics; (2) using fitness values of a given objective function; and (3) having very simple search rules which search agents follow. As such, SI algorithms have been applied to various black-box optimization problems. Particle swarm optimization is one of powerful SI algorithms, in which a swarm consists of plural particles as solution candidates. Particles directly fly a search space and share their own information each other, and thus PSO can find good quality of solutions. However, a PSO swarm is easily stuck in solving optimization problems whose search space is high-dimensional and complicated. In order to solve such problems, large numbers of particles and reference frame invariance are needed for PSO algorithms. Herein, we suggest a piecewise-linear particle swarm optimizer (PPSO) which is a deterministic PSO. PPSO has two simple search modes switched to another mode dynamically, whose search dynamics are complex. As such, PPSO algorithm can be implemented on hardware with low hardware costs because PPSO algorithm must not require many random number generators. In addition, PPSO algorithm can find a good quality of solution in solving complex optimization problems. We studied search performances of PPSO compared to PSO algorithms and provide theoretical analysis of reference frame invariance for PPSO. In order to verify search performances and theoretical analysis, we performed numerical simulations.
AN EFFICIENT BREAST CANCER RECURRENCE PREDICTION USING SIMULATED ANNEALING INERTIA WEIGHT-BASED CHICKEN SWARM OPTIMIZATION ALGORITHM WITH WEIGHTED MINKOWSKI RADIAL BASIS FUNCTION-BASED SUPPORT VECTOR MACHINE

Nowadays the more often diagnosed type of cancer in women is breast cancer. Approximately 12 percent of females worldwide are afflicted by it. Recurrent breast cancer refers to breast cancer that recurs despite having been successfully treated. Breast cancer recurrence is one of the most feared outcomes for cancer patients. As a result, their quality of life is impacted. Even though early-stage breast cancer prediction has always been a difficult research challenge, data mining algorithms can be of considerable assistance in addressing this issue. The prior method used naive Bayes, REPTree, and K-nearest neighbor, Particle Swarm Optimization (PSO) based feature selection for breast cancer recurrence prediction. It is simple for the Particle Swarm Optimization (PSO) method to fall into local optimum in high-dimensional space and has a poor rate of convergence in the iterative process. Furthermore, because of the Nave Bayes’ class conditional independence and the resulting loss of accuracy, it is not recommended. To deal with this problem, the proposed system devised a Simulated Annealing Inertia Weight-based Chicken Swarm Optimization (SAIWCSO) algorithm with a Weighted Minkowski Radial Basis Function-based Support Vector Machine (WMRBF-SVM) for the diagnosis of the reputation of breast cancer. The WBCD is used as an initial source of data. Z-score normalization is then used to ensure that the data is in a consistent format. An algorithm called SAIWCS selects the best features. It enhances the categorization accuracy. To improve the high detection rate, a Weighted Minkowski Radial Basis Function-based Support Vector Machine (WMRBF-SVM) is used to diagnose breast cancer. Python is being used to model the experiments. The experimental findings reveal that the suggested system outperforms the present system with regard to the accuracy, precision, recall, specificity, and f-measure.
Text Classification Models for Form Entity Linking

Forms are a widespread type of template-based document used in a great variety of fields including, among others, administration, medicine, finance, or insurance. The automatic extraction of the information included in these documents is greatly demanded due to the increasing volume of forms that are generated in a daily basis. However, this is not a straightforward task when working with scanned forms because of the great diversity of templates with different location of form entities, and the quality of the scanned documents. In this context, there is a feature that is shared by all forms: they contain a collection of interlinked entities built as key-value (or label-value) pairs, together with other entities such as headers or images. In this work, we have tacked the problem of entity linking in forms by combining image processing techniques and a text classification model based on the BERT architecture. This approach achieves state-of-the-art results with a F1-score of 0.80 on the FUNSD dataset, a 5% improvement regarding the best previous method. The code of this project is available at https://github.com/mavillot/FUNSD-Entity-Linking.
FitAct: Error Resilient Deep Neural Networks via Fine-Grained  Post-Trainable Activation Functions

Deep neural networks (DNNs) are increasingly being deployed in safety-critical systems such as personal healthcare devices and self-driving cars. In such DNN-based systems, error resilience is a top priority since faults in DNN inference could lead to mispredictions and safety hazards. For latency-critical DNN inference on resource-constrained edge devices, it is nontrivial to apply conventional redundancy-based fault tolerance techniques. In this paper, we propose FitAct, a low-cost approach to enhance the error resilience of DNNs by deploying fine-grained post-trainable activation functions. The main idea is to precisely bound the activation value of each individual neuron via neuron-wise bounded activation functions so that it could prevent fault propagation in the network. To avoid complex DNN model re-training, we propose to decouple the accuracy training and resilience training and develop a lightweight post-training phase to learn these activation functions with precise bound values. Experimental results on widely used DNN models such as AlexNet, VGG16, and ResNet50 demonstrate that FitAct outperforms state-of-the-art studies such as Clip-Act and Ranger in enhancing the DNN error resilience for a wide range of fault rates while adding manageable runtime and memory space overheads.
A Method for Evaluating the Capacity of Generative Adversarial Networks  to Reproduce High-order Spatial Context

Generative adversarial networks are a kind of deep generative model with the potential to revolutionize biomedical imaging. This is because GANs have a learned capacity to draw whole-image variates from a lower-dimensional representation of an unknown, high-dimensional distribution that fully describes the input training images. The overarching problem with GANs in clinical applications is that there is not adequate or automatic means of assessing the diagnostic quality of images generated by GANs. In this work, we demonstrate several tests of the statistical accuracy of images output by two popular GAN architectures. We designed several stochastic object models (SOMs) of distinct features that can be recovered after generation by a trained GAN. Several of these features are high-order, algorithmic pixel-arrangement rules which are not readily expressed in covariance matrices. We designed and validated statistical classifiers to detect the known arrangement rules. We then tested the rates at which the different GANs correctly reproduced the rules under a variety of training scenarios and degrees of feature-class similarity. We found that ensembles of generated images can appear accurate visually, and correspond to low Frechet Inception Distance scores (FID), while not exhibiting the known spatial arrangements. Furthermore, GANs trained on a spectrum of distinct spatial orders did not respect the given prevalence of those orders in the training data. The main conclusion is that while low-order ensemble statistics are largely correct, there are numerous quantifiable errors per image that plausibly can affect subsequent use of the GAN-generated images.
Arbitrary Shape Text Detection using Transformers

Recent text detection frameworks require several handcrafted components such as anchor generation, non-maximum suppression (NMS), or multiple processing stages (e.g. label generation) to detect arbitrarily shaped text images. In contrast, we propose an end-to-end trainable architecture based on Detection using Transformers (DETR), that outperforms previous state-of-the-art methods in arbitrary-shaped text detection. At its core, our proposed method leverages a bounding box loss function that accurately measures the arbitrary detected text regions' changes in scale and aspect ratio. This is possible due to a hybrid shape representation made from Bezier curves, that are further split into piece-wise polygons. The proposed loss function is then a combination of a generalized-split-intersection-over-union loss defined over the piece-wise polygons and regularized by a Smooth-$\ln$ regression over the Bezier curve's control points. We evaluate our proposed model using Total-Text and CTW-1500 datasets for curved text, and MSRA-TD500 and ICDAR15 datasets for multi-oriented text, and show that the proposed method outperforms the previous state-of-the-art methods in arbitrary-shape text detection tasks.
Three Level Password Authentication System Mechanism

Abstract: A protection breach may be a hazard to countrywide private records or the non-public records of a business enterprise or a person. The maximum famous sort of password used for protection functions is text-based. However, those passwords may be without difficulty breached and one may also lose his/her non-public records to the incorrect hands. With the upward thrust in cyber-crime, protection threats associated with logins &amp; accesses have grown to be a prime concern. Also, using unmarried protection authentication isn't enough sufficient to maintain you blanketed from cyber threats. In spite of severe endeavour’s taken nowadays nonetheless protection risks may be visible all around the place. Also, from the start, we're using simply unmarried degree mystery key validation factors, which is not good enough to offer more protection. To be more secure we will believe in Three Level Password Authentication. In these studies paintings, three-degree password authentication is proposed and suggested experimental results. From the end result evaluation, its miles discovered that the three-degree authentication offers a dependable protection degree in assessment to the present mechanisms.
Neuromuscular Control of the Face-Head-Neck Biomechanical Complex With  Learning-Based Expression Transfer From Images and Videos

The transfer of facial expressions from people to 3D face models is a classic computer graphics problem. In this paper, we present a novel, learning-based approach to transferring facial expressions and head movements from images and videos to a biomechanical model of the face-head-neck complex. Leveraging the Facial Action Coding System (FACS) as an intermediate representation of the expression space, we train a deep neural network to take in FACS Action Units (AUs) and output suitable facial muscle and jaw activation signals for the musculoskeletal model. Through biomechanical simulation, the activations deform the facial soft tissues, thereby transferring the expression to the model. Our approach has advantages over previous approaches. First, the facial expressions are anatomically consistent as our biomechanical model emulates the relevant anatomy of the face, head, and neck. Second, by training the neural network using data generated from the biomechanical model itself, we eliminate the manual effort of data collection for expression transfer. The success of our approach is demonstrated through experiments involving the transfer onto our face-head-neck model of facial expressions and head poses from a range of facial images and videos.
Augmented reality applications in manufacturing and its future scope in Industry 4.0

Augmented reality technology is one of the leading technologies in the context of Industry 4.0. The promising potential application of augmented reality in industrial production systems has received much attention, which led to the concept of industrial augmented reality. On the one hand, this technology provides a suitable platform that facilitates the registration of information and access to them to help make decisions and allows concurrent training for the user while executing the production processes. This leads to increased work speed and accuracy of the user as a process operator and consequently offers economic benefits to the companies. Moreover, recent advances in the internet of things, smart sensors, and advanced algorithms have increased the possibility of widespread and more effective use of augmented reality. Currently, many research pieces are being done to expand the application of augmented reality and increase its effectiveness in industrial production processes. This research demonstrates the influence of augmented reality in Industry 4.0 while critically reviewing the industrial augmented reality history. Afterward, the paper discusses the critical role of industrial augmented reality by analyzing some use cases and their prospects. With a systematic analysis, this paper discusses the main future directions for industrial augmented reality applications in industry 4.0. The article investigates various areas of application for this technology and its impact on improving production conditions. Finally, the challenges that this technology faces and its research opportunities are discussed.
SFPN: Synthetic FPN for Object Detection

FPN (Feature Pyramid Network) has become a basic component of most SoTA one stage object detectors. Many previous studies have repeatedly proved that FPN can caputre better multi-scale feature maps to more precisely describe objects if they are with different sizes. However, for most backbones such VGG, ResNet, or DenseNet, the feature maps at each layer are downsized to their quarters due to the pooling operation or convolutions with stride 2. The gap of down-scaling-by-2 is large and makes its FPN not fuse the features smoothly. This paper proposes a new SFPN (Synthetic Fusion Pyramid Network) arichtecture which creates various synthetic layers between layers of the original FPN to enhance the accuracy of light-weight CNN backones to extract objects' visual features more accurately. Finally, experiments prove the SFPN architecture outperforms either the large backbone VGG16, ResNet50 or light-weight backbones such as MobilenetV2 based on AP score.
Gradients without Backpropagation

Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is a special case within the general family of automatic differentiation algorithms that also includes the forward mode. We present a method to compute gradients based solely on the directional derivative that one can compute exactly and efficiently via the forward mode. We call this formulation the forward gradient, an unbiased estimate of the gradient that can be evaluated in a single forward run of the function, entirely eliminating the need for backpropagation in gradient descent. We demonstrate forward gradient descent in a range of problems, showing substantial savings in computation and enabling training up to twice as fast in some cases.
Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows

As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparency in clinical decisions. Existing explainers discover an explanatory subgraph relevant to the prediction. However, such a subgraph is insufficient to reveal all the critical biological substructures for the prediction because the prediction will remain unchanged after removing that subgraph. Hence, an explanatory subgraph should be not only necessary for prediction, but also sufficient to uncover the most predictive regions for the explanation. Such explanation requires a measurement of information transferred from different input subgraphs to the predictive output, which we define as information flow. In this work, we address these key challenges and propose IFEXPLAINER, which generates a necessary and sufficient explanation for GNNs. To evaluate the information flow within GNN's prediction, we first propose a novel notion of predictiveness, named $f$-information, which is directional and incorporates the realistic capacity of the GNN model. Based on it, IFEXPLAINER generates the explanatory subgraph with maximal information flow to the prediction. Meanwhile, it minimizes the information flow from the input to the predictive result after removing the explanation. Thus, the produced explanation is necessarily important to the prediction and sufficient to reveal the most crucial substructures. We evaluate IFEXPLAINER to interpret GNN's predictions on breast cancer subtyping. Experimental results on the BRACS dataset show the superior performance of the proposed method.
Developing new business models from a controller perspective

The Fourth Industrial Revolution and its effects, the pandemic and the new market trends that are emerging as a result, pose significant challenges for companies. Flexibility is no longer enough, adapting to digitalization, developing a new business model using an integrated work environment and self-service business intelligence are needed. The controller plays a key role in this process, as a business partner is actively involved in building the models. The controller has several new or refocused tools at their disposal, such as multidimensional decision-making procedures, digital reports, scenario analysis, specialized KPIs, and more. This study reviews how these tools can help shape a new business model, and how this digitalization can help.
Pembentukan Model Pohon Keputusan pada Database Car Evaluation Menggunakan Statistik Chi-Square

The study discusses problems related to the formation of a decision tree based on a collection of evaluation data records obtained from a number of car buyers. This secondary data was obtained from the UCL machine learning website. The purpose of this research is to produce a prototype algorithm for obtaining an inductive decision tree based on Chi-square statistics. An inductive decision tree formation method based on the Chi-square contingency test was compared with a decision tree obtained using a machine learning algorithm which was done using RapidMiner5 software. The work to produce an inductive decision tree was carried out by first processing data using Microsoft excel and next processed using SPSS software, on the crosstabs descriptive menu. The results of the two methods provide some kind of similar rules, in terms of the order of priority of the variables that most influencing people's decision to accept an automotive product. The formation of the decision tree uses a random sampling of size 300 data records among 1729 respondent data records in the car evaluation database. The resulting decision tree should have a minimal structure like a binary tree. This is possible because its formation is based on the statistical inferential method, so it does not require a separate pruning process as an addition step in the C4.5 algorithm, which actually this algorithm also considers aspects of the statistical significance.
Attention Based Communication and Control for Multi-UAV Path Planning

Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning. The ISHA mechanism is run by a communication helper collecting the state embeddings of UAVs and distributing an attention score vector to each UAV. The attention scores computed by ISHA identify how many interactions with other UAVs should be considered in each UAV's control decision-making. Simulation results corroborate that the ISHA-based communication and control framework achieves faster travel with lower inter-UAV collision risks than an MHA-aided baseline, particularly under limited communication resources.
Interpretation of Black Box NLP Models: A Survey

An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing efforts for researchers to develop methods to interpret these black-box models. Post hoc explanations based on perturbations, such as LIME, are widely used approaches to interpret a machine learning model after it has been built. This class of methods has been shown to exhibit large instability, posing serious challenges to the effectiveness of the method itself and harming user trust. In this paper, we propose S-LIME, which utilizes a hypothesis testing framework based on central limit theorem for determining the number of perturbation points needed to guarantee stability of the resulting explanation. Experiments on both simulated and real world data sets are provided to demonstrate the effectiveness of our method.
Adversarial Memory Networks for Action Prediction

Action prediction aims to infer the forthcoming human action with partially-observed videos, which is a challenging task due to the limited information underlying early observations. Existing methods mainly adopt a reconstruction strategy to handle this task, expecting to learn a single mapping function from partial observations to full videos to facilitate the prediction process. In this study, we propose adversarial memory networks (AMemNet) to generate the "full video" feature conditioning on a partial video query from two new aspects. Firstly, a key-value structured memory generator is designed to memorize different partial videos as key memories and dynamically write full videos in value memories with gating mechanism and querying attention. Secondly, we develop a class-aware discriminator to guide the memory generator to deliver not only realistic but also discriminative full video features upon adversarial training. The final prediction result of AMemNet is given by late fusion over RGB and optical flow streams. Extensive experimental results on two benchmark video datasets, UCF-101 and HMDB51, are provided to demonstrate the effectiveness of the proposed AMemNet model over state-of-the-art methods.