Reviewerly/exHarmony huggingface dataset:
Multi-objective genetic algorithm for mobile robot path planning in industrial automation

Due to the problem’s high level of complexity, the optimization strategies used for the mobile robot path planning problem are quite expensive. The Mobile Robot Path Search based on a Multi-objective Genetic Algorithm (MRPS-MOGA) is suggested as a solution to the complexity. The MRPS-MOGA resolves path planning issues while taking into account a number of different factors, including safety, distance, smoothness, trip duration, and a collision-free path. In order to find the best approach, the suggested MRPS-MOGA takes into account five main objectives. The MOGA is used to pick the best path from a variety of viable options. Paths produced at random are used to initialise the population with viable paths. By using objective functions for various objectives, the fitness value is assessed for the quantity of potential candidate paths. In order to achieve diversity in the population, another GA operator mutation is carried out at random on the sequence. Once more, the individual fitness criterion is supported in order to derive the best path from the population. With various situations, an experimental research of the suggested MRPS-MOGA is conducted. The outcome shows that the suggested MRPS-MOGA performs better when choosing the best path with the least amount of time complexity. MRPS-MOGA is more effective than the currently used approaches, according to the experimental analysis.
Multi-target trajectory tracking in multi-frame video images of basketball sports based on deep learning

INTRODUCTION: There is occlusion interference in the multi-target visual tracking process of basketball video images, which leads to poor accuracy of multi-target trajectory tracking. This paper studies the multi-target trajectory tracking method in multi-frame video images of basketball sports based on deep learning. OBJECTIVES: Aiming at the problem of target occlusion in the tracking process and the problem of trajectory tracking anomaly caused by target occlusion, a modified algorithm is proposed. METHODS: The method is divided into two parts: detection and tracking. In the detection part, the YOLOv3 algorithm in deep learning technology is used to detect each target in the video, and the original YOLOv3 backbone network Darknet-53 is replaced by the lightweight backbone network MobileNetV2 to extract the target features. RESULTS: Based on the target detection results, the Kalman filter is used to predict the next position and bounding box size of the target to obtain the target trajectory prediction results according to the current target position, then a hierarchical data association algorithm is designed, and multi-target tracking of the same category is completed based on the target appearance feature similarity and feature similarity. CONCLUSION: The experimental results show that the method can accurately detect the targets in multi-frame video images in basketball sports and obtain high-precision target trajectory tracking results.
Quality metrics for search engine deterministic sort orders

eCommerce search engines such as eBay and Amazon often allow the user to order their search results on deterministic features such as price, time, or distance from the seller. Using metrics such as precision at 10 documents (P@10), others have already shown that the quality of deterministically sorted results is lower than that of best-match (or relevance) sorted results, and that work is needed in order to improve result quality. But metrics such as P@10 are based purely on relevance, and do not reflect the order-feature: cost (be it price, time, or otherwise) — and it is hard to see how to improve a system without a metric that reflects the quality of the ordering. In this contribution we introduce a set of metrics that, using relevance and cost, measure the quality of deterministically sorted search engine results. We examine metrics from the perspective of the buyer, the seller, and the systems engineer. Using our new metrics (buying power ( bp ), buying power for K ( bp4k ), selling power ( sp ), and cheapest precision ( P c )) we re-evaluate the results of the “eBay SIGIR 2019 eCommerce Search Challenge: High Accuracy Recall Task” and demonstrate how to include cost in a metric designed to evaluate the quality of deterministically ordered lists of results. • Metrics to measure the quality of search engine deterministic sort orderings. • Search engine metrics including both a relevance and cost component. • Relevance from the perspective of the buyer, the seller, and the systems engineer. • Multiple user models with well-grounded metrics for each.
DGAC: Dialogue Graph Auto Construction Based on Data with a Regular Structure

A scripted graph is a way to represent a dialogue scenario in a dialogue system. It is often used in development when the dialogue has a regular structure. In this paper, we propose a method for recovering or extracting regular structures from the data by building a dialogue graph. The dialogue graphs constructed by our method can be used for a more accurate pre-selection of candidates for response selection models on the MultiWOZ dataset. Quality improvements are demonstrated for various response selection models: statistical, pre-trained, and fine-tuned. Obtained results demonstrate the applicability of our approach to the automatic construction of a dialogue graph for the tasks of creating scenario-driven dialogue assistants and improving response selection models.
Does a Recurrent Neural Network Use Reflection During a Reflexive Game?

We tested the ability of simple recurrent neural networks to use the reflection of playmate during the “Even-Odd” reflexive game. Reflection is understood as an internal representation of an external environment. To determine if subject uses reflection in the game, three criteria were proposed and applied: formal, neuronal, behavioral. The formal criterion is a win in the game, the neuronal one is a dynamic attractor’s formation of the player’s neural activity, the behavioral one is the deviation of a move sequence from the natural strategy “win-stay, lose-shift”. Three configurations of simple recurrent neural networks were used: 1) the basic one; 2) the one with an additional input which receives information about the success of the last game move, 3) the one which has off-game cycles for processing the received stimulus and making instant decision. We considered the following game conditions: 1) round-robin tournaments of various neural network configurations with modifiable and fixed synapses, 2) playing against fixed sequences of moves. It was found that the simplest neural network model is able to play as good as and, in some cases, better than neural networks of a more complex configuration. The off-game cycles for processing the received stimuli contribute to the non-reflexive behavior of the neural network. The performance of neural networks in a reflexive game correlates 1) with the presence of a self-sustaining dynamic attractor and 2) with the use of moves other than the natural strategy.
A new approach for histological classification of breast cancer using deep hybrid heterogenous ensemble

Purpose Hundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis. Design/methodology/approach The present study proposes and evaluates a novel approach which consists of 24 deep hybrid heterogenous ensembles that combine the strength of seven deep learning techniques (DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for feature extraction and four well-known classifiers (multi-layer perceptron, support vector machines, K-nearest neighbors and decision tree) by means of hard and weighted voting combination methods for histological classification of BC medical image. Furthermore, the best deep hybrid heterogenous ensembles were compared to the deep stacked ensembles to determine the best strategy to design the deep ensemble methods. The empirical evaluations used four classification performance criteria (accuracy, sensitivity, precision and F1-score), fivefold cross-validation, Scott–Knott (SK) statistical test and Borda count voting method. All empirical evaluations were assessed using four performance measures, including accuracy, precision, recall and F1-score, and were over the histological BreakHis public dataset with four magnification factors (40×, 100×, 200× and 400×). SK statistical test and Borda count were also used to cluster the designed techniques and rank the techniques belonging to the best SK cluster, respectively. Findings Results showed that the deep hybrid heterogenous ensembles outperformed both their singles and the deep stacked ensembles and reached the accuracy values of 96.3, 95.6, 96.3 and 94 per cent across the four magnification factors 40×, 100×, 200× and 400×, respectively. Originality/value The proposed deep hybrid heterogenous ensembles can be applied for the BC diagnosis to assist pathologists in reducing the missed diagnoses and proposing adequate treatments for the patients.
Data Balancing and Hyper-parameter Optimization for Machine Learning Algorithms for Secure IoT Networks

Nowadays, many industries rely on machine learning (ML) algorithms and their ability to learn from existing data to make inferences about new unlabeled data. Applying ML algorithms to the network security domain is not new. However, without proper data pre-processing and proper optimization of the hyper-parameters (HPs) of ML algorithms, these algorithms might not achieve their full potential. This paper proposes a framework that applies pre-processing steps, including data balancing, and utilizes optimization techniques to tune the HPs of random forest, gradient boosting machine, and deep neural networks. In this paper, we compare the performance of three different optimization algorithms: Tree-structured Parzen Estimator (TPE), Bayesian Optimization and Hyperband (BOHB), and Particle Swarm Optimization (PSO). We show that through data balancing and optimization of the HPs and architecture of deep neural networks, their performance can improve significantly: false alarm rate of 0% and only 1.79% using the BoT-IoT and the ToN-IoT benchmark datasets, respectively.
Black Box Models for eXplainable Artificial Intelligence

Machine learning algorithms are becoming popular nowadays in cyber security applications like Intrusion Detection Systems (IDS). Most of these models are anticipated as a Black Box. Previously black box was a model where the user cannot see the internal logic. To reach the goal of overwhelming the crucial weakness, the cost may vary. This is related to both ethical and practical problems. Explainable Artificial Intelligence (XAI) is crucial to converting the machine learning algorithms to appreciate the management by accepting the human experts to understand the data evidence. Important role of trust management is to accept the impact of malicious data to identify the intrusions. This chapter addresses the XAI method to appreciate trust management using the decision tree models. Basic decision tree models are used to simulate a human contact to decision making by dividing the options into multiple small options for the IDS area. This chapter aims to implement the arrangement of issues labeled in the various black box methods. This survey helps the researcher to understand the classification of various black box models.
A Pre-Process Enhanced Digital Image Correlation Approach for Smart Structure Monitoring

This research provides a practical guideline for Digital Image Correlation (DIC) data variations minimization in structural engineering through simple image processing techniques. The main objective of this research is to investigate the Pixel Averaging (P.A.) effect on the differential strain Diff(εx) variations. Three concrete arches were tested with three-point bending using the DIC technique for strain measurements. The measured strains are obtained through two virtual horizontal extensometers in the middle of each arch. The Diff(εx) was selected to avoid other 2D-DIC issues, such as the sample-camera out-of-plane movement. Three image cases, namely, one, ten, and twenty averaged images, were used for DIC analysis of each arch. The conditions of each image case are assessed by computing the Diff(εx) variance and the linear least square criterion (R2) between the two extensometers. The second objective is to examine the speckles’ dilation effects on the speckle pattern density and surface component quality utilizing the Image Erode (I.E.) technique. The (P.A.) technique provided consistent differential strain Diff(εx) values with a variance reduction of up to (90%) when averaged images were used. The (R2) has considerably increased (from 0.46, 0.66, 0.91 to 0.90, 0.96, 0.99), respectively, for the three samples. Moreover, the (I.E.) technique provided qualitatively denser speckles with a highly consistent DIC surface component.
Weight-guided loss for long-tailed object detection and instance segmentation

The long-tailed characteristic leads to a significant performance drop for various models on long-tailed distribution datasets. Existing works mainly focus on mitigating the data shortage in tail classes at dataset level by data re-sampling, loss re-weighting or knowledge transfer from head to tail. While in this paper, we focus on another perspective which is also related to the performance drop: the gap between total dataset class number and training batch size. To address this issue, we propose a Weight-Guided (WG) loss which utilizes the classifier weights as auxiliary tail samples. It can be easily deployed to different models. By simply adding WG loss to Mask R-CNN with ResNet-50 backbone, we improve the performance by (i) 0.5 box AP and 0.4 mask AP on COCO dataset, (ii) 0.4 box and mask AP (1.8 mask AP for rare classes) on LVIS v1.0 dataset. Codes will be released.
FastDARTSDet: Fast Differentiable Architecture Joint Search on Backbone and FPN for Object Detection

Neural architecture search (NAS) is a popular branch of automatic machine learning (AutoML), which aims to search for efficient network structures. Many prior works have explored a wide range of search algorithms for classification tasks, and have achieved better performance than manually designed network architectures. However, few works have explored NAS for object detection tasks due to the difficulty to train convolution neural networks from scratch. In this paper, we propose a framework, named as FastDARTSDet, to directly search on a larger-scale object detection dataset (MS-COCO). Specifically, we propose to apply differentiable architecture search method (DARTS) to jointly search backbone and feature pyramid network (FPN) architectures for object detection task. Extensive experimental results on MS-COCO show the efficient and efficacy of our method. Specifically, our method achieves 40.0% mean average precision (mAP) on the test set, outperforming many recent NAS methods.
Embedding Biometric Information in Interpolated Medical Images with a Reversible and Adaptive Strategy

How to hide messages in digital images so that messages cannot be discovered and tampered with is a compelling topic in the research area of cybersecurity. The interpolation-based reversible data hiding (RDH) scheme is especially useful for the application of medical image management. The biometric information of patients acquired by biosensors is embedded into an interpolated medical image for the purpose of authentication. The proposed scheme classifies pixel blocks into complex and smooth ones according to each block's dynamic range of pixel values. For a complex block, the minimum-neighbor (MN) interpolation followed by DIM embedding is applied, where DIM denotes the difference between the block's interpolated pixel values and the maximum pixel values. For a smooth block, the block mean (BM) interpolation is followed by a prediction error histogram (PEH) embedding and a difference expansion (DE) embedding is applied. Compared with previous methods, this adaptive strategy ensures low distortion due to embedding for smooth blocks while it provides a good payload for complex blocks. Our scheme is suitable for both medical and general images. Experimental results confirm the effectiveness of the proposed scheme. Performance comparisons with state-of-the-art schemes are also given. The peak signal to noise ratio (PSNR) of the proposed scheme is 10.32 dB higher than the relevant works in the best case.
A parallel derivation theory of adjuncts

<title xmlns="http://www.ncbi.nlm.nih.gov/JATS1" /><p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">I present and argue for a theory of adjuncts according to which, adjuncts and their respective hosts are derived as separate, parallel objects that are not combined until forced to by the process of linearization. I formalize the notion of the workspace, and the workspace-based operation MERGE. Finally, I show that this approach to adjuncts naturally accounts for Adjunct Islands and Parasitic Gaps and is consistent with adjective ordering constraints.
Modeling Different Deployment Variants of a Composite Application in a Single Declarative Deployment Model

For automating the deployment of composite applications, typically, declarative deployment models are used. Depending on the context, the deployment of an application has to fulfill different requirements, such as costs and elasticity. As a consequence, one and the same application, i.e., its components, and their dependencies, often need to be deployed in different variants. If each different variant of a deployment is described using an individual deployment model, it quickly results in a large number of models, which are error prone to maintain. Deployment technologies, such as Terraform or Ansible, support conditional components and dependencies which allow modeling different deployment variants of a composite application in a single deployment model. However, there are deployment technologies, such as TOSCA and Docker Compose, which do not support such conditional elements. To address this, we extend the Essential Deployment Metamodel (EDMM) by conditional components and dependencies. EDMM is a declarative deployment model which can be mapped to several deployment technologies including Terraform, Ansible, TOSCA, and Docker Compose. Preprocessing such an extended model, i.e., conditional elements are evaluated and either preserved or removed, generates an EDMM conform model. As a result, conditional elements can be integrated on top of existing deployment technologies that are unaware of such concepts. We evaluate this by implementing a preprocessor for TOSCA, called OpenTOSCA Vintner, which employs the open-source TOSCA orchestrators xOpera and Unfurl to execute the generated TOSCA conform models.
Classification and measurement of multipartite entanglement by reconstruction of correlation tensors on an NMR quantum processor

We introduce a protocol to classify three-qubit pure states into different entanglement classes and implement it on an NMR quantum processor. The protocol is designed in such a way that the experiments performed to classify the states can also measure the amount of entanglement present in the state. The classification requires the experimental reconstruction of the correlation matrices using 13 operators. The rank of the correlation matrices provides the criteria to classify the state in one of the five classes, namely separable, biseparable (of three types), and genuinely entangled (of two types, GHZ and W). To quantify the entanglement, a concurrence function is defined which measures the global entanglement present in the state, using the same 13 operators. Global entanglement is zero for separable states and nonzero otherwise. We demonstrate the efficacy of the protocol by implementing it on states chosen from each of the six inequivalent (under stochastic local operations and classical communication) classes for three qubits. We also implement the protocol on states picked at random from the state space of three-qubit pure states.
Compensation and pricing strategies in cloud service SLAs: Considering participants’ risk attitudes and consumer quality perception

Appropriate pricing and compensation terms in service level agreements (SLAs) help cloud service providers (CSPs) attract users and make profits. However, the research related to cloud services mainly has focused on the issue of pricing strategies, underestimating the role of compensation. Moreover, few studies have considered all possible risk attitudes of both the CSP and customers when guiding the CSP to make decisions. Based on mathematical game models, this study develops the CSP's profit-maximizing compensation and pricing strategies for existing and new cloud service scenarios, respectively. We investigate the interaction between compensation and price, as well as the effect of participants' risk attitudes on the CSP's optimal decisions. Considering the different information acquired by users in the existing and new service scenarios, we further explore the signaling effect of compensation on consumer quality perception. These findings inform CSPs in designing and implementing SLAs by setting pricing and compensation terms wisely.
DRE: density-based data selection with entropy for adversarial-robust deep learning models

Abstract Active learning helps software developers reduce the labeling cost when building high-quality machine learning models. A core component of active learning is the acquisition function that determines which data should be selected to annotate.State-of-the-art (SOTA) acquisition functions focus on clean performance (e.g. accuracy) but disregard robustness (an important quality property), leading to fragile models with negligible robustness (less than 0.20%). In this paper, we first propose to integrate adversarial training into active learning (adversarial-robust active learning, ARAL ) to produce robust models. Our empirical study on 11 acquisition functions and 15105 trained deep neural networks (DNNs) shows that ARAL can produce models with robustness ranging from 2.35% to 63.85%. Our study also reveals, however, that the acquisition functions that perform well on accuracy are worse than random sampling when it comes to robustness. Via examining the reasons behind this, we devise the density-based robust sampling with entropy (DRE) to target both clean performance and robustness. The core idea of DRE is to maintain a balance between selected data and the entire set based on the entropy density distribution. DRE outperforms SOTA functions in terms of robustness by up to 24.40%, while remaining competitive on accuracy. Additionally, the in-depth evaluation shows that DRE is applicable as a test selection metric for model retraining and stands out from all compared functions by up to 8.21% robustness.
Improving source-in-the-middle continuous-variable quantum key distribution using a heralded hybrid linear amplifier

A hybrid linear amplifier is inserted at the output of the source-in-the-middle distribution protocol to overcome the shortcomings of the transmission distance. The modified protocol aims to maintain a high key rate for long-distance transmission under high noise. It has the potential to significantly broaden the application range of the continuous variable quantum key distribution protocol. The effects of amplifier parameters and noise on the modified protocol are analyzed in detail with regard to applying it to a practical system. To make the simulation more realistic, the effect of finite size on the new protocol is taken into account. It will serve as a guideline for the future use of hybrid linear amplifiers. Different parameters can be adjusted to achieve the best performance for key rates of different quantum channels.