Reviewerly/exHarmony huggingface dataset:
Dynamic Multidimensional Knapsack Problem benchmark datasets

With increasing research on solving Dynamic Optimization Problems (DOPs), many metaheuristic algorithms and their adaptations have been proposed to solve them.However, from currently existing research results, it is hard to evaluate the algorithm performance in a repeatable way for combinatorial DOPs due to the fact that each research work has created its own version of a dynamic problem dataset using stochastic methods.Up to date, there are no combinatorial DOP benchmarks with replicable qualities.This work introduces a non-stochastic consistent Dynamic Multidimensional Knapsack Problem (Dynamic MKP) dataset generation method that is also extensible to solve the research replicability problem.Using this method, generated and published 1405 Dynamic MKP benchmark datasets using existing famous static MKP benchmark instances as the initial state.Then the datasets are quantitatively and qualitatively analyzed.Furthermore, 445 datasets have the optimal result found of each state using a linear solver.The optimal results and result scores are included with published datasets.
Advanced NLP Procedures as Premises for the Reconstruction of the Idea of Knowledge

This is an outline of contributions from the 13th International Workshop on Natural Computing (IWNC), which was one of the ten conferences of the 2021 Summit of the International Society for the Study of Information held online on 12&ndash;19 September 2021. Each of the ten conferences contributing to the summit had a 3&ndash;6 h block of plenary time with the program (usually invited lectures and panel discussions) intended for all participants of the Summit. Outside of the assigned plenary time, conferences had parallel programs of more specialized, technical, or focused presentations. All plenary events and some parallel internal events of the conferences federated into the summit were recorded. Links to several of these recordings and extended abstracts of all presentations are available at the IS4SI website. Five of the papers presented at the IWNC were submitted and accepted for publication in the Special Issue of Proceedings.
ANOSY: approximated knowledge synthesis with refinement types for declassification

Non-interference is a popular way to enforce confidentiality of sensitive data. However, declassification of sensitive information is often needed in realistic applications but breaks non-interference. We present ANOSY, an approximate knowledge synthesizer for quantitative declassification policies. ANOSY uses refinement types to automatically construct machine checked over- and under-approximations of attacker knowledge for boolean queries on multi-integer secrets. It also provides an AnosyT monad to track the attacker knowledge over multiple declassification queries and checks for violations against user-specified policies in information flow control applications. We implement a prototype of ANOSY and show that it is precise and permissive: up to 14 declassification queries are permitted before a policy violation occurs using the powerset of intervals domain.
Score-CAM++: Class Discriminative Localization with Feature Map Selection

Abstract In recent years, explaining convolutional neural networks (CNN) has received increasing attention since it helps to build humans’ trust on CNNs by exposing their inference basis. In this field, generating intuitive saliency maps that highlight the input regions most related to model decisions is one of the popular approaches. Building on a state-of-the-art saliency method named Score-CAM, we propose Score-CAM++ to generate better saliency maps with higher efficiency (when compared to Score-CAM). Different from Score-CAM, which adopts all the feature maps in target layer to produce saliency maps, we propose the “feature map selection” operation to select the feature maps that capture the “positive” information (i.e., the features whose increment in intensity leads to an increase in target score). Then the selected feature maps are up-sampled as masks to perturb the input and compute the weights of the corresponding feature maps. Finally, the linear combination of the weighted feature maps forms the saliency map. Compared with Score-CAM, the experiments based on VGG-16 show that our method saves 25%-30% of the time when generating saliency maps. Meanwhile, our conducted evaluations, both subjective and objective, show that our method provides better visual explanations when compared to the previous saliency methods.
Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images

The inner parts of the human body are usually inspected endoscopically using special equipment. For instance, each part of the female reproductive system can be examined endoscopically (laparoscopy, hysteroscopy, and colposcopy). The primary purpose of colposcopy is the early detection of malignant lesions of the cervix. Cervical cancer (CC) is one of the most common cancers in women worldwide, especially in middle- and low-income countries. Therefore, there is a growing demand for approaches that aim to detect precancerous lesions, ideally without quality loss. Despite its high efficiency, this method has some disadvantages, including subjectivity and pronounced dependence on the operator's experience. The objective of the current work is to propose an alternative to overcoming these limitations by utilizing the neural network approach. The classifier is trained to recognize and classify lesions. The classifier has a high recognition accuracy and a low computational complexity. The classification accuracies for the classes normal, LSIL, HSIL, and suspicious for invasion were 95.46%, 79.78%, 94.16%, and 97.09%, respectively. We argue that the proposed architecture is simpler than those discussed in other articles due to the use of the global averaging level of the pool. Therefore, the classifier can be implemented on low-power computing platforms at a reasonable cost.
Integrating Interactive Technology Concepts With Material Expertise in Textile Design Disciplines

Textile and fashion designers are increasingly interested in integrating interactive technologies into their practice. However, traditional design education typically lacks support for them to develop technical digital and electronics skills alongside their expertise in materials. Reflecting on outputs from an e-textile design workshop and 8-week design projects with four textile design students using an e-textile toolkit, and follow-up data collection with the students one year after the projects, we argue that starting technical explorations with raw materials results in a better understanding and more flexible use of technical knowledge. We also argue that this newly acquired knowledge is then more fully integrated with their pre-existing material knowledge as it is applied to physical interface design. The results contribute to the development of tools and approaches in supporting designers with material expertise to learn tangible interaction design skills.
Single-image night haze removal based on color channel transfer and estimation of spatial variation in atmospheric light

The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.
Selective Classification of Sequential Data Using Inductive Conformal Prediction

Cyber-Physical Systems (CPS) operate in dynamic and uncertain environments where the use of deep neural networks (DNN) for perception can be advantageous. However, DNN integration in CPS is not straightforward. Perception outputs must be complemented with assurance metrics that represent if they can be trusted or not. Further, the inputs to DNNs are typically sequential capturing time-correlated data that can affect the accuracy of the predictions since machine learning models require inputs to be independent and identically distributed. In this paper, we propose a selective classification approach that rejects predictions that are not trustworthy. We quantify the credibility and confidence of each prediction by computing aggregate p-values from multiple subsequent inputs. We examine different multiple hypothesis testing approaches for combining p-values computed using Inductive Conformal Prediction (ICP) focusing on their ability to produce valid p-values for sequential data. Empirical evaluation results using the German Traffic Sign Recognition Benchmark demonstrate that ICP validity can be recovered when p-values from sequential inputs are combined and selective classification based on aggregate p-values produces predictions with less risk.
Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection

We propose a three-step concept and visual design for supporting the visual exploration of high-dimensional data in scatterplots through eye-tracking. First, we extract subsets in the underlying data using existing classifications, automated clustering algorithms, or eye-tracking. For the latter, we map gaze to the underlying data dimensions in the scatterplot. Clusters of data points that have been the focus of the viewers’ gaze are marked as clusters of interest (eye-mind hypothesis). In a second step, our concept extracts various properties from statistics and scagnostics from the clusters. The third step uses these measures to compare the current data clusters from the main scatterplot to the same data in other dimensions. The results enable analysts to retrieve similar or dissimilar views as guidance to explore the entire data set. We provide a proof-of-concept implementation as a test bench and describe a use case to show a practical application and initial results.
Face Recognition Technique based Student Attendance Management System

Teachers and administrators alike have a difficult time keeping track of student attendance. As a result, deep learning’s most recent developments are used to automate this process. This paper presents a facial recognition-based student attendance monitoring system built on Open CV (open-source computer vision library). It can detect objects like photos, educated face, or even just the writing of a person by processing photographs and videos. This system requires OpenCV, Pillow, and NumPy. The face characteristics of the students are detected and extracted from the collected photographs, which are then compared to the database and the attendance is recorded. A pattern recognition model will then be used to try to guess their identities, without human intervention, the process of keeping track of student attendance is maintained.
Analysis of the Impact Mechanism of Occupational Identity on Occupational Well-Being Based on Big Data

Occupational identity is an individual's view, recognition, and approval of his long-term occupation, and its importance to every professional is self-evident. Only when a professional person agrees with the profession he is engaged in from the bottom of his heart can he devote himself wholeheartedly to it and unreservedly exert his greatest potential. On the basis of sorting out and analyzing the prevailing theoretical and empirical research results, this paper deliberates the empirical research on the influence mechanism between employees' occupational identity and occupational well-being. In this study, through big data analysis, literature search, questionnaire survey, and other methods, this paper obtained the professional identity data of employees in different companies and used a method of big data analysis, namely, BP neural network (BPNN) to design in this paper to verify the data, and finally obtain an effective theoretical model of the influence mechanism of occupational identity and occupational well-being. The main work of this paper is as follows: (1) it introduces the interpretation of the concept of "professional identity" by different scholars at home and abroad and makes a brief review of the researches on professional identity and professional well-being made by foreign scholars in recent years. (2) The basic knowledge and algorithm process of artificial neural network (ANN) are introduced, and the design of the evaluation model of the influence mechanism of occupational identity on occupational well-being based on BPNN is proposed. (3) The simulation software validates the neural network (NN) assessment system developed in this paper. Experiments reveal that the BPNN system is a reasonable and feasible evaluation approach for analyzing the impact of occupational identity on occupational well-being.
Retracted: The Application of Virtual Metacognitive Network Model in Preschool Guiding Art Network Teaching

The rapid development of the Internet age has led to the reform of the education industry and has given birth to the rapid development of the blended teaching model. By analyzing the problems existing in the teaching process of preschool education major art disciplines in the blended teaching mode, the author starts from the concept of metacognition and the characteristics of online teaching, and analyzes the new connotation of metacognition in the online teaching environment, and here On the basis of this, a three-dimensional meta-cognition training structure based on technology platform, teachers and learners in the network environment is proposed, trying to provide a comprehensive perspective for the meta-cognitive training of learners, combined with the blended teaching model in the preschool education professional art discipline. Based on the advantages of application, the implementation suggestions of blended teaching are put forward in a targeted manner, in order to provide reference for higher vocational colleges to adopt blended teaching mode for teaching.
Side-channel Attacks and Countermeasures in Cloud Services and Infrastructures

Cloud computing relies on the sharing of resources between users of the same physical machine, to reduce costs through optimizing and increasing utilization. However, sharing these resources may be with malicious users, which could lead to confidentiality violations through co-residency attacks. These attacks may exploit the sharing of resources such as cache memory to reveal a legitimate user’s recent activities. Multiple techniques and factors can be exploited to perform side-channel attacks and other microarchitectural attacks successfully. Therefore, despite all its benefits, multi-tenancy remains a risk factor in cloud computing. Without appropriate mitigation, this security risk could become the primary concern hindering cloud adoption. This doctoral paper proposes the integrated use of three approaches to provide the necessary protection for shared virtualized systems. These approaches provide self-protection for the virtual machine (VM) on which they are used by monitoring activities within shared virtualized systems, determining the threat level of suspicious VMs, and providing periodic scanning of the virtualized system against microarchitectural attacks and viruses.
A comprehensive approach for performance evaluation of Indian language identification systems

Good feature representation is the chief requirement for improving Language Identification (LID) system recognition performance. In this work LID system for Indian languages is proposed based on unsupervised feature learning utilizing Deep Belief Network (DBN). The proposed methodology is implemented in two parts. The first phase of this work is based on extracting MFCC features combined with SDC hybrid features. The resultant hybrid features are further stacked to Deep Belief Network (DBN). The second phase of the proposed work is investigating the performance of various Feed forward back propagation neural network models for classification using different training algorithms. Effect of combining different activation functions and varying the hidden neurons is also investigated The performance of the resultant models is evaluated on the basis of some performance metrics such as the epochs, training time, Mean Square Error, Regression and Mean Absolute Percentage Error. Results indicate that optimal performance is achieved in model trained with Levenberg Marquardt (LM) training algorithm. The activation functions used in the hidden and output layer are “tansig” and “purelin”. Similarly, the effect of varying the number of neurons in the hidden layer is not significant in improving the performance of the derived models. FFBPNN models trained with PL and TS activation functions gave best performance indices. A user defined language database in four different languages Hindi, English, Tamil and Malayalam is used for this work.
Towards Practical Privacy-Preserving Solution for Outsourced Neural Network Inference

When neural network model and data are outsourced to a cloud server for inference, it is desired to preserve the privacy of the model/data as the involved parties (i.e., cloud server, and model/data providing clients) may not trust mutually. Solutions have been proposed based on multi-party computation, trusted execution environment (TEE) and leveled or fully homomorphic encryption (LHE or FHE), but they all have limitations that hamper practical application. We propose a new framework based on integration of LHE and TEE, which enables collaboration among mutually-untrusted three parties, while minimizing the involvement of resource-constrained TEE but fully utilizing the untrusted but resource-rich part of server. We also propose a generic and efficient LHE-based inference scheme, along with optimizations, as an important performance-determining component of the framework. We implemented and evaluated the proposed scheme on a moderate platform, and the evaluations show that, our proposed system is applicable and scalable to various settings, and it has better or comparable performance when compared with the state-of-the-art solutions which are more restrictive in applicability and scalability.
Hybrid outranking of geospatial data: Multi attributive ideal-real comparative analysis and combined compromise solution

Identifying mineralization areas with an acceptable level of reliability is a complex matter demanding the implementation of supervised methods for mapping mineralization exploration aims. In this study, further collecting all available exploratory information and data of the region of study, their good processing and analysis, and then integrating them with an appropriate method, multi-criteria decision-making (MCDM) approaches were utilized to outranking porphyry Cu mineralization areas. This paper describes an acceptable procedure for obtaining evidential layers for recognizing porphyry Cu mineralization, assigning weight to the layers, and combining them. For this aim, first, a data-driven multi-class index overlay (DMIO) approach is applied as it is a proven method with proper results in mineral prospectivity mapping (MPM) to integrate evidential layers (criteria and sub-criteria emanated from geoscience data including geological, geochemical, remote sensing, and geophysical datasets). After that, two recent MCDM models, combined compromise solution (CoCoSo) and multi attributive ideal-real comparative analysis (MAIRCA), were used to prioritize the porphyry Cu potential areas producing MPM. Concentration-area (C–A) fractal method and prediction-area (P–A) plots by considering the areas of occurrence of the known mineralization and normalized density (Nd) as traditional methods were applied for weighting and integration evidential layers and evaluation of the final maps in a data-driven manner. Consequently, verifying the verisimilitude of the MAIRCA prospectivity map (Nd = 3.17) is similar to the DMIO map in delimiting the priority areas. The results of the CoCoSo model (Nd = 2.7) show this methodology was also successfully applied in mapping the porphyry Cu mineralization areas in the study.
A Frog based Nature Inspired Algorithm for Solving Optimization Problem

Humans have always been inspired by nature, and they have continued to adapt and absorb various environmental systems. Several Nature Inspired Algorithms have been created and used to a wide range of challenging engineering optimization problems. The self-defense strategy used by prey animals prompted us to write this paper. In nature, we see that one species is the food of another, implying that one animal's survival is dependent on the survival of another. As a result, individuals are exposed to a variety of dangers in their daily lives. As a result, many species develop defense mechanisms to avoid becoming prey to another predator animal. The suggested Improved Anti-predatory Nature Inspired algorithm (IAPNIA) is inspired by frogs' self-defense strategies for survival. We have improved the directional flee functionality of the algorithm by observing that the frog can only observe the nearest peer frog and thus makes a jump towards it. The suggested method's findings were compared to the existing Anti-predatory Nature Inspired Algorithm (APNIA). We tested the algorithm on 10 benchmark functions and compared it with the existing APNIA, in which we performed better in 5 benchmarks and similar in 4 benchmarks. We also compared the convergence plots on 6 benchmark functions on which our algorithm converges faster than the existing APNIA. This demonstrates the value of the proposed algorithm in terms of convergence and quality of the solution achieved.