Reviewerly/exHarmony huggingface dataset:
A Multi-Perspective Feature Approach to Few-Shot Classification of IoT Traffic

The realization of Internet of Things (IoT) traffic classification is crucial to the management and monitoring of the IoT network. Performing IoT traffic classification under a few-shot scenario is vital due to extremely rapid growth in the number of IoT devices and necessity to save computational costs. In this paper, we propose MAFFIT, a multi-perspective feature approach to few-shot classification of IoT traffic. The purpose is to comprehensively consider the information of network traffic and to achieve accurate classification of IoT traffic using a limited number of samples. MAFFIT is based on our key observation that traffic behaviour and traffic composition are highly consistent across IoT traffic of the same class. For a flow, MAFFIT will first extract the packet length sequences and packet byte sequence, then encodes the features of the corresponding sequences using feature construction, and finally uses comparative learning to obtain the class of the flow without the additional cost of training a comparison model. We conduct extensive experiments on two real-world IoT traffic datasets, the results demonstrate that MAFFIT can achieve accurate IoT traffic classification using a limited number of flow samples and MAFFIT outperforms three existing network traffic classification methods.
Single Image Reflection Removal Based on Dark Channel Sparsity Prior

The major task of reflection removal methods is to restore a reflection-free image from a reflection-contaminated image taken through glass. We propose an algorithm to remove reflections from a single image by means of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula> -regularized dark channel sparsity prior and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula> gradient sparsity prior. In addition, we analyze the difference between the dark channel map in the reflection-contaminated image and the reflection-free image empirically and mathematically. Moreover, a new data fidelity term is introduced to handle strong reflections and preserve high-frequency details in the recovered transmission image. Different from the model used in most state-of-the-art methods, our reflection removal model does not rely on the assumption of out-of-focus objects in the reflection layer. Quantitative evaluation on several publicly available real-world image datasets including ground-truth demonstrates the high accuracy of our algorithm. Qualitative evaluation of extensive experimental results on real-world images shows the competitive performance of the proposed method compared with the state-of-the-art reflection removal methods.
Implementation Framework for Blockchain-Based Traceability to Tackle Drug-Counterfeiting: Embracing Sustainable Pharma Logistics Networks

Abstract Fighting counterfeit drugs in pharma logistics networks, is one of the most important challenges in the industry. In order to contribute to the UN Sustainable Development Goal of “global health and well-being” the integration of counterfeit substances and drugs has to be stopped but is still causing significant human and economic damage. Although the problem is known for decades no approach is known that eradicates the problem. Blockchain technology is promoted as a potential solution to counterfeit drugs since it combines the properties of tamper-proof data storage and secure information transfer but its’ application in the pharma industry is still lacking behind the theoretical potential. This study seeks to assess the applicability of blockchain technology to tackle drug counterfeiting in pharma networks and to develop an implementation framework that outlines industry-specific implementation barriers and mitigation measures as well as their interdependencies. Building on nine interviews with industry experts, Grounded Theory was applied. Accordingly, the interview results were condensed into a theory by applying a three-stage coding process. Building on this analysis, an implementation framework for blockchain technology to tackle drug counterfeiting in the pharma industry is developed. The framework explains the enablers, barriers, and implementation strategies as well as the relation between them. It is shown that blockchain implementation is not hindered by a lack of technological maturity, but mostly by opposing incentive mechanisms of stakeholders involved.
Cross-Chain Data Auditing for Medical IoT Data Sharing

Secure medical IoT data sharing significantly improves medical collaboration and facilitates patients' medical treatment. Since block chain provides integrity and traceability management for medical data, many IoT medical data choose block chain as a storage medium. However, the isolation of block chain hinders data sharing between heterogeneous chains, so how to realize the secure sharing of medical IoT data in heterogeneous block chains and allow users to obtain correct and credible shared data is still a challenge. Existing data integrity verification techniques ensure the correctness of shared data by comparing off-chain data with metadata stored on-chain. However, these schemes ignore the consistency of shared data and the correctness of cross-chain data. This paper builds a cross-chain medical IoT data-sharing framework, introduces a relay chain, and verifies the consistency between data requests and actual storage through registration, auditing, and other methods. Based on this framework, this paper uses homomorphic signature technology and batch auditing to design a cross-chain audit protocol to verify the consistency of registered data attributes and the correctness of shared data. Security analysis and simulation experiments based on security reduction demonstrate the security and effectiveness of the proposed scheme.
A Survey of Energy-Aware Server Consolidation in Cloud Computing

In Cloud Data Centers (CDC), dynamic consolidation of Virtual Machines (VMs) is an efficient technique to increase resource usage and energy efficiency. Energy efficiency has become a new frontier in the virtualized cloud computing paradigm. Data centers must decide how, which, and when VMs must be merged onto a few physical servers, which is a difficult task. Server consolidation necessitates VMs migration, which has a direct influence on service response time. It means that overloading of server results in resource scarcity and application performance deterioration. Most of the existing solutions for VMs consolidation are based on the heuristic methods. The drawbacks of these methods are they produce sub-optimal outcomes and prevent the precise description of a Quality of Service (QoS) target. Further, the requirement for energy efficiency has grown as the number and scale of CDC has grown. Virtual machine migration is widely utilized cloud computing technology that is thus focused in this work to conserve energy. We conduct an in-depth study of the existing literature on that address server consolidation and energy consumption issues and provide optimal solutions. We conclude the survey by describing key challenges for future research on constructing effective and accurate server consolidation techniques with power modeling.
Analysis of Blood Smear Images Using Dark Contrast Algorithm and Morphological Filters

In recent years, Biomedical Imaging has emerged as an effective tool in diagnosis of various diseases. In order to perform anatomy or histology of cells, Blood Smear Images are used. To process these images, enhancement plays a major role in order to increase visual quality of the image and for accurate segmentation of Region of Interest (ROI). The motive of this work is to perform enhancement using the Dark Contrast Algorithm (DCA) since it increases the intensity of darker regions, which in case of Blood Smear Images are nucleus. Further, the quality of enhanced image is evaluated using suitable Image Quality Assessment (IQA) metric. This enhanced image is segmented using Morphological Filters with appropriate structuring element to extract ROI which is nucleus and cell periphery. This helps to identify irregularities in cell periphery to detect various blood disorders. The performance of segmentation technique is assessed using Jaccard Coefficient (JC).
Experimental Analysis of Oversampling Techniques in Class Imbalance Problem

The abstract should summarize the contents of the paper and should Class Imbalance is consistently being faced by real-world datasets, where one class have a high number of instances (i.e., majority class) while the other one is having a smaller number of instance (minority class). Current data mining and machine learning algorithm struggle to handle imbalanced data by reducing the error rate while ignoring the minority class. To tackle this issue, various strategies that range from modifying the training data to modifying the algorithm itself. The most widely used approach is data resampling, either oversampling the minority class instances or undersampling the majority class instances. This paper aims at the performance analysis of various oversampling techniques. The resampling technique includes oversampling based on synthetic minority oversampling technique (SMOTE) and clustering. The experimental results are obtained using 15 publically available UCI datasets with varying imbalance ratios. It also addresses the issues and challenges of SMOTE technique when applied alone. The performance of these techniques is assessed through performance matrices like ROC-AUC, F-measure and Recall. Through experimental analysis, it is found that the cluster-based oversampling technique outperforms the SMOTE-based techniques.
Content-Based Secure Image Retrieval in an Untrusted Third-Party Environment

In this digital world, where availability of the image-generating tools is quite common and owing to the rapid growth of Internet knowledge, people use to exchange massive volume of images every day which results in creating large image repositories. So, retrieving appropriate image available on these repositories is one of the vital tasks. This problem leads to evolving content-based image retrieval (CBIR). As the generation of image increases, people start transferring these images to a remote third-party server, but these images may have personal information. This leads to adding privacy concerns toward the system as transferring personal data to some other place might be a cause of leakage of information or transfer to an unauthorized person. So, to keep this in mind, sensitive images like medical and personal images require encryption before being a contracted out for the privacy-preserving resolutions. In this work, we have deployed ACM for image encryption as well as asymmetric scalar product preserving encryption (ASPE) for feature vector encryption and similarity matching. We have demonstrated our results based on various benchmark databases.
The Students’ Response Toward Indirect Corrective Feedback Used By The Lecturer In Teaching Writing At Cokroaminoto Palopo University

This research aims to find out the students’ response toward indirect corrective feedback used by the lecturer in teaching writing. This research was conducted at Cokroaminoto Palopo University in class VIA. This research applied descriptive quantitative method. There are 9 students as the sample of this research taken by purposive sampling technique. The result of this research shows that the mean score of students’ response is 35.5. It means that students’ response toward indirect corrective feedback used by the lecturer in teaching writing at Cokroaminoto Palopo University is very negative. It indicates that the null hypothesis (H0) is accepted and the alternative hypothesis (H1) is rejected. It means that the indirect corrective feedback used by the lecturer in teaching writing does not give a positive response to students.&#x0D; &#x0D; Keywords:
Special issue of applied cognitive psychology: Rethinking cognition in a digital environment

Abstract The digital environment has become increasingly pervasive in our lives, and as a result, may be profoundly impacting our cognitive processes. This special issue of Applied Cognitive Psychology brings together a collection of articles adopting a variety of methodological approaches and applied settings to examine how human cognition transforms in the context of digital environments. Some articles take a functional approach, examining how people use digital media as memory technology in relatively naturalistic contexts. Others adopt experimental methods to examine how basic cognitive processes such as memory and metacognition transform when embedded in digital activities such as Internet search or digital storage. Still others examine how design features of online media platforms shape how content from those different platforms are recalled. In all cases, the studies in this issue indicate how the integration of “onboard” cognitive resources with digital technologies transforms the nature and capacity of human cognition.
Evaluation of feature selection methods utilizing random forest and logistic regression for lung tissue categorization using <scp>HRCT</scp> images

Abstract Categorization of lung tissue patterns with interstitial lung diseases (ILD) utilize high‐resolution computed tomography (HRCT) lung images of the TALISMAN dataset which is challenging due to high intra‐class variation and inter‐class ambiguity. To tackle this, major contributions are made in three aspects. First, a novel shape‐based feature is proposed to quantify the amount of fibrotic and nodular components in a lung tissue pattern which helps to minimize intra‐class variation and inter‐class ambiguity. Second, we address the curse of dimensionality which often arises due to huge feature space. Third, to prevent an overfitting issue, the Grid Search optimization algorithm is utilized by tuning the Random Forest hyper‐parameters. In this manuscript, a framework is proposed to categorize lung tissue patterns by integrating four types of feature domains (a) intensity‐based, (b) texture‐based, (c) wavelet‐based, and (d) shape‐based along with the novel shape‐based feature. As a result, we encounter a large feature space (i.e., ), which leads to high dimensionality. To address this issue, we reduce the feature space using filter‐based f‐statistic, reliefF, minimum Redundancy Maximum Relevance (mRMR), and embedded‐based decision trees, regularization models. We found that the regularization model shrinks the feature space by 2.5 times in just 90 s whereas mRMR methods reduce the feature space by 10 times in 13 min. Using the proposed feature set, we employ Random Forest and Logistic Regression as potential classifiers to classify lung tissue patterns. Experiential results reveal that the proposed framework categorizes lung tissue patterns more effectively than state‐of‐the‐art hand‐crafted and deep learning‐based approaches.
Explainable fault prediction using learning fuzzy cognitive maps

Abstract IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short‐Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state‐of‐the‐art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what‐if analysis, could provide more information about the black‐box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability.
ARToolKit Target Tracking and Animation Fusion Technology Based on Edge Computing and Augmented Reality

Since the beginning of the 21st century, computer network technology has been developing at a rapid speed. Many things that people thought were remote or even impossible 10 years ago can now become a reality, such as AR. AR is augmented reality. After simulating computer-generated text, image, 3D models, music, video, and other virtual information and applied to the real world, the two kinds of information complement each other, so as to achieve the “enhancement” of the real world. AR is augmented reality, a new field that is currently emerging. Using AR technology can add a realistic and virtual atmosphere to the real environment, giving users a better visual experience. Edge computing is a more efficient algorithm based on cloud computing. Traditional cloud computing, which requires all terminal devices to go from the cloud to the edge, consumes more energy and time, while using edge computing is more efficient. Applying edge computing to AR can render videos and pictures in real time, giving users a better experience. This paper is aimed at studying the ARToolKit target tracking and animation fusion technology based on edge computing and augmented reality and using this technology to propose and design an AR computing edge model. And compared with the traditional AR technology, the final experimental results show that the use of ARToolKit target tracking and animation fusion technology based on edge computing and augmented reality can reduce the minimum delay time by 1/3 compared with traditional cloud computing. The signal-to-noise ratio of the fused animation effect is significantly increased. Compared with the traditional gray value fusion, it only needs 1/2 of the number of calculations to achieve the same fusion effect.
Quantum Key Distribution with Trusted Relay using an ETSI-compliant Software-Defined Controller

Quantum Key Distribution (QKD) is a mechanism that allows two entities to agree on a secret key over a quantum channel.Recently, commercially viable QKD technology appeared on the market and the European Telecommunications Standards Institute (ETSI) established a working group to standardize QKD for commercial applications.Several testbeds and pre-commercial QKD networks are being deployed in the world, all requiring sophisticated control mechanisms to enable key exchanges between non-adjacent nodes for remote applications.In this paper, we implement and evaluate a prototype that realizes the architecture defined by ETSI and uses trusted relay nodes to exchange keys between non-adjacent nodes.In particular, we present the Software-Defined Network (SDN) Controller and the Software-Defined QKD nodes designed to control the QKD experimental testbed in Milan, named PoliQI.We test the proposed prototype in an emulation environment, showing its advantages in inter-datacenter communication scenarios in terms of key delivery time and application acceptance ratio.
Evaluation of keyness metrics: performance and reliability

Abstract The methodological debates surrounding keyword analysis have given rise to a wide range of keyness metrics. The present paper delineates four dimensions of keyness, which distinguish between frequency- and dispersion-related perspectives. Existing measures are then organized according to these dimensions and evaluated with regard to their performance on a specific keyword analysis task: The identification of key verbs in academic writing. To this end, the rankings produced by 32 different metrics are evaluated against an established academic word list. Further, the reliability of measures is assessed, to determine whether they produce stable rankings across repeated studies on the same pair of text varieties. We observe notable differences among metrics with regard to these criteria. Our findings provide further support for the superiority of the Wilcoxon rank sum test and text-dispersion–based measures, and allow us to identify, within each dimension of keyness, metrics that may be given preference in applied work.
Homogeneous ensemble model built from artificial neural networks for fault detection in navigation systems

In this paper, authors present a modern approach to the detection of malfunctioning sensory systems. The proposed solution is based on artificial neural networks. The application example uses a navigation system based on a 9-axis IMU (Inertial Measurement Unit), the signal fusion data is converted into quaternions. The form of quaternions is then analyzed along with sensor samples by an artificial neural network. If the network detects data processing inadequate to the pattern then we obtain information about the malfunction of a specific sensing axis from the sensors. The results compare fault detection capabilities using an ensemble structure built from three types of artificial neural networks: fully-connected, recurrent and convolutional. We provide a comprehensive analysis of all models; the proposed measures include RMSE (Root-Mean-Square Error), NRMSE (Normalized RMSE), t-SNE (t-distributed stochastic neighbor embedding) visualization, ROC (Receiver Operating Characteristic) curve, precision vs. recall curve, AUC (Area Under Curve) and F-score.
An intention inference method for the space non-cooperative target based on BiGRU-Self Attention

Intention inference for space non-cooperative targets is the key to space situational awareness and assistant decision for collision avoidance. Given that the problem of target intention inference is essential to learn the dynamically changing time-series characteristics of space non-cooperative target intentions and infer their relative motion patterns for threat warning, this paper adopts a deep learning-based approach, introduces a bidirectional propagation mechanism and self-attention mechanism based on Gated Recurrent Unit (GRU) and proposes a bidirectional Gated Recurrent Unit (BiGRU)-Self Attention-based space non-cooperative target intention inference model. BiGRU is used to learn deep information in time-series characteristics of the space non-cooperative target, and self-attention mechanism is used to adaptively extract and assign weights to key characteristics to capture the internal correlations in time-series information, thus improving model performance. The line-of-sight measurements are used as the characteristics of target intention inference, and the typical target motion intentions are defined. Subsequently, the proposed model is trained and tested on the test set, with the accuracy reaching 97.1%. Besides, the effectiveness and advantages of the proposed model are verified by the simulation of a case study and comparison evaluations. The results demonstrate that our proposed model could significantly improve the accuracy, computational efficiency, and noise resistance for the space non-cooperative target intention inference compared with the existing intention inference models.