Reviewerly/exHarmony huggingface dataset:
Segmentation techniques for morphometric measurements of blood cells: Overview and research challenges

Measurement of Morphological Blood Cell Parameter (MBCP) plays a key role in hematological examination and diagnosis of various diseases. Recently, Cellular Image Analysis (CIA) techniques have been widely used in clinical diagnosis. Nowadays, these CIA techniques are mainly based on image acquisition followed by digital processing for image segmentation and measurement of MBCP. This paper presents an overview of segmentation methods currently used for measurement of MBCP. Furthermore, main challenges and limitations related to metrological aspects that can affect the performances of these methods are also highlighted.
Bioeconomy Towards Green Deal. Case Study of Citric Acid Production through Fuzzy Cognitive Maps

Abstract The rapid consumption of resources, as well as the increase in the number of people, has raised awareness of the urgent need to change Europe’s existing methods and attitudes towards the consumption of biological resources in production, processing, storage, reuse and disposal. One of the key principles of the European Green Deal is to make the EU economy sustainable. Achieving this goal requires promoting resource efficiency through the transition to a clean circular economy, restoring biodiversity and, above all, reducing pollution in order to mitigate climate change. The aim of the research is to create and offer bioeconomy opportunities, by demonstrating, analysing, and describing possible solution with the help of various examples. In order to compare different production process methods, which helps to understand which of them best meets the set sustainability criteria, fuzzy cognitive maps (FCM) modelling method was used. Alternatives to 16 bio-products are evaluated using the FCM (fuzzy cognitive maps) method using the Mental Modeller tool, according to four criteria – environmental, economic, social and technological aspects. Obtained results are reliable and objectively reflect the validity of the FCM method, and the use of this type of integrated analysis is appropriate to compare the various alternative production processes considered in the work.
Analysis of Application Data Mining to Capture Consumer Review Data on Booking Websites

The rapid development of the Internet has led to the prevalence of big data analysis. Data mining is crucial to extracting potentially valuable information from big data and has therefore received considerable attention from researchers. Python is a common programming language used in data mining. Because of its rich database and robust capacity for scientific calculations, Python is considered an irreplaceable tool for data mining. This study adopted Python to perform a data mining analysis on visitor comments on Booking.com. The study was divided into several stages, namely, data source selection, data acquisition, data saving, data preprocessing, indexing of comments on Booking.com through the Python-based Scrapy framework, and user operation simulation through Selenium to analyze the performance of the spider program. Data mining can be used to identify useful information, which can serve as references for consumers to make purchase decisions. Extraction of data from booking sites through spider programs enables site administrators to attract more visitors. Analysis of extracted data also facilitates the elimination of misjudged comments and helps hotels improve their service quality, hardware, and personnel training.
Grover’s Algorithm for Data Lake Optimization Queries

Now-a-days, the use of No-SQL databases is one of the potential options for storing and processing big data lakes.However, searching for large data in No-SQL databases is a complex and time-consuming task.Further, information retrieval from big data management suffers in terms of execution time.To reduce the execution time during the search process, we propose a fast and suitable approach based on the quantum Grover algorithm, which represents one of the best-known approaches for searching in an unstructured database and resolves the unsorted search query in O (√ ) time complexity.To assess our proposal, a comparative study with linear and binary search algorithms was conducted to prove the effectiveness of Grover's algorithms.Then, we perform extensive experiment evaluations based on ibm_qasm_simulator for searching one item out of eight using Grover's search algorithm based on three qubits.The experiments outcomes revealed encouraging results, with an accuracy of 0.948, well in accordance with the theoretical result.Moreover, a discussion of the sensitivity of Grover's algorithm through different iterations was carried out.Then, exceeding the optimal number of iterations round ( √ ), induces low accuracy of the marked state.Furthermore, the incorrect selection of this parameter can outline the solution.
Classification of Breast Ultrasound Images: An Analysis Using Machine Intelligent Based Approach

Purpose: Breast Cancer (BC) is considered as one of the most dangerous diseases, especially in women. The survivability of the patient is a challenging task if the breast cancer is in severe stage. It is very much essential for the early classification of breast ultrasound images (BUIs) into several categories such as benign (BN), malignant (MG) and normal (NL), etc. so that preventive measures can be taken accordingly at the earliest. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of BUIs into the BN, MG and NL types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 750 TLDIs having 250 numbers of each type such as BN, MG and NL are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of BUIs into several types such as BN, MG and NL. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.
Addressing label ambiguity imbalance in candidate labels: Measures and disambiguation algorithm

Partial Label Learning (PLL) is a weakly supervised learning framework where each training instance is associated with more than one candidate label. However, label ambiguity in the case of label imbalance has not been well solved. In this paper, a novel label imbalance-aware algorithm named PL-LIMAP is proposed to effectively solve this problem. Firstly, a partial manifold constrained low-rank representation method is proposed to learn the instance affinity matrix, which can identify instances with the same label by fully mining the manifold structure of the data. Moreover, for making full use of the information hidden in candidate labels, the Jaccard distance is introduced to measure the similarity between candidate labels to obtain the label affinity matrix. The combination of these two affinity matrices can achieve the purpose of disambiguation through complementary information. Meanwhile, aiming to determine the imbalance of ambiguous labels, specialized measures are proposed to evaluate the imbalance level in PLL. According to the imbalance level of different labels, an alternative weighting scheme is proposed to mitigate the imbalance of ambiguous labels. As a consequence, the ground-truth label of an instance can be found out even though its label information is not available in advance. Experiments reveal the overwhelming performances of PL-LIMAP.
Advanced Tools for the Explosive Materials Identification

Nowadays terrorism is one of the largest problems. From among the different forms of appearance of terrorism the bomb attacks maybe the most considerable ones. Fortunately, the number of bomb attacks decreases from a year to a year in the concern of the airports, and this can be attributed for the fast and efficient development of reconnaissance devices. Unfortunately, not only these devices develop, the terror organizations train their people and develop the procedures with which the applied safety devices may be dodged are drawn up continuously. The newest explosive contraband procedure the explosives hidden into implants smuggled inside the human body. The defense options associated with the procedure are not reassuring, so anyone traveling by plane may be at risk.
Education for Democracy: Balancing Intellectual Rigor and Political Action

Democracy is often perceived of as institutions, laws, and procedures, exemplified by parliaments, city councils, constitutions, and elections. All are essential but none can function without a culture of democracy, understood as a set of attitudes and behaviors that makes democracy possible in practice. Majorities decide but minorities have inalienable rights, conflicts are resolved by peaceful means, and diversity is accepted as enriching rather than threatening. The chapter explores the background for the Council of Europe’s work on competences for democratic culture (CDC) and analyzes. The Reference Framework (RFCDC) developed in 2012–18. The chapter further considers the relevance of the RFCDC for higher education, in particular with reference to the European Higher Education Area and the ongoing work on the fundamental values of higher education. A guidance document on the use of the RFCDC in higher education was finalized in spring 2020; Pavel Zgaga was a key member of the group developing the guidance document as well as of the one developing the RFCDC.
Critical Infrastructure Protection in the Production and Use of Explosives Industry Products

The explosives industry products are a very fast-growing and extremely dangerous manufacture. The entire expanding product range and its multifunctional use is now unquestionable. In parallel with the renewal of explosives products and the application of modern production technology, new hazards and risks are emerging. The harmful effects of explosives of the modern age, even in small quantities, can many times exceed the damaging effects of conventional and well-known materials.
The End of Encryption? – The Era of Quantum Computers

The twenty-first century cannot be imagined without ICT and the excessive use of the internet. Even more so in our current state of (post-) Covid. People work, communicate, and live online. For this reason, information security is more important than ever. However, the awareness regarding ICT related security threats have not increased significantly in the past decades. In present paper we introduce the traditional identification methodologies focusing on the computational capacity these require to be cracked, since the purpose is to demonstrate, how quantum computers will bring about a new era of encryption, where traditional methodologies will be easily cracked by brute force. In current paper we try to draw attention to the fact, that cryptography needs to undergo radical changes, in order to adapt to the coming challenges posed by quantum computers.
Bridging between Soft and Hard Thresholding by Scaling

In this article, we developed and analyzed a thresholding method in which soft thresholding estimators are independently expanded by empirical scaling values. The scaling values have a common hyper-parameter that is an order of expansion of an ideal scaling value that achieves hard thresholding. We simply call this estimator a scaled soft thresholding estimator. The scaled soft thresholding is a general method that includes the soft thresholding and non-negative garrote as special cases and gives an another derivation of adaptive LASSO. We then derived the degree of freedom of the scaled soft thresholding by means of the Stein's unbiased risk estimate and found that it is decomposed into the degree of freedom of soft thresholding and the reminder connecting to hard thresholding. In this meaning, the scaled soft thresholding gives a natural bridge between soft and hard thresholding methods. Since the degree of freedom represents the degree of over-fitting, this result implies that there are two sources of over-fitting in the scaled soft thresholding. The first source originated from soft thresholding is determined by the number of un-removed coefficients and is a natural measure of the degree of over-fitting. We analyzed the second source in a particular case of the scaled soft thresholding by referring a known result for hard thresholding. We then found that, in a sparse, large sample and non-parametric setting, the second source is largely determined by coefficient estimates whose true values are zeros and has an influence on over-fitting when threshold levels are around noise levels in those coefficient estimates. In a simple numerical example, these theoretical implications has well explained the behavior of the degree of freedom. Moreover, based on the results here and some known facts, we explained the behaviors of risks of soft, hard and scaled soft thresholding methods.
Video Source Characterization Using Encoding and Encapsulation Characteristics

We introduce a new method for camera-model identification. Our approach combines two independent aspects of video file generation corresponding to video coding and media data encapsulation. To this end, a joint representation of the overall file metadata is developed and used in conjunction with a two-level hierarchical classification method. At the first level, our method groups videos into metaclasses considering several abstractions that represent high-level structural properties of file metadata. This is followed by a more nuanced classification of classes that comprise each metaclass. The method is evaluated on more than 20K videos obtained by combining four public video datasets. Tests show that a balanced accuracy of 91% is achieved in correctly identifying the class of a video among 119 video classes. This corresponds to an improvement of 6.5% over the conventional approach based on video file encapsulation characteristics. Furthermore, we investigate a setting relevant to forensic file recovery operations where file metadata cannot be located or are missing but video data is partially available. By estimating a partial list of encoding parameters from coded video data, we demonstrate that an identification accuracy of 57% can be achieved in camera-model identification in the absence of any other file metadata.
GJTD-LR: A Trainable Grouped Joint Tensor Dictionary With Low-Rank Prior for Single Hyperspectral Image Super-Resolution

Reconstructing a high-resolution hyperspectral image (HR-HSI) by using a single low-resolution hyperspectral image (LR-HSI) is a significant technique for increasing the spatial resolution of HSIs and overcoming the physical limitation of the HSI sensor. Most single HSI super-resolution methods have achieved great success recently. However, owning to the difficulty of acquiring an HSI, the available training samples are relatively few, which will inevitably lead to relatively low performance. To address this issue, in the paper, we propose a novel single HSI super-resolution method by combining a trainable grouped joint tensor dictionary and a low-rank prior (GJTD-LR). First, we design a trainable grouped joint tensor dictionary, which can build an accurate mapping relationship between training HR-HSIs and their corresponding LR-HSIs with relatively few training samples. To be specific, the training HR-HSI and LR-HSI pairs are decomposed into a joint tensor dictionary and a set of sparse coefficients by using tensor-tensor product to fully preserve the spectral correlation. In addition, we apply a grouped strategy to divide the training images into several groups and learn a compact joint dictionary for each group. Second, a tensor low-rank model is forced into the reconstruction model to further capture the spatial correlation. At last, GJTD-LR is optimized by employing alternating direction method of multipliers (ADMM), soft threshold algorithm, singular value decomposition and fourier domain transform. The experimental results on both remote sensed HSIs and indoor HSIs show the superiority of GJTD-LR to some other traditional and advanced single HSI super-resolution methods.
A Novel Two-Stage Knowledge Distillation Framework for Skeleton-Based Action Prediction

This letter addresses the challenging problem of action prediction with partially observed sequences of skeletons. Towards this goal, we propose a novel two-stage knowledge distillation framework, which transfers prior knowledge to assist the early prediction of ongoing actions. In the first stage, the action prediction model (also referred to as the student) learns from a couple of teachers to adaptively distill action knowledge at different progress levels for partial sequences. Then the learned student acts as a teacher in the next stage, with the objective of optimizing a better action prediction model in a self-training manner. We design an adaptive self-training strategy from the perspective of undermining the supervision from the annotated labels, since this hard supervision is actually too strict for partial sequences without enough discriminative information. Finally, the action prediction models trained in the two stages jointly constitute a two-stream architecture for action prediction. Extensive experiments on the large-scale NTU RGB+D dataset validate the effectiveness of the proposed method.
Channel identification with Improved Variational Mode Decomposition

The identification of the type of wireless propagation channel (e.g., Line of Sight (LOS) or Non Line of Sight (NLOS)) is an important function in the wireless communication design and deployment especially in rich propagation environments. The wireless channel characteristics can be quite specific not only between Line of Sight (LOS) and Non Line of Sight (NLOS) wireless propagation conditions but also in different NLOS environments. In recent times, machine learning approaches have been increasingly used to differentiate and classify channel characteristics and this paper is part of this trend. In particular, this paper proposes the combination of machine learning with a recently proposed signal processing tool called Variational Mode Decomposition (VMD), which is a decomposition algorithm that decomposes a time series into several modes which have specific sparsity properties. VMD itself is a refinement of the Empirical Mode Decomposition (EMD) and demonstrated a superior performance to EMD for classification problems. One issue for the practical deployment of VMD in channel identification problems is the presence of hyper-parameters, which must be tuned for the applied context. The main contribution of this paper is to propose a novel approach for channel identification based on an improvement of VMD called Improved Variational Mode Decomposition (IVMD), where the optimal values of the hyper-parameters of VMD are automatically identified on the basis of the Shannon entropy of the signal output from the channel. Then, various features are extracted from the modes generated by IVMD and a sequential feature selection algorithm is applied to select the optimal features. This paper applies the proposed approach with IVMD to a data set generated by the authors with a wireless channel emulator, where 6 different propagation scenarios (including no fading conditions) are created for WiFi 802.11g signals, where only the preamble is used for channel identification. Even if channel identification based on the normalized preamble is a challenging classification problem, the proposed IVMD is able to outperform significantly the application of basic VMD, EMD and the time and frequency domain representations (as commonly done in literature) of the WiFi signals.
Application of Nanomaterials in the Field of New Energy Environment and Economic Benefit Analysis

The depletion of traditional fossil energy and the air pollution caused by the burning of fossil fuels have forced science authors from all over the world to gradually turn their attention to the development and utilization of renewable energy. However, limited by the defects of less exposed active surface and slow diffusion of traditional materials, nanomaterials have advantages that traditional materials cannot match and can significantly improve the performance of energy conversion and storage devices. Based on this, this paper studies the application and economic benefits of nanomaterials in the field of new energy and environment and provides a new idea for the development direction of nanomaterials in the field of new energy and environment. This article selects three different survey objects to correctly measure the energy consumed by the three groups of experimental vehicles traveling 1 km, 5 km, 10 km, and 20 km and the content of substances that pollute the environment. At the same time, the three traditional electrodes are connected. The electrochemical workstation of the system measures the electrochemical performance of nanomaterials to analyze the application of nanomaterials in the field of new energy and environment and the economic benefits brought by them. The experiment proves that AgPdMo/MoS2 NWs has the smallest charge transfer resistance of 259.3 Ω, the smallest impedance, and the fastest electron transfer rate. The charge transfer resistances of MoS2 NSs, AgPd NWs, and AgPdMo NWs are 37079 Ω, 398.4 Ω, and 916.6 Ω.
End-to-End Autonomous Exploration for Mobile Robots in Unknown Environments through Deep Reinforcement Learning

Autonomous exploration in unknown environments is a significant capability for mobile robots. In this paper, we present an end-to-end autonomous exploration model based on deep reinforcement learning (DRL), which takes the sensor data and a novel exploration map as inputs, and directly outputs the motion control commands of the robot. In contrast to the existing DRL-based exploration methods, the proposed model has no requirements to be combined with the traditional exploration or navigation algorithms, resulting in lower computational complexity. We directly transfer the DRL-based model trained in the training map to four test maps with different sizes and layouts, and the results show that the robot can rapidly adapt to unknown scenes. Besides, a comparison study with RRT-exploration algorithm indicates that the proposed model can reach a higher map exploration rate within less distance and time. Furthermore, we also conduct experiments on the real physical robot to demonstrate the transferability of learned policy from simulation to reality. A video of our experiments in the Gazebo simulator and real world can be found here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>