Reviewerly/exHarmony huggingface dataset:
Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for Sensor Array Optimization Problem

A feature selection algorithm (FSA) is used to eliminate redundant and irrelevant features.Obviously, it can reduce dimensionality as well as the complexity of the original problem.Furthermore, the stability of FSA output becomes a major issue in real-world applications.Stability refers to the consistency of its feature preference related to the perturbation of data samples.In sensor array optimization, an FSA is used to find the best sensor combination in a sensor array.Typically, the main objectives of sensor array optimization are reducing data dimensions, electrical consumption, production cost, computational and traffic overhead, etc.Furthermore, the stable outputs of FSA in several observations are necessary to make a firm conclusion of selected sensors.The contribution of this research is to investigate the stability of FSAs in twelve homogeneous datasets in relation to the sensor array optimization problem.In this study, the stability of seventeen filter-based FSAs is compared across twelve homogeneous datasets.These datasets are generated from the electronic nose (e-nose) used to monitor twelve types of beef cuts.In this case, gas sensor array must have good generalization to differentiate all beef types.The experimental results show that a single FSA cannot guarantee stable sensors recommendation in sensor array optimization.Thus, it becomes a caution to researchers and practitioners to use a proper approach when performing sensor array optimization.
Generalized quantum scissors for noiseless linear amplification

We generalize the concept of optical state truncation and noiseless linear amplification to enable truncation of the Fock-state expansion of an optical state to higher order and to simultaneously amplify it using linear optics. The resulting generalized quantum scissors are more efficient for noiseless linear amplification than employing multiple scissors in parallel and are experimentally practical. As a particular example, we focus on a third-order scissor device and demonstrate advantages in terms of fidelity with the target state, probability of success, distillable entanglement, and the amount of non-Gaussianity introduced.
Generation of a broadband chaotic laser by active optical feedback loop combined with a high nonlinear fiber

We propose and demonstrate a method to generate a flat broadband chaotic laser by using an active optical feedback loop combined with a high nonlinear fiber. The feedback strength and nonlinear effect, especially the four-wave mixing effect of high nonlinear fiber, are studied to improve the bandwidth and flatness of chaos. When the feedback strength is 6.6 and injected fiber power is 1.0 W, a chaotic signal with a frequency range over 50 GHz, 80% bandwidth of 38.9 GHz, and flatness of 4.2 dB are experimentally achieved.
A fully distributed hierarchical attribute-based encryption scheme

With the development of cloud computing, many enterprises have been interested in outsourcing their data to cloud servers to decrease IT costs and rise capabilities of provided services. To afford confidentiality and fine-grained data access control, attribute-based encryption (ABE) was proposed and used in several cloud storage systems. However, scalability and flexibility in key delegation and user revocation mechanisms are primary issues in ABE systems. In this paper, we introduce the concept of a fully distributed revocable ciphertext-policy hierarchical ABE (FDR-CP-HABE) and design the first FDR-CP-HABE scheme. Our scheme offers a high level of flexibility and scalability in the key delegation and user revocation phases. Moreover, our scheme is efficient and provides lightweight computation in the decryption phase. Indeed, by exploiting a computation outsourcing technique, most of the operations are executed by the powerful cloud server, and very few computations are left to the users. Also, the storage cost on the user side is significantly decreased as compared to similar schemes. Furthermore, using the hardness assumption of DBDH problem, we prove that our scheme is adaptively secure in the standard model. Our security analyses and implementation results indicate that our scheme is efficient, secure, and scalable.
Impact of WhatsApp Interaction on Improving L2 Speaking Skills

Mobile Assisted Language Learning (MALL) not only provides access to authentic learning resources and facilitates second language (L2) learning anytime and anywhere but also offers scope for informal learning beyond the classroom. Social media with instant messaging and multi-modal communication and information sharing provide platforms for interaction with peers and collaborative learning to hone their L2 skills. There is little research on informal learning through WhatsApp in enhancing L2 speaking skills. This paper studies the impact of interaction and informal learning in a WhatsApp group on improving a) fluency and coherence, b) lexical resource, c) grammatical range and accuracy and d) pronunciation which form the criteria for assessing speaking skills in IELTS. It also studies how participants perceive the changes in their speaking skills based on the band descriptors of IELTS. Mixed methods approach was adopted to obtain data from the group consisting of mixed ability participants (n=110) with pre and post speaking assessments and pre and post surveys. The participants were given collaborative learning activities and problem solving tasks at regular intervals for over two semesters. The results indicated statistically significant difference in their speaking skills and considerable change in their perceptions. The study has implications for both teachers and researchers of second language acquisition (SLA) for incorporating social media for interaction in the target language beyond the classroom.
Testing and verification of neural-network-based safety-critical control software: A systematic literature review

Context: Neural Network (NN) algorithms have been successfully adopted in a number of Safety-Critical Cyber-Physical Systems (SCCPSs). Testing and Verification (T&V) of NN-based control software in safety-critical domains are gaining interest and attention from both software engineering and safety engineering researchers and practitioners. Objective: With the increase in studies on the T&V of NN-based control software in safety-critical domains, it is important to systematically review the state-of-the-art T&V methodologies, to classify approaches and tools that are invented, and to identify challenges and gaps for future studies. Method: By searching the six most relevant digital libraries, we retrieved 950 papers on the T&V of NN-based Safety-Critical Control Software (SCCS). Then we filtered the papers based on the predefined inclusion and exclusion criteria and applied snowballing to identify new relevant papers. Results: To reach our result, we selected 83 primary papers published between 2011 and 2018, applied the thematic analysis approach for analyzing the data extracted from the selected papers, presented the classification of approaches, and identified challenges. Conclusion: The approaches were categorized into five high-order themes, namely, assuring robustness of NNs, improving the failure resilience of NNs, measuring and ensuring test completeness, assuring safety properties of NN-based control software, and improving the interpretability of NNs. From the industry perspective, improving the interpretability of NNs is a crucial need in safety-critical applications. We also investigated nine safety integrity properties within four major safety lifecycle phases to investigate the achievement level of T&V goals in IEC 61508-3. Results show that correctness, completeness, freedom from intrinsic faults, and fault tolerance have drawn most attention from the research community. However, little effort has been invested in achieving repeatability, and no reviewed study focused on precisely defined testing configuration or defense against common cause failure.
An Image Encryption Scheme Based on Hyper Chaotic System and DNA With Fixed Secret Keys

A novel image encryption scheme combining the 5D hyper chaotic system and DNA technology is proposed in this paper.The proposed scheme is related to the plaintext and external secret key, which does not need to manage the huge amounts of dynamic secret keys and does not to design synchronization method as the one-time-pad encryption scheme.The proposed scheme consists of four parts: pixel-level diffusion, pixel-level permutation, DNA-level diffusion and second permutation.In pixel-level diffusion process, chaotic sequences iterated by 5D hyper chaotic system with initial values (which are set as secret keys) are used to rewrite the pixel values of plaintext image and they are also used to generate second permutation rule.Then the pixel-level permutation rules are obtained by chaotic system with modified initial values that are related to the plaintext image and external secret key.In this case, the permutation rules are different when the plaintext images are different.In the DNA-level diffusion process, we select a part of pixel values of the pixel-level permutated image and external secret key to generate key streams used in DNA-level diffusion process.In this case, the decryption part can obtain the selected pixel values during the decryption process, which avoids transmitting huge secret keys and synchronizing them with plaintext images.In the second permutation, we rearrange the position of the selected pixel values, which can enhance the security of the proposed scheme.Finally, experimental results and security analysis verify that the proposed scheme can achieve good encryption effect and resist various attacks.
Locality adaptive preserving projections for linear dimensionality reduction

Dimensionality reduction techniques aim to transform the high-dimensional data into a meaningful reduced representation and have been consistently playing a fundamental role in the study of intrinsic dimensionality estimation and the design of an intelligent expert system towards real-world applications. From the perspective of manifold learning, locality preserving projections is a classical and commonly used dimensionality reduction method and it essentially learns the low-dimensional embedding under the constraint of preserving the local geometry of data. However, since it determines the neighborhood relationships in the original feature space that probably contains noisy and irrelevant features, the derived similarity between the neighbors are unreliable and the corresponding local data manifold tends to be error-prone, which inevitably leads to degraded performance for subsequent data analyses. Hence, how to accurately identify the true neighbor relationships for each sample remains crucial to the robustness improvement. In this work, we propose a novel approach, termed locality adaptive preserving projections (LAPP), to adaptively determine the neighbors and their relationships in the optimal subspace rather than in the original space. Specifically, due to the absence of prior knowledge of local properties of the underlying manifold, LAPP adopts a coarse-to-fine strategy to iteratively update the projected low-dimensional subspace and optimize the identification of the local structure of the data. Moreover, an iterative algorithm with fast convergence is utilized to solve the transformation matrix for explicit out-of-sample extension. Besides, LAPP is easy to implement and its key idea can be potentially extended to other methods for neighbor-finding and similarity measurement. To evaluate the performance of LAPP, we conduct comparative experiments on numerous synthetic and real-world datasets. Experimental results show that seeking the local structure in the original feature space misleads the selection of neighbors and the calculation of similarity and that the proposed method helps alleviate the negative effect of noisy and irrelevant features, which demonstrates its effectiveness. Besides, this study has the potential to enlighten relevant studies to consider the problem of optimizing the neighborhood relationships.
Out of Oddity – New Cryptanalytic Techniques Against Symmetric Primitives Optimized for Integrity Proof Systems

The security and performance of many integrity proof systems like SNARKs, STARKs and Bulletproofs highly depend on the underlying hash function. For this reason several new proposals have recently been developed. These primitives obviously require an in-depth security evaluation, especially since their implementation constraints have led to less standard design approaches. This work compares the security levels offered by two recent families of such primitives, namely GMiMC and HadesMiMC. We exhibit low-complexity distinguishers against the GMiMC and HadesMiMC permutations for most parameters proposed in recently launched public challenges for STARK-friendly hash functions. In the more concrete setting of the sponge construction corresponding to the practical use in the ZK-STARK protocol, we present a practical collision attack on a round-reduced version of GMiMC and a preimage attack on some instances of HadesMiMC. To achieve those results, we adapt and generalize several cryptographic techniques to fields of odd characteristic.
A one-class classification decision tree based on kernel density estimation

One-class Classification (OCC) is an important field of machine learning which aims at predicting a single class on the basis of its lonely representatives and potentially some additional counter-examples. OCC is thus opposed to traditional classification problems involving two or more classes, and addresses the issue of class unbalance. There is a wide range of one-class models which give satisfaction in terms of performance. But at the time of explainable artificial intelligence, there is an increasing need for interpretable models. The present work advocates a novel one-class model which tackles this challenge. Within a greedy and recursive approach, our proposal for an explainable One-Class decision Tree (OC-Tree) rests on kernel density estimation to split a data subset on the basis of one or several intervals of interest. Thus, the OC-Tree encloses data within hyper-rectangles of interest which can be described by a set of rules. Against state-of-the-art methods such as Cluster Support Vector Data Description (ClusterSVDD), One-Class Support Vector Machine (OCSVM) and isolation Forest (iForest), the OC-Tree performs favorably on a range of benchmark datasets. Furthermore, we propose a real medical application for which the OC-Tree has demonstrated effectiveness, through the ability to tackle interpretable medical diagnosis aid based on unbalanced datasets.
A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Recently, with the rapid increase in the number of web services, QoS-aware Web Service Composition(QWSC) has become a popular topic in both industry and academia.Meta-heuristic algorithm, as an effective way to solve classical optimization problems, has been successfully applied to QWSC nowdays.However, such approach has intrinsic drawbacks and usually lack of good performance in large-scale scenarios.For example, some meta-heuristic algorithms are suitable for continuous search space, while the search space of QWSC is discrete.For solving those problems which were commonly faced when applying meta-heuristic algorithm on QWSC, in this research, we firstly introduce a preprocessing approach for constructing fuzzy continuous neighborhood relations of concrete services, which makes the local search strategy of meta-heuristic algorithms be as effective in discrete space as in continuous space, thus improving the optimization performance.Second, we combine Harris Hawks Optimization (HHO) algorithm and logical chaotic sequence to propose an improved meta-heuristic algorithm named CHHO for solving QWSC.The ergodic and chaotic characteristics of chaotic sequences are used to enhance the ability of the CHHO to jump out of the local optimum for further optimization.Experimental results show that the CHHO has better optimization performance by comparing with the existing mainstream algorithms when solving QWSC problems.Additionally, the preprocessing approach not only greatly improves the optimization performance of the CHHO but also can be freely utilized in other meta-heuristics based approaches.
The Hydrochemistry and Recent Sediment Geochemistry of Small Lakes of Murmansk, Arctic Zone of Russia

This paper presents the first study of five small lakes located in the city of Murmansk. Field work was carried out during 2018–2019. Water samples were collected using a bathometer, while the sediments were sampled using an Ekman grab and Limnos gravity corer. It was found that the water of the studied lakes in Murmansk belong to the sodium group of the chloride class and to the calcium group of the hydrocarbonate class. Compared to the background level, elevated pH, concentrations of the main cations of alkali and alkaline-earth metals, N compounds, total dissolved solids, and heavy metals were found in the lakes, which indicate exposure to anthropogenic impacts. The sediments of the lakes, composed of organomineral and mineral silts, also have an elevated content of heavy metals compared to the background. The most significant excessive concentrations were found for V, Ni, Sb, Pb, Co, Cr, and W. Based on the calculated pollution load index and geoaccumulation index of the sediments, the studied water bodies in Murmansk can be classified as lakes with heavy and extremely heavy pollution levels. The primary pollution sources are emissions from the Murmansk thermal power plant, coal port, road and, rail transport.
Blended learning for sustainable education: Moodle-based English for Specific Purposes teaching at Kryvyi Rih National University

The article deals with the experience of implementing Information Communication Technologies (ICT) into ESP (English for Specific Purposes) teaching and learning. Informatization and application of innovations in education has resulted in emergence of e-learning. While Moodle is one of the most popular Learning Management Systems (LMS) and it enables and facilitates the shift to the student-centered education, the article highlights its implementation and adjustment to the specific nature of teaching and learning languages for specific purposes at Kryvyi Rih National University. The article touches upon reasons for applying Moodle to language teaching/learning and its advantages as a complement to the traditional face-to-face, or classroom, mode, thus combining them into what is referred to as blended learning, or b-learning. Both the teachers and learners interviewed demonstrated positive attitudes to using the platform in their practices. Besides, the article touches upon Moodle-based opportunities of managing the content and monitoring students’ activities both in general and by individual courses. As Moodle is a web-based distance education platform not initially developed for language learning, the article invites discussion on advantages and disadvantages of its application to teaching/learning foreign languages and finding out which factors may allow language teachers and learners to boost its use and reach the set goals.
Nonnegative and Nonlocal Sparse Tensor Factorization-Based Hyperspectral Image Super-Resolution

Hyperspectral image (HSI) super-resolution refers to enhancing the spatial resolution of a 3-D image with many spectral bands (slices). It is a seriously ill-posed problem when the low-resolution (LR) HSI is the only input. It is better solved by fusing the LR HSI with a high-resolution (HR) multispectral image (MSI) for a 3-D image with both high spectral and spatial resolution. In this article, we propose a novel nonnegative and nonlocal 4-D tensor dictionary learning-based HSI super-resolution model using group-block sparsity. By grouping similar 3-D image cubes into clusters and then conduct super-resolution cluster by cluster using 4-D tensor structure, we not only preserve the structure but also achieve sparsity within the cluster due to the collection of similar cubes. We use 4-D tensor Tucker decomposition and impose nonnegative constraints on the dictionaries and group-block sparsity. Numerous experiments demonstrate that the proposed model outperforms many state-of-the-art HSI super-resolution methods.
OSLNet: Deep Small-Sample Classification With an Orthogonal Softmax Layer

A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.To mitigate overfitting in small-sample classification, learning more discriminative features from small-sample data is becoming a new trend.To this end, this paper aims to find a subspace of neural networks that can facilitate a large decision margin.Specifically, we propose the Orthogonal Softmax Layer (OSL), which makes the weight vectors in the classification layer remain orthogonal during both the training and test processes.The Rademacher complexity of a network using the OSL is only 1 K , where K is the number of classes, of that of a network using the fully connected classification layer, leading to a tighter generalization error bound.Experimental results demonstrate that the proposed OSL has better performance than the methods used for comparison on four small-sample benchmark datasets, as well as its applicability to large-sample datasets.Codes are available at:
Learning Strategies Using Augmented Reality Technology in Education: Meta-Analysis

The learning environment is changing rapidly with the advent of the Industrial Revolution 4.0.One of the trending technologies in education is Augmented Reality.The Augmented Reality technology allows users to interact with virtual objects that are integrated into the real world and appear in the same space in real-time.The purpose of this meta-analysis is to identify the types of learning strategies that have been implemented using the Augmented Reality technology.The research methodology is based on a systematic literature search in online databases, namely, Scopus, Web of Science, Science Direct, Taylor Francis, and Springer.Keywords used in the search include Augmented Reality in education, learning strategies, integration strategies, as well as Augmented Reality teaching and learning.The results of this meta-analysis reveal that interactive learning, game-based learning, collaborative learning, and experiential learning are the dominant strategies in education that use Augmented Reality.Such findings will provide educators with guidance on the learning strategies that use Augmented Reality and its potential in education, which will subsequently lead to further research on how learning strategies using the Augmented Reality technology can be implemented in teaching and learning effectively.