Reviewerly/exHarmony huggingface dataset:
FS-MLC: Feature selection for multi-label classification using clustering in feature space

Multi-label classification (MLC) has attracted many researchers in the field of machine learning as it has a straightforward problem statement with varied solution approaches. Multi-label classifiers predict multiple labels for a single instance. The problem becomes challenging with the increasing number of features, especially when there are many features and labels which depend on each other. It requires dimensionality reduction before applying any multi-label learning method. This paper introduces a method named FS-MLC (Feature Selection forMulti-Label classification using Clustering in feature-space). It is a wrapper feature selection method that uses clustering to find the similarity among features and example-based precision and recall as the metrics for feature rankings to improve the performance of the associated classifier in terms of sample-based measures. First, clusters are created for features considering them as instances then features from different clusters are selected as the representative of all the features for that cluster. It reduces the number of features as a single feature represents multiple features within a cluster. It neither requires any parameter tuning nor the user threshold for the number of features selected. Extensive experimentation is performed to evaluate the efficacy of these reduced features using nine benchmark MLC datasets on twelve performance measures. The results show an impressive improvement in terms of sample-based precision, recall, and f1-score with up to 23%-93% discarded features.
Continuous authentication using biometrics: An advanced review

Abstract The shortcomings of conventional access control systems for high‐security environments have led to the concert of continuous authentication. Contrary to traditional verification, in which users are authenticated only once at the start of their session, continuous authentication systems regularly check users' identities to prevent hijackings. The challenges in this area involve balancing the security of protected assets by quickly detecting intruders with the system usability for genuine users. Biometric recognition plays a major role within this context, as it is the main way to assure that users are who they claim to be. A comparative analysis of the latest works revealed different aspects of this problem. First, some biometrics traits among those applied for continuous authentication are more suitable for this task than others. Second, systems combining multiple traits have advantages over those relying on a single one. Finally, many works fail to report proper evaluation metrics. With this in mind, we were able to identify new opportunities for researchers in the field. We highlight the potential for mining new datasets on the internet, which would benefit validation and benchmarking, and how recent deep learning techniques could address some of the open challenges in the area. This article is categorized under: Technologies &gt; Prediction Technologies &gt; Machine Learning Application Areas &gt; Science and Technology
On Recurrent Neural Networks for Sequence-based Processing in Communications

In this work, we analyze the capabilities and practical limitations of neural networks (NNs) for sequence-based signal processing which can be seen as an omnipresent property in almost any modern communication systems. In particular, we train multiple state-of-the-art recurrent neural network (RNN) structures to learn how to decode convolutional codes allowing a clear benchmarking with the corresponding maximum likelihood (ML) Viterbi decoder. We examine the decoding performance for various kinds of NN architectures, beginning with classical types like feedforward layers and gated recurrent unit (GRU)-layers, up to more recently introduced architectures such as temporal convolutional networks (TCNs) and differentiable neural computers (DNCs) with external memory. As a key limitation, it turns out that the training complexity increases exponentially with the length of the encoding memory ν and, thus, practically limits the achievable bit error rate (BER) performance. To overcome this limitation, we introduce a new training-method by gradually increasing the number of ones within the training sequences, i.e., we constrain the amount of possible training sequences in the beginning until first convergence. By consecutively adding more and more possible sequences to the training set, we finally achieve training success in cases that did not converge before via naive training. Further, we show that our network can learn to jointly detect and decode a quadrature phase shift keying (QPSK) modulated code with sub-optimal (anti-Gray) labeling in one-shot at a performance that would require iterations between demapper and decoder in classic detection schemes.
MLCR: A Fast Multi-label Feature Selection Method Based on K-means and L2-norm

Feature selection is an essential step in data mining and machine learning that increases classification accuracy and reduces the computational time by eliminating redundant and unrelated features. In this paper, a fast feature selection algorithm is introduced based on clustering ranking in feature-label space and L2-norm, called MLCR. This method is a filter-based method for multi-label datasets. We used a two-step strategy for this method. First, we used the k-means algorithm to cluster the features based on their correlation with labels. Then we sorted the features in each cluster based on L2-norm in descending order and finally set rank to each feature. This will allow similar features to be grouped into one cluster. In the second step, the features with the same rank are sorted like the previous step and added to the feature ranking vector. To verify the efficiency of MLCR, we have compared the obtained results of this method with five well-known multi-label feature selection algorithms based on various real-world multilabel datasets in different dimensions. The results demonstrate that our proposed method outperforms the other methods in the classification measures and run-time.
Influence of Stark-shift on quantum coherence and non-classical correlations for two two-level atoms interacting with a single-mode cavity field

An exact analytic solution for two two-level atoms coupled with a multi-photon single-mode electromagnetic cavity field in the presence of the Stark shift is derived. We assume that the field is initially prepared in a coherent state and the two atoms are initially prepared in an excited state. Considering the atomic level shifts generated by the Stark shift effect, the dynamical behavior of both quantum coherence (QC) measured using a quantum Jensen-Shannon divergence and of quantum correlations captured by quantum discord (QD) are investigated. It is shown that the intensity-dependent Stark-shift in the cavity and the number of coherent state photons plays a key role in enhancing or destroying both QC and QD during the process of intrinsic decoherence. We remarked that increasing the Stark-shift parameters, the frequencies of the transition for the mode of the cavity field, and photons number destroy both the amount of QC and QD and effected their periodicity. More importantly, QC and QD exhibit similar behavior and both show a revival phenomenon. We believe that the present work shows that the quantum information protocols based on physical resources in optical systems could be controlled by adjusting the Stark-shift parameters.
Hardness of LWE on General Entropic Distributions

The hardness of the Learning with Errors (LWE) problem is by now a cornerstone of the cryptographic landscape. In many of its applications the so called “LWE secret” is not sampled uniformly, but comes from a distribution with some min-entropy. This variant, known as “Entropic LWE”, has been studied in a number of works, starting with Goldwasser et al. (ICS 2010). However, so far it was only known how to prove the hardness of Entropic LWE for secret distributions supported inside a ball of small radius. In this work we resolve the hardness of Entropic LWE with arbitrary long secrets, in the following sense. We show an entropy bound that guarantees the security of arbitrary Entropic LWE. This bound is higher than what is required in the ball-bounded setting, but we show that this is essentially tight. Tightness is shown unconditionally for highly-composite moduli, and using black-box impossibility for arbitrary moduli. Technically, we show that the entropic hardness of LWE relies on a simple to describe lossiness property of the distribution of secrets itself. This is simply the probability of recovering a random sample from this distribution s, given $$s+e$$ , where e is Gaussian noise (i.e. the quality of the distribution of secrets as an error correcting code for Gaussian noise). We hope that this characterization will make it easier to derive entropic LWE results more easily in the future. We also use our techniques to show new results for the ball-bounded setting, essentially showing that under a strong enough assumption even polylogarithmic entropy suffices.
Implementation and Analysis of Blockchain Based DApp for Secure Sharing of Students' Credentials

The paper aims to resolve security issues revolving around the sharing of students' credentials by leveraging the blockchain technology. It proposes a novel blockchain-based architecture followed by its implementation as a decentralized application (DApp). Further, the cost & the performance analysis are carried out based on the experiments conducted.
A Mobile Edge Computing (MEC)-Enabled Transcoding Framework for Blockchain-Based Video Streaming

Blockchain technology is reshaping the video streaming industry by changing how humans create, distribute and consume videos. Leveraging blockchain technology, some emerging video streaming platforms aim to eliminate the centralized video streaming servers by building peer-to-peer content distribution architectures and cryptocurrency-based payment systems. On these blockchain-based platforms, the source video streams are required to be transcoded into multiple versions to meet heterogeneous user demands, which is a computationally intensive and energy consuming process. Meanwhile, the block size of the blockchain has significant impacts on the performance of blockchain-based video streaming systems. In this article, we propose a novel MEC-enabled transcoding framework for blockchain-based video streaming while an adaptive block size scheme is designed for the underlying blockchains. The system architecture of the proposed framework is presented to enable coordination of different aspects. Then we address the video transcoding and block size issues by using the ADMM method, and smart contract is used to facilitate distributed optimization among non-trusting entities. Simulation results are presented to show the effectiveness of the proposed framework.
Classifying Upper Limb Activities Using Deep Neural Networks

This paper presents a classification method using Inertial Measurement Unit (IMU) in order to classify six human upper limb activities. The study was also carried out to investigate whether theses activities are being performed normally or abnormally using two different neural networks: Artificial neural network (ANN) and convolutional neural network (CNN). Human activities that were included in the study: arm flexion and extension, arm pronation and supination, shoulder internal and external rotations. Before activities categorization, training data was obtained by the means of an IMU sensor fixed on an armband worn around the forearm. The training data obtained were positions, velocities, accelerations and jerks around x, y and z axes. Training samples of 264 have been collected from 10 participants, 2 women and 8 men from ages 19 to 23. Then, 204 features were extracted from IMU data, nonetheless, 15 features only have been used as inputs to the proposed neural networks because they were the most distinguished ones. After all, the networks classify the data into one of 6 classes and their results were compared. Furthermore, these proposed methods of classification have been validated by real experiments showing that ANN network gives the best performance.
An Empirical Study on Spatial Presence in Immersive Geo-Environments

Abstract Research on immersive virtual reality (VR) indicates that VR technologies facilitate understanding through the formation of spatial presence, which is the user’s feeling of “being there” in a virtual place. These findings make VR a highly interesting tool for geographic information science and the visualization of geospatial phenomena. However, immersion and presence are often studied under laboratory conditions, while research on virtual representations of real-world environments is still rare. This paper aims to reduce this gap empirically through a user study on spatial presence in immersive geo-environments. A total of 60 subjects entered a VR replica of a coral reef in the Mexican Caribbean built upon WorldView2 imagery. After testing within- and between-subject performance under both VR headset- and desktop-based visualization conditions, all users completed the IPQ self-report questionnaire to evaluate the sense of presence experienced. The results indicate a strong effect of immersion, leading to statistically significant higher levels of spatial presence in immersive geo-environments.
An Image Encryption Algorithm Based on Chaotic Selection of Robust Cryptographic Primitives

With the spread of digital images, the security of these data has become a serious problem. In this study, an image encryption algorithm is designed to ensure the confidentiality of digital images. The original aspect of the proposed algorithm is that it has an architecture that combines unpredictable and random-like features of chaotic systems and cryptographic primitives that have been proven as a result of many years of research. By making use of the advantages of chaotic systems, it has provided the design of an image encryption algorithm that is more resistant to application attacks than mathematical designs. Analysis results and security evaluations have shown that the proposed method can be used successfully in many applications related to security of digital image.
Integrating Learning and Reasoning with Deep Logic Models

Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable of take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overcomes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning.
Enhancing Privacy through “Smart Contract” using Blockchain-based Dynamic Access Control

Blockchain helps to deliver Computerized working, on spot verification, cost efficiency, enhanced work. One of the fast initiatives for the implementation of Blockchain is taken by financial sector. As more than 60% of the market percent of the market value focuses on new technology. In the coming time, blockchain will start into influence into many domain including accountancy on it auxiliaries. Smart contract can play a very important role in banking and insurance. This can be very vital tool for the auditing purpose, because all transactions can be updated without any third party interference. All the accounting standards can be evaluated through blockchain systems thus giving professional accountants such as Chartered Accountants and Certified Public Accountants to focus more on the company policies and how it can improve its operations ethically. To access any resource in blockchain network. This paper presents a framework which use Fair access using Dynamic Access control. All process of fair access is recorded in smart contract and token allocation can be done with the help of Digital Signature. This makes system more perfect. For the very purpose of Blockchain intervening into accounting policies is to ensure that auditors are working to the potential in terms of verifying the accounts and financial records in accordance to the International Accounting Standards. This step hopes to minimize minimal frauds that might happen in the workplaces which could halt to the end of the business life as the privacy of smart contract can be enhanced.
Evaluation of web service clustering using Dirichlet Multinomial Mixture model based approach for Dimensionality Reduction in service representation

In recent years, mainly the functionality of services are described in a short natural text language. Keyword-based searching for web service discovery is not efficient for providing relevant results. When services are clustered according to the similarity, then it reduces search space and due to that search time is also reduced in the web service discovery process. So in the domain of web service clustering, basically topic modeling techniques like Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Hierarchical Dirichlet Processing (HDP), etc. are adopted for dimensionality reduction and feature representation of services in vector space. But as the services are described in the form of short text, so these techniques are not efficient due to lack of occurring words, limited content, etc. In this paper, the performance of web service clustering is evaluated by applying various topic modeling techniques with different clustering algorithms on the crawled dataset from ProgrammableWeb repository. Gibbs Sampling algorithm for Dirichlet Multinomial Mixture (GSDMM) model is proposed as a dimensionality reduction and feature representation of services to overcome the limitations of short text clustering. Results show that GSDMM with K-Means or Agglomerative clustering is outperforming all other methods. The performance of clustering is evaluated based on three extrinsic and two intrinsic evaluation criteria. Dimensionality reduction achieved by GSDMM is 90.88%, 88.84%, and 93.13% on three real-time crawled datasets, which is satisfactory as the performance of clustering is also enhanced by deploying this technique.
Discrete artificial electric field algorithm for high-order graph matching

High-order graph matching is a problem of establishing the correspondences between two sets of visual features subject to high-order matching constraints. This is an NP-hard combinatorial optimization problem and formulated as a maximization problem of matching score over all permutations of features. Artificial electric field algorithm (AEFA) (Yadav et al., 2019) is a proven optimization algorithm in the family of meta-heuristic and performed well for continuous optimization problems. In this article, we extended the AEFA algorithm for combinatorial high-order graph matching problems and introduced a discrete artificial electric field algorithm (DAEFA). This framework incorporates the redefine position and velocity representation scheme, addition–subtraction operation, velocity and position update rules, and a problem specific initialization by using heuristic information. The efficiency of the proposed algorithm is tested over three well-known datasets: synthetic, CMU house and real-world datasets. The computational results measured the matching score, accuracy of matching and established the correspondences between two graphs. The computational results show the outperformance of the proposed algorithm over the other state-of-art algorithms in terms of good matching score and accuracy both.
Monazite chemistry and its distribution along the coast of Neendakara–Kayamkulam belt, Kerala, India

The beach sands along the coasts of India contain large reserves of strategic minerals like ilmenite, monazite, zircon, etc. Monazite mineral is primarily used as an ore for extracting rare earths particularly cerium and lanthanum. In the present study, the chemistry and distribution of monazite found in the beach sands of Neendakara–Kayamkulam belt in Kerala, south India, is studied using advanced techniques and standardized methods. Beach sediments were collected and analyzed for the texture, and mineralogy reveals that the study area contains characteristically fine sand with maximum 98.9% total heavy minerals (THM) content with ilmenite as predominant mineral species. The content of monazite ranges from 0.1 to 1.4%. A combination of unit operations like gravity separation, magnetic separation and electrostatic separation techniques were applied to the beach sand to recover monazite mineral. The data indicate that samples show a maximum yield of about 81% for THM and 0.5% for monazite. The ED-XRF, XRD and SEM–EDS analysis gives reliable results on the chemistry of monazite. The major chemical constituents of monazite mineral like Ce2O3, La2O3, P2O5 corresponds to 26.658, 13.421 and 23.649%, respectively. The SEM reveals the weathering mechanism, both mechanical and chemical, occurred in monazite mineral. Advanced characterization of monazite mineral will influence positively the efficiency of determining their potential applications.
Subjective and objective quality assessment for image restoration: A critical survey

Image restoration is the process of restoring latent clear images from degraded images, and it has received substantial attention in various restoration scenarios recently. Contrary to the considerable progress of image restoration algorithms, the quality evaluation for image restoration falls behind, which may hinder the further development of advanced image restoration techniques. So far, only several objective quality metrics have been proposed to evaluate the quality of restored images for specific restoration scenarios, and little work has been dedicated to the applications of image restoration quality metrics. Besides, the performance of these metrics remains an open problem, and an obvious disadvantage of these metrics is that their robustness is quite weak. To bridge this gap, we present this survey paper. First, the difference between the traditional image quality assessment and quality assessment for image restoration was analyzed profoundly. Then, for different image restoration scenarios, this paper provides (i) a comprehensive description of the existing subjective quality databases, (ii) a thorough insight into existing objective quality metrics and (iii) two applications of image restoration quality metrics, namely, parameter selection and benchmarking image restoration algorithms. After that, the experimental results and a detailed performance analysis on (i) quality assessment of restored images, (ii) benchmarking image restoration algorithms and (iii) time complexity are given based on public restored image databases. Finally, the paper outlines the challenges and future trends of subjective and objective quality assessment for image restoration in various aspects.