Reviewerly/exHarmony huggingface dataset:
A fusion representation for face learning by low-rank constrain and high-frequency texture components

Face image processing has always been an important field of artificial intelligence. In machine learning tasks, samples are vectorized into a data matrix. Hence, different faces lie in several respective low-rank subspaces, which can be effectively captured by Low-Rank Representation (LRR) learning. However, most technologies usually use the raw data directly but ignore the weight among different information of the image itself. Following the intuition that human beings distinguish different people through the lineaments and wrinkles of faces which are indeed the texture components of face images, it is believed that representation learning will benefit from introducing the texture components of samples into the information fusion. Inspired by Multi-view learning models which can reconcile the knowledge gained from different views, a fusion framework is proposed in this letter, named High-Frequency texture components Low-Rank Representation (HFLRR). In HFLRR, the high-frequency texture components of samples will be extracted by Fast Fourier Transformation and Butterworth filtering firstly. Subsequently, the texture information will be introduced into the subspace learning procedure. An effective optimization solution is presented for this framework and the experimental results show that the proposed algorithm outperforms the state-of-art methods on several real-world face datasets.
On methods of calculation of pi on quantum computers

A simple one-qubit algorithm to compute &pi; is considered and compared to another algorithm proposed by Noto with respect to the precision offered and its quantum computation requirements. We find that &pi; = 3:157&plusmn;0:017 with our algorithm on a real quantum computer and that Noto's algorithm offers accuracy comparable to that, but only on a simulator; heavy use of two-qubit gates would cause Noto's proposed algorithm for calculation of &pi; to perform much worse on quantum computers currently publicly available.
An adaptive registration algorithm for zebrafish larval brain images

Zebrafish (Danio rerio) in their larval stages have grown increasingly popular as excellent vertebrate models for neurobiological research. Researchers can apply various tools in order to decode the neural structure patterns which can aid the understanding of vertebrate brain development. In order to do so, it is essential to map the gene expression patterns to an anatomical reference precisely. However, high accuracy in sample registration is sometimes difficult to achieve due to laboratory- or protocol-dependent variabilities.In this paper, we propose an accurate adaptive registration algorithm for volumetric zebrafish larval image datasets using a synergistic combination of attractive Free-Form-Deformation (FFD) and diffusive Demons algorithms. A coarse registration is achieved first for 3D volumetric data using a 3D affine transformation. A localized registration algorithm in form of a B-splines based FFD is applied next on the coarsely registered volume. Finally, the Demons algorithm is applied on this FFD registered volume for achieving fine registration by making the solution noise resilient.Results Experimental procedures are carried out on a number of 72 hpf (hours post fertilization) 3D confocal zebrafish larval datasets. Comparisons with state-of-the-art methods including some ablation studies clearly demonstrate the effectiveness of the proposed method.Our adaptive registration algorithm significantly aids Zebrafish imaging analysis over current methods for gene expression anatomical mapping, such as Vibe-Z. We believe the proposed solution would be able to overcome the requirement of high quality images which currently limits the applicability of Zebrafish in neuroimaging research.
Text Compression Based on an Alternative Approach of Run-Length Coding Using Burrows-Wheeler Transform and Arithmetic Coding

In modern life, communication via text is becoming one of the most popular means of communication. As a result, storing text in a small format or transferring it quickly over the internet has become a challenging issue, and text compression has become an important research field. Many algorithms for text compression have already been developed, and new algorithms are being devised to fulfil the demands of current technology. This research article proposes a text compression technique based on: (i) the Burrows-Wheeler transform; (ii) an alternative method of run-length coding; (iii) finding repeated patterns more frequently; and (iv) arithmetic coding. The proposed approach is compared with other state-of-the-art methods, and gives better performance in terms of compression ratios.
EUROPE IS GOING DIGITAL – A GLANCE AT THE DIGITAL COMPETENCES OF THE ROMANIAN CITIZENS

The technological ascension represents one of the main phenomena encountered by today’s society, influencing almost every field of activity of the modern world. In fact, technology has become part of our daily life, whether we refer to active participation in society, learning, work, or other activities. In order to gain a favorable position compared to other states considered as global powers, the European Union is constantly striving to advance in various fields, placing special importance on the digitalization of the Member States. In addition to the technological side, the process of digitalization takes place with and through the human factor. Thus, we are concerned with the human capital and its digital competences, as their deficiency or absence can have negative effects both on the general life chances of citizens and on the digital progress of EU Member States. Romania is striving to make the most of the digital revolution, the possibilities and benefits offered by it, trying to contribute to the digital progress of the European Union and to consolidate its position among the other Member States. However, given the aforementioned issues, it is natural to wonder if citizens’ skills support the digitalization process or represent a shortcoming in this regard. Thus, the fundamental purpose of this article is to provide an overview of the digital competences of Romanian citizens, dealing with aspects such as the evolution of the last years and the current status related to the analyzed phenomenon.
Transformation of the Role of University Teacher in the Digital Age

The article presents the results of the study of the role positions of the teacher, which are of particular importance for the modern student in the context of the digitalization of the educational process, as well as the ideas of teachers about their role in the digital educational environment of the university. We assume that, despite the digital transformation of the educational process, the leading role of the teacher in its organization remains. Students are interested in active communication with the teacher even in the conditions of automated online learning. The study will be attended by 100 students and 60 teachers of an engineering and technical university. The research results are planned to be used in the implementation of professional retraining and advanced training programs for teachers of an engineering and technical university and college.
Balanced Spectral Clustering Algorithm Based on Feature Selection

High dimensional data clustering faced some problems such as sparse samples, difficulty in calculating similarity and so on. In addition, the clustering results sometimes be extremely unbalanced with too many or too few category samples. Therefore, we propose a novel algorithm, that is a balanced spectral clustering algorithm based on feature selection. Firstly, the least square method is used to calculate the target loss error. Secondly, the method of feature selection is used to reduce the influence of noise and redundant features. Thirdly, a balanced regularization term exclusive lasso is introduced to balance the clustering results. Finally, the locality preserving projection is used to maintain the feature structure of the samples. A large number of experimental results show that the proposed algorithm outperformed the comparison algorithms on the two indicators (accuracy and normal mutual information) in most cases, which proves the effectiveness of the proposed spectral clustering algorithm.
Distributed Neural Network with TensorFlow on Human Activity Recognition Over Multicore TPU

There have been increasing interests and success of applying deep learning neural networks to their big data platforms and workflows, say Distributed Deep Learning. In this paper, we present distributed long short-term memory (dLSTM) neural network model using TensorFlow over multicore Tensor Processing Unit (TPU) on Google Cloud. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. This model could extract human activity features automatically and classify them with a few model parameters. In the proposed model, the raw data collected by mobile sensors was fed into distributed multi-layer LSTM layers. Human activity recognition data from UCI machine-learning library have been applied to the proposed distributed LSTM (dLSTM) model to compare the efficiency of TensorFlow over CPU and TPU based on execution time, and evaluation metrics: accuracy, precision, recall and F1 score along with the use of Google Colab Notebook.
TEACHER MOTIVATION AND ITS SOCIO-PROFESSIONAL FEATURES

This article, along with the general concept and theoretical foundations of motivation, considers the professional and social features of teacher motivation. The work independently studied the intrinsic and extrinsic motivations of teachers on the basis of the theories of motivation of Maslow, McClelland and Herzberg. The main points of connection between the types of motivation were introduced.
Secure Data Sharing Algorithm for Privacy Protection of Industrial Internet

With the integration of artificial intelligence, 5g and cloud computing technologies, the development direction of industrial Internet tends to be more intelligent and digital. The privacy protection and security sharing methods of equipment production data are very important. In the face of a large number of sensitive industrial control data uploaded to the cloud, there are great security risks using the traditional plaintext or symmetric encryption transmission methods. This paper proposes a privacy protection and safe and efficient data sharing algorithm for the industrial Internet. The fog computing layer is introduced between the sensing layer and the cloud of the industrial Internet to perform some intensive computing, such as aggregation and processing of collected data Re-encryption and decryption, mutual authentication and trust relationship are established between the sensing layer devices. Only the devices in the authorization list can access the shared data, which achieve confidentiality and security of end-to-end data sharing. The fog computing layer uploads the data to the cloud after storing and computing, reduces the computing and communication overhead, and reduces the trust and dependence on the cloud platform. The algorithm can solve the semi trusted problem of cloud centralized database and realize the secure sharing of data. The experimental results show that the algorithm has less communication and computing overhead, can effectively reduce the transmission delay and improve the efficiency.
Learning Based Approach for Facial Fiducial Points Prediction of Non Frontal Faces

Facial features points (FFP) are used for localization and representation of the prominent features of the face that are traced on tips, corners, and mid-points of facial segments. The Proposed algorithm to detect facial landmarks on frontal and non-frontal faces on video sequences. It syndicates a regression-based approach with an advanced block matching technique. In the novel methodology, it will be shown that how trees are used to take the estimated position of facial landmarks, straight from a scant subsection of pixel intensities with less time and by handling missing labeled data, especially for non-frontal images. The Proposed algorithm, encompasses the regression-based model to provide a quality measure of each prediction and use the shape model to restrict and correct the sampling region. Our approach is an extension of the existing LEAR that combines the low computational cost with the selection of important features based on 22 facial points. The proposed algorithm is tested on five datasets. Results presented significant enhancement over the existing approach.
Private Set Intersection with Delegated Blind Quantum Computing

Private set intersection is an important problem with implications in many areas, ranging from remote diagnostics to private contact discovery. In this work, we consider the case of two-party PSI in the honest-but-curious setting. We propose a protocol that solves the server-aided PSI problem using delegated blind quantum computing. More specifically, the proposed protocol allows Alice and Bob (who do not have any quantum computational resources or quantum memory) to interact with Steve (who has a quantum computer) in order for Alice and Bob to obtain set intersection such that privacy is preserved. In particular, Steve learns nothing about the clients' input, output, or desired computation. The proposed protocol is correct, secure and blind against a malicious server, and characterized by a quantum communication complexity that is linear in the input size.
Bifurcations and catastrophes at the boundaries separating the phases of quantum correlations

The spin-1/2 XXZ chain in a uniform magnetic field at thermal equilibrium is considered. For this model, we give phase diagrams for the one-way quantum work deficit (thermal discord). The diagrams can contain regions with both stationary and variable (state-dependent) angles of optimal measurement. We also established a relationship between the behavior of optimal measurement angles near the boundaries separated different regions and Landau’s theory of phase transitions of the second and first kind. Namely, when crossing the boundary from a region with a stationary optimal measurement angle (0 or &pi;/2) to the region with a variable angle of optimal measurement (0 &lt; ϑ &lt; &pi;/2), quantum discord and one-way quantum work deficit experience a sudden change (“catastrophe”) in their behavior. The sudden change is accompanied by a splitting of the minimum characterizing stationary point of quantum correlation into two minima (i.e., bifurcation of the initial single minimum occurs). Such phenomena are analogous to phase transitions described within the Landau theory, or, more generally, by the mathematical formalism of catastrophe theory.
Stability Analysis of Recurrent Neural Networks by IQC with Copositive Mutipliers

This paper is concerned with the stability analysis of the recurrent neural networks (RNNs) by means of the integral quadratic constraint (IQC) framework. The rectified linear unit (ReLU) is typically employed as the activation function of the RNN, and the ReLU has specific nonnegativity properties regarding its input and output signals. Therefore, it is effective if we can derive IQC-based stability conditions with multipliers taking care of such nonnegativity properties. However, such nonnegativity (linear) properties are hardly captured by the existing multipliers defined on the positive semidefinite cone. To get around this difficulty, we loosen the standard positive semidefinite cone to the copositive cone, and employ copositive multipliers to capture the nonnegativity properties. We show that, within the framework of the IQC, we can employ copositive multipliers (or their inner approximation) together with existing multipliers such as Zames-Falb multipliers and polytopic bounding multipliers, and this directly enables us to ensure that the introduction of the copositive multipliers leads to better (no more conservative) results. We finally illustrate the effectiveness of the IQC-based stability conditions with the copositive multipliers by numerical examples.
Use of Probabilistic Risk Assessment Methodology for Providers of Services in a Virtual Environment

Information and data in today’s world are the most valuable assets of an organization. Every business system is exposed to certain risks and threats. One of the critical parameters that affect an organization’s exposure to risk is information security. Its goal is to protect information from threats. The information security risk management system establishes a mechanism for controlling and managing an organization’s acceptable risk level. Selecting an appropriate risk assessment methodology allows the organization’s managers to prioritize risks according to their severity or other criteria. Given the complexity of today’s information business, the Probabilistic Risk Assessment methodology has begun to be applied in information security risk assessment. By creating a use case scenario, it is possible to conduct qualitative and quantitative risk assessments. As part of the above methodology, event trees and fault trees are used to indicate possible scenarios, find the causes of their occurrence, and model the possible failure of controls applicable to risk mitigation. This chapter will present one of the use cases of information security risk assessment using these trees. Also, it will show the application of these trees in a complex information system’s conditions on the example of an organization in the Republic of Croatia.
Revocable Outsourcing Multi-Authority ABE for Medical Data in Mobile Cloud

With the improvements in mobile healthcare em-boldened by the ubiquity of mobile devices, personal health records are increasingly being outsourced to remote servers to enjoy customer-centric health services. Multi-authority ciphertext-policy attribute-based encryption has been proposed and adopted mainly in recent years, thanks to its flexibility, to guarantee data security, privacy, and fine-grained access control over distant cloud data. However, its practicality still faces three main drawbacks: key escrow, high encryption, and decryption costs, and finally, improper implementation of user and attribute revocations. We introduce in this paper a revocable outsourcing multi-authority attribute-based access control for medical data in the mobile cloud. The advantages of our construction are multifold. First, the expensive encryption operation is outsourced to a semi-honest cloud storage server and is verifiable. Second, the costly decryption operation in current schemes exhibits lower computation in our work and is partially outsourced to the cloud. Third, user and attribute revocations are performed by the semi-trusted cloud storage server, therefore, saving resources on end computing devices. Fourth, our scheme is secure and verifiable under the decisional bilinear Diffie-Hellman assumption.
VHAS: Video Highlight based Adaptive Streaming

As video streaming has become one of the most important services on the Internet, exploration of efficient video transmission methods have attracted vast research attractions during last several years. Existing ABR (Adaptive Bit Rate) algorithms assign the same bitrate as much as possible, assuming that the user watches all video scenes with the same attention and interest. However, because users engage more in highlight scenes than other parts of videos, the playback quality of the highlights should be higher than that of non-highlights. In this paper, we propose a highlight-based bitrate adaptation scheme called VHAS, Video Highlight based Adaptive Streaming. VHAS first extracts a highlight score per frame using graph neural network and object detection techniques. VHAS employs a DRL (Deep Reinforcement Learning) technique to learn the optimal policy from an incomprehensibly complicated operating environment. VHAS incorporates the extracted highlight scores into the reward model and decides the bitrate of video chunks based on their highlight scores. Performance experiments on real video streaming service environments show that VHAS can adaptively provide bitrate according to the highlight scores of chunks. In our study, the average QoE of VHAS is about 56%- 150% higher than that of state-of-the-art baselines.
High-tech criminality

Cybercrime is an activity that originates from a hacker's workstation, spreads through the web and affects the endpoint infrastructure. From a professional point of view, it's one of the most requiring criminologist cases in all crime areas, because it needs a plenty of skill to be recognized, managed and resolved. The modern global landscape shows a certain tendency of not being capable to respond to such a security challenge. In other words, the cyber criminals are well-motivated and work for a high profit, so it's obvious why that branch of criminality cannot be fully covered at present. Indeed, we need much better capacities to tackle such a concern and as the entire world suffers so obvious cyber skill shortage it's clear why we are dealing in maze. In this paper, we discuss high-tech crime as a social, economical and above all; security challenge trying to indicate some recommendations that could provide us a deeper understanding of the topic.
The Analysis of Chinese People’s Recognition of Digital Currency Electronic Payment Policy

The purpose of this paper is to explore the degree and reasons of Chinese people’s recognition of Digital Currency Electronic Payment policy, through literature review, expert interviews and research results. Personal factors also affect the degree and reasons of DC/EP policy identification. There is a significant positive correlation between the people’s recognition of DC/EP policy (policy purpose, policy content, policy process) and the reasons for policy recognition (policy cognition, policy expectation, policy emotion, policy evaluation). Personal factors will also affect the degree of identification and the reasons for identification. The age and education level of the people have a significant impact on the degree of DC/EP policy identification and the identification reasons. The use of mobile payment devices has a significant impact on policy emotion and policy purpose.
Distributed Semisupervised Partial Label Learning Over Networks

Partial label learning (PLL) deals with the classification from sufficient training data associated with a candidate set of labels but not the only correct one. In this article, we focus on PLL with some ambiguously labeled and many unlabeled data collected from multiple nodes distributed over a network. To solve this problem, a distributed semisupervised PLL (dS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> PLL) framework is formulated, in which the weighted logistic loss with respect to partially labeled (PL) and unlabeled data is utilized. In the proposed dS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> PLL algorithm, the parameters of the classifier are adapted in a collaborative manner, meanwhile the weights of each training sample and the ground-truth confidence of candidate labels are adaptively learned to disambiguate the correct label from the candidate label set. The performance of the proposed algorithm is analyzed theoretically and verified by simulations on both synthetic and real datasets. Results show that the proposed algorithm achieves good classification performance and robustness to the ambiguity in the labels.