Reviewerly/exHarmony huggingface dataset:
Digital transformation of higher education: technologies and digital competencies

The article considers the issues of digital transformation of higher education. The approaches to the definition and content of the term “digital transformation” are analyzed, the specifics and conditions of digitalization of the economy and the development of higher education are investigated. As prerequisites for the digital transformation of higher education, one can designate not only the penetration into the educational field of various information technologies that have changed the learning process itself, but also a number of regulatory legal acts. The reasons for the digital transformation of universities are identified. There are considered the issues of the modern content of digital technologies in higher education and a set of digital competencies that a university teacher must master. The experience of individual universities (Tyumen State University, National Research University Higher School of Economics, Toliatti State University) in building a digital educational environment has been analyzed. The digital transformation of higher education involves the restructuring of all areas of activity based on information and communication technologies. Changes should take place in technologies and training programs, methods and means, forms of educational activities, planned educational outcomes, and assessment results. It is noted that digital competencies are a set of skills and knowledge necessary to carry out professional activities using digital technologies. There have been listed the digital competencies that the future university graduates should master: working with applied programs, digital equipment, digital information, communication skills in a digital environment, self-writing software, creating their own digital products, working in various programs, etc. Retraining the teaching staff implies developing advanced training programs aimed at mastering digital competencies that are in demand by the digital economy, and which must be formed among university graduates. It has been inferred that the totality of information systems, digital devices, sources, interfaces, services, which are developed and aimed at implementing the educational process and university management, constitutes a digital educational environment.
Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.
Unsupervised Proxy Selection for Session-based Recommender Systems

Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i.e., session). Unlike sequence-aware recommender systems where the whole interaction sequence of each user can be used to model both the short-term interest and the general interest of the user, the absence of user-dependent information in SRSs makes it difficult to directly derive the user's general interest from data. Therefore, existing SRSs have focused on how to effectively model the information about short-term interest within the sessions, but they are insufficient to capture the general interest of users. To this end, we propose a novel framework to overcome the limitation of SRSs, named ProxySR, which imitates the missing information in SRSs (i.e., general interest of users) by modeling proxies of sessions. ProxySR selects a proxy for the input session in an unsupervised manner, and combines it with the encoded short-term interest of the session. As a proxy is jointly learned with the short-term interest and selected by multiple sessions, a proxy learns to play the role of the general interest of a user and ProxySR learns how to select a suitable proxy for an input session. Moreover, we propose another real-world situation of SRSs where a few users are logged-in and leave their identifiers in sessions, and a revision of ProxySR for the situation. Our experiments on real-world datasets show that ProxySR considerably outperforms the state-of-the-art competitors, and the proxies successfully imitate the general interest of the users without any user-dependent information.
Translation correction of English phrases based on optimized GLR algorithm

Abstract Basic syntactic analysis refers to sentence-level syntactic analysis. In the process of developing the Mat Link English–Chinese machine translation system, the Generalized Maximum Likelihood Ratio algorithm was improved, and a basic English syntax analyzer for English–Chinese translation was designed and implemented. The analyzer approves the structure of the analysis table with a variety of export products, introduces the character mapping function to realize the automatic recognition of the sentence boundary, uses the children of the same level to describe the grammatical structure of the sentence, and realizes the proverb from the original sentence to the target sentence stage conversion. Finally, through the analysis of example sentences, the design concept and working process of the basic grammar are explained.
A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts

In recent years, Vietnam witnesses the mass development of social network users on different social platforms such as Facebook, Youtube, Instagram, and Tiktok. On social medias, hate speech has become a critical problem for social network users. To solve this problem, we introduce the ViHSD - a human-annotated dataset for automatically detecting hate speech on the social network. This dataset contains over 30,000 comments, each comment in the dataset has one of three labels: CLEAN, OFFENSIVE, or HATE. Besides, we introduce the data creation process for annotating and evaluating the quality of the dataset. Finally, we evaluated the dataset by deep learning models and transformer models.
First-Order Trotter Error from a Second-Order Perspective

Simulating quantum dynamics beyond the reach of classical computers is one of the main envisioned applications of quantum computers. The most promising quantum algorithms to this end in the near term are the simplest, which use the Trotter formula and its higher-order variants to approximate the dynamics of interest. The approximation error of these algorithms is often poorly understood, even in the most basic and topical cases where the target Hamiltonian decomposes into two realizable terms: H=H_{1}+H_{2}. Recent studies have reported anomalously low approximation error with unexpected scaling in such cases, which they attribute to quantum interference between the errors from different steps of the algorithm. Here, we provide a simpler picture of these effects by relating the Trotter formula to its second-order variant for such H=H_{1}+H_{2} cases. Our method generalizes state-of-the-art error bounds without the technical caveats of prior studies, and elucidates how each part of the total error arises from the underlying quantum circuit. We compare our bound to the true error numerically, and find a close match over many orders of magnitude in the simulation parameters. Our findings further reduce the required circuit depth for the least experimentally demanding quantum simulation algorithms, and illustrate a useful method for bounding simulation error more broadly.
High-frequency expansions for time-periodic Lindblad generators

Floquet engineering of isolated systems is often based on the concept of the effective time-independent Floquet Hamiltonian, which describes the stroboscopic evolution of a periodically driven quantum system in steps of the driving period and which is routinely obtained analytically using high-frequency expansions. The generalization of these concepts to open quantum systems described by a Markovian master equation of Lindblad type turns out to be nontrivial: On the one hand, already for a two-level system two different phases can be distinguished, where the effective time-independent Floquet generator (describing the stroboscopic evolution) is either again Markovian and of Lindblad type or not. On the other hand, even though in the high-frequency regime a Lindbladian Floquet generator (Floquet Lindbladian) is numerically found to exist, this behavior is, curiously, not correctly reproduced within analytical high-frequency expansions. Here, we demonstrate that a proper Floquet Lindbladian can still be obtained from a high-frequency expansion, when treating the problem in a suitably chosen rotating frame. Within this approach, we can then also describe the transition to a phase at lower driving frequencies, where no Floquet Lindbladian exists, and show that the emerging non-Markovianity of the Floquet generator can entirely be attributed to the micromotion of the open driven system.
Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction

Background: Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). Objectives: This study aimed to use DL models to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart failure with reduced ejection fraction (HFrEF). Methods: We analyzed the data of adult HFrEF patients from the IBM® MarketScan® Commercial and Medicare Supplement databases between January 1, 2015 and December 31, 2017. A sequential model architecture based on bi-directional long short-term memory (Bi-LSTM) layers was utilized. For DL models to predict HF hospitalizations and worsening HF events, we utilized two study designs: with and without a buffer window. For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was assessed by area under the curve (AUC) values, precision, and recall on an independent testing dataset. Results: A total of 47 498 HFrEF patients were included; 9427 with at least one HF hospitalization. The best AUCs of DL models without a buffer window in predicting HF hospitalizations and worsening HF events in the total patient cohort were 0.977 and 0.972; with a 7-day buffer window the best AUCs were 0.573 and 0.608, respectively. The best AUCs in predicting 30- and 90-day readmissions in all adult patients were 0.597 and 0.614, respectively. An AUC of 0.861 was attained for prediction of 90-day readmission in patients aged 18-64. For all outcomes assessed, the DL approach outperformed traditional machine learning models. Discussion: The DL approach can automate feature engineering during the model learning, which can increase the clinical applicability and lead to comparable or better model performance. However, the lack of granular clinical data, and sample size and imbalance issues may have limited the model's performance. Conclusions: A DL approach using Bi-LSTM was shown to be a feasible and useful tool to predict HF-related outcomes. This study can help inform the future development and deployment of predictive tools to identify high-risk HFrEF patients and ultimately facilitate targeted interventions in clinical practice.
Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique

OBJECTIVE The aim of this study was to assess the precision and feasibility of 3D-printed marker–based augmented reality (AR) neurosurgical navigation and its use intraoperatively compared with optical tracking neuronavigation systems (OTNSs). METHODS Three-dimensional–printed markers for CT and MRI and intraoperative use were applied with mobile devices using an AR light detection and ranging (LIDAR) camera. The 3D segmentations of intracranial tumors were created with CT and MR images, and preoperative registration of the marker and pathology was performed. A patient-specific, surgeon-facilitated mobile application was developed, and a mobile device camera was used for neuronavigation with high accuracy, ease, and cost-effectiveness. After accuracy values were preliminarily assessed, this technique was used intraoperatively in 8 patients. RESULTS The mobile device LIDAR camera was found to successfully overlay images of virtual tumor segmentations according to the position of a 3D-printed marker. The targeting error that was measured ranged from 0.5 to 3.5 mm (mean 1.70 ± 1.02 mm, median 1.58 mm). The mean preoperative preparation time was 35.7 ± 5.56 minutes, which is longer than that for routine OTNSs, but the amount of time required for preoperative registration and the placement of the intraoperative marker was very brief compared with other neurosurgical navigation systems (mean 1.02 ± 0.3 minutes). CONCLUSIONS The 3D-printed marker–based AR neuronavigation system was a clinically feasible, highly precise, low-cost, and easy-to-use navigation technique. Three-dimensional segmentation of intracranial tumors was targeted on the brain and was clearly visualized from the skin incision to the end of surgery.
Performance comparison between Chaos and quantum-chaos based image encryption techniques

Today's digital era has undertaken most of the responsibilities of public and private sectors, not only the industries or big organizations dependent on the internet but individual's household needs also lying on it. To make the data transmission/reception confidential and secure for both internet users and internet service providers, a large number of researches have been done in this field. It has proved that cryptography is the best solution for solving this purpose. Mostly, digital images are continuously transferring on the network rather than texts. Enciphering a digital image is a much bulkier and a complex task. It has been evident from many types of research that chaotic logistic map-based equations provide a great level of randomness. Hence Chaotic logistic maps-based image encryption techniques (also called chaos techniques) were implemented to obtain highly random cipher images. On the other hand, time consumption must be as low as it can be possible to sustain real-time communication. Presently, the advanced encryption schemes based on quantum technology have enhanced efficiency and security because of having a large key-space and less time complexity along with randomness. The quantum-chaos based encryption is done by utilizing uncertainty principles of quantum mechanics on logistic maps. This paper is an effort to compare chaos and quantum chaos-based image encryption schemes. MATLAB 2016a software is used for the execution and the comparison is made based on various security attack analyses. Based on the study and experimental results, the quantum chaos techniques used for bit plane scrambling provides better results in terms of effectiveness, efficiency, and trustworthy that can be adopted for highly secured image encryption.
Suppression of midcircuit measurement crosstalk errors with micromotion

Midcircuit measurement and reset are crucial primitives in quantum computation, but such operations require strong interactions with selected qubits while maintaining isolation of neighboring qubits, which is a significant challenge in many systems. For trapped ion systems, measurement is performed with laser-induced fluorescence. Stray light from the detection beam and fluorescence from the measured ions can be significant sources of decoherence for unmeasured qubits. We present a technique using ion micromotion to reduce these sources of decoherence by over an order of magnitude. We benchmark the performance with a different method, based on randomized benchmarking, to estimate the magnitude of crosstalk errors on nearby qubits. Using the Honeywell System Model H0, we demonstrate measurement and reset on select qubits with low crosstalk errors on neighboring qubits.
An improved Wolf pack algorithm for optimization problems: Design and evaluation

Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of O pposition-based learning and G enetic algorithm with L evy’s flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy’s flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.
High-capacity and high-power collective charging with spin chargers

Quantum battery works as a micro- or nanodevice to store and redistribute energy at the quantum level. Here we propose a spin-charger protocol, in which the battery cells are charged by a finite number of spins through a general Heisenberg $XY$ interaction. Under the isotropic interaction, the spin-charger protocol is endowed with a higher capacity in terms of the maximum stored energy than the conventional protocols, where the battery is charged by a continuous-variable system, e.g., a cavity mode. By tuning the charger size, a tradeoff between the maximum stored energy and the average charging power is found in comparison to the cavity-charger protocol in the Tavis-Cummings model. Quantum advantage of our protocol is manifested by the scaling behavior of the optimal average power with respect to the battery size, in comparing the collective charging scheme to its parallel counterpart. We also discuss the detrimental effect on the charging performance from the anisotropic interaction between the battery and the charger, the nonideal initial states for both of them, and the crosstalk among the charger spins. A strong charger-charger interaction can be used to decouple the battery and the charger. Our findings about the advantages of the spin-charger protocol over the conventional cavity-charger protocols, including the high capacity of energy storage and the superior power law in the collective charging, provide an insight to exploit an efficient quantum battery based on the spin-spin-environment model.
Unsupervised Person Re-Identification With Stochastic Training Strategy

Unsupervised person re-identification (re-ID) has attracted increasing research interests because of its scalability and possibility for real-world applications. State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning. This approach suffers two problems. First, the centroid generated by unsupervised learning may not be a perfect prototype. Forcing images to get closer to the centroid emphasizes the result of clustering, which could accumulate clustering errors during iterations. Second, previous instance memory based methods utilize features updated at different training iterations to represent one centroid, these features are inconsistent due to the change of encoder. To this end, we propose an unsupervised re-ID approach with a stochastic learning strategy. Specifically, we adopt a stochastic updated memory, where a random instance from a cluster is used to update the cluster-level memory for contrastive learning. In this way, the relationship between randomly selected pair of images are learned to avoid the training bias caused by unreliable pseudo labels. By picking a sole last seen sample to directly update each cluster center, the stochastic memory is also always up-to-date for classifying to keep the consistency. Besides, to relieve the issue of camera variance, a unified distance matrix is proposed during clustering, where the distance bias from different camera domains is reduced and the variances of identities are emphasized. Our proposed method outperforms the state-of-the-arts in all the common unsupervised and UDA re-ID tasks. The code will be available at https://github.com/lithium770/ Unsupervised-Person-re-ID-with-Stochastic-Training-Strategy.
Deep Learning for Multimedia Forensics

Over the past few decades, we have witnessed a huge increase in the use of multimedia content on the internet, for multiple applications ranging from innocuous to critical ones. This development has led to threats posed when content can be manipulated/used for malicious purposes. For example, fake media can be used to drive personal opinions, or for criminal activities such as terrorist propaganda and cyberbullying. This research and practice activity gave rise to the creation of the multimedia forensics field. In this survey, the latest trends and deep-learning-based techniques for multimedia forensics are introduced, in both architectural and data-processing. Firstly, different techniques used to manipulate content are presented, followed by image and video forgery techniques. Thereafter, deep learning methods for source identification and recent solutions for deepfake detection are covered. Datasets and evaluation metrics are included, and conclusions are presented. The publication is intended for researchers, students and professionals active in the fields of Deep Learning and Multimedia Forensics.