Reviewerly/exHarmony huggingface dataset:
Impact of the COVID-19 Pandemic on the Processes of Internationalization and Virtualization of Higher Education: Antecedents, Current Events, and Possible Scenarios in the Cases of Chile and Mexico

Using a case study approach, this chapter examines two primary changes to global health education prompted and exacerbated by the COVID-19 pandemic. First, is the acceleration of digital education into the typical global health classroom. Second, is the integration of transnational networks and partnerships between intergovernmental organizations (IGOs), nongovernmental organizations (NGOs), and higher education institutions (HEIs) to create, adapt, and disseminate digital materials in fast-changing information environments. The chapter examines the potential positive and negative impacts of the proliferation of digital content created by IGOs and international NGOs that is being rapidly integrated into use in HEIs for Global Health. The growth of tools and access through these new partnerships may lead to increased access to global health information for professionals. However, these changes may have long-term implications for global health regarding equitable access to culturally appropriate and accurate information. Furthermore, there may be additional implications for equity, as the proliferation of tools may still leave out the most vulnerable that do not have access to digital platforms.
Building a Chatbot for Libraries

Natural language processing (NLP) is a very important science because it helps make human language understandable to machines. In addition, it helps add numeric structure to the unstructured data that is needed for many applications, such as speech recognition or text analytics. The importance of improving speech recognition is not limited to individual applications that make life easier. Speech recognition technology is very important for many businesses, as it can provide customer service benefits and enrich the customer experience. For example, voice recognition technologies have proven to be very secure, and even banks use them to authorize access to individuals' accounts. This chapter consists of the execution of a chatbot using different machine learning algorithms to help provide answers to users and provide basic customer service support.
Analysis of Cases of Corruption Across the African Continent

This chapter's main objective is to critically analyze the phenomenon of corruption in Africa, and in the process highlight some major cases of corruption in the continent, describe the evolutions of the phenomenon, as well as recommend some possible ways and mechanisms for fighting the menace. The study looks at figures from Transparency International and United Nations Human Development Index and juxtaposed them with the changing dynamics of corruption in Africa. The method of study is critical analysis of individual cases, Africa corruption ranking and reviews of relevant empirical literature dealing with corruption in Africa. The study also adopts phenomenology as a study tool. Thus, the study relied heavily on secondary sources of information. The summary of the findings of the chapter shows the problem of corruption in Africa as endemic; something that has entered all the major sectors of modern African society.
Keeping Close From a Distance

The upsurge of the COVID-19 pandemic has drastically changed the delivery of library services. With the physical closure of library doors, libraries were forced to provide expanded and strengthened access to library collections. This study aims to document the curbside book delivery service of Nazarbayev University Library during the pandemic specifically from Fall 2020 to Fall 2021. The study revealed that the patrons found the delivery service effective, especially during the pandemic. Despite the challenge of having a limited number of staff to work, the library personnel found themselves untiringly serving the patrons and were considered as front liners who provide the service despite the harm of getting infected with the virus. Although no one would hope to have another crisis, the library has proven itself as flexible as it can be and can go the extra mile in response to the changing needs of the time.
Memtrade: Marketplace for Disaggregated Memory Clouds

We present Memtrade, the first practical marketplace for disaggregated memory clouds. Clouds introduce a set of unique challenges for resource disaggregation across different tenants, including resource harvesting, isolation, and matching. Memtrade allows producer virtual machines (VMs) to lease both their unallocated memory and allocated-but-idle application memory to remote consumer VMs for a limited period of time. Memtrade does not require any modifications to host-level system software or support from the cloud provider. It harvests producer memory using an application-aware control loop to form a distributed transient remote memory pool with minimal performance impact; it employs a broker to match producers with consumers while satisfying performance constraints; and it exposes the matched memory to consumers through different abstractions. As a proof of concept, we propose two such memory access interfaces for Memtrade consumers -- a transient KV cache for specified applications and a swap interface that is application-transparent. Our evaluation shows that Memtrade provides significant performance benefits for consumers (improving average read latency up to 2.8X) while preserving confidentiality and integrity, with little impact on producer applications (degrading performance by less than 2.1%).
Duo: A High-Throughput Reconfigurable Datacenter Network Using Local Routing and Control

The performance of many cloud-based applications critically depends on the capacity of the underlying datacenter network. A particularly innovative approach to improve the throughput in datacenters is enabled by emerging optical technologies, which allow to dynamically adjust the physical network topology, both in an oblivious or demand-aware manner. However, such topology engineering, i.e., the operation and control of dynamic datacenter networks, is considered complex and currently comes with restrictions and overheads.
Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models

Deep Neural Network (DNN) models are becoming ubiquitous in a variety of contemporary domains such as Autonomous Vehicles, Smart cities and Healthcare. They help drones to navigate, identify suspicious activities from safety cameras, and perform diagnostics over medical imaging. Fast DNN inferencing close to the data source is enabled by a growing class of accelerated edge devices such as NVIDIA Jetson and Google Coral which host low-power Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) along with ARM CPUs in a compact form-factor to offer a superior performance-to-energy ratio. E.g., the NVIDIA Jetson AGX Xavier kit has a 512-core Volta GPU, an 8-core ARM CPU and 32GB LPDDR4x memory, that operates within 65W of power, costs US999 and is smaller than a paperback novel.
Leveraging the Properties of mmWave Signals for 3D Finger Motion Tracking for Interactive IoT Applications

Wireless signals, which are mainly used for communication networks, also have the potential to extend our senses, enabling us to see behind closed doors and track moving objects through walls [3, 10]. Accordingly, there is a growing interest in the community recently to develop novel IoT applications for sensing by exploiting radio frequency signals [7]. Given the compact size of modern wireless devices, this enables ubiquitous applications in the areas of smart healthcare, sports analytics, AR/VR etc. Specifically, as these signals travel in the medium, they traverse occlusions and bounce off different objects before arriving at a receiver; hence, the reflected signals carry information about the environment. By exploiting this property, this paper shows the feasibility of tracking precise 3D finger motion using mmWave signals that are popularly used in 5G networks.
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning

We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of their entropy-regularized long-term rewards. To overcome the curse of dimensionality and to reduce communication, we propose a Localized Policy Iteration (LPI) algorithm that provably learns a near-globally-optimal policy using only local information. In particular, we show that, despite restricting each agent's attention to only its κ-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in κ. In addition, we show the finite-sample convergence of LPI to the global optimal policy, which explicitly captures the trade-off between optimality and computational complexity in choosing κ. Numerical simulations demonstrate the effectiveness of LPI. This extended abstract is an abridged version of [12].
Online Education During the Covid-19 Pandemic: Differences by Education Levels and Years of Study

Abstract The present article identifies, at the level of university education, the difficulties encountered by students from the humanities profile, within two universities in Romania, by levels and years of study, during the online courses. 143 students, aged between 18 and 50, from undergraduate, master’s and doctoral studies participated in the study. The results of the study revealed difficulties in the online education of students such as: disorganization in the personal schedule, interests in other activities during the courses, low motivation and interest, insufficiently developed digital skills, the existence of a less performing computer, the service.
Split consensus for object security

We present a set of security mechanisms supporting a form of split consensus between subjects that grant permissions to exert rights to access objects, and subjects that hold these rights. Our solution is based on lock-key pairs. A key specifies a collection of access rights, and a category for each access right. For each category, a lock states whether an access right in that category is valid, or not. We illustrate a few examples of applications, including file security, a form of priority-based access control, access control lists, and capability lists supporting hierarchical object structures.
Probability Distribution Functions

Random phenomena are described with stochastic variables and associated probability distribution functions. Stochastic variables take sets of possible values, and their probability distribution functions are maps of the stochastic variables that show the distributions of what to be observed.
Metropolis–Hastings Algorithm for Ising Model

Models in statistical physics have vast degrees of freedom and finding exact solutions are very challenging.
Blockchain: Uncertainty in Transactions

Think how many times you buy a product in your everyday life. This could be anything from a small transaction (e.g., a sandwich), down to a significant one (e.g., a new car). As a consumer, you are exposed only to one layer of complexity in this transaction: that of the buyer and seller. But think how many additional steps are required for the car to arrive at the dealership. A car is made of many components and consists of many materials, from metal to plastic to glass. The manufacturer has to transact with different companies to get access to those components. Then, the car has to be tested, and once the tests are passed, it has to be assembled in an assembly line. Each car that is being produced could be faulty, which might require additional tests. Then, the cars have to be transferred to different destinations all over the world, which can include the use of various transportation companies. The transportation companies have to use trucks and ships, either by buying them or arranging agreements with other companies that can carry their goods for them.
INTELLECTUAL ANALYSIS OF WEB CONTENT FOR THE FORMATION OF SOCIAL AND DIGITAL IDENTITY OF WEB USERS

The article discusses the growing need to analyze and understand web user behavior due to the unprecedented amount of digital content being generated and distributed on the Internet. Web content intelligence is presented as an effective way to explore and extract valuable information from online content, including websites, social media platforms, and other digital sources, to better understand web users' interests, preferences, and behaviors. The ability to identify web users based on their online behavior is important for industries such as marketing, psychology, and law enforcement. However, there are certain problems associated with this approach, such as ensuring the privacy and security of web users’ data, as well as assessing the accuracy and reliability of web content analysis tools. The purpose of the article is to review the current state of web content analysis, its potential applications in various industries, and its role in shaping the digital future. The article emphasizes the importance of an interdisciplinary approach to the study of virtual identification and self-presentation in online communities, taking into account the socio-demographic characteristics of a web personality involved in social interactions. The article also explores the latest trends and developments in the field of web data mining, including web content analysis, web structure analysis, web page usage analysis, and social media data analysis. A software solution for conducting intelligent analysis of web content is proposed to form a social and digital identity of a web user using a specialized dictionary of content markers of a web community member.
Neural Network-Based Prediction for Secret Key Rate of Underwater Continuous-Variable Quantum Key Distribution through a Seawater Channel

Continuous-variable quantum key distribution (CVQKD) plays an important role in quantum communications, because of its compatible setup for optical implementation with low cost. For this paper, we considered a neural network approach to predicting the secret key rate of CVQKD with discrete modulation (DM) through an underwater channel. A long-short-term-memory-(LSTM)-based neural network (NN) model was employed, in order to demonstrate performance improvement when taking into account the secret key rate. The numerical simulations showed that the lower bound of the secret key rate could be achieved for a finite-size analysis, where the LSTM-based neural network (NN) was much better than that of the backward-propagation-(BP)-based neural network (NN). This approach helped to realize the fast derivation of the secret key rate of CVQKD through an underwater channel, indicating that it can be used for improving performance in practical quantum communications.
Correction: Witanto et al. Distributed Data Integrity Verification Scheme in Multi-Cloud Environment. Sensors 2023, 23, 1623

The authors make the following corrections to the published paper [...].
Qutrit-Based Orthogonal Approximations with Inverse-Free Quantum Gate Sets

The efficient compiling of arbitrary single-qubit gates into a sequence of gates from a finite gate set is of fundamental importance in quantum computation. The exact bounds of this compilation are given by the Solovay–Kitaev theorem, which serves as a powerful tool in compiling quantum algorithms that require many qubits. However, the inverse closure condition it imposes on the gate set adds a certain complexity to the experimental compilation, making the process less efficient. This was recently resolved by a version of the Solovay–Kitaev theorem for inverse-free gate sets, yielding a significant gain. Considering the recent progress in the field of three-level quantum systems, in which qubits are replaced by qutrits, it is possible to achieve the quantum speedup guaranteed by the Solovay–Kitaev theorem simply from orthogonal gates. Nevertheless, it has not been investigated previously whether the condition of inverse closure can be relaxed for these qutrit-based orthogonal compilations as well. In this work, we answer this positively, by obtaining improved Solovay–Kitaev approximations to an arbitrary orthogonal qutrit gate, with an accuracy ε from a sequence of O(log8.62(1/ε)) orthogonal gates taken from an inverse-free set.
Refactoring with domain-driven design in an industrial context

Abstract Context Software developers need to constantly work on evolving the structure and the stability of the code due to changing business needs of the product. There are various refactoring approaches in industry which promise improvements over source code composition and maintainability. Objective In our research, we want to improve the maintainability of an existing system through refactoring using Domain-Driven Design (DDD) as a software design approach. We also aim for providing empirical evidence on its effect on maintainability and the challenges as perceived by developers. Method In this study, we applied the action research methodology, which facilitates close academia-industry collaboration and regular presence in the studied product. We utilized focus groups to discover problems of the existing system with a qualitative approach. We reviewed the subject codebase to construct our own expert opinion as well and identified problems in the codebase and matched them with the ones raised by engineers in the team. We refactored the existing software system according to DDD principles. To measure the effects of our actions, we utilized Technology Acceptance Model (mTAM) questionnaire, and also semi-structured interviews with the development team for data collection, and card sorting methodology for qualitative analysis. For minimizing bias that might affect our results with the existing software engineers in the team, we extended our measurement with three new joiner software engineers in the team through the think aloud protocol. Results We have identified that engineers mostly gave positive answers to our interview questions, which are mapped to software maintainability metrics defined by ISO/IEC 25010. Our DDD refactoring scored 85 in PU and 83 in PEU, leading to an overall mTAM score of 84. This means acceptable on the acceptability scale, B on the grade scale, and good on the adjective rating scale. Conclusion Our research led us to conclude that a powerful design approach, like DDD, is an effective tool for restructuring and resolving software issues in this situation. It offers standardization to the software and the refactoring efforts. We realized that DDD entails a certain degree of complexity and cognitive load, which is a barrier for software engineers, but they are aware of its benefits.
TCRN: A Two-Step Underwater Image Enhancement Network Based on Triple-Color Space Feature Reconstruction

The underwater images acquired by marine detectors inevitably suffer from quality degradation due to color distortion and the haze effect. Traditional methods are ineffective in removing haze, resulting in the residual haze being intensified during color correction and contrast enhancement operations. Recently, deep-learning-based approaches have achieved greatly improved performance. However, most existing networks focus on the characteristics of the RGB color space, while ignoring factors such as saturation and hue, which are more important to the human visual system. Considering the above research, we propose a two-step triple-color space feature fusion and reconstruction network (TCRN) for underwater image enhancement. Briefly, in the first step, we extract LAB, HSV, and RGB feature maps of the image via a parallel U-net-like network and introduce a dense pixel attention module (DPM) to filter the haze noise of the feature maps. In the second step, we first propose the utilization of fully connected layers to enhance the long-term dependence between high-dimensional features of different color spaces; then, a group structure is used to reconstruct specific spacial features. When applied to the UFO dataset, our method improved PSNR by 0.21% and SSIM by 0.1%, compared with the second-best method. Numerous experiments have shown that our TCRN brings competitive results compared with state-of-the-art methods in both qualitative and quantitative analyses.
Group codes over symmetric groups

&lt;abstract&gt;&lt;p&gt;Let $ \Bbb F_{q} $ be a finite field of characteristic $ q $ and $ S_n $ a symmetric group of order $ n! $. In this paper, group codes in the symmetric group algebras $ \Bbb F_{q}S_n $ with $ q &amp;gt; 3 $ and $ n = 3, 4 $ are proposed. We compute the unique (linear and nonlinear) idempotents of $ \Bbb F_q S_n $ corresponding to the characters of symmetric groups and use the results to characterize the minimum distances and dimensions of group codes. Furthermore, we construct MDS group codes and almost MDS group codes in $ \Bbb F_q S_3 $ and $ \Bbb F_q S_4 $.&lt;/p&gt;&lt;/abstract&gt;
Design, fabrication and experiment of dataset acquisition apparatus for dual imaging plane

Abstract Target tracking plays an important role in monitoring, security, pedestrian recognition and other fields. The imaging system of a single imaging plane is limited by hardware conditions, and sometimes is not competent for increasingly complex image processing tasks. Therefore, it is a natural choice to obtain better tracking effect by multi imaging plane cooperative system. When the target tracking network algorithm is implemented for the dual imaging plane system, the data set acquisition of image is indispensable. According to this requirement, we designed and fabricated an image data acquisition apparatus, which includes a set of optical system, two sets of imaging circuits and structural components. After the apparatus integration is completed, we acquire the image data and implement the target tracking experiment based on correlation filtering method. The experiment shows that the tracking accuracy and tracking success rate of correlation filtering algorithm are significantly improved compared with the method using single imaging plane information after using the dataset constructed by this apparatus.
Continuous-Variable Quantum Key Distribution: Background and Perspectives

The review examines the essential characteristics of continuous-variable quantum key distribution protocols. In these protocols, information is encoded into the complex amplitude of the optical field, which can have a continuous range of values, unlike a qubit. An advantage over many other quantum key distribution protocols is provided by the fact that the practical implementation of these protocols does not require sources and single-photon detectors. Additionally, they offer a relatively high rate of secret key generation. However, continuous-variable protocols require a specific analysis of secrecy and special consideration for the influence of noise that may arise during practical implementation. This presents a broad area for further research.
Estimating the Feasibility of Images Simulation in Unity 3D for Sub-Pixel Processing Algorithms

Computer image simulation is used in the development of optical measurement systems and processing algorithms. It allows the performance of a system or algorithm to be verified and its measurement uncertainty to be assessed. Modern algorithms make it possible to achieve sub-pixel accuracy in digital image processing. An example of such algorithms is the cross-correlation processing in Particle Image Velocimetry. Often, standard graphics engines such as Unity 3D and Unreal Engine are used to simulate the images. The purpose of this work is to evaluate the possibility of using these tools to generate images for further processing by subpixel resolution algorithms. This paper presents the results of a computational experiment to estimate the maximum spatial resolution for simulations in Unity 3D. A qualitative and quantitative analysis of the results confirms the possibility of using the tested graphics engine for the analysis of subpixel resolution algorithms for digital image processing.