Reviewerly/exHarmony huggingface dataset:
Bandit Approach to Conflict-Free Parallel Q-Learning in View of Photonic Implementation

Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example in reinforcement learning. However, the bandit problem deals with a static environment where the agent’s actions do not influence the reward probabilities. This study aims to extend the conventional approach to a more general type of parallel reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of the agent’s actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, a modified bandit Q-learning method, in view of a potential photonic implementation. Here, state–action pairs in the environment are regarded as slot machines in the context of the bandit problem and a change in Q -value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a parallel architecture in which multiple agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state–action pair selections among agents. We demonstrate that parallel reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.
The Where of Mineral Names: Insizwaite Waterfall Gorge, Insizwa, Alfred Nzo District Municipality Eastern Cape Province, South Africa

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSOT. Ljøstad is thanked for his photo of insizwaite. William Besse kindly drafted the locality map. Mindat.org was a source of locality information and references. BC acknowledges that the research for this work was partly funded by the NRF (National Research Foundation) and CIMERA (the DSI-NRF Centre of Excellence for Integrated Mineral and Energy Resource Analysis). GC also acknowledges funding from the NRF. Malcolm Southwood and John White reviewed the manuscript and improved its content. Any opinions, findings and conclusions or recommendations expressed are those of the authors, and therefore the NRF and DSI-NRF CIMERA do not accept liability in regard thereto.Additional informationNotes on contributorsBruce CairncrossDr. Bruce Cairncross, a consulting editor of Rocks & Minerals, is professor emeritus at the University of Johannesburg.R. Grant CawthornDr. R. Grant Cawthorn is professor emeritus in the School of Geosciences at the University of the Witwatersrand, Johannesburg. He has published extensively on the Bushveld Complex and other layered complexes, including Insizwa.
A dynamic conjunctive keywords searchable symmetric encryption scheme for multiple users in cloud computing

With the continuous advancement of cloud computing technology, massive amounts of heterogeneous data are being outsourced to cloud servers. Dynamic searchable symmetric encryption (DSSE) is widely used for dynamically updating and searching ciphertext. Although many DSSE schemes have been developed in recent years, few can satisfy the requirement for multi-keyword search. To address this problem, we proposed a novel Dynamic Searchable Symmetric Encryption scheme supporting Multiple users and Multiple keywords (MMDSSE) in this paper, specifically designed for the multi-keyword cloud computing paradigm, while also enabling multi-user search capability. To achieve multi-keyword search, a tag is computed using an iso-or operation that records the relationship between the keyword and identifier. In addition, a unique key is assigned by the data owner to each user with different file access rights. This allows users to gain fine-grained control over the ciphertext, and the essential security properties, including forward and backward privacy, can be achieved accordingly. Furthermore, this scheme is resistant to collusion attacks. Finally, the experiment was simulated on the IDEA platform using the Java language. The experimental results demonstrate that this scheme performs well.
Categorical Data Clustering Using Meta Heuristic Link-Based Ensemble Method

Conventional ensemble clustering is a consensus function that fails to produce final clusters. Such poor clusters partitioning creates poor stability with reduced clustering accuracy. This motivates to improve the final clustering quality using a hybrid ensemble-based model. In this study, an optimized link-based ensemble clustering approach is proposed to refine the incomplete datasets and to refine unknown entries in categorical dataset. The proposed work uses link-based similarity measure to find the availability of unknown datasets from link network of clusters. The ensemble clustering generates a refined cluster-association matrix in the form of weighted graphs. The final cluster partitioning acquires the final clustering partitions with a refined matrix as its input that decomposes the graph into clusters. The comparison with conventional methods is made against performance metrics to evaluate the model efficacy.
Opportunities and Barriers for Entrepreneurship in the Metaverse Age

The metaverse is a 3D (three-dimensional) virtual world synchronized with the real world, where users interact with a digital avatar using augmented reality technologies. The transformative impact of this virtual world is making itself felt in many areas, as are the opportunities and challenges it creates. Entrepreneurs' capacity to foresee these risks and assess opportunities may be crucial to the survival of their businesses in the future. This study aims to examine potential business opportunities, obstacles, and risks for entrepreneurs in the metaverse universe. To this end, studies in the metaverse literature were examined, and as a result of these studies, an attempt was made to determine the potential business opportunities that the metaverse universe could create. As a result, it was found that the metaverse offers very different business opportunities for entrepreneurs in different fields. This study considers it essential for entrepreneurs to invest in the metaverse to evaluate these business opportunities.
A Blockchain IoT Hybrid Framework for Security and Privacy in a Healthcare Database Network

Deep concern is rising in healthcare systems regarding medical data and computing medical customary. With advancement in blockchain technology, an advanced and more secure way of medical data management, sharing, and other services can be implemented with simple innovations in IoT and blockchain. This technology relies on grouping data into block and hold sets of information that once filled are closed and linked to other blocks of data creating a chain of data. This innovation grants high fidelity and a secure way of storing data without any involvement of third-party systems. In this research, the authors harness opportunity and trends that blockchain provides in advanced healthcare systems and how integration of other technologies can lead one of most secure and automatic systems in the health sector and medicine, records serving.
Intelligent System to Provide Support in the Analysis of Colposcopy Images Based on Artificial Vision and Deep Learning: A First Approach for Rural Environments in Ecuador

According to the World Health Organization (WHO), cervical cancer (CC) is an illness that has taken more than 342,000 female lives in 2020 and is considered the fourth cause of death in the world. In the rural areas of countries like Ecuador, there is no existence of low-cost tools for women who need to perform a self-screening exam and doctors who need to report cases based on artificial vision. For these reasons, in this article, we present the results of the first stage of development of the ecosystems aimed at the early detection of CC in rural areas. This ecosystem is based on a mobile application used to take photos during self-screening, a web tool to store and manage the image and diagnosis, and a module to classify images using deep learning.
NeSSA: Near-Storage Data Selection for Accelerated Machine Learning Training

Large-scale machine learning (ML) models rely on extremely large datasets to learn their exponentially growing number of parameters. While these models achieve unprecedented success, the increase in training time and hardware resources required is unsustainable. Further, we find that as dataset sizes increase, data movement becomes a significant component of overall training time. We propose NeSSA, a novel SmartSSD+GPU training architecture to intelligently select important subsets of large datasets near-storage, such that training on the subset mimics training on the full dataset with a very small loss in accuracy. To the best of our knowledge, this is the first work to propose such a near-storage data selection model for efficient ML training. We have evaluated our method for the CIFAR-10, SVHN, CINIC-10, CIFAR-100, TinyImageNet, and ImageNet-100 datasets. We also test across ResNet-20, ResNet-18, and ResNet-50 models.
Neural Cloud Storage: Innovative Cloud Storage Solution for Cold Video

Cloud storage providers offer different pricing tiers based on the access frequency of stored data. This pricing plan offers cost benefits for videos that are accessed less than once per month. However, the stringent requirement falls short in addressing the large number of "cold" videos stored today. This paper proposes Neural Cloud Storage (NCS), a pioneering approach to address the problem by applying neural enhancement, specifically content-aware super-resolution (SR). According to our preliminary cost-benefit analysis, NCS can further save an annual 14% total cost of ownership (TCO) compared to the cheapest AWS storage service for cold video. By reducing the cost, it expands the cold video coverage (from 25% to 38%) that can benefit from the multi-tiered service. As deep learning and computational resources continue to advance, we believe that neural enhancement will revolutionize the field of cloud storage.
P2Cache: An Application-Directed Page Cache for Improving Performance of Data-Intensive Applications

We propose P2Cache, an application-directed kernel-level page cache that allows an application developer to build a custom kernel-level page cache that matches the I/O characteristics of a target application. P2Cache extends a Linux kernel page cache by adding new probe points that are used to support application-programmable kernel page caches by eBPF programs. Our experimental results show that custom page caches implemented with our P2Cache achieve up to 32% performance improvement in data-intensive graph applications with little effort.
Hide-and-Seek: Hiding Secrets in Threshold Voltage Distributions of NAND Flash Memory Cells

In this paper, we propose a new page-writing technique to hide secret information using the threshold voltage variation of programmed memory cells. We demonstrate the proposed technique on the state-of-the-art commercial 3D NAND flash memory chips by utilizing common user mode commands. We explore the design space metrics of interest for data hiding: bit accuracy of public and secret data and detectability of holding secret data. The proposed method ensures more than 97% accuracy of recovered secret data, with negligible accuracy loss in the public data. Our analysis shows that the proposed technique introduces negligible distortions in the threshold voltage distributions. These distortions are lower than the inherent threshold voltage variations of program states. As a result, the proposed method provides a hiding technique that is undetectable, even by a powerful adversary with low-level access to the memory chips.
SAND: A Storage Abstraction for Video-based Deep Learning

Deep learning has gained significant success in video applications such as classification, analytics, and self-supervised learning. However, when scaling out to a large volume of videos, existing approaches suffer from a fundamental limitation; they cannot efficiently utilize GPUs for training deep neural networks (DNNs). This is because video decoding in data preparation incurs a prohibitive amount of computing overhead, making GPU idle for the majority of training time. Otherwise, caching raw videos in memory or storage to bypass decoding is not scalable as they account for from tens to hundreds of terabytes.
Classification on Unsupervised Deep Hashing With Pseudo Labels Using Support Vector Machine for Scalable Image Retrieval

The content-based image retrieval (CBIR) method operates on the low-level visual features of the user input query object, which makes it difficult for users to formulate the query and also does not provide adequate retrieval results. In the past, image annotation was suggested as the best possible framework for CBIR, which works on automatically signing keywords to images that support image retrieval. The recent successes of deep learning techniques, especially Convolutional Neural Networks (CNN), in solving computer vision applications have inspired me to work on this paper to solve the problem of CBIR using a dataset of annotated images
Performing Corporate Culture: Analysing Meta-Narratives and Online Interactivity Through Quigital

Home Comfort Advisor is a collaborative composition for online choir, presented by the fictional corporation ‘Quigital’. This paper details the work's co-opting of corporate aesthetics, analysis, productivity, design strategies and their implications in musical language, collaboration, performer agency and audience reception. The resulting choir performance masquerades as an interactive online product launch, making unconventional analytical demands on individual choir members as singers, community members, distributed decision makers and content creators. To manage the logistical challenges presented by an interdisciplinary collaboration featuring 70 agents and a creative development team, linear approaches to composition were replaced by a modified iterative design loop. (Collaborative creation ⇒ analysis ⇒(re)-interpretation ⇒ interactive audience engagement: repeat/deploy:). More specifically, this paper examines emergent materials, structures and processes used to amplify collaboration. Home Comfort Advisor's deeply embedded corporate aesthetic and technological infrastructure reveals novel interactions between the score, text, code, design assets, analysist and performer. An analysis of the non-linear, collaborative relationships provides a model for creating similar data-driven, interdisciplinary collaborations. Quigital's manipulation of a mutually understood code hidden in plain sight approaches what Limor Shifman refers to as hypersignification. This cultivation of technologically amplified, collaborative metanarrative lies at the heart of Quigital's success and its ability to be both approachable, subversive and deeply disturbing.
Planning and implementation of the administrative node of the institutional virtual learning environment of a linguistic university

The research is aimed at developing and testing a node of the institutional virtual learning environment of a linguistic university to monitor the learning process and to assist the management of the educational programs in decision-making, which would be organically combined with the corresponding lear¬ning nodes – both technically and conceptually. Scientific novelty lies in the fact that the notion of an ad-ministrative node is introduced into the concept of the virtual learning environment for the first time; in addition, the paper proposes an approach to the development of software solutions that are secondary to methodological aspects, that is, they proceed from the needs of the learning process and not from any “technical” circumstances. In the course of the research, firstly, the requirements for the functions of the administrative node were drawn up and the technical tools for its implementation were listed, sec-ondly, its complete programming code was written, including the construction of an interactive graphical user interface, and, thirdly, the created software package was tested and its viability as a node of the insti-tutional virtual learning environment of a language university was established. As a result, the correctness of the approach to creating software for the needs of the learning process as an add-on to the basis, which is made up of methodological components, has been proved. This approach is opposed to choosing ready-made software solutions, which lack flexibility and are poorly compatible with the actual processes of or-ganizing and implementing educational programs in specific conditions.
YOUNG PEOPLE’S REAL-TIME EXPERIENCES OF DIGITAL EDUCATION: "THE SCHOOL IS COMFORTABLE" "YEAH, RIGHT, LIKE GETTING SUFFOCATED IN A MASK ALL DAY IS COMFORTABLE”

YOUNG PEOPLE’S REAL-TIME EXPERIENCES OF DIGITAL EDUCATION: "THE SCHOOL IS COMFORTABLE" "YEAH, RIGHT, LIKE GETTING SUFFOCATED IN A MASK ALL DAY IS COMFORTABLE”
Teacher Education beyond the Pandemic in Spain

In the wake of the pandemic, there has been a clear need to understand the “new normal” contexts of teaching and learning and how they affect teacher education. In order to rethink and reimagine teacher education, a priority should be to have a firm grasp on the traits that young people bring with them when they enroll in Education degree programs. In other words, we must understand their distinctive generational traits and how the various crises of recent years have affected them. This paper has three main aims, each of which is addressed in a corresponding section of the article. The first objective is to gain a better understanding of the educational needs and learning styles of this new generation of students and to examine how they are influenced by present-day realities. A second section strives to identify the most prominent challenges that are reshaping teacher education programs on a global scale. The third and final part explores the bidirectional interaction between these two previous variables by examining the educational approaches and methodologies that have been implemented recently in Spain and discussing the extent to which they have been able to meet students’ evolving needs. Given that these changes, transformations and pedagogical concerns are observable worldwide, these analyses and reflections are potentially relevant beyond the Spanish context.
Behavioral Analysis of an Interval Type-2 Fuzzy Controller Designed with Harmony Search Enhanced with Shadowed Type-2 Fuzzy Parameter Adaptation

The challenges we face in today’s world are increasingly complex, and effectively managing uncertainty when modeling control problems can yield significant benefits. However, the complexity of these models often leads to higher computational costs. Therefore, the main contribution of this article is the use of the theory of shadowed type-2 fuzzy sets to address these challenges and to control the search space exploration in the harmony search algorithm by employing two alpha planes, and with this, it was possible to reduce the computational cost and obtain effective results. Furthermore, the application of this approach aims to find optimal parameters for the membership functions of a type-2 fuzzy controller and analyze its behavior. By adopting the proposed methodology, it becomes possible to minimize computational costs while still achieving feasible solutions for interval type-2 control problems. A key aspect is that symmetry is considered in the design of the controller to also obtain good results. To validate the effectiveness of the approach, extensive simulations were conducted with varying levels of noise introduced to the type-2 controller. This comprehensive analysis allowed for a thorough examination of the results obtained. The findings of the simulations are presented, showcasing the advantages of the proposed methodology. By incorporating noise into the system, it was observed that the objective function, in this case, the root mean square error (RMSE), was reduced. Moreover, the signal obtained with the presence of noise demonstrated a superior performance compared to the noise-free reference. In conclusion, the proposed approach of utilizing shadowed type-2 fuzzy systems, combined with the harmony search algorithm, offers a promising solution for managing complex control problems. By carefully analyzing the behavior of the system through simulations, it is evident that the inclusion of noise helps improve the system’s performance.
Flower pollination optimization algorithm with stacked temporal convolution network-based classification for financial anomaly fraud detection

As internet use increases dramatically, an increasing number of industries, including the financial one, are operationalizing their services online. Due to the significant financial losses, they cause that financial fraud is becoming a big issue as it spreads globally and grows in both volume and variety. Financial fraud detection systems should be used to identify dangers like unauthorized access and erratic assaults. During the last several years, this problem has been extensively addressed using machine learning and data mining approaches. These methods still need to be developed in order to cope with massive data, compute quickly, and spot new attack patterns. As a result, in this research, a deep learning-based approach based on the stacked temporal convolution network technique is provided for the identification of financial fraud. This model aims to improve both the efficiency and accuracy of existing detection methods in the context of large data. In addition, a flower pollination optimization process is incorporated for feature selection, which addresses any side effects that may result from choosing the best features. An actual dataset of credit card, loan, and insurance frauds is used to assess the proposed model, and the results are contrasted with those of current deep learning methods. The experimental results show that the suggested FPO_QCNN achieves 99.95%, 99.95%, 99.95% of accuracy for credit card, insurance, and Mortgage Data set.