Reviewerly/exHarmony huggingface dataset:
Factors Influencing Sttudents’ Speaking Passivity on Social Media

Speaking is one of the essential skills that every individual, especially students, must possess to support their other abilities. In this highly modern era, social media needs to be explored, particularly concerning students' speaking skills. Many students remain passive when speaking through social media. This case is intriguing to explore and investigate. This research aims to investigate the factors that can influence students' speaking passivity on social media, using a qualitative approach. Data were obtained by interviewing the English subject teachers of the 10th grade in a private high school in Surabaya. The researcher found that several factors influencing the speaking passivity of 10th-grade students include fear of making mistakes, bad pronounciation, and a lack of vocabulary. The results of this research can serve as a reference for both students and teachers to study solutions for these factors.
Information maximization-based clustering of histopathology images using deep learning

Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis. However, the pancreas remains so deep inside the body that experts sometimes struggle to detect cancer in it. Computer-aided diagnosis can come to the aid of pathologists in this scenario. It assists experts by supporting their diagnostic decisions. In this research, we carried out a deep learning-based approach to analyze histopathology images. We collected whole-slide images of KPC mice to implement this work. The pancreatic abnormalities observed in KPC mice develop similar histological features to human beings. We created random patches from whole-slide images. Then, a convolutional autoencoder framework was used to embed these patches into an integrated latent space. We applied ‘information maximization’, a deep learning clustering technique to cluster the identical patches in an unsupervised manner since our dataset does not have annotation. Moreover, Uniform manifold approximation and projection, a nonlinear dimension reduction technique was utilized to visualize the embedded patches in a 2-dimensional space. Finally, we calculated a few internal cluster validation metrics to determine the optimal cluster set. Our work concentrated on patch-based anomaly detection in the whole slide histopathology images of KPC mice.
Evolution of Requirements for Library Interior Design in the Context of Digitalisation, Reflecting Public Opinion

The purpose of the article is to determine the evolution of requirements for library interior design under the influence of digitalisation, taking into account the public’s attitude to the role and functions of libraries in the modern world. The research methods are based on a systematic approach to analysing the impact of innovative services on library interior design and public opinion about the role and status of libraries in the information society. It is shown that positive attitudes towards libraries are influenced by the quality of service and the relevance of the information services package, which, in turn, are associated with the functionality of the workspace planning and the library interior design. The survey revealed the public’s requirements for library interior design. The trends in the evolution of library interior design are analysed, taking into account the impact of digitalisation of information services on its requirements. Scientific novelty. The article demonstrates the relationship between the relevance of the package of information library services, the functionality of interior design, and the public positive attitude towards visiting libraries. The evolution of requirements for library interior design is characterised by a list of requirements generated by innovative services and refined by the results of a sociological survey on public attitudes to the functions of modern libraries. Conclusions. The study identified possible trajectories of sustainable development of libraries in the context of digitalisation, when libraries lose their monopoly on access to knowledge. The features of implementing the concept of «well-being of presence» in the design of library interiors using multisensory interaction of light, heat, acoustic, and olfactory environments are considered. It is shown that the implementation of this concept in the practice of managing the functioning of a modern library is based on a combination of architectural and design solutions with distributed «cloud» means of monitoring and managing the «well-being of the presence environment».
NAS-SE: Designing A Highly-Efficient In-Situ Neural Architecture Search Engine for Large-Scale Deployment

The emergence of Neural Architecture Search (NAS) enables an automated neural network development process that potentially replaces manually-enabled machine learning expertise. A state-of-the-art NAS method, namely One-Shot NAS, has been proposed to drastically reduce the lengthy search time for a wide spectrum of conventional NAS methods. Nevertheless, the search cost is still prohibitively expensive for practical large-scale deployment with real-world applications. In this paper, we reveal that the fundamental cause for inefficient deployment of One-Shot NAS in both single-device and large-scale scenarios originates from the massive redundant off-chip weight access during the numerous DNN inference in sequential searching. Inspired by its algorithmic characteristics, we depart from the traditional CMOS-based architecture designs and propose a promising processing-in-memory design alternative to perform in-situ architecture search, which helps fundamentally address the redundancy issue. Moreover, we further discovered two major performance challenges of directly porting the searching process onto the existing PIM-based accelerators: severe pipeline contention and resource under-utilization. By leveraging these insights, we propose the first highly-efficient in-situ One-Shot NAS search engine design, named NAS-SE, for both single-device and large-scale deployment scenarios. NAS-SE is equipped with a two-phased network diversification strategy for eliminating resource contention, and a novel hardware mapping scheme for boosting the resource utilization by an order of magnitude. Our extensive evaluation demonstrates that NAS-SE significantly outperforms the state-of-the-art digital-based customized NAS accelerator (NASA) with an average speedup of 8.8 × and energy-efficiency improvement of 2.05 ×.
CASA: An Energy-Efficient and High-Speed CAM-based SMEM Seeding Accelerator for Genome Alignment

Genome analysis is a critical tool in medical and bioscience research, clinical diagnostics and treatment, and disease control and prevention. Seed and extension-based alignment is the main approach in the genome analysis pipeline, and BWA-MEM2, a widely acknowledged tool for genome alignment, performs seeding by searching for super maximal exact match (SMEM). The computation of SMEM searching requires high memory bandwidth and energy consumption, which becomes the main performance bottleneck in BWA-MEM2. State-of-the-Art designs like ERT and GenAx have achieved impressive speed-ups of SMEM-based genome alignment. However, they are constrained by frequent DRAM fetches or computationally intensive intersection calculations for all possible k-mers at every read position.
Research and Development of Holographic Teaching Application System for Automobile Engine Construction, Disassembly and Maintenance Based On Mixed Reality (MR) Technology

Abstract: or the purpose of preventing misoperation from damaging the automobile engine and saving the teaching cost, this paper uses MR technology to carry out relevant research on the holographic teaching application system of automobile engine structure, disassembly and maintenance. Use UG and 3ds-max and other software to model the 3D model of the automobile engine at a scale of 1:1, and the model is consistent with the rendering effect of the real engine. Use Unity 3D as the MR development platform, and use C # language programming to realize the interaction and animation functions of the system. The developed system includes six functional modules, including exploded view, disassembly and assembly, working principle, transparent sectioning, structure introduction and free movement. The system combines MR technology with engine structure cognition, working principle analysis and disassembly and maintenance, realizes the integration of virtual equipment, interactive animation and real space equipment, and solves the practical problems such as high purchase cost due to high engine price and complex maintenance and disassembly, which are easy to damage the engine by misoperation [1].
Toward Visually Analyzing Dynamic Social Messages and News Articles Containing Geo-Referenced Information

Abstract The number of social media posts and news articles that are being published every day is high. This makes them an attractive source of human-generated information for different domain experts such as journalists and business analysts but also emergency responders, particularly if posts contain references to geolocations. Visual analytics approaches can help to gain insights into such datasets and inform decision-makers. However, the high volume and the veracity of the data, as well as the velocity in the case of streaming data, pose challenges when supporting explorative analysis with interactive visualization. Based on four exemplary approaches, we outline recently proposed strategies to tackle these challenges. We describe how geo-aware filtering and anomaly detection methods can help to inform stakeholders based on geolocated tweets. We show that data-aware tag maps can provide analysts with an overview-first, details-on-demand visual summary of large amounts of text content over time. With space-filling curves, we can visualize the temporal evolution of geolocations in a two-dimensional plot without relying on animations that would impede comparative analyses. Additionally, we discuss the use of an efficient dynamic clustering algorithm for enabling large-scale visual analyses of streaming posts.
SPATIAL INTERPRETATION OF A. EINSTEIN'S ENERGY EQUATION

It is known that A. Einstein's theory is well applicable for macro objects. However, for micro systems it does not give valid results. The totality of all physical processes is described by four interrelated components – energy, substance (matter), space, time. There is no space component in the structure of the energy equation. Based on the unity of physical nature, it is of interest to create such a theory that was equally applicable to both macro and micro systems. An interpretation of this equation is proposed which contains the volume of space with a certain mass and energy. The resulting expression containing the specific energy of space, and the density of matter in this space, allows you to combine energy, space, time and matter into a single pattern. The directions of perspective researches are determined using the developed interpretation of the energy equation.
Transformer based model for offensive content recognition in dravidian languages

This paper describes a model for spotting offensive data from the comments being collected from social media. The comments posted will include expressions, emoticons and will mostly be in code mixed language and classifying these code-mixed language comments is tricky. The proposed system uses a multi-head attention model to extract features from the code-mixed Tamil input data. Various classification algorithms are applied to these extracted features to categorize offensive comments. The generated labels are optimized by performing majority voting on labels generated by different algorithms. This system is validated on the validation set and is evaluated by applying the Tamil CodeMix test data from the dataset published by the HASOC task (Task2-subtask1) at FIRE 2021. The evaluation yields an average weighted F1 score of 0.83 and is ranked 3rd position in the official ranking.
Who’s Who in Mineral Names: Arthur L. Flagg (1883–1961)

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSA thank you is extended to the many members of the Flagg Mineral Foundation for all their help furthering Arthur Flagg’s interest in mineralogy. Thanks are also given to Bill Yedowitz, Phil Richardson, Peter Megaw, and Steve Chamberlain for their reviews of this article and helpful suggestions for its improvement, and to Dr. Anthony Kampf for the photograph of flaggite.Additional informationNotes on contributorsRon GibbsRon Gibbs, a retired mining engineer, is a research associate with the Department of Geosciences at the University of Arizona in Tucson and a past contributor to Rocks & Minerals.
An Energy-Efficient Scheduling Method for Real-Time Multi-workflow in Container Cloud

Cloud computing has a powerful ability to handle a large number of tasks. Correspondingly, it also consumes a lot of energy. Reducing the energy consumption of cloud service platforms while ensuring the quality of service has become a crucial issue. In this paper, we propose a heuristic energy-saving scheduling algorithm named Real-time Multi-workflow Energy-efficient Scheduling (RMES) with the aim to minimize the total energy consumption in container cloud. RMES executes tasks as parallel as possible to enhance the resource utilization of the running machines in cluster, therefore reducing the time of the global process, saving energy as a result. RMES takes advantage of the affinity between containers and machines to meet the resource quantity and performance requirements of containers during scheduling. In order to follow the change of the system state overtime, we introduce the re-scheduling mechanism, which can automatically adjust the scheduling decisions of the tasks that have not yet been executed in the scheduling scheme. The experimental results show that RMES has obvious advantages over other scheduling algorithms in terms of energy consumption and success ratio.
The Pegmatite Interest Group (PIG) Is Hog Heaven!

"The Pegmatite Interest Group (PIG) Is Hog Heaven!." Rocks & Minerals, 99(1), pp. 16–17 Additional informationNotes on contributorsDavid LondonDr. David London is a professor emeritus of the School of Geosciences at the University of Oklahoma. Granitic pegmatites have been the focus of his research for the past fifty years.
9 Dynamische Arrays

No AccessBusiness Intelligence mit ExcelDec 20239 Dynamische Arrayshttps://doi.org/10.3139/9783446480025.009SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2023Pages: 491-504Print ISBN: 978-3-446-47952-4eISBN: 978-3-446-48002-5 PDF downloadLoading ...
Elastic Kubernetes Service from AWS

AWS Elastic Kubernetes Service has been generally available since 2018. It is one of the major implementations of Kubernetes-as-a-Service on a public cloud. It has since then grown and offers tailored solutions for running container-based workloads in AWS as we will see in this chapter.
Humanness, pronoun types, and orphan prepositions in French

Abstract This paper is about the distribution of null and strong pronouns in French prepositional phrases in relation to the humanness of the preposition’s complement. The existing literature suggests that the mapping between pronoun type and humanness is characterized by soft complementarity, linking human complements to strong pronouns, and non-human complements to null pronouns. In the empirical coverage of the phenomenon experimental studies are still underrepresented: so far, no data on pronoun choice is available, and regarding interpretation data, the only existing study does not include null pronouns. Two experiments were conducted with the goal to fill this research gap. The results show that soft complementarity only applies to pronoun choice: human complements are typically referred to by strong pronouns, and non-human complements by null pronouns. As related to interpretation, however, both strong and null pronouns are preferably interpreted as human. We account for this asymmetry based on the general preference of pronouns to be interpreted as human unless prevented by strong constraints (as in the case of Engl. it ).
NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.
SignIt! An Android game for sign bilingual play that collects labelled sign language data

The Deaf or Hard-of-Hearing (DHH) community constitutes over 430 million people globally, with about 70 million of them using sign language as their primary means of communication. India has around 63 million DHH individuals. The DHH community in India faces several challenges, particularly in learning sign language and English, due to delayed diagnosis, stigma, oralism, and a diversity of languages. Digital games for spoken and sign language learning have gained popularity due to their advantages over traditional language learning methods, such as enhanced engagement and socialization, driving increased research and adoption over traditional methods. Moreover, the development of robust machine learning models for sign language recognition, which could significantly improve access for signers, is currently impeded by the scarcity of labelled sign language data. To address these challenges, we collaborated with NISH, an academic institution for the DHH community and developed SignIt!, an accessible and inclusive quiz platform that facilitates the learning of English and Indian Sign Language (ISL) with a secondary goal of data collection. To assess the game's usability, we conducted a study with 20 members of the DHH community, followed by interviewing 15 participants. Overall, our participants answered 2160 quiz questions and created 210 questions. The quiz creation resulted in the collection of three hours of labelled real-world sign language data. The interviews revealed novel insights, such as a preference for playing competitively with friends, empowerment by their agency to be content creators, and early signs of learning English, sign language, and quiz content by playing and creating quizzes. We plan to open-source and release SignIt! to increase its adoption among diverse DHH communities.
Know Your Customer and Customer Identification Program

Preventing criminals from opening up accounts at a financial institution starts with understanding who you are dealing with. We would not want Al Capone to open up a bank account. Therefore, being able to identify the customers is where the money laundering fight begins. Terms such as “Know Your Customer (KYC)” and “Customer Identification Program (CIP)” are simply phrases used that mean a financial institution should obtain some basic pedigree information on their customers. Items such as name, DOB, address, phone, and SSN. In other words, there should be no anonymity within the ranks of the customers. This chapter details what is required, how to obtain that information, and how that intelligence comes in handy when a money laundering investigation has begun.
Research on Multi-Domain Intelligent Customer Service Dialog Modeling with Integrated Transfer Learning Strategies

Abstract The intelligent customer service dialog model is centered on human-machine dialog, which has good prospects for commercial applications in multiple domains. In this paper, we use the Siamese-LSTM model to do vectorization of questions in the FAQ question and answer database to get the semantic representation vector of sentences, and then use the approximate retrieval algorithm to index the question and answer database and perform approximate nearest-neighbor retrieval of the query. After completing the question query, migration learning is employed to create a mapping between input questions and human responses, enabling the model to produce sentences that are similar to human responses. Tests show that the task success rate gradually stabilizes around 0.80 at about the 100th round and fluctuates up to around 0.986 after that. For the average number of conversation rounds, migration learning improves the conversation efficiency of intelligent customer service, and the average number of conversation rounds gradually stabilizes at about 150 rounds and eventually stabilizes at about 4.2 rounds as the number of training rounds increases. The transfer learning strategy helps machine responses to be as close to human responses as possible.
A Multimodal Approach for Detecting AI Generated Content using BERT and CNN

With the advent of Generative AI technologies like LLMs and image generators, there will be an unprecedented rise in synthetic information which requires detection. While deepfake content can be identified by considering biological cues, this article proposes a technique for the detection of AI generated text using vocabulary, syntactic, semantic and stylistic features of the input data and detecting AI generated images through the use of a CNN model. The performance of these models is also evaluated and benchmarked with other comparative models. The ML Olympiad Competition dataset from Kaggle is used in a BERT Model for text detection and the CNN model is trained on the CIFAKE dataset to detect AI generated images. It can be concluded that in the upcoming era, AI generated content will be omnipresent and no single model will truly be able to detect all AI generated content especially when these technologies are getting better.