Reviewerly/exHarmony huggingface dataset:
Multidimensional query processing algorithm by dimension transformation

Multidimensional query processing is an important access pattern for multidimensional scientific data. We propose an in-memory multidimensional query processing algorithm for dense data using a higher-dimensional array. We developed a new array system namely a Converted two-dimensional Array (C2A) of a multidimensional array of dimension n ([Formula: see text]) where the n dimensions are transformed into 2 dimensions. Using the C2A, we design and analyze less complex algorithms that show improve performance for data locality and cache miss rate. Therefore, improved performance for data retrieval is achieved. We demonstrate algorithms for single key and range key queries for both Traditional Multidimensional Array(TMA) and C2A. We also compare the performance of both schemes. The cost of index computation gets high when the number of dimensions increases in a TMA but the proposed C2A based algorithm shows less computation cost. The cache miss rate is also lower for in C2A based algorithm than TMA based algorithm. Theoretical and experimental results show that the performance of C2A based algorithm outperforms the TMA-based algorithms.
Role of IPR (Intellectual Property Rights) in Fashion Designing Industry: A Quantitative Investigation

The apparel market is expanding rapidly on a worldwide scale.It must be protected by intellectual property rights in order to support its expansion since it's grown into an important component of the international business.Refreshing and modern designs sit in the centre of the burgeoning apparel sector.Plagiarism in fashion design, nevertheless, hinders the development of this business.The fashion design sector in India is struggling to address this issue.The fashion design sector cannot be adequately protected by the country 's existing intellectual property laws since they are neither adequate nor effective.To improve present intellectual property laws and enhance their efficacy at preventing copying of fashion designs, changes must be made.But before that we need to look into the role of IPR (Intellectual Property Rights) in the fashion designing industry.
Quantum hash function based on controlled alternate lively quantum walks

Quantum hash function is an important area of interest in the field of quantum cryptography. Quantum hash function based on controlled alternate quantum walk is a mainstream branch of quantum hash functions by virtue of high efficiency and flexibility. In recent development of this kind of schemes, evolution operators determined by an input message depend on not only coin operators, but also direction-determine transforms, which usually are hard to extend. Moreover, the existing works ignore the fact that improper choice of initial parameters may cause some periodic quantum walks, and further collisions. In this paper, we propose a new quantum hash function scheme based on controlled alternate lively quantum walks with variable hash size and provide the selection criteria for coin operators. Specifically, each bit of an input message determines the magnitude of an additional long-range hop for the lively quantum walks. Statistical analysis results show excellent performance in the aspect of collision resistance, message sensitivity, diffusion and confusion property, and uniform distribution property. Our study demonstrates that a fixed coin operator, along with different shift operators, can effectively work on the design of a quantum hash function based on controlled alternate quantum walks, and shed new light on this field of quantum cryptography.
Role of Social Media in Development Context: An Empirical Study in the Context of Rural India

The use of social media has emerged as a potential tool for promoting development initiatives in rural India.Social media platforms such as Twitter, Facebook, and WhatsApp have transformed the communication, education, health, and agriculture sectors in rural areas.Social media has facilitated communication among rural communities and enabled them to access information, services, and markets that were once inaccessible.Platforms such as YouTube, Khan Academy, and Coursera provide free access to high-quality educational content, enabling rural communities to access education and improve their skills.Furthermore, social media platforms such as WhatsApp and Facebook have become popular channels for sharing health-related information, connecting with doctors, and accessing telemedicine services.The use of social media in agriculture has provided access to market information, enabling farmers to connect with buyers, and facilitating the exchange of goods and services.Despite challenges such as limited internet connectivity and digital literacy, the potential of social media in promoting rural development is significant.
The Future Metavertainment Application development

The metaverse is a virtual environment where users can communicate with each other and a computer-generated environment. Nowadays, discussions about metaverse technology are prevalent in both online and print media. The concept of the metaverse is still not fully developed. Most people see it as a glimpse into how people will interact with their environment and how the internet will change over the years. According to a Technavio report, the metaverse in the entertainment sector is anticipated to grow to $28.92 billion in 2026. Over time, market growth will be accelerated by changing entertainment preferences and increased use of cutting-edge gaming technology.This paper aims to explore the trends of metaverse entertainment applications and how they will shape our future. The methods used in this research are based on narrative reviews of recent literature on Metaverse applications and the future of metaverse entertainment and a survey conducted on 100 participants. Results show that the future of metaverse entertainment is very promising but is also very dangerous and its security should be considered carefully.The study concluded that the metaverse has a future of benefits in different fields, not only in entertainment but also in education, business, and even the arts.
FPGA Implementation of N-BEATS for Time Series Forecasting Using Block Minifloat Arithmetic

The block minifloat (BM) number format uses an 8-bit floating point format with additional shared exponent bias to enable low-precision representation with large dynamic range. While it has been shown that the BM format can support low-precision training of convolutional neural networks such as ResNet on ImageNet at precisions down to 6 bits, its applicability to inference-only applications has not been studied. We present a BM implementation of N-BEATS, a deep neural architecture for univariate time series forecasting. N-BEATS utilises residual and fully connected (FC) blocks to achieve high accuracy. It was found that 8-bit BM had similar area and speed as 8-bit integer arithmetic with NBEATS accuracy similar to 16-bit floating point.
Influence of Typical Meteorological Years on the Optimization of Incident Solar Radiation for PV Applications in Portugal

In Portugal, the values used for forecasting the production of solar thermal systems, photovoltaic and thermal simulation of buildings are periodically updated due to climate changes. There is a special interest in optimizing these systems since Portugal has high insolation levels and the sector has benefited from technological developments that improved its efficiency and costs. The main objective is the analysis of typical meteorological years influence on the optimization of incident solar radiation, applied to the municipality of Braga (north of Portugal). A study was carried out on the fundamental concepts related to solar geometry, and the best methods for data processing were identified to develop an analysis tool, in MS Excel, to compare the selected climatic databases. Different solar tracking configurations were also considered: biaxial, polar axis, horizontal axis and fixed tracking systems. The analysis shows that the climatic database update resulted in the increase of total incident solar radiation when comparing both typical meteorological years. The polar axis present values very close to the orthogonal incidence biaxial system, which maximizes the incident solar radiation in the panel. The application of simple atmospheric models to estimate/predict the total radiation incidence presents more conservative values when compared to the more complex models.
Two-step Domain Adaptive Semantic Segmentation Algorithm of Driving Scene

Semantic segmentation of driving scenes is an important task in the field of automatic driving perception.The existing semantic segmentation based on deep learning method needs a lot of manpower cost to label data, and the changeable driving scenes will also lead to the performance degradation of semantic segmentation model in practical application.Therefore, in this paper, labeled computer-generated images are used as source domain data, unlabeled real driving scene images are used as target domain data, and unsupervised domain adaptive method is used to transfer knowledge when the target domain data is unlabeled.Based on the one-step domain adaptive semantic segmentation algorithm, a two-step domain adaptive semantic segmentation algorithm is proposed, and an image quality evaluation module is introduced to optimize the algorithm, so as to improve the performance index of cross-domain semantic segmentation tasks to some extent and reduce the dependence on data labeling and labeling cost.
BLOCKCHAIN DRIVEN INTERNATIONAL BUSINESS: A NEW FRONTIER

ABSTRACT This paper discusses the impact blockchain technology has on international business. There are great opportunities for incorporating the blockchain within global value chain activities and cross-border trade facilitation, along with business functions across borders – i.e., blockchain driven international business (BDIB). The paper posits that while initial investments in BDIB would be higher, there would be better performance realized over the long term. It is critical, then, to better understand and research the incorporation of this technology across the various needs of multinational enterprises (MNEs) as well as international small & medium sized enterprises (SMEs). Implications are discussed. Keywords Blockchain, Global Value Chains, International Business
SEM Image Quality Improvement and MTF Measurement Technique for Image Quality Evaluation Using Convolutional Neural Network

As the size of semiconductor devices gradually decreases, it is important to measure and analyze semiconductor devices, to improve the image quality of semiconductors. We use VDSR, one of the Super-Resolution methods to improve the quality of semiconductor devices’ SEM images. VDSR is also a convolutional neural network that can be optimized with various parameters. In this study, a VDSR model for semiconductor devices’ SEM images was optimized using parameters such as depth of layers and amount of training data. Meanwhile, the quantitative evaluation and the qualitative evaluation did not match at the low scale factor. To solve this problem, we proposed an MTF measurement method using the slanted edge for better quantitative evaluation. This method was verified by comparing the results with the PSNR and SSIM index results, which are known as quality indicators. Based on the results, it was confirmed that using the MTF value could be a better approach for the evaluation of SEM images of the semiconductor device than using PSNR and SSIM.
Implementing Artificial Neural Networks In Predicting Non-routine Discharge And Mortality In Patients Hospitalized With Diastolic Heart Failure

Introduction Diastolic heart failure is a relatively common cause of hospitalization nationwide. Stratifying patient risk factors can help serve as an instrumental tool in assessing patient disposition and mortality. We aim to explore risk factor importance derived from artificial neural networks (ANN) and their ability to classify patients hospitalized with diastolic heart failure at risk for non-routine discharge and mortality Methods Patient data was collected for years 2011-2018 from the National inpatient sample (NIS) using International Classification of Disease-Revision Codes (ICD-9 and 10). We selected patients hospitalized with a primary diagnosis of diastolic heart failure. Multilayer Layer Perceptron (MLP) ANNs were utilized to calculate classification scores and Normalized Importance Scores. Training and testing models were utilized to achieve this. Area under the curve (AUC) was calculated to determine MLP model efficacy. Models were utilized to determine if select comorbid conditions were associated with increased risk of non-routine discharge and mortality. Patient characteristics were tabulated to determine prevalence. Results A total of 365,522 patients were included in our analysis. 62.9% of our patient population was female. 70.3% were Caucasian. 81.2% of our patient sample had Medicare as their primary payer. MLP analysis was able to predict non-routine discharge with an overall accuracy of 65.5% with its testing model. Specifically we predicted non-routine discharge at 76.1%. Receiver operator characteristic curve (ROC) yielded an AUC for non-routine discharge model of 0.708. Overall accuracy in the testing model for mortality predictions was 97.5%. AUC for ROC mortality model was 0.801. Notably, highest predictors in mortality normalized importance scores were: Charlson Score, Elixhauser Score, acute hypoxic respiratory failure, acute kidney injury, obesity, transfer status, and age. Factors that most contributed to non-routine discharge derived from normalized importance scores were: Charlson Score, Elixhauser Score, transfer status, age, insurance payer, acute hypoxic respiratory failure, and venous insufficiency. Conclusions We found that there were similar risk factors that helped in building ANN prediction models for non-routine discharge and mortality in patients hospitalized with diastolic heart failure. ANN models in our analysis placed a higher weight on patient complexity scores. Social determinants of health played a notable role in non-routine discharge prediction compared to mortality. AUC analysis showed good efficacy in both non-routine discharge and mortality model accuracy, however the former does have room for further optimization.
QR CODE FOR DATA SECURITY

Distributed storage has turned into an essential industry in far off information the executives administration yet in addition draws in security concerns, where the most ideal that anyone could hope to find approach for forestalling information revelation is encryption.Among them the public key encryption with watchword search (PKSE) is viewed as a promising method, since clients can productively look through over encoded information records.That is, a client initially creates a pursuit token when to question information records, the cloud server utilizes the hunt token to continue the inquiry over scrambled information documents.In any case, a serious assault is raised when PKSE meets cloud.Officially talking, the cloud server can become familiar with the data of a recently added encoded information record containing the catchphrase that recently questioned by utilizing the pursuit tokens it has gotten, and can additionally find the security data.To resolve this issue, we propose a forward secure public key accessible encryption conspire, in which a cloud server can't gain proficiency with any data about a recently added encoded information record containing the catchphrase that recently questioned.To all the more likely comprehend the plan guideline, we present a structure for developing forward secure public key accessible encryption plans in light of property based accessible encryption.At last, the trials show our plan is productive.
BUILD A SOCIAL BOT THAT MAKES AUDIO TRANSLATED VIDEO

The demand for tools that can help with video creation is rising as a result of the success of YouTube as a platform for sharing and consuming content.The automated video maker we show in this work makes use of a variety of approaches, including text-to-speech, speech-to-text, video downloading development, audio translation and video creation.we have also used React js to create a secure web page.The website will be kept on a server from which it is possible to access the platform.The system creates audio files using text-to-speech technology, which are then mixed with video files created by video downloading functionalities.The device also comes with an audio translator that can convert audio files between different languages.The technology merges the audio and video files after they have been generated to create a desired video.To make it simple for content producers to share their videos with the world, the platform then immediately uploads the video to YouTube.Our findings demonstrate that the suggested method may efficiently and quickly produce high-quality videos to whom want to produce videos with little effort.
Machine Learning-Enabled Workflow To Identify Patients With Advanced Heart Failure In An Integrated Health System

Introduction Timely referral for specialist evaluation in patients with advanced heart failure (HF) is a Class 1 recommendation. However, the transition from stage C HF to advanced or stage D HF often goes undetected in routine care, resulting in delayed referral and higher mortality rates. Hypothesis We hypothesized that we could develop and embed within the electronic health record (EHR) a workflow enabled by machine learning (ML) to facilitate the identification of stage D HF and streamline specialist referral. Methods Data on HF patients (n=14,846) with encounters from 01/01/07-11/30/20 was extracted from an HF Registry within the Healthy Planet module of Epic Systems EHR (Epic Systems Corp, Verona, WI) and the Northwestern Medicine Enterprise Data Warehouse. Labels for model training were generated through text search for phrases “stage C” or “stage D” in cardiology notes. Features were selected from established HF mortality risk models and by expert consensus (Table 1). We trained and validated three models, with the final classification made by vote score. Model performance was evaluated on a holdout dataset of clinician-adjudicated patients (n=100), and the model output was embedded within a workflow in the EHR (Figure). Results The ML classifier had good precision (0.58) and low recall (0.30) for identifying stage D HF in the holdout dataset (Table 2). From 4/1/22 to 2/15/22, 384 patients were classified stage D after review by a clinical coordinator (precision 0.46). Twenty-four patients have been scheduled for evaluation in a HF clinic, 4 patients started an evaluation for advanced therapies, and 1 patient received a left ventricular assist device. Conclusions An ML-enabled workflow was embedded into the EHR to identify patients with advanced HF. Endeavors such as this require a multi-disciplinary team with experience in design thinking, clinical informatics, quality improvement, operations, and health IT. Continuous revision is needed to reduce the burden and inefficiency of human review.
Efficient Algorithms for High-Dimensional Quantum Optimal Control of a Transmon Qubit

Hybrid quantum classical optimization using near-term quantum technology is an emerging direction for exploring quantum advantage in high-dimensional systems. However, precise characterization of all experimental parameters is often impractical and challenging. A viable approach is to use algorithms that rely entirely on black-box inference rather than analytical gradients. Here, we combine randomized perturbation gradient estimation with adaptive momentum gradient updates and propose AdamSPSA and AdamRSGF algorithms. We prove the asymptotic convergence of the proposed algorithms in a convex setting and benchmark them against other gradient-based black-box optimization algorithms on nonconvex quantum optimal control tasks. Our results indicate that these algorithms accelerate the optimization rate, lower the optimization loss, and efficiently tune up high-fidelity Hann-window single-qubit gates from trivial initial conditions with up to 80 variables for a transmon qubit.
POLICING PLATFORMS: ADDRESSING POWER AND INEQUALITIES IN PLATFORM POLICIES

Social media platforms had for a long time successfully positioned themselves in the “sweet spot” between beneficial legislative protections and a remarkable absence of obligations (Gillespie 2010: 348), yielding little need to take direct responsibility for the content of users. Increasingly, and specifically since 2016, public and policy pressure has pushed platforms to become something different: not the allegedly neutral tech companies, but powerful intermediaries responsible for the functioning of public discourse and democracy. Platforms have struggled to develop their positions and processes for handling contested and delicate issues such as hate speech and misinformation, and the (at times unwritten) policies are still changing regularly. The panel examines four interrelated aspects of platform policies, including (1) the complexification and commodification of copyright content moderation, (2) platform verification processes and policies to classify some users, things, and places as ‘official,’ or ‘authentic’, (3) the relationship between human reviewers and AI in the enforcement of content moderation policies, and (4) the dilemma platforms face when turning to human rights as a standard for their platform policies. Taken together, the papers of this panel analyze crucial power dynamics and inequalities embedded within and extending beyond platform policies. For this, the panel convenes a productive multidisciplinary conversation and methodological exchange. This examination of the social, political, legal and economic underpinnings of recent changes in platform policies from a global perspective will allow us to better understand the ability of platforms to “re-fashion the world in their image” and to foster change.
UNCIVIL FOR CIVIL RIGHTS: A MACHINE LEARNING AND QUALITATIVE ANALYSIS OF INCIVILITY IN THE TWITTER-BASED CONVERSATION ABOUT BLACK LIVES MATTER

This study uses a combination of machine-learning and fine-tuned qualitative analysis to explore the online disinhibition effect in Twitter-based discourse around #BlackLivesMatter. Our analysis shows that uncivil tweets in the nonmobile dataset are twice as likely to be overtly racist and challenge Black Lives Matter. And in both nonmobile and mobile tweets, uncivil language is deployed in a variety of ways that are sometimes consistent with how we understand the online disinhibition effect, but sometimes not. As such, this study advances our understanding of instances where actors indulge in the more harmful forms of disinhibition such as personal attacks, vulgar language, and hate speech, which are often termed “toxic disinhibition.” In sum, these findings add nuance to the way we understand the online disinhibition effect and responds to a vital gap in the existing body of knowledge.