Reviewerly/exHarmony huggingface dataset:
Exponential Quantum Speedup in Simulating Coupled Classical Oscillators

We present a quantum algorithm for simulating the classical dynamics of 2n coupled oscillators (e.g., 2n masses coupled by springs). Our approach leverages a mapping between the Schrödinger equation and Newton’s equation for harmonic potentials such that the amplitudes of the evolved quantum state encode the momenta and displacements of the classical oscillators. When individual masses and spring constants can be efficiently queried, and when the initial state can be efficiently prepared, the complexity of our quantum algorithm is polynomial in n, almost linear in the evolution time, and sublinear in the sparsity. As an example application, we apply our quantum algorithm to efficiently estimate the kinetic energy of an oscillator at any time. We show that any classical algorithm solving this same problem is inefficient and must make 2Ω(n) queries to the oracle, and when the oracles are instantiated by efficient quantum circuits, the problem is bounded-error quantum polynomial time complete. Thus, our approach solves a potentially practical application with an exponential speedup over classical computers. Finally, we show that under similar conditions our approach can efficiently simulate more general classical harmonic systems with 2n modes.Received 20 April 2023Revised 29 August 2023Accepted 9 October 2023DOI:https://doi.org/10.1103/PhysRevX.13.041041Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum algorithms & computationQuantum computationQuantum simulationQuantum Information, Science & Technology
Is GPT-4 a Good Data Analyst?

As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by artificial intelligence (AI). This controversial topic has drawn great attention in public. However, we are still at a stage of divergent opinions without any definitive conclusion. Motivated by this, we raise the research question of “is GPT-4 a good data analyst?” in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. We propose a framework to tackle the problems by carefully designing the prompts for GPT-4 to conduct experiments. We also design several task-specific evaluation metrics to systematically compare the performance between several professional human data analysts and GPT-4. Experimental results show that GPT-4 can achieve comparable performance to humans. We also provide in-depth discussions about our results to shed light on further studies before reaching the conclusion that GPT-4 can replace data analysts.
The Impact of Using AI Chat GPT on Marketing Effectiveness: A Case Study on Instagram Marketing

This study aims to investigate the impact of using AI Chat GPT on marketing effectiveness, particularly on the Instagram platform. Marketing, specifically on the Instagram platform. In obtaining structured information and effective content suggestions, increasing desired, enhancing consumer emotions and experiences, and understanding the performance of their posts in order to make informed decisions regarding content strategy. Descriptive qualitative method with virtual ethnography that looks at AIDA (Attention, Interest, Desire, Action) is used to measure the effectiveness of Instagram marketing by observing the effectiveness of Instagram marketing. The effectiveness of Instagram marketing was measured by observing the number of users who viewed, liked, visited the profile, or took a specific action after seeing the ad. The results of the study showed that marketing content created using AI Chat GPT can achieve high levels of attention and engagement among users, increasing their interest in the products or services offered by the company. These findings can help businesses improve their marketing effectiveness by using AI Chat GPT on the Instagram platform.
Using Multi-Agent System Technologies in Risk Bots

We present a multi-agent architecture for playing the game of Risk which is a multi-player board game in which the players control armies and try to conquer the world through attacking each others territories. Our solution puts an agent in every territory, and let them negotiate about what actions to priori- tize in the phases of placing armies, attack, and fortify. The results of a tournament of 13 participating bots, shows out- standing results for our solution, MA R S which ends first or second (out of six participants) in 507 out of 792 matches.
Implementing Fast and Flexible Parallel Genetic Algorithms

Genetic algorithms are used to solve harder problems and it is becoming necessary to use more efficient implementations to find good solutions fast. This chapter describes the implementation of a fast and flexible parallel genetic algorithm. Since our goal is to help others to implement their own parallel codes, we describe some of the design decisions that we faced and discuss how the code can be improved even further.
Process Modelling: A Critical Analysis

This paper uses a software development environment construction case study as a framework fora critical analysis of software process modelling. It outlines a research agenda based on thisanalysis.
Petrogenesis of Early Cretaceous High-Mgo Diorites and Granitoids in the Rangnim Massif, North Korea: Implications for Tectonic Setting of Magmatism and Gold Mineralization

A large scale of gold deposit-hosted intrusive rocks are distributed in North Korea.A combination of major and trace element, whole-rock Sr-Nd-Hf isotope and zircon U-Pb-Hf-O isotope data are reported for the Early Cretaceous granitoids from the Rangnim Massif, North Korea, in order to constrain their sources, petrogenesis and tectonic setting.The intrusive rocks mainly consist of high-MgO diorites to granodiorites and porphyric granites.Zircon SIMS U-Pb dating gives coeval emplacement ages of 111-112 Ma for the magmatism.The high-MgO diorites and granodiorites have high MgO (up to 4.8 wt.%) at intermediate SiO2 contents, indicating a mantle source.They have high initial 87 Sr/ 86 Sr rations and negative εNd(t) and εHf(t) values, suggesting that the mantle source had been metasomatized by continental crustal materials for a long term or by ancient crustal materials before magma generation.However, the coupling whole rock Nd and Hf isotopes and high δ 18 O values of the high-MgO diorites argue for the later.The high-MgO diorites and granites have variable major and trace elements and whole rock Sr-Nd-Hf isotopes.The well relationship between SiO2 (or MgO) and Sr, Nd or Hf isotopes suggest that the Ealy Cretaceous high-MgO diorites to granodiorites and granites were the result of pyroxene-and amphibole-dominated crystal fractionation of mafic magma, coupled with extensive crustal assimilation.Importantly, the identification of ancient continental crustal materials in the mantle reveals a lithospheric delamination event in North Korea before or at the timing of magma generation.Therefore, our new data suggest that the Early Cretaceous magmatism of Rangrim Massif is generated by AFC of enriched lithospheric mantle-derived magmas, and the magmatism and metallogenesis are the result of lithospheric delamination in North Korea during Early Cretaceous.
Task Scheduling Allocation based on Task Completion Time in Cloud Environment

Cloud computing is a prominent computing model wherein shared resources can be given as per the customer request at a time. The available resources in the cloud are gathered to execute several tasks that are submitted by the customer. While implementing the tasks, there is a need to optimize performance in terms of execution time, response time and resource utilization of the cloud. The optimization of the mentioned factors in the Cloud Computing can be achieved by one of the major areas known as Load balancing which refers to dealing with client requests from diverse application servers that are functioning in the cloud. An efficient Load Balancing algorithm enables the cloud to be more proficient and enhances customer contentment. So, this survey paper highlights the latest studies regarding the application of Load Balancing techniques for task allocation such as resource allocation (RA) strategies, cloud task scheduling centered on Load Balancing, dynamic Resource Allocation schemes, and cloud resource provisioning scheduling heuristics. Finally, Load Balancing performance for task allocation methods is compared based on task completion time.
Developing Ovarian Cancer Sensors Using Molecular Perceptron

Ovarian cancer patient prognosis and quality of life are significantly affected by the failure to accurately diagnose disease at early stage. Currently, less than 15% of ovarian cancer patients are diagnosed at stage 1, when 5-year survival rates are near 93%. Early detection may save more than 80% of women diagnosed with ovarian cancer. Our goal is to develop long-term implantable carbon nanotube-based sensors for ovarian cancer biomarkers to transiently detect and monitor their levels in patients. The proposed sensors will harness the unique optical properties and sensitivity of single-walled carbon nanotubes. To improve sensitivity and specificity of such sensors, previously developed to detect ovarian cancer biomarkers in vivo, we used molecular perceptron—data analytics-based methods to detect specific spectroscopic fingerprints from the binding of protein biomarkers to the nanotube surface.
PC-Spaces

Abstract The present study concentrates on the new generalizations of the Jordan curve theorem. In order to achieve our goal, new spaces namely PC-space and strong PC-space are defined and studied their properties. One of the main concepts that use to define the related classes of spaces is paracompact space. In addition, the property of being PC-space and strong PC-space is preserved by defining a new type of function so called para-perfect function.
Supplemental Material for How Does Aging Impact Decision Making? The Contribution of Cognitive Decline and Strategic Compensation Revealed in a Cognitive Architecture

Details on why Participants Were ExcludedIn this section, we give an overview of the number of excluded participants and the underlying reasons: 24 were excluded because they could not successfully complete the learning phase or main phase of the experiment (17 in the take-the-best group of older adults, seven in the tallying group of older adults), seven because of a technical problem in recording the responses in one of the cognitive covariates, that is, the working memory test battery (two in the take-the-best group of older adults, two in the take-the-best group of younger adults, one in the tallying group of older adults, and two in the tallying group of younger adults), 12 because we had measured timing errors of more than 300 ms for the presentation of tones in more than 10 of their trials (seven in the take-the-best group of older adults, four in the tallying group of older adults, and one in the tallying group of younger adults), four because of crashes of the experimental software triggered by accidentally pressing one of the Windows keys (one in the take-the-best group of older adults, two in the take-the-best group of younger adults, and one in the tallying group of younger adults), and three because a different version of the experiment was run (one in the take-the-best group of older adults, one in the take-the-best group of younger adults, and one in the tallying group of older adults). Learning Phase of the Empirical StudyThe learning phase consisted of four tasks designed to familiarize participants with the materials and tasks of the study.In a tone-familiarization task, participants first adjusted the
A Real Time Haze Removal &amp; Mosaicing u sing Rapid Prototype Hardware

In this paper, a pivotal technique was proposed that reduces the haze and combines the haze free image to increase the Field of View (FoV) in real-time with a rapid prototype hardware device. The Initial focus is to reduce the haze in an image with Dark Channel Prior Technique and the FSD method is utilized to mosaic the haze free images. Low contrast may occur due to the scattering light, air particles or fog in nature which results in a haze image that needs to be reduced and enhance the image for better vicinity. Haze reduction approach depends on entropy and information fidelity. Our Haze free algorithm executes multiple phases such as dark channel prior computation, estimation and refinement of transmission map and restoration of RGB values. The second technique is the mosaic process that improves the field of view of a scene and the phases that execute are corner detection, extraction, geometric computation and blending. Our experimental results have shown better when compared to the other algorithms. The whole process is executed in real-time with a standalone device called Intel compute stick.
A STUDY ON USING SENTENCE-COMPLETING ELEMENTS OF CHINESE-MAJORED STUDENTS AT VNU UNIVERSITY OF LANGUAGES AND INTERNATIONAL STUDIES

The sentence-completion component has the function of making the sentence structurally and expressively complete to perform the communication function in the condition that the sentence is not clear in a specific communication context. In Chinese, the sentence-completion components belong to the following categories: intonation, modal particle, the category denoting degree, negation, trend, state and quantity. Students in the process of learning Chinese, because of not knowing the sentence-completion component, not having mastered the use of sentence-completion components or due to negative effects from Vietnamese, tend to speak and write incomplete sentences. The study aims to specifically evaluate the use of sentence-completion components of students majoring in Chinese, assess the ability to use sentence-completion components, find out which sentence-completion components that are often misused, explain the cause of the errors and give specific solutions to improve the efficiency of teaching Chinese for students.
MR Image Analysis of Neonatal Brain using Adaptive Mathematical Morphological Processing and Region based Segmentation

In the study of evaluation of infant brain development, the segmentation of obtained MR images is an important step. When compared with the MR images of adult brain it is difficult to identify the different regions in infant brains with that of the methods used for the analysis of adult brains. This is due to the size difference of the brain and the differences in the properties of the brain tissues. So, for analyzing these MR images it requires manual interaction with the images resulting in the bias of the results. For this problem we propose another approach for the segmentation of neonatal brain MR images. This method doesn’t require any manual interaction and produces unbiased results. Our algorithm segments the different layers (right hemisphere, left hemisphere, cerebellum, brain stem) and the different tissues like sub cortical gray matter, Militated &amp; un mylinated gray matter and cerebrospinal fluid, resulting in the better understanding of the development of different parts of the brain. Our algorithm can be used for the analysis of MR images of infant brains of age as less as 3to6 months.
Multiclass Sentiment Analysis of Social Media Data using Neural Networks

Sentiment analysis, also known as Opinion Mining is one of the hottest topic Nowadays. in various social networking sites is one of the hottest topic and field nowadays. Here, we are using Twitter, the biggest web destinations for people to communicate with each other to perform the sentiment analysis and opinion mining by extracting the tweets by various users. The users can post brief text updates in twitter as it only allows 140 characters in one text message. Hashtags helps to search for tweets dealing with the specified subject. In previous researches, binary classification usually relies on the sentiment polarity(Positive , Negative and Neutral). The advantage is that multiple meaning of the same world might have different polarity, so it can be easily identified. In Multiclass classification, many tweets of one class are classified as if they belong to the others. The Neutral class presented the lowest precision in all the researches happened in this particular area. The set of tweets containing text and emoticon data will be classified into 13 classes. From each tweet, we extract different set of features using one hot encoding algorithm and use machine learning algorithms to perform classification. The entire tweets will be divided into training data sets and testing data sets. Training dataset will be pre-processed and classified using various Artificial Neural Network algorithms such as Reccurent Neural Network, Convolutional Neural Network etc. Moreover, the same procedure will be followed for the Text and Emoticon data. The developed model or system will be tested using the testing dataset. More precise and correct accuracy can be obtained or experienced using this multiclass classification of text and emoticons. 4 Key performance indicators will be used to evaluate the effectiveness of the corresponding approach.
THE ONLINE LISTENING STRATEGY INSTRUCTION AND VOCABULARY KNOWLEDGE ON LISTENING SKILL OF GRADE 12 JAPANESE LANGUAGE LEARNERS IN BEKASI WEST JAVA

ABSTRACT&#x0D; This article presents the result of empirical research that investigates the effect of online listening strategies instruction and vocabulary knowledge on Japanese language learners' listening skill. Listening strategies instruction used are the integrative model and the Oxford model. Participants in the study were 44 Japanese language learners of grade 12 from SMAN 21 Bekasi. This research used a quantitative approach and a quasi-experimental method. The data was collected by pre-test and post-test related to the learners’ Japanese listening skills. The learners' vocabulary knowledge was also measured using a vocabulary knowledge test. The result showed that the online listening strategy instruction, integrative model could not be given to students of class XII of SMAN 21 Bekasi due to the absence of a significant effect of the method and vocabulary knowledge on listening skills of Japanese language learners of grade 12 from SMAN 21 Bekasi. The research shows the possibility of other factors such as learning independency and learner autonomy that can affect the effectiveness of online listening strategy instruction, integrative model on Japanese listening skills. &#x0D; &#x0D; Keywords: Listening Strategy Instruction; Listening Skill; Vocabulary Knowledge; Japanese language.
Development of a Mobile Time Banking system on Blockchain (MTBB) for Community Elderly Care (Preprint)

<sec> <title>BACKGROUND</title> Time banking is a good mechanism to provide elderly care in community services with members having mutual benefits, besides social welfare and out-of-pocket fee payment mechanisms. With further integration with off-line works, mobile time banking may provide a better way, compared to traditional web access. On the other hand, blockchain technology has been long encountering difficulty in integrating with real-world economies or activities. Development of a mobile time banking system on blockchain (MTBB) may provide a realistic solution for community elderly care. Besides, the tracking mechanism from blockchain technology itself may also help track the elderly care service transaction records in order to measure better Sustainable Development Goals (SDGs) set by United Nations (UN). </sec> <sec> <title>OBJECTIVE</title> The aim of this study was to develop the MTBB, which enables tracking service transaction records in community elderly care through mutual helps. </sec> <sec> <title>METHODS</title> The MTBB was developed to empower organizations, either Corporate Social Responsibility (CSR) organizations, or Non-Profit Organizations (NPOs), to issue time tokens of their proprietary token types to the members who participate in the volunteer activities organized by the organizations respectively. In the service activities, members sign in and sign out before and after the services by using a smartphone app, and then get the time tokens afterwards. Members with time tokens can then exchange time tokens for elderly care services using the same smartphone app. MultiChain is used as the blockchain technology stack, as one of its features to support multiple token types is critical. </sec> <sec> <title>RESULTS</title> Database applications with smartphone apps integrated with MultiChain were developed. The whole set of the database schema was integrated with two smartphone apps, one for members, and the other for organizations, in addition to the two backend operations modules, one for organizations, and the other for managing all organizations and members. The MultiChain wallet was also integrated into the member app, as well as the organization backend modules for keeping track of the service transactions and time tokens. Metadata with the service transaction information is stored in the MultiChain blocks so that the transaction records are immutable and can thus be analyzed in the future. </sec> <sec> <title>CONCLUSIONS</title> The twelve characteristics of Cahn’s time banking are the guidelines of developing this MTBB with integration of MultiChain blockchain technology for tracking service transaction records. The study also combines the 1-to-1 member service exchange with organizations holding volunteer activities and issuing proprietary time tokens. With the blockchain transaction tracking mechanism, all of the elderly care service records through or within organizations can be tracked and analyzed to align with UN’s five SDGs. </sec>
Accessing Dynamic Health Records using K-Vertex Search Scheme Model towards Hierarchical Users mod Obscure Servers

Improvement appertaining to automations paved a major way to the security systems which in turn revamps the quality and instigates services. In order to alleviate those challenges, attribute based encryption schemes have also ideated solutions to control contrivances. This proposed system accords the description of k vertex leading in the access control using encoded data over scrutinizing encryption model at the equivalent time stage. The K vertex is incapable of precise data retrieval. Orderly to rig out these issues, k-vertex search scheme has been proffered by building up a cipher text-policy hierarchical encryption (CP-HE) swank the R score counting from descending order to enumerate the file views. The data user retrieves hold, allow along with convinced strategical provisions. The previous works define the ensemble Signature paradigm that has been taken advantage of mulct signer formats. It is ascertained to take the edge off the quantum attacks. Though the data owner involves the reclamation of data, it necessitates the demand for a group of signatures. Generation of aggregate key and the arrangement in hierarchy comprehends to tortuous ingressions which are swamped down by the R score to the corresponding k values with illustrious security in the current paper with entailment of K-vertex trapdoor (TD=(K vertex(TF) &amp; K vertex(IDF))). The results are gauged out by TF (U) and IDF (U) besides k-vertex (IDF).