Reviewerly/exHarmony huggingface dataset:
Data Analysis and Core Findings of Pakistan’s National Population Census for a Blockchain-Based Solution

The national population census provides a holistic picture of the country’s stability situation in terms of health and economy indexes and sources in policy formation, electoral support and fund allocation. Any flaws in the census process can have serious consequences for national planning and interests. Our previous research revealed six challenges in the national census process, in general, such as population coverage, ethnic and racial discrimination, privacy concerns, etc. Taking into account these challenges, we conducted a case study of Pakistan and interviewed the National Census Bureau to identify their challenges and propose a blockchain-based robust and scalable solution to address them, encapsulating census data sensitivity, privacy and national security concerns. Our research contributes to the field by presenting the challenges and findings of the current system of Pakistan’s national population census and outlining the requirements for a new permissioned blockchain system.
Robust and efficient verification of graph states in blind measurement-based quantum computation

Abstract Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. Measurement-based quantum computation (MBQC) is a promising approach for realizing BQC. To obtain reliable results in blind MBQC, it is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. However, previous verification protocols for this task are too resource-consuming or noise-susceptible to be applied in practice. Here, we propose a robust and efficient protocol for verifying arbitrary graph states with any prime local dimension in the adversarial scenario, which leads to a robust and efficient protocol for verifying the resource state in blind MBQC. Our protocol requires only local Pauli measurements and is thus easy to realize with current technologies. Nevertheless, it can achieve optimal scaling behaviors with respect to the system size and the target precision as quantified by the infidelity and significance level, which has never been achieved before. Notably, our protocol can exponentially enhance the scaling behavior with the significance level.
Leveraging GPT-4 for Automatic Translation Post-Editing

While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality under critical settings. In this work, we formalize the task of direct translation post-editing with Large Language Models (LLMs) and explore the use of GPT-4 to automatically post-edit NMT outputs across several language pairs. Our results demonstrate that GPT-4 is adept at translation post-editing, producing meaningful and trustworthy edits to translations that help improve its general quality as well as remove different classes of major errors in translations. In particular, human evaluations on assessing edit trustworthiness show that GPT-4 exhibits a large improvement over the prior state-of-the-art LLM. Notably, we improve upon state-of-the-art performance on WMT-22 English-Chinese, English-German, Chinese-English and German-English language pairs using GPT-4 based post-editing, as evaluated by state-of-the-art MT quality metrics. However, we also show that GPT-4 could produce hallucinated edits, thereby urging caution in its use as an expert translation post-editor.
We’re Afraid Language Models Aren’t Modeling Ambiguity

Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, Yejin Choi. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
The $10 Million ANA Avatar XPRIZE Competition: How It Advanced Immersive Telepresence Systems

Imagine how your life would change if you could instantly transport yourself anywhere in the world. Transcending the barriers of distance and travel time would enable many activities that are impossible today, including being present at remote events, connecting with family, friends, or colleagues, and efficiently distributing skills and hands-on expertise to locations wherever they are needed, e.g., to provide critical care and to deploy immediate emergency response in natural disaster scenarios. While the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Star Trek</i> transporter technology is yet to be invented, the closest thing to beaming is telepresence in an avatar robot.
Promoting Collaborations in Cross-Silo Federated Learning: Challenges and Opportunities

In cross-silo federated learning (FL), companies or organizations collectively train a shared model while keeping the raw data local. The success of cross-silo FL relies on client cooperation, effective communication, and sufficient resource contributions for model training. However, several unique challenges make client collaboration in cross-silo FL difficult. First, as the global model is a public good, clients may choose to free ride on the process instead of actively contributing to the training process. Second, market competition among clients also discourages their collaborations in training, as clients may not want their business competitors to obtain a high-quality model. Third, repeated interactions among clients may further decentivize collaboration, as one can free ride on others' long-term active contributions. This paper focuses on designing effective economic mechanisms to address the above challenges. Specifically, we propose an incentive mechanism to address the public good issue, a revenue-sharing mechanism to mitigate business competition, and a cooperative strategy to enable clients' longterm collaboration. Our results provide insights into better design of collaboration mechanism and communication in practical cross-silo applications. We further discuss some future directions and open issues that merit research efforts from the community.
THE NATURE OF DARK MATTER

The statement is made that Dark Matter is one of the seven phase (aggregate) states of space in the series: black holes - baryonic matter - dark matter - standard space - radiation (light) - plasma - electricity. The conclusion is drawn that dark matter is not a separate substance consisting of particles. It is the standard but aging space with all its other properties, acquiring a new property - mass. This mass gradually accumulates and increases due to by reducing the speed of the passage of time (energy condensation according to Newton). Therefore, the identification of individual particles of dark matter is impossible. Two driving forces for the concentration of dark matter and its distribution by densities in the halo (which are giant cosmic spherical colloidal systems in the universe) are considered. A forecast is made that in the near future, in the laboratory of Professor Lene Hau (Harvard, Boston), one of the most important physical laws of the 21st century will be discovered - the law of correlation of the speed of light with the mass of dark matter. Collaboration is proposed for the Harvard Museum of Natural History - the creation of the first-ever section in the history of science, "Space, Time, and the Origin of material Life." It is suggested to fill it with exhibits, educational models and manuals for practical work, paintings, and lectures.
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model

In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. We aim to integrate the advantages of the physical model and the generative network. Furthermore, we hope to supplement and even deduce the information missing in the low-light image through the generative network. Therefore, Diff-Retinex formulates the lowlight image enhancement problem into Retinex decomposition and conditional image generation. In the Retinex decomposition, we integrate the superiority of attention in Transformer and meticulously design a Retinex Transformer decomposition network (TDN) to decompose the image into illumination and reflectance maps. Then, we design multi-path generative diffusion networks to reconstruct the normal-light Retinex probability distribution and solve the various degradations in these components respectively, including dark illumination, noise, color deviation, loss of scene contents, etc. Owing to generative diffusion model, Diff-Retinex puts the restoration of low-light subtle detail into practice. Extensive experiments conducted on real-world low-light datasets qualitatively and quantitatively demonstrate the effectiveness, superiority, and generalization of the proposed method.
Semantic Graph Based Convolutional Neural Network for Spam e-mail Classification in Cybercrime Applications

Spam is characterized as unnecessary and garbage E-mails. Due to the increasing of unsolicited E-mails, it is becoming more and more crucial for mail users to utilize a trustworthy spam E-mail filter. The shortcomings of spam classifier are defined by their increasing inability to manage large amounts of relevant messages and to effectively detect and effectively detect spam messages. Numerous characteristics in spam classifications are problematic. Given that selecting features is one of the most often used and successful techniques for feature reduction, it is a crucial duty in the identification of keyword content. As a result, features that are unnecessary and pointless yet potentially harm effciency would be removed. In this study, we present SGNNCNN (Semantic Graph Neural Network With CNN) as a solution to tackle the diffcult task of mail identification. By projections E-mails onto a graph and by using the SGNN-CNN model for classifications, this technique transforms the E-mail classification issue into a graph classification challenge. There is no need to integrate the word into a representation since the E-mail characteristics are produced from the semantic network. On several open databases, the technique's effectiveness is evaluated. Some few public databases were used in experiments to demonstrate the high accuracy of the proposed approach for classifying E-mails. In term of spam classification, the performance is superior to state-of-the-art deep learning-based methods.
Cross-scale infrared pedestrian detection based on dynamic feature optimization mechanism

Abstract：Multi-scale and partial occlusions in infrared pedestrian images for target detection make it difficult for traditional algorithms to achieve accurate detection.This study developed a cross-scale infrared pedestrian detection algorithm based on a dynamic feature optimization mechanism.First，to alleviate the
America’s Foreign Policy and the Challenges of Liberal Democracy in Africa

The paper examines the United States of America’s foreign policy toward Africa in terms of promoting liberal democracy, as well as the challenges that African countries face in putting liberal democracy principles into practice, thereby impeding expected dividends and development. It is based on secondary data sources and descriptive data analysis methods. The findings revealed a gap between what the United States proposed for Africa in terms of good governance, anti-corruption, and social development and how liberal democracy actually works on the continent. The paper observed that, despite America’s celebration of liberal democracy, the effect in Africa is contextualized by the majority of African countries’ experiences of impressive governance, ineffective leadership, and endemic corruption. The various bilateral agreements between the United States of America and some African countries to promote liberal democracy have also been called into question due to deviations from expected outcomes. The paper suggests a philosophical approach to liberal democracy in Africa that incorporates pre-colonial African leadership traditions and governance cultures. African governments should invest adequately in youths and galvanize their energies for productivity through employment opportunities in order to uphold and protect liberal democracy. On the other hand, the United States should strengthen bilateral relations with each African country to ensure that they achieve good governance and economic development for mutual benefit.
Keefektifan Model Pembelajaran Example Non-Example pada Pembelajaran Menulis Karangan Narasi Siswa Kelas VII SMP IT Nurul Ikhwah Kabupaten Nagan Raya

This study aims to determine the effectiveness of applying the example non example learning model in learning to write narrative essays in class VII students of SMP IT Nurul Ikhwah, Nagan Raya. The informants of this study were 20 female class VII students. The approach used in this study is a qualitative approach to the type of research, classroom action research (CAR). The design of PTK includes the steps of planning, action, observation, and reflection using two cycles. Data collection techniques used in this study were observation and tests. Observation or observation is a data collection method used to collect research data through observation and sensing. The test is a data collection instrument to measure students' abilities in cognitive aspects or the level of mastery of learning material. The use of examples non examples learning models is very effective in learning to write student narrative essays, in addition to higher student activity when the learning process takes place, student scores also increase after using the examples non examples learning model. This can be seen from the grade VII students of SMP IT Nurul Ikhwah who can meet the minimum completeness criteria which is higher than the assignment to write descriptive essays without using an example non example model. Before using the example non example model, there were 11 students out of 20 students who did not meet the minimum completeness criteria, after using the model, there were 3 students out of 20 who did not pass.
When a Quantum Scientist Interviews ChatGPT

&lt;p&gt;The following is a conversation with ChatGPT, a large language machine learning model, on the topic of quantum. In the discussion section, the responses of ChatGPT are analyzed. Although comprehensive, impressive, and full of surprises, ChatGPT is not yet free of errors, typos, or obscure suggestions. A particular recommendation is to include awareness to ChatGPT about the source of its knowledge. &lt;/p&gt;
Penggunaan Media Pembelajaran Berbasis Smartphone Dalam Mengatasi Kesulitan Belajar Siswa Pada Mata Pelajaran Aqidah Akhlak Siswa Kelas VIII MTs Tarbiyah Waladiyah Pulau Banyak

The use of smartphones by teachers and students is done well and is very positive which can be used as a learning resource that provides a very broad scope so as to increase students' insight. Smartpones are used to help solve problems in learning aqidah morality. Efforts to overcome students' learning difficulties are carried out by utilizing a smartphone as a medium that helps each student's learning difficulties such as in overcoming different student learning abilities, improving students' memorization skills and instilling the character of Islamic teachings in students. by utilizing articles and videos as well as toutube related to the subject matter of the moral aqidah being taught. The use of smartphones can overcome student learning difficulties effectively because students are easier to understand the lessons of aqidah morals and make smartphones as the main learning resource because all materials can be accessed easily to get the required information related to the material discussed. Teachers can also use smartphones to facilitate the provision of learning information and assignments to students. The use of Smartphones in overcoming students' learning difficulties in the Aqidah Akhlak subject of class VIII MTs Tarbiyah Waladiyah Pulau Banyak has been done well. Smartphones can be used as learning resources for students to facilitate the teaching and learning process. Learning difficulties faced by students can be overcome properly by utilizing smartphones optimally and positively.
Integrating System Failure Diagnostics Into Model‐based System Engineering

ABSTRACT Ever‐increasing system complexity is challenging for development engineers and service personnel troubleshooting system failures in the field. This paper presents a systematic, scalable approach to attain a diagnostic model. Automatic transformation into computational models is used 1) at design time to improve the diagnosability of the system, and 2) during operation for guided root cause analysis by calculating the most probable failures and suggesting diagnostic procedures based on available data and observations. The approach combines nicely with model‐based systems engineering, showing the added value of using diagnostic models both during the design of a system and during operation when the system needs to be diagnosed.
USING GIS DATA AND MACHINE LEARNING FOR MINERAL MAPPING. STUDY CASE, BOU SKOUR EASTERN ANTI-ATLAS, MOROCCO

Abstract. The continued demand for mineral deposits in recent years has led exploration geologists for each stage of mineral exploration; find more effective and innovative ways of processing different data types. The use of Geographic Information Systems (GIS) allows various features, such as elevation, slope, tectonic structures, lithological units and indicator minerals of Bou Skour region, Eastern Anti-Atlas, Morocco to be mapped making targeted mining decisions easier. In this paper, a methodology was developed to enable the automated mapping of mineral using machine learning methods such Random Forest (RF) and Artificial Neural Network (ANN) achieves approximately 98% classification accuracy on a single Intel® Core™ i5-5300U CPU core with 16GB of memory, and come up with predictive maps representing the probable potentially mineralized areas.
How Blockchain Network Factors and Market Forces Determine Bitcoin Returns

The creation of distributed ledger technology resulted in the use of secured peer-to-peer interactions that pave way for the invention of Bitcoin and other cryptocurrencies. Since its invention, the price of Bitcoin has exhibited excessive volatility and has attracted increasing attentions. This paper considers the isolated influence of network activities (confirmed payments and users’ adoptions), mining information (network difficulty, Hashrate and transaction fees) and market factors (such as, bitcoin supply and trade volume) as key drivers of Bitcoin price. Using the vector autoregressive model (VECM), the results identified the existence of both long-term equilibrium and short-term dynamic relationship amongst the endogenous system’s variables. The cointegration relation has reversed adjustment effects on the bitcoin return. Accordingly, any deviation from the equilibrium dynamics due to perturbations of network events, market forces and mining data would be minimised. This explains why the Bitcoin price, and by implication its return, continues to experience different massive run-up, spiky protrusions, resistance, reversals, strong supports and consolidations. Based on the finding, the study recommends increased regulatory efforts to curb the excessive fluctuations in Bitcoin price in order to prevent significant loss which could discourage digital investors in the cryptocurrency markets.
Application for Reading and Studying Ayurvedic Scriptures

The main purpose of this application is to provide better understandings and learning of the Ayurvedic sutras. This is because most Ayurvedic sutras are based on Sanskrit. It is difficult for everyone to learn and understand the same things. Thus this applications allow users to understand and carry out the scriptures. The basic ideas of this application is to provide the users with the audio data of the scriptures and the context of the particular scripture. Keywords: Ayurvedic Sutras, Ayurvedic Shloka, Meaning Sandhi, Sandhi Divider
Effects of Innovation on Business Performance: Empirical Evidence from Manufacturing Firms in Tanzania

Regardless of its relevance for business performance, the influence of innovation on the performance of manufacturing firms in Tanzania is not well documented. Thus, this study aimed to examine the effects of innovation on business performance of manufacturing firms in Tanzania. The study used cross-sectional design and quantitative approach. Copies of a structured questionnaire were administered to 420 participants from 28 manufacturing companies in Dar es Salaam and Coast Regions. An impressive response rate of 93.1% was achieved. Data were tested for reliability using Cronbach’s alpha coefficient. Tests for normality, multicollinearity and autocorrelation were conducted, and the results showed the data were reliable, normally distributed, free of multicollinearity and autocorrelation problems. Descriptive and multiple regression statistical techniques were employed. The results suggested that a significant positive effect existed between performance of manufacturing firms and product innovation (B=0.705, p=0.001), process innovation (B=0.640, p=0.000) and marketing innovation (B=.818, p=0.000). The obvious implication to industry is that innovation is important to business success of the manufacturing companies, thus the governments should motivate firms to innovate continuously by giving incentives to invest in R&amp;D. Past studies linking innovation and performance have focused on financial measures of performance. The major contribution of the current study is to use non-financial measures of performance such as business growth and responsiveness to change.
A Literature Review of Music in Computer Science

Music has been a big part of human life since ancient civilizations, and listening to music is fun and for the benefit of humans.Over time, computer science continues to develop, and music begins to be recorded digitally and belongs to the group of unstructured data whose data needs to be managed to become structured data.Much academic work has analyzed how music is digitized, and there is also mixed analysis of the negative effects of music, but most research agrees that digitization has many positive effects in the computer science-backed music world.This paper analyzes and reviews papers that discuss the application of computer science technology that plays a role in managing digital music data, which is better known in the form of sound or sound.Many papers analyze what people think about the use of technology in music education, and several have proposed different ideas that can be applied.The literature review discussed in this paper is divided into three parts: the rise of digital music, how computer science can be integrated into music education, and music creation through computer science.The literature review in this paper is carried out in 4 steps: paper collection, selective screening, classification, and summary analysis.