Reviewerly/exHarmony huggingface dataset:
Examine the Feasibility of Implementing Blockchain Technology in the Sri Lankan Banking System

In the era of digitization, it is important that Banks and Financial institutions stay up to date with the latest technology. Blockchain technology is one such technology which has become a widely discussed topic when it comes to finance in the digital era. The objectives of this study are to identify the factors that affect Blockchain adoption as well to identify the possible applications of it in the Sri Lanka banking sector. The study applied qualitative research approach. An interview guide was created and banking professionals were interviewed in order to understand their take or perception on Block Chain implementation in the Sri Lanka Banking sector. This study discovered that the application of Blockchain technology for shared KYC, trade finance, and Central Bank Digital Currency (CBDC) was possible. When deploying Blockchain technology, interoperability problems, regulatory limits, and lack of knowledge were the main obstacles. Additionally, the presence of Fin Tech companies, the regulator's attitude, the cooperation of banks, customers' perceptions, and the banking system's capability functioned as catalysts to enable such an adoption. The results and suggestions of this study would benefit bankers, educators, and practitioners in their efforts to examine the factors that affect the adoption of Blockchain technology. It will also help them to develop workable solutions for their problems. This will also help authorities like the Central Bank in order to create a proper framework for the implementation of block chain technology in the Sri Lanka banking sector. The study will contribute to extend the existing literature on Blockchain adoption and that it will help to create the much-needed framework in order to successfully implement Blockchain technology in banking system while set precedence for future research on this area.
Qualité des institutions et croissance économique dans la région MENA : une analyse par la méthode des moments généralisés

Institutions, whether they are political, legal, financial, or administrative, provide the framework and necessary rules for a stable and prosperous economy. Strong governance, the rule of law, transparency, and the fight against corruption are all factors that foster an environment conducive to investments, innovation, and job creation. The objective of this article is to examine the influence of institutions on the economic performance of countries in the MENA region. The study focuses on a panel of 14 countries for the period 1996-2021, using the Generalized Method of Moments (GMM) dynamic system approach proposed by Blundell and Bond (1998). The results demonstrate that institutions have a positive and significant effect on the economic growth of countries in the MENA region.
Neuroarchitecture applied in the design of educational centers and cognitive development at the initial level in the district of Villa El Salvador

The objective of this research is to propose archetypes of accessible design with a neuroarchitec-ture approach for the design of initial level educational centers, in Peru, especially in areas of ur-ban growth, many children do not manage to access initial education, there is even school drop-out. . At this educational level, with an increase of 4.4% in recent years, the process of developing mental abilities such as memory, perception, thinking, reactions, attention, language, among oth-ers, is of the utmost importance. in the first years of life. As a methodology, sensory design strate-gies were applied, likewise through instruments such as interviews, observation, photographic taking and measurement carried out during the field visit for the analysis of the space-child in-teraction and spatial stimuli, supported by digital tools (Autocad, Google Earth, Andrés Marsh). As results, accessible design archetypes were obtained based on neuroarchitecture based on 10 spatial indicators (Lighting; Green area; roofs; shape; size; distribution; accessibility; scale; colors; materiality). In conclusion, the provision of adequate spaces that provide exploratory facilities greatly contributes to cognitive development in early childhood. The environment where these cognitive activities take place needs to be conditioned to allow children to develop, bond and ap-propriate the space.
Environmental health risk analysis of Hydrogen Sulfide (H2S) exposure to Jabon Sidoarjo landfill scavengers

Scavengers work every day by interacting with waste in landfill making them a group with a high risk of contracting diseases due to unhealthy working conditions and exposure to air pollutant gases, one of which is hydrogen sulfide gas (H2S). H2S gas can cause respiratory problems for scavengers such as coughing, flu, fever, headache, shortness of breath, sore throat, and sore nose. This study aims to analyze the environmental health risks analysis of hydrogen sulfide gas (H2S) exposure in Jabon Sidoarjo landfill scavengers. This research is a descriptive study with cross sectional design. The approach used is Environmental Health Risk Analysis. Measurement of H2S gas concentration was carried out in the scavenger work area. The research respondents were 46 scavengers who worked in old landfill. Data analysis using univariate analysis and risk analysis. The results for the concentration of H2S in the scavenger work area point 1 and point 2 were 0.00242 ppm and 0.00081 ppm. Analysis of the risk quotient (RQ) in real time, the next 10 years, and in real life, the result is 0.13; 0.25; 0.37. The results showed that the measurement of H2S gas concentrations in the scavenger work area was still below the provincial government's environmental quality standards. Risk quotient analysis (RQ) shows results below 1. This shows that scavengers are still safe from non-carcinogenic risks or the H2S gas produced is not at risk of causing health problems.
Consistency and Ability of Students Using DINA and DINO Models

There are two prevalent models for studying cognitive diagnosis Models for appraisement and diagnostics, each of these two models is the definite Input Noisy Output “and” gate and the Deterministic Input Noisy Output “or” gate models. The usage of them is to display various views of the ways cognitive skills are related and the probability of how an responds correctly. The aim of study is to compare the mentioned models and girls and boys with the modeling of cognitive diagnosis. The aims of this study to identify differences in performance of Afghan boys’ and girls’ students in the basic mathematical attributes and cognitive skills of the eighth grade in TIMMS (2011). As well as rankings among the countries that participated in TIMMS. Two commonly used CDMs were employed to fit the response data, including these two models (DINA and DINO). With the assistance of CDMs, we could obtain not only the item parameters, but also the skill profile for each student. Results show that the examinees do best in number domain while do worst in data and chance.
Computational energy gap estimation for strontium titanate photocatalyst using extreme learning machine method

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Unleashing the Power of Consolidate Cloud Computing: Secure and Energy-Efficient Virtual Machines at Your Service

Abstract Cloud computing has revolutionized the way businesses and individuals store, process, and access their data. As the demand for cloud services continues to grow, the need for efficient and secure cloud computing systems becomes increasingly vital. This paper introduces the concept of consolidating cloud computing systems by utilizing secure and energy-efficient virtual machines. The consolidation of cloud computing systems involves the optimization of resources, such as servers and storage, to maximize efficiency and minimize costs. By consolidating virtual machines onto fewer physical servers, resource utilization can be improved, resulting in reduced energy consumption and increased cost savings. Additionally, the consolidation approach enhances the overall security of the cloud computing environment, as it enables better monitoring and control of virtual machine instances. To ensure the security of virtual machines, various techniques can be implemented, such as encryption, access control mechanisms, and intrusion detection systems. These measures protect sensitive data and prevent unauthorized access, mitigating potential risks and vulnerabilities associated with cloud computing systems. Energy efficiency is another critical aspect addressed in this paper. By optimizing resource allocation and workload distribution, energy consumption can be minimized, leading to reduced carbon footprint and operational costs. The proposed system aims to strike a balance between energy efficiency and performance, ensuring that cloud computing resources are utilized effectively without compromising user experience. Overall, this paper presents a comprehensive approach to consolidate cloud computing systems, integrating secure and energy-efficient virtual machines. The proposed system not only enhances the security and reliability of cloud services but also contributes to environmental sustainability. The results of this research demonstrate the potential of consolidating cloud computing systems to revolutionize the way cloud services are provisioned, offering businesses and individuals secure and energy-efficient solutions for their computing needs..
A Review on Security Techniques in Image Steganography

Given the increased popularity of the internet, the exchange of sensitive information leads to concerns about privacy and security.Techniques such as steganography and cryptography have been employed to protect sensitive information.Steganography is one of the promising tools for securely exchanging sensitive information through an unsecured medium.It is a powerful tool for protecting a user's data, wherein the user can hide messages inside other media, such as images, videos, and audios (cover media).Image steganography is the science of concealing secret information inside an image using various techniques.The nature of the embedding process makes the hidden information undetectable to human eyes.The challenges faced by image steganography techniques include achieving high embedding capacity, good imperceptibility, and high security.These criteria are inter-related since enhancing one factor undermines one or more others.This paper provides an overview of existing research related to various techniques and security in image steganography.First, basic information in this domain is presented.Next, various kinds of security techniques used in steganography are explained, such as randomization, encryption, and region-based techniques.This paper covers research published from 2017 to 2022.This review is not exhaustive and aims to explore state-of-the-art techniques applied to enhance security, crucial issues in the domain, and future directions to assist new and current researchers.
State of-the-Art Analysis of Multiple Object Detection Techniques using Deep Learning

Object detection has experienced a surge in interest due to its relevance in video analysis and image interpretation.Traditional object detection approaches relied on handcrafted features and shallow trainable algorithms, which limited their performance.However, the advancement of Deep learning (DL) has provided more powerful tools that can extract semantic, highlevel, and deep features, addressing the shortcomings of previous systems.Deep Learning-based object detection models differ regarding network architecture, training techniques, and optimization functions.In this study, common generic designs for object detection and various modifications and tips to enhance detection performance have been investigated.Furthermore, future directions in object detection research, including advancements in Neural Network-based learning systems and the challenges have been discussed.In addition, comparative analysis based on performance parameters of various versions of YOLO approach for multiple object detection has been presented.
Hierarchical and Efficient Identity-bsed Encryption Against Side Channel Attacks

Hierarchical and identity-based encryption (HIBE) is very valuable and widely used in many occasions.In the Internet of Things based on cloud services, efficient HIBE is likely to be applied to cloud service scenarios for the limited computing ability of some terminal devices.What's more, because of the insecurity of cryptographic systems caused by side channel attacks, the design of leakage resilient cryptographic scheme has attracted more and more cryptography researchers' attention.In this study, an efficient leakage resilient HIBE is constructed.(1) In essence, this given scheme contains a hierarchical ID-based key encapsulation system.By using the extractor to act on the encapsulated symmetric key, this proposed scheme may resist the disclosure for the symmetric key due to side channel attacks.The relative leakage ratio of the encapsulated key is close to 1. (2) We also construct a hierarchical identity-based hash proof system that provides the security of our scheme.The proposed scheme can not only resist side channel attacks, but also has short public key parameters and computational efficiency, which is very suitable for applications in the Internet of Things environment.(3) There is no limit to the hierarchy depth of the system, and only the maximum hierarchy length is required to be given when the system is initialized.
Speaker Recognition Improvement for Degraded Human Voice using Modified-MFCC with GMM

Speaker's audio is one of the unique identities of the speaker.Nowadays not only humans but machines can also identify humans by their audio.Machines identify different audio properties of the human voice and classify speaker from speaker's audio.Speaker recognition is still challenging with degraded human voice and limited dataset.Speaker can be identified effectively when feature extraction from voice is more accurate.Mel-Frequency Cepstral Coefficient (MFCC) is mostly used method for human voice feature extraction.We are introducing improved feature extraction method for effective speaker recognition from degraded human audio signal.This article presents experiment results of modified MFCC with Gaussian Mixture Model (GMM) on uniquely developed degraded human voice dataset.MFCC uses human audio signal and transforms it into a numerical value of audio characteristics, which is utilized to recognize speaker efficiently with the help of data science model.Experiment uses degraded human voice when high background noise comes with audio signal.Experiment also covers, Sampling Frequency (SF) impacts on human audio when "Signal to Noise Ratio" (SNR) is low (up to 1dB) in overall speaker identification process.With modified MFCC, we have observed improved speaker recognition when speaker voice SNR is upto 1dB due to high SF and low frequency range for mel-scale triangular filter.
Analisis Gangguan Bahasa Pada Anak Melalui Kajian Psikolinguistik

This analysis focuses on interdisciplinary psycholinguistics regarding language disorders in children. This article analyzes two children with language disorders. Language disorders in children often occur due to problems such as what will be discussed about what factors affect children's language disorders, knowing the indicators of language disorders in children. This analysis is intended for children aged over 3 years who have language disorders as well as the research we have done on children aged 9 years and over 6 years who have speech delays in children, the factors that influence and how speech delays occur in children. . The research method used is descriptive qualitative research method. The method aims to describe in a systematic, actual, and accurate way. This research is a research analysis of official documents such as books, articles, journals, and the internet. The accuracy of the data is obtained from the observations of the researchers themselves. The research instrument is the human instrument. Researchers become the main data collection tool as well as directly analyze the data
Self-Consuming Generative Models Go MAD

Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this process creates an autophagous (self-consuming) loop whose properties are poorly understood. We conduct a thorough analytical and empirical analysis using state-of-the-art generative image models of three families of autophagous loops that differ in how fixed or fresh real training data is available through the generations of training and in whether the samples from previous generation models have been biased to trade off data quality versus diversity. Our primary conclusion across all scenarios is that without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease. We term this condition Model Autophagy Disorder (MAD), making analogy to mad cow disease.
Does entanglement enhance single-molecule pulsed biphoton spectroscopy?

It depends. For a single molecule interacting with one mode of a biphoton probe, we show that the spectroscopic information has three contributions, only one of which is a genuine two-photon contribution. When all the scattered light can be measured, solely this contribution exists and can be fully extracted using unentangled measurements. Furthermore, this two-photon contribution can, in principle, be matched by an optimised but unentangled single-photon probe. When the matter system spontaneously emits into inaccessible modes, an advantage due to entanglement can not be ruled out. In practice, time-frequency entanglement does enhance spectroscopic performance of the oft-studied weakly-pumped spontaneous parametric down conversion (PDC) probes. For two-level systems and coupled dimers, more entangled PDC probes yield more spectroscopic information, even in the presence of emission into inaccessible modes. Moreover, simple, unentangled measurements can capture between 60% - 90% of the spectroscopic information. We thus establish that biphoton spectroscopy using source-engineered PDC probes and unentangled measurements can provide tangible quantum enhancement. Our work underscores the intricate role of entanglement in single-molecule spectroscopy using quantum light.
Shortcuts to adiabaticity in superconducting circuits for fast multi-partite state generation

Abstract Shortcuts to adiabaticity provide a flexible method to accelerate and improve a quantum control task beyond adiabatic criteria. However, their application to the fast generation of multi-partite quantum gates is still not optimized. Here we propose the reverse-engineering approach to design the longitudinal coupling between a set of qubits coupled to several field modes, for achieving a fast generation of multi-partite quantum gates in photonic or qubit-based architecture. We show that the enhancing generation time is at the nanosecond scale that does not scale with the number of system components. In addition, our protocol does not suffer noticeable detrimental effects due to the dissipative dynamics. Finally, the possible implementation is discussed with the state-of-the-art circuit quantum electrodynamics architecture.
A Survey on Path Planning for Mobile Robot Systems

During the past three decades, it is witnessed that a lot of intense interests and quick advancements have been poured down to the field of robot vehicles. Multiple types of robots, like sweeping robots, greeting robots and robotic toys have become more and more common in people's daily life. As a core part of robot technology, path planning is to find a fast and safe route from the initial position to the target destination. Motivated by making convenience for scholars who are ambitious to study the path planning technologies, this paper has conducted a round review on this topic. Throughout this paper, the path planning technologies of robot vehicles have been divided into three categories, namely, traditional, intelligent and other approaches. After the main concerns of path planning are concluded for the mobile robot vehicles, the future research directions are subsequently discussed.
WHORU: Improving Abstractive Dialogue Summarization with Personal Pronoun Resolution

With the abundance of conversations happening everywhere, dialogue summarization plays an increasingly important role in the real world. However, dialogues inevitably involve lots of personal pronouns, which hinder the performance of existing dialogue summarization models. This work proposes a framework named WHORU to inject external personal pronoun resolution (PPR) information into abstractive dialogue summarization models. To reduce time and space consumption, we further propose a simple and effective PPR method for the dialogue domain. Experiments demonstrated the superiority of the proposed methods. More importantly, we achieve new SOTA results on SAMSum and AMI datasets.
Model Mitigasi Non Struktural Bencana Banjir di Kecamatan Dayeuhkolot Kabupaten Bandung

Dayeuhkolot District is the area with the heaviest impact in the flood disaster that occurred in Bandung Regency, high rainfall during the rainy season is the main factor causing flooding. The larger the disaster victims, the greater the impact that will occur, both directly felt by the community and local government, ironically the above flood disaster management activities are still focused on structural mitigation activities which of course require very large costs. In contrast to structural mitigation, non-structural mitigation is the reduction of the possibility of risk through engineering on human behavior including changes in behavior towards nature. This study uses direct observation in the field observing the mitigation that has been done, making documentation, conducting interviews. All data is processed in a reduced pattern and re-verified to produce a conclusion. The method of non-structural flooding from Coppola is a study that is specifically discussed in this study.
ESTIMASI INTENSITAS RADIASI MATAHARI DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPRPAGATION DI KOTA JAYAPURA

Eastern Indonesia has enormous potential for solar energy. For the utilization of this energy, data on the intensity of solar radiation is needed that can describe the availability of solar energy that can be utilized. Information on the availability of solar energy will be used to estimate the intensity of solar radiation, so that the use of solar energy can be optimal. In this study, the intensity of solar radiation was estimated. The data used to estimate the intensity of solar radiation were air temperature, humidity, duration of solar radiation, and rainfall. The method used in this study is an artificial neural network (ANN) with backpropagation training. This study uses variations in the number of neurons in 1 hidden layer to get the best group based on the RMSE value and correlation. The best group from each training simulation is then used to estimate solar radiation. The estimation results for the city of Jayapura have an RMSE value of 1,970 kWh/m2. The solar radiation received in the Jayapura area has a high enough potential to be used as alternative energy with an average monthly radiation value of 4,5 kWh/m2.