Reviewerly/exHarmony huggingface dataset:
The Effect of Monetary Policy on Economic Growth of selected SAARC countries

The study intended to examine the effect of monetary policy on economic growth of selected SAARC countries. Inorder to fulfill these objectives analysis is based on panel data over 25 years of period from 1996 to 2020 of selected five SAARC nations. Economic growth is measured by GDP per capita growth where exchange rate, inflation, broad money to GDP, external reserve are used as monetary policy variables. The Descriptive and casual relationship research design has been used using secondary data. Hausman test is run to select between the fixed and random effect model and correlation matrix is used in this study to summarize the relationship between variables. Among the variables, external reserve, broad money was found to have positive significant effect on GDP while exchange rate has negative significant effect. Also, inflation was found to have negative insignificant effect on economic growth at 5% level of significance. This study concludes that the inability of monetary policies to effectively maximize their policy objectives most of the time is due to flaws in the policy instruments used, which limits their contribution to growth even though monetary policies have made impressive contributions over the years.
Bayesian Approach for Numerical Calculation of the Risk of Medical Evacuation by Vehicles

An original logical-probabilistic method for calculating the risk of critical patients’ medical evacuation by vehicles is proposed. A comprehensive estimation of road conditions and risk factors for transporting an intensive care patient as well as the calculation of a formal quantitative measure of transportation risk by the Bayesian inference method is considered. An effective instrument for estimating transportation risks for management decision making is developed.
Six-qubit silicon quantum processor sets a record

Researchers in the Netherlands have placed a record number of silicon spin qubits on a chip and showed that they could operate the new six-qubit chip with high fidelity.
That’s the way the cookie QRumbles

Researchers at Japan’s Osaka University have developed a way of creating QR codes inside cookies using a 3D printer.
PENINGKATAN LITERASI AKSARA DI KELURAHAN PETUK BERUNAI, KOTA PALANGKA RAYA

Covid-19 causes face-to-face learning cannot be carried out and is replaced by using online learning. Online learning causes new problems, especially in schools located in areas where internet access and transportation facilities are difficult, such as in Petuk Berunai, Palangka Raya City. Online learning in Petuk Berunai causes the teaching and learning process, especially in the aspects of reading and writing to experience problems. The effect is that many students have problems with reading and writing skills. Departing from these problems, this service activity aims to improve the reading and writing skills of elementary school students in Petuk Berunai, Palangka Raya City. This service is carried out using an Asset Based Community Development (ABCD) approach. The implementation of the literacy improvement program in the Petuk Berunai was carried out in four steps. First, conduct a reading ability test for students. Second, divide the class. Third, the implementation of the program starts from August 5 to 26, 2021. The learning program is carried out every Monday to Thursday. Meanwhile, from Friday to Sunday, motivational learning is carried out with a religious approach. Fourth, evaluate students' reading and writing abilities. Based on the comparison of pre-test and post-test assessments, it can be concluded that this service activity has been carried out well and that it has succeeded in improving the reading and writing skills of elementary school students in Petuk Berunai, Palangka Raya.Kata Kunci: Literasi Aksara, Petuk Berunai, Asset Based Community Development (ABCD)
Holistic education of personality: historical-pedagogical and innovative dimensions of definitions

Ruslan Basenko. Holistic education of personality: historical-pedagogical and innovative dimensions of definitions
Mob Attack on Doctors

Mob attacks on doctors have become common now a days. Almost every doctor is worried about their safety in workplace. Only few of us are trained to avoid or tackle the situation. There are many cases in which doctors were injured, beaten badly, harassed, murdered which we came across in social media. Doctors need to be alert and must know the warning signs of an attack. Violence against doctors can be prevented at several levels with the help of government, media, and educational institutions. We need to handle the situation timely to protect ourselves from such attacks.
MULTI-JOIN-ORDERING QUERY OPTIMIZATION ALGORITHM FOR HIVE WAREHOUSE WITH MAPREDUCE

According to the Digital Report of July, 2021, Billions of users around the world uses Mobile Phones, Internet, social media every second. This huge range of heterogeneous digital data is called Big Data, and is measured in terms of terabytes or petabytes. It is difficult to the conventional relational databases to handle these heterogeneous data for data analytics, but is still in use significantly in the growth of Big Data. To handle SQL-based structured queries, Hadoop is one of the prominent and well-suited solution that allows Big Data to be stored and processed. Hive support SQL queries on Hadoop. Hive warehouse, is the oldest SQL-engine on the top of the Hadoop framework and to store the processed data, it uses HDFS (Hadoop Distributed File System). On the Hadoop, MapReduce is an execution engine that executes SQL-based queries. In the Query Optimization, join ordering always plays a significant role because when the order of tables in joining operation is changed, execution time of the query is reduced to a greater extent. The main problem of the Hive is that it does not enhance the order of the join for an SQL-query and also does not give assurance for an optimal execution plan. Its time complexity is measured in exponential (Shan, Y., &amp; Chen, Y., 2015).The main focus of this paper is to discover the finest join ordering solution for a Hive query optimization problem through appropriate search algorithms and to improve SQL-based Hive queries performance with MapReduce–based system.
The Effect Of The Learning Model Naval Collaboration Flexible Learning (NCFL) On The Quality Of Education Outcomes: An Innovative Approach To Islamic Religious Education In The Naval College Of Technology

The Naval Collaboration Flexible learning (NCFL) learning model is the result of innovation in the field of learning technology to make it more effective, efficient and increase attractiveness in order to achieve learning success. The NCFL learning model was developed by synthesizing various learning and learning theories, system-based, referring to the suitability of the needs and the latest science and technology developments, and still maintaining the distinctive cultural features of the Navy, and based on the values ​​of Pancasila. The problem studied is how the influence of the NCFL learning model in the field of Islamic Religious Education at the Naval College of Technology. The method used is a quantitative method with the process of collecting data through questionnaires to 35 students of the Naval College of Technology and data analysis using SPSS 25.0 software. The results showed that there was a significant influence on the application of the NCFL learning model at the Naval College of Technology, especially in the field of Islamic Religious Education. From the simple linear regression test, the regression equation formula Y = 9.657 + 0.579 X1, meaning that if the X1 value is increased by 1 point, the strength of the Y relationship will increase by the value of the X1 variable, namely 0.579. Furthermore, from the formulation of the regression equation Y = 24.352 + 0.461 X2, which means that if the X2 value is increased by 1 point, the strength of the Y relationship will increase by the value of the X2 variable, namely 0.461. In the multiple linear regression test, the equation formula is Y = 8.274 + 0.476 X1 + 0.187 X2, meaning that if the two variables X1 and X2 are increased by a constant 1, there will be an increase in the Y output value of the X1 and X2 variables, namely (0.476 + 0.187 = 0.663).
Study on abnormal data acquisition method of industrial internet of things communication based on node clustering

In order to overcome the problems of low efficiency and poor accuracy of traditional abnormal data capture, this paper proposes an industrial IoT communication abnormal data capture method based on node clustering. First, analyse the structure of Industrial Internet of Things (IIoT), collect communication data and extract the characteristics of industrial communication data; then, the node subordination function is constructed, and the abnormal data types are divided by common neighbour node analysis. Finally, the matching degree between features is extracted through node clustering calculation, and the matching degree is input into the classifier function and the communication exception data capture result is output. The results show that the malicious node capture rate of this method is always higher than 92%, the capture time is always less than 3.6 seconds and the effect of abnormal data capture of IoT communication is improved.
Optimisation of energy consumption in cloud video surveillance centre based on monitoring and placement of virtual machines

Cloud computing is one of the most popular computational models, which requires plenty of physical devices where services are provided based on user demand. A majority of data centres need plenty of energy consumption which has become a challenge in recent years. Regarding cloud video surveillance - as a contemporary research field of cloud computing and big data, the service, due to the high demand for monitoring remote places, continually consumes surplus energy to process the high-volume data. This study considers the importance of energy consumption in cloud video surveillance, and it has been tried to increase the efficiency of servers concerning energy usage. The proposed method employs virtual machine placement in two steps, including monitoring and placement, to reduce energy consumption and increase the efficiency of servers. Implementation results in Cloudsim showed that it reduces energy consumption and increases resource efficiency.
Proyecto de vida y bienestar en estudiante en la modalidad virtual Una mirada desde el configuracionismo

The Life Project construct, in its historical trajectory, has been described and characterized with different approaches and approaches.However, the Systematic Review of the literature, the frequent use of the construct in various settings and the experiences and experiences with students of the virtual modality, reveal the little theoretical foundation that exists in this field, from the configurational point of view, as a holistic theoretical model, systemic, dialectical and complex, in which ideas, theories and knowledge are logically interconnected with each other, to form a harmonious and coherent whole.This book chapter is derived from the doctoral thesis Psychological Configuration of the life project construction process in students of the virtual modality and reveals essential elements associated with the construct from the configurational vision.The data construction technique was a systematic review that was carried out with documents such as articles, books, monographs and research projects.The findings show that the life project can be analyzed as a configuration from characterological features and being sensitive to considering it as a social network, with subjective codes, structures, continuities and discontinuities, functionalities and contradictions and with a projective look of permanent search for well-being and around which knowledge can be built from the configurationist methodology.
Textual Explanations for Automated Commentary Driving

The provision of natural language explanations for the predictions of deep-learning-based vehicle controllers is critical as it enhances transparency and easy audit. In this work, a state-of-the-art (SOTA) prediction and explanation model is thoroughly evaluated and validated (as a benchmark) on the new Sense--Assess--eXplain (SAX). Additionally, we developed a new explainer model that improved over the baseline architecture in two ways: (i) an integration of part of speech prediction and (ii) an introduction of special token penalties. On the BLEU metric, our explanation generation technique outperformed SOTA by a factor of 7.7 when applied on the BDD-X dataset. The description generation technique is also improved by a factor of 1.3. Hence, our work contributes to the realisation of future explainable autonomous vehicles.
Benchmarking Faithfulness: Towards Accurate Natural Language Explanations in Vision-Language Tasks

With deep neural models increasingly permeating our daily lives comes a need for transparent and comprehensible explanations of their decision-making. However, most explanation methods that have been developed so far are not intuitively understandable for lay users. In contrast, natural language explanations (NLEs) promise to enable the communication of a model's decision-making in an easily intelligible way. While current models successfully generate convincing explanations, it is an open question how well the NLEs actually represent the reasoning process of the models - a property called faithfulness. Although the development of metrics to measure faithfulness is crucial to designing more faithful models, current metrics are either not applicable to NLEs or are not designed to compare different model architectures across multiple modalities. Building on prior research on faithfulness measures and based on a detailed rationale, we address this issue by proposing three faithfulness metrics: Attribution-Similarity, NLE-Sufficiency, and NLE-Comprehensiveness. The efficacy of the metrics is evaluated on the VQA-X and e-SNLI-VE datasets of the e-ViL benchmark for vision-language NLE generation by systematically applying modifications to the performant e-UG model for which we expect changes in the measured explanation faithfulness. We show on the e-SNLI-VE dataset that the removal of redundant inputs to the explanation-generation module of e-UG successively increases the model's faithfulness on the linguistic modality as measured by Attribution-Similarity. Further, our analysis demonstrates that NLE-Sufficiency and -Comprehensiveness are not necessarily correlated to Attribution-Similarity, and we discuss how the two metrics can be utilized to gain further insights into the explanation generation process.
Compositional Probabilistic and Causal Inference using Tractable Circuit Models

Probabilistic circuits (PCs) are a class of tractable probabilistic models, which admit efficient inference routines depending on their structural properties. In this paper, we introduce md-vtrees, a novel structural formulation of (marginal) determinism in structured decomposable PCs, which generalizes previously proposed classes such as probabilistic sentential decision diagrams. Crucially, we show how mdvtrees can be used to derive tractability conditions and efficient algorithms for advanced inference queries expressed as arbitrary compositions of basic probabilistic operations, such as marginalization, multiplication and reciprocals, in a sound and generalizable manner. In particular, we derive the first polytime algorithms for causal inference queries such as backdoor adjustment on PCs. As a practical instantiation of the framework, we propose MDNets, a novel PC architecture using md-vtrees, and empirically demonstrate their application to causal inference.
Examining Computational Performance of Unsupervised Concept Drift Detection: A Survey and Beyond

Concept drift detection is crucial for many AI systems to ensure the system's reliability. These systems often have to deal with large amounts of data or react in real time. Thus, drift detectors must meet computational requirements or constraints with a comprehensive performance evaluation. However, so far, the focus of developing drift detectors is on detection quality, e.g.~accuracy, but not on computational performance, such as running time. We show that the previous works consider computational performance only as a secondary objective and do not have a benchmark for such evaluation. Hence, we propose a set of metrics that considers both, computational performance and detection quality. Among others, our set of metrics includes the Relative Runtime Overhead RRO to evaluate a drift detector's computational impact on an AI system. This work focuses on unsupervised drift detectors, not being restricted to the availability of labeled data. We measure the computational performance based on the RRO and memory consumption of four available unsupervised drift detectors on five different data sets. The range of the RRO reaches from 1.01 to 20.15. Moreover, we measure state-of-the-art detection quality metrics to discuss our evaluation results and show the necessity of thorough computational performance considerations for drift detectors. Additionally, we highlight and explain requirements for a comprehensive benchmark of drift detectors. Our investigations can also be extended for supervised drift detection.
Use of social media and Natural Language Processing (NLP) in natural hazard research

Twitter is a microblogging service for sending short, public text messages (tweets) that has recently received more attention in scientific comunity. In the works of Sasaki et al. (2010) and Earle et al., (2011) the authors explored the real-time interaction on Twitter for detecting natural hazards (e.g., earthquakes, typhoons) baed on users' tweets. An inherent challenge for such an application is the natural language processing (NLP), which basically consists in converting the words in number (vectors and tensors) in order to (mathematically/ computationally) make predictions and classifications. Recently advanced computational tools have been made available for dealing with text computationally. In this report we implement a NLP machine learning with TensorFlow, an end-to-end open source plataform for machine learning applications, to process and classify evenct based on files containing only text.
Low Seesaw Scale Solution for $M_W$ and $(g-2)_μ$

In this short talk, we present a renormalizable model that can i) generate neutrino masses via a low-scale seesaw mechanism and ii) solve the long-standing $(g-2)_μ$ and the more recent CDF II $M_W$-anomalies. This is minimally achieved by introducing two sterile neutrinos and a single electroweak-doublet vector-like lepton, with masses $&lt; 2$ TeV. We focus on the one-generation scenario and the requirements to extend it to three generations.
Deep Learning Criminal Networks

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of complex networks. These advances have sparked a surge of interest at the interface between network science and machine learning. Despite this, the use of machine learning methods to investigate criminal networks remains surprisingly scarce. Here, we explore the potential of graph convolutional networks to learn patterns among networked criminals and to predict various properties of criminal networks. Using empirical data from political corruption, criminal police intelligence, and criminal financial networks, we develop a series of deep learning models based on the GraphSAGE framework that are able to recover missing criminal partnerships, distinguish among types of associations, predict the amount of money exchanged among criminal agents, and even anticipate partnerships and recidivism of criminals during the growth dynamics of corruption networks, all with impressive accuracy. Our deep learning models significantly outperform previous shallow learning approaches and produce high-quality embeddings for node and edge properties. Moreover, these models inherit all the advantages of the GraphSAGE framework, including the generalization to unseen nodes and scaling up to large graph structures.
Improving Autoregressive NLP Tasks via Modular Linearized Attention

Various natural language processing (NLP) tasks necessitate models that are efficient and small based on their ultimate application at the edge or in other resource-constrained environments. While prior research has reduced the size of these models, increasing computational efficiency without considerable performance impacts remains difficult, especially for autoregressive tasks. This paper proposes modular linearized attention (MLA), which combines multiple efficient attention mechanisms, including cosFormer, to maximize inference quality while achieving notable speedups. We validate this approach on several autoregressive NLP tasks, including speech-to-text neural machine translation (S2T NMT), speech-to-text simultaneous translation (SimulST), and autoregressive text-to-spectrogram, noting efficiency gains on TTS and competitive performance for NMT and SimulST during training and inference.
Learning to Render Novel Views from Wide-Baseline Stereo Pairs

We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and due to the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. We demonstrate that our method learns powerful multi-view geometry priors while reducing the rendering time. We conduct extensive comparisons on held-out test scenes across two real-world datasets, significantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis.
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing

Despite the success in large-scale text-to-image generation and text-conditioned image editing, existing methods still struggle to produce consistent generation and editing results. For example, generation approaches usually fail to synthesize multiple images of the same objects/characters but with different views or poses. Meanwhile, existing editing methods either fail to achieve effective complex non-rigid editing while maintaining the overall textures and identity, or require time-consuming fine-tuning to capture the image-specific appearance. In this paper, we develop MasaCtrl, a tuning-free method to achieve consistent image generation and complex non-rigid image editing simultaneously. Specifically, MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency. To further alleviate the query confusion between foreground and background, we propose a mask-guided mutual self-attention strategy, where the mask can be easily extracted from the cross-attention maps. Extensive experiments show that the proposed MasaCtrl can produce impressive results in both consistent image generation and complex non-rigid real image editing.