Reviewerly/exHarmony huggingface dataset:
A probabilistic autoencoder for causal discovery

The paper addresses the problem of finding the causal direction between two associated variables. The proposed solution is to build an autoencoder of their joint distribution and to maximize its estimation capacity relative to both the marginal distributions. It is shown that the resulting two capacities cannot, in general, be equal. This leads to a new criterion for causal discovery: the higher capacity is consistent with the unconstrained choice of a distribution representing the cause while the lower capacity reflects the constraints imposed by the mechanism on the distribution of the effect. Estimation capacity is defined as the ability of the auto-encoder to represent arbitrary datasets. A regularization term forces it to decide which one of the variables to model in a more generic way i.e., while maintaining higher model capacity. The causal direction is revealed by the constraints encountered while encoding the data instead of being measured as a property of the data itself. The idea is implemented and tested using a restricted Boltzmann machine.
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations

Natural language explanations promise to offer intuitively understandable explanations of a neural network's decision process in complex vision-language tasks, as pursued in recent VL-NLE models. While current models offer impressive performance on task accuracy and explanation plausibility, they suffer from a range of issues: Some models feature a modular design where the explanation generation module is poorly integrated with a separate module for task-answer prediction, employ backbone models trained on limited sets of tasks, or incorporate ad hoc solutions to increase performance on single datasets. We propose to evade these limitations by applying recent advances in large-scale multi-task pretraining of generative Transformer models to the problem of VL-NLE tasks. Our approach outperforms recent models by a large margin, with human annotators preferring the generated explanations over the ground truth in two out of three evaluated datasets. As a novel challenge in VL-NLE research, we propose the problem of multi-task VL-NLE and show that jointly training on multiple tasks can increase the explanation quality. We discuss the ethical implications of high-quality NLE generation and other issues in recent VL-NLE research.
Fast Parallel Exact Inference on Bayesian Networks: Poster

Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact inference solution named Fast-BNI on multi-core CPUs. Fast-BNI enhances the efficiency of exact inference through hybrid parallelism that tightly integrates coarse- and fine-grained parallelism. We also propose techniques to further simplify the bottleneck operations of BN exact inference. Fast-BNI source code is freely available at https://github.com/jjiantong/FastBN.
Improving block-based compensated wavelet lifting by reconstructing unconnected pixels

This paper presents a new approach for improving the visual quality of the lowpass band of a compensated wavelet transform. A high quality of the lowpass band is very important as it can then be used as a downscaled version of the original signal. To adapt the transform to the signal, compensation methods can be implemented directly into the transform. We propose an improved inversion of the block-based motion compensation by processing unconnected pixels by a reconstruction method. We obtain a better subjective visual quality while furthermore saving up to 2.6% of bits for lossless coding.
Leveraging Unlabeled Data to Track Memorization

Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, called susceptibility, to gauge such memorization for neural networks. Susceptibility is simple and easy to compute during training. Moreover, it does not require access to ground-truth labels and it only uses unlabeled data. We empirically show the effectiveness of our metric in tracking memorization on various architectures and datasets and provide theoretical insights into the design of the susceptibility metric. Finally, we show through extensive experiments on datasets with synthetic and real-world label noise that one can utilize susceptibility and the overall training accuracy to distinguish models that maintain a low memorization on the training set and generalize well to unseen clean data.
Learning Video Representations from Large Language Models

We introduce LaViLa, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual input, and finetune them to create automatic video narrators. Our auto-generated narrations offer a number of advantages, including dense coverage of long videos, better temporal synchronization of the visual information and text, and much higher diversity of text. The video-text embedding learned contrastively with these additional auto-generated narrations outperforms the previous state-of-the-art on multiple first-person and third-person video tasks, both in zero-shot and finetuned setups. Most notably, LaViLa obtains an absolute gain of 10.1% on EGTEA classification and 5.9% Epic-Kitchens-100 multi-instance retrieval benchmarks. Furthermore, LaViLa trained with only half the narrations from the Ego4D dataset outperforms baseline models trained on the full set, and shows positive scaling behavior on increasing pre-training data and model size.
An Interpretable Model of Climate Change Using Correlative Learning

Determining changes in global temperature and precipitation that may indicate climate change is complicated by annual variations. One approach for finding potential climate change indicators is to train a model that predicts the year from annual means of global temperatures and precipitations. Such data is available from the CMIP6 ensemble of simulations. Here a two-hidden-layer neural network trained on this data successfully predicts the year. Differences among temperature and precipitation patterns for which the model predicts specific years reveal changes through time. To find these optimal patterns, a new way of interpreting what the neural network has learned is explored. Alopex, a stochastic correlative learning algorithm, is used to find optimal temperature and precipitation maps that best predict a given year. These maps are compared over multiple years to show how temperature and precipitations patterns indicative of each year change over time.
Petrography and Geochemical Studies of Basement Rocks around Zango-Daji and Its Environs, North Central Nigeria

The geology of the Zango-Daji area was investigated petrographically and geochemically to determine the study area’s rock types and mineralization potential. The study area is underlain by rocks of the basement complex characterized by hilly and undulating rocks, which include granitic gneiss, migmatite gneiss, biotite hornblende granite gneiss, and pegmatites. Observation from the field shows that the study area is dominantly underlain by granitic gneiss. The granitic gneiss is dark grey, medium-coarse-grained, and characterized by weak foliation defined by the alignment of a streak of light and dark coloured minerals. They are widespread in the area constituting about 70% of rock types found in the study area. The average modal percentage of minerals in the rocks from petrographic studies shows that granitic gneiss had quartz 45%, plagioclase 10%, microcline 20%, hornblende 2%, biotite 10%, muscovite 5%, kyanite 8% and other minerals 5%. Also, the pegmatite of the study area has no evidence of mineralization; it contains minerals like quartz, feldspars (microcline and orthoclase), and micas (mostly muscovite). Geochemical analysis of the granitic gneiss of the study area shows that silica is by far the most abundant with a value of 53.5%, Na2 O value of 32.5%, Al2 O3, and k2 O of 6.1% and 4.0%, respectively. CaO value of 2.630% accounts for plagioclase feldspar in the granitic gneiss. The QAP diagram was used to determine the petrogenesis of the granitic gneiss. The plot shows the parent rock was a monzogranite with a low percentage of plagioclase in a thin section with a high percentage of quartz and alkali feldspar. The pegmatites of the study area are barren, as confirmed by the XRD result.
DEVELOPMENT OF EDUCATORS’ DIGITAL COMPETENCE AT MEDICAL UNIVERSITY

As the situation in the world is extremely fluid, university educators must be ready and able to provide all educational, informational, consulting services using the Internet system. That is why the issue of the digital competence development is of a great importance. The purpose of this article is to highlight innovative strategies to promote the development of digital competence of medical university educators. In order to meet the demands of the modern informational society, it is critical to be responsive to the latest requirements and keep on learning. The formation of new organizational values and norms of electronic education require additional time, physical, and material resources. The main mission of methodological department of education quality assurance at the Poltava State Medical University established 5 years ago is to create conditions for educators to update their digital and methodological levels by organizing the digital space and moving work to qualitatively new standards. The matter of improving digital competence is of particular importance, as well as an updating the ideology regarding the organization and control of the teaching staff work. The University Library also contributes much in creating the modern educational environment. The library personnel try to combine traditional service and the latest information technologies. The creation, administration and moderation of the Poltava State Medical University repository are also very important. The university website is organized in such a way that it allows educators and students to use it easily and conveniently. Thus, today it is essential to train modern scientists, teachers, and managers to meet new demands, use a wide range of possibilities of innovative digital technologies and teach them to convey creative and critical use of these digital technologies.
Ghanaian Language Students' Acceptance of E-Learning amid the COVID-19 Pandemic

This study examined Ghanaian language students' acceptance of e-learning using the Unified Theory of Acceptance and Use of Technology (UTAUT). A cross-sectional survey design was adopted for this research. Data for the research were gathered with an adapted UTAUT questionnaire. A total of 204 Ghanaian language students filled out the questionnaire. The gathered data were analyzed by computing the Means and Standard Deviation of the Likert Scale items. Also, a Multivariate Analysis of Variance (MANOVA) was run to test the hypotheses set to guide the study. The results of the study indicate a low acceptance of e-learning among Ghanaian language students. Also, the MANOVA results indicate no significant difference between students' e-learning acceptance based on gender. The difference in students' acceptance of e-learning was, however, found in their academic year in school; thus, as final-year students show a higher behavioural intention to study through e-learning, first, second and third years resisted e-learning.
Simulation of thermal fragility of vacuum squeezing in two photon lossy cavity

In this research, squeezing fragility due to thermal bath, in quantum squeezed state generation, is simulated. For this purpose, single mode dissipative cavity with non-zero second-order susceptibility is used. Cavity nonlinear medium is driving by laser pump with known frequency, and pairs of identical photons are created, with one-half frequency of driving pump. This process known as degenerate parametric down conversion. In the absence of any dissipation, simulation shows linear time dependent squeezing parameter, which is in agreement with theoretical results. In two photon loss of cavity in contact with cold reservoir, competition between gain and two photon loss, results in stable squeezing of initial vacuum, in steady state of system. At the end, has been shown that, non-zero thermal reservoir, omits the squeezing and leads the final cavity field to the thermal mixture of Fock states.
Online Newspaper Distribution System

There is one industry in modern society where technology is not yet fully integrated with automation mechanisms for Distributing Newspapers. The distribution mechanism for online newspapers is what we use. For Vendors and Customers who fall under the distributor's management. Based on the distribution of online newspapers utilizing technologies akin to the internet buying system. The Internet and the usage of new technologies enhance the newspaper delivery system. Present-day newspapers are distributed by newspaper vendors manually, with the various issues with the manual system are described in more detail. There are several issues with manual approaches', including the high likelihood of adding human error and other outside factors. These customer and newspaper delivery issues may both be resolved by using our system.
Influence of social media on EFL undergraduate students’ academic learning performances during Covid-19 lockdown period

Due to the spread of the pandemic, the learning system has changed which affected the students’ performance to master the English language appropriately inside classrooms. Social media is an essential means that facilitate the learning process for both learners and teachers. Therefore, this study aims to find out if the students are affected negatively or positively in their learning process through the use of social media during the COVID-19 pandemic, and how social media contributes to EFL students. This study has been carried out using a qualitative interview conducted with 9 (male and female) students. In this study, the findings revealed that third-year EFL students beneficially used social media applications; they were impacted positively by the adoption of social media in learning the English language. So, students’ learning performances were improved. Also, the COVID-19 pandemic affected their English language skills positively. Keywords: Academic learning performances; COVID-19 pandemic; Lockdown; online learning social media.
High speed and Power efficient Digital VLSI Architecture of Artificial Neural Network for reliable in-situ Water Quality Application

This paper presents a low-power portable Digital VLSI architecture using an Artificial Neural Network (ANN) for Water Quality Monitoring. The study uses Posit number representation to implement ANN on both FPGA and ASIC platform. The proposed ANN Posit architecture has 50% improvement over IEEE 754 in terms of Power and Silicon Area, and 13% improvement in speed it achieves the comparable accuracy. The same design using FPGA consumes 6 orders higher power than VLSI architecture.
Encryption Symmetric secret Key in Wireless Sensor Network Using AES Algorithm

Wireless sensor network (WSN) security is an important component for protecting data from an attacker. For improving security, cryptography technologies are divided into two kinds: symmetric and asymmetric. Therefore, the implementation of protocols for generating a secret key takes a long time in comparison to the sensor’s limitations, which decrease network throughput because they are based on an asymmetric method. The asymmetric algorithms are complex and decrease network throughput. In this paper, an encryption symmetric secret key in wireless sensor networks (WSN) is proposed. In this work, 24 experiments are proposed, which are encryption using the AES algorithm in the cases of 1 key, 10 keys, 25 keys, and 50 keys. In each experiment, two chains are combined by using a hash function (SHA-2) to produce secret keys. The Network Simulator Version 2 (NS2) was used to assess the network throughput for the generated key. The randomness of the suggested LWM method has been tested by using the Diehard statistical test and the Entropy test. The results of the tests show that the encryption secret keys have a high level of data randomness.
Prediction of Explicit Features for Recommendation System Using User Reviews

With the explosive growth of data, it has become very difficult for a person to process the data and find the right information from it. So, to discover the right information from the colossal amount of data that is available online, we need information filtering systems. Recommendation systems (RS) help users find the most interesting information among the options that are available. Ratings given by the users play a vital role in determining the purposes of recommendations. Earlier, researchers used a user’s rating history to predict unknown ratings, but recently a user’s review has gained a lot of attention as it contains a lot of relevant information about a user’s decision. The proposed system makes an attempt to deal with the problem of uncertainty in the rating histories by using textual reviews. Two datasets are used to experimentally analyze the proposed framework. In this approach, clustering techniques are used with natural language processing (NLP) for prediction. It also compares how different algorithms, such as K-mean, spectral, and hierarchical clustering algorithms, produce a varied outcome and concludes which method is appropriate for the given recommendation scenarios. We also validate how the proposed method outperforms the non-clustering-based methods.
AN ASSESSMENT OF THE SEVEN POINT AGENDA OF PRESIDENT UMARU MUSA YAR’ADUA’S ADMINISTRATION IN NIGERIA

This study explores President Umaru Musa Yar'adua's Seven-Point Agenda policy, which sought to propel Nigeria to the twenty-first position in the world's economic development by 2020. The question that this article sought to address was if the issue of policy discontinuity and collapse in Nigeria afflicted the Seven-Point Agenda. The Marxist theory served as the theoretical foundation for this research. The study adopted qualitative method of research enquiry with data collected from secondary source such as text books, journals, and other internet sources while content analysis was used to analyse the data collected. It discovered that while the Agenda had a good influence on a few areas of the economy, the impact was insufficient to solve the difficulties of poverty, unemployment, security, and industrialisation caused by the administration of Umaru Musa Yar'adua's brief tenure. The research also discovered that the problems impeding the successful execution of the Seven Point Agenda's objectives include, but are not limited to, corruption, bad governance, and inconsistency in policy design and implementation. This study stated that with competent leadership and real dedication to the goals, the aim may be met because Nigeria is endowed with ample natural and human resources. The paper recommended taking decisive efforts to maximise the advantages, opportunities, and strengths of policies in Nigeria while also decreasing the obstacles they face via effective governance.
When a Party Paper Meets COVID-19: Crisis and Legitimacy in Renmin Ribao’s Commentary Articles

Similar to many other countries, journalistic production in China during the first half of 2020 was dominated on a thematic level by the COVID-19 pandemic. The trend included the mouthpiece of the Central Committee of the Chinese Communist Party (CCP), Renmin Ribao. Its news and commentary articles, which are widely republished by other media, influence the country’s political life, and contribute to the process of meaning generation in the news discourse and to the CCP’s effort to align Chinese citizens to official narratives. Renmin Ribao can therefore provide a relevant source to investigate the relationship between political power and the COVID-19 pandemic, starting from the hypothesis that the paper’s narrative would aim at discursively turning the international health crisis into a piece of positive propaganda. This study aims to verify whether and how the paper did so, and to question the assumption that the outlet’s media discourse can be equated to the CCP official discourse.&#x0D; The research is developed as a case study and blends quantitative and qualitative tools grounded on Applied Linguistics, Discourse-Historical Approach, and Frame Analysis, in order to analyse a corpus of commentaries that have appeared in the outlet’s online edition in 2020. By applying a Genre Analysis approach, the study also offers insights into the possibility to identify distinguishing characteristics and communicative goals in the different sub-genres of Renmin Ribao commentaries.
Jargon Dalam Perlombaan Burung Murai Batu Di Desa Kemantran Dan Ciledug Serta Implikasinya Terhadap Pembelajaran Di SMA

This study aims to describe the jargon form of the stone magpie competition in Kemantran Village and describe the implications of the research results on Indonesian learning in high school. This research uses a qualitative descriptive approach. The technique of providing data in this study is to use library techniques, reading techniques, and listening and recording techniques. Data analysis in this study used the presentation of the results of data analysis using informal methods. The results showed that there are 5 forms of jargon which include (1) basic word jargon, (2) compound jargon, (3) reword jargon, (4) reword jargon, (5) acronym word jargon. The results of this study can be implied in Indonesian language learning in high school class X odd semester with basic competence X KD 3.5 evaluating anecdotal texts from the aspect of implied meaning, with material assessing the content and aspects of implied meaning in anecdotal texts class X semester 1.
Majas Dalam Antologi Puisi Aksara Rasa Karya Aulia Irmawati Dkk Dan Implikasinya Dalam Pembelajaran Sastra Di SMP

The purpose of this research; describes the figure of speech used in the poetry anthology of Aksara Rasa by Aulia Irmawati et al and describes the implementation of the results of the figure of speech research in teaching literature in junior high schools. This study used a descriptive analytical research method with the data source in this study being the poetry anthology of Aksara Rasa by Aulia Irmawati et al. The form of data is in the form of words, phrases and sentences that contain figures of speech in poetry quotes. The data analysis technique uses observing and note-taking techniques. Data analysis technique uses semantic analysis. The results of the study proved that the figurative language contained in the poetry anthology of Aksara Rasa by Aulia Irmawati et al. analyzed as many as 131 data. The data is divided into 4 classifications, namely 50 data for comparison, 30 for conflict, 27 for linking, and 24 for repetition. This maja research is implemented into teaching literature in junior high school on basic competencies 3.7 and 3.8 in the grade 8 Indonesian syllabus which identify elements of poetry and present ideas by paying attention to elements of poetry