Reviewerly/exHarmony huggingface dataset:
LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

Abstract Motivation: Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant", while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods of compositional analysis such as ANCOM or ANCOM-BC use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren, Willis and Callahan [1] have recently proposed a model for how these biases affect relative abundance data. Methods: Motivated by [1], we show that the (log) odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose LOCOM, a robust logistic regression approach to compositional analysis, that does not require pseudocounts. We use a Firth bias-corrected estimating function to account for sparse data. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for continuous and/or discrete confounding covariates is supported. Results: Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, ANCOM often had inflated FDR; ANCOM-BC largely controlled FDR but still had modest inflation occasionally; ALDEx2 generally had low sensitivity. LOCOM and ANCOM were robust to experimental biases in every situation, while ANCOM-BC and ALDEx2 had elevated FDR when biases at causal and non-causal taxa were differentially distributed. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Availability and implementation: Our R package LOCOM is available on GitHub at https://github.com/yijuanhu/LOCOM in formats appropriate for Macintosh or Windows.
A Pilot Analysis of Factors Affecting Defense Against Social Engineering Attacks in the Armed Forces Environment

Social engineering is a technique of deceiving people into giving away confidential information that could be useful to gain unauthorized access to an information system. Even to the most secured system, social engineering is a formidable threat. It is one of the most devastating threats to organizations and businesses. Unlike traditional hacking, social engineering is less or non-technological. It manipulates characteristics of human nature, exploiting people’s desire to be kind and helpful. The psychology leverage makes social engineering hard to defend against. This paper presents the identification of factors related to social engineering in the context of armed forces through a review of related literature. Prior works from previous studies are discussed, and factors have been identified based on certain criterion. This study executed a pilot analysis on 30 samples of respondents among Malaysian armed forces personnel. As a result, nine factors are identified that may affect defense against social engineering in the armed forces: Authority, Reciprocation, Commitment and Consistency, Diffusion of Responsibility, Scarcity, Friendliness and Liking, Awareness, Social Proof, and Trust.
On the complexity of computing Markov perfect equilibrium in general-sum stochastic games

ABSTRACT Similar to the role of Markov decision processes in reinforcement learning, Markov games (also called stochastic games) lay down the foundation for the study of multi-agent reinforcement learning and sequential agent interactions. We introduce approximate Markov perfect equilibrium as a solution to the computational problem of finite-state stochastic games repeated in the infinite horizon and prove its PPAD-completeness. This solution concept preserves the Markov perfect property and opens up the possibility for the success of multi-agent reinforcement learning algorithms on static two-player games to be extended to multi-agent dynamic games, expanding the reign of the PPAD-complete class.
Case Study on Distance Learning for K-12 Education in Japan: The Nagasaki-Takaoka Model

Abstract A pre-existing partnership between the Japanese Government and Keio University paved the way for K-12 education to embrace distance learning. The university has been engaging in the revision of educational ICT policies in Japan for many years. In Japan, universities have been accumulating knowledge in distance learning practices since the emergence of the Internet, though earlier educational ICT policy required the ICT system in K-12 education to depend on dedicated Internet lines. In December 2019, the Japanese Ministry of Education, Culture, Sports, Science, and Technology (MEXT) announced a new ICT policy to allow K-12 education to launch distance learning classes via public cloud on the Internet. The university’s experience with running distance learning, bundled with the stipulations of the new educational ICT policy, enabled K-12 education to carry out distance learning. New subjects can be taught daily, and continuity of education is ensured in disaster situations, such as the Covid-19 pandemic. The research team at Keio University built the “Nagasaki-Takaoka Model” as a reference model with the added aim of ensuring data security and trust in the open network. In December 2020, Takaoka City successfully deployed the “Nagasaki-Takaoka Model” across all public schools.
Inclusion and Exclusion Criteria in Software Engineering Tertiary Studies

Context: Tertiary studies in software engineering ([email protected]) are widely used to synthesise evidence on a research topic systematically. As part of their protocol, [email protected] define inclusion and exclusion criteria (IC/EC) aimed at selecting those secondary studies (SS) to be included in the analysis. Aims: To provide a state of the art on the definition and application of IC/EC in [email protected], and from the results of this analysis, we outline an emerging framework, TSICEC, to be used by SE researchers. Method: To provide the state of the art, we conducted a systematic mapping (SM) combining automatic search and snowballing over the body of SE scientific literature, which led to 50 papers after application of our own IC/EC. The extracted data was synthesised using content analysis. The results were used to define a first version of TSICEC. Results: The SM resulted in a coding schema, and a thorough analysis of the selected papers on the basis of this coding. Our TSICEC framework includes guidelines for the definition of IC/EC in [email protected] Conclusion: This paper is a step forward establishing a foundation for researchers in two ways. As authors, understanding the different possibilities to define IC/EC and apply them to select SS. As readers, having an instrument to understand the methodological rigor upon which [email protected] may claim their findings.
A Deep Convolutional Neural Network-Based Approach for Handwritten Recognition System

Handwritten recognition model characters have been reported by various researchers in past years and it becomes the most challenging day by day in this age of the digital world. Many machine learning algorithms have a great influence over the handwritten recognition system. In this segment, deep learning is one of the enhanced techniques to solve pattern recognition problems. Such complex problem we have imposed with convolutional neural network (CNN) for the handwritten characters. In this paper, we have made an attempt to build a recognition system for multi-scripts such as Odia and Bangla scripts. Here we have defined the data-driven learning mechanism of CNN along with deriving the discriminate features of handwritten images. This light-weighted CNN provides a feasible solution and reports a high recognition rate. Such deep learning-based approach is a new state of art method for developing an automatic recognition model and to meet real-time challenges.
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging

Machine learning models commonly exhibit unexpected failures post-deployment due to either data shifts or uncommon situations in the training environment. Domain experts typically go through the tedious process of inspecting the failure cases manually, identifying failure modes and then attempting to fix the model. In this work, we aim to standardise and bring principles to this process through answering two critical questions: (i) how do we know that we have identified meaningful and distinct failure types?; (ii) how can we validate that a model has, indeed, been repaired? We suggest that the quality of the identified failure types can be validated through measuring the intra- and inter-type generalisation after fine-tuning and introduce metrics to compare different subtyping methods. Furthermore, we argue that a model can be considered repaired if it achieves high accuracy on the failure types while retaining performance on the previously correct data. We combine these two ideas into a principled framework for evaluating the quality of both the identified failure subtypes and model repairment. We evaluate its utility on a classification and an object detection tasks. Our code is available at https://github.com/Rokken-lab6/Failure-Analysis-and-Model-Repairment.
Reduction of Spatially Correlated Speckle in Textured SAR Images

Synthetic aperture radars (SARs) provide a lot of images that can be used for numerous applications. A problem with acquired images is that they are corrupted by speckle which is a noise-like phenomenon with multiplicative nature. In addition, speckle is non-Gaussian and it is often spatially correlated. A typical task in SAR image processing is despeckling and many methods have been already proposed. However, most of them do not take noise spatial correlation into account during denoising. In this paper, we show how this can be done in despeckling based on discrete cosine transform. The use of frequency-dependent thresholds leads to sufficient improvement of denoising efficiency in terms of visual quality metrics. Moreover, we consider quite complex structure texture images for which noise removal is usually problematic and can lead to information loss. Comparison to the well-known local statistic Lee and Frost filters, extended DCT-based filter is carried out for different remote sensing systems including Sentinel-1 and Sentinel-2.
Design and Testing of Automatic Machine Translation System Based on Chinese-English Phrase Translation

With the development of linguistics and the improvement of computer performance, the effect of machine translation is getting better and better, and it is widely used. The automatic expression translation method based on the Chinese-English machine takes short sentences as the basic translation unit and makes full use of the order of short sentences. Compared with word-based statistical machine translation methods, the effect is greatly improved. The performance of machine translation is constantly improving. This article aims to study the design of phrase-based automatic machine translation systems by introducing machine translation methods and Chinese-English phrase translation, explore the design and testing of machine automatic translation systems based on the combination of Chinese-English phrase translation, and explain the role of machine automatic translation in promoting the development of translation. In this article, through the combination of machine translation experiments and machine automatic translation system design methods, the design and testing of machine automatic translation systems based on Chinese-English phrase translation combinations are studied to cultivate people's understanding of language, knowledge, and intelligence and then help solve other problems. Language processing issues promote the development of corpus linguistics. The experimental results in this article show that when the Chinese-English phrase translation probability table is changed from 82% to 51%, the BLEU translation evaluation system for the combination of Chinese-English phrases is improved. Automatic machine translation saves time and energy of translation work, which shows that machine translation shows its advantages due to its short development cycle and easy processing of large-scale corpora.
Instability of terms of employment of scientific and pedagogical personnel as a factor of formation of the academic precariat

The article discusses the factors of changing the employment conditions of scientific and pedagogical workers in the conditions of transformation of Russian scientific research organizations. The precarization of scientific and pedagogical personnel is not specific to the Russian Federation; it is inscribed in the global trend. However, there are also prerequisites associated with the transformation of post-Soviet science and education. The factors for this kind of precarization are fixed-term contracts, regular professional extension is required to continue professional activities, and the contract term itself is an indication of the status of the employee; insufficient pay, its instability and dependence on an effective contract and other external factors; multiple jobs; terms of an effective contract; increase in extracurricular workload of teachers, including administrative; grant system for financing research; increase in extracurricular and "home" workload of academic staff. The signs of labor instability of scientific and pedagogical personnel are revealed and analyzed, such as an increase in part-time work, an increase in the share of labor compensation, which depends on the fulfillment of the terms of an effective contract; decline in prestige of the academic profession; increased fears of job loss, reduction in the number of scientific and pedagogical personnel in universities and research organizations. The combined effect of various manifestations of instability not only transforms the academic profession itself, but also reduces the staffing potential of researchers and teachers, and is the reason for the outflow of specialists to other areas. The empirical basis of this study is also the analysis of secondary data and the data of our own qualitative research, a series of in-depth interviews of researchers on the problem of the deformation of the professional activity of scientific and pedagogical personnel, they also indicate the unproductive impact of the new conditions of the academic contract on the content of the activity.
Development of a Hybrid Algorithm for efficient Task Scheduling in Cloud Computing environment using Artificial Intelligence

Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms.
Ridge regression modeling in overcoming multicollinearity problems in multiple linear regression models (case study: Life expectancy in Maluku Province)

Life expectancy rate is one of the indicators used to measure the health status of the population which describes the quality of life. The Life Expectancy Rate of Maluku Province from year to year has always increased. However, there are still disparities or gaps between regions and there are still 7 districts that have a life expectancy below the provincial and national life expectancy rates. This cannot be separated from various influencing factors. So it is necessary to identify the factors that rationally affect the Life Expectancy Rate, these factors tend to have high collinearity, causing multicollinearity cases. If the multicollinearity case is not resolved, it can cause the variance of the parameter estimation results to be large so that it can result in the number of predictor variables that are not significant. To overcome this problem, one of them is by modeling the Life Expectancy Rate by using ridge regression. The results of this study show that the factors that influence the Life Expectancy Rate in Maluku Province are the factor of infant mortality, the factor of toddlers aged 1-4 years who get complete immunization, factors for infants aged 0-11 months who are breastfed for 4-6 months, Education Level factors, Population Literacy Rate factors aged 10 years and over, Labor Force Participation Level factors, Population Growth Rate factors, GRDP factors, and Economic Growth Rate factors. The magnitude of the influence of the independent variables on the Life Expectancy Rate in Maluku Province is 100%.
Study about Chinese Speech Synthesis Algorithm and Acoustic Model Based on Wireless Communication Network

Chinese speech synthesis refers to the technology that machines transform human speech signals into corresponding texts or commands through recognition and understanding. This paper combines the classic VAD and GSM VAD1 algorithm simulations, improves on the above two algorithms to recognize and collect speech, and analyzes their Chinese proficiency by amplifying the signal through a filter, in order to study the adulthood of Zhengzhou University in Southeast Asian students (mother tongues are Indonesian and Thai) as the research objects, to explore the relationship between the Chinese phonetic proficiency and the acquisition motivation of Southeast Asian students. This article combines algorithm and language disciplines. According to the results of Praat and SPSS: 55-80 points account for 70%, 55 points below 20% and 80 points above 10%, we find that intrinsic motivation plays a role in CSL acquisition, a vital role. Intrinsic motivation can help mature learners from Southeast Asia to acquire Chinese better and better. The earlier you learn Chinese, the higher your motivation, and the easier it is to set your Chinese learning goals. The greater the enthusiasm for learning Chinese, the better the Chinese scores (such as HSK test scores and Chinese phonetic test scores). Therefore, the Chinese proficiency of international students has a great relationship with their interest in Chinese language, that is, the greater the interest in Chinese, the stronger their motivation to learn, and the Chinese proficiency will be very good.
Minimum Word Error Training For Non-Autoregressive Transformer-Based Code-Switching ASR

Non-autoregressive end-to-end ASR framework might be potentially appropriate for code-switching recognition task thanks to its inherent property that present output token being independent of historical ones. However, it still under-performs the state-of-the-art autoregressive ASR frameworks. In this paper, we propose various approaches to boosting the performance of a CTC-mask-based non-autoregressive Transformer under code-switching ASR scenario. To begin with, we attempt diversified masking method that are closely related with code-switching point, yielding an improved baseline model. More importantly, we employ Minimum Word Error (MWE) criterion to train the model. One of the challenges is how to generate a diversified hypothetical space, so as to obtain the average loss for a given ground truth. To address such a challenge, we explore different approaches to yielding desired N-best-based hypothetical space. We demonstrate the efficacy of the proposed methods on SEAME corpus, a challenging English-Mandarin code-switching corpus for Southeast Asia community. Compared with the cross-entropy-trained strong baseline, the proposed MWE training method achieves consistent performance improvement on the test sets.
Design of Remote Real-Time Positioning Method for Long-Distance Running under Wireless Network Technology

Aiming at the problems of low precision, long time, and poor effect of current remote real-time motion location methods, a long-distance real-time motion location method based on wireless network technology was proposed. Through the analysis of wireless network technology, the basic structure and positioning technology of wireless sensor networks are studied. In addition, this paper adopts the core matrix Isomap node location algorithm based on partial least squares (PLS). The distance of geodesic between long-distance running nodes is used to characterize the dissimilarity between nodes, and the sample contribution rate of the partial least squares method is used to find and eliminate the phenomenon of short circuits in long-distance running fields. High-dimensional feature interval is mapped by centroid transformation and kernel transformation. The experimental results show that the method can improve the positioning accuracy and shorten the positioning time effectively.
Calculation of the regulator for the object with a delay

The task of managing some systems is complicated due to the fact that real technical objects contain delay links. That is, there is a certain period of time when there is no reaction from the object of regulation to the control action. Usually, the presence of a delay link negatively affects the quality of management of such a system. There are various ways to synthesize a control system for such systems. These include: Smith predictors, specialized control tuning algorithms, the use of self-adjusting systems with active adaptation. However, they impose additional requirements on the dynamics of the system or are complex in technical implementation and configuration. Within the framework of this article, an attempt is made to calculate the regulator by the polynomial method for an object with a delay. The mathematical model of the delay is obtained by approximating the delay link next to the Pade. To ensure the necessary dynamics of the transition process from the system, we require the preservation of the poles of the delay link. Then the regulator, calculated for a system with a delay link in the form of a series of Pads, is applied to a system with an "ideal" delay. For clarity of the calculations carried out, an object in the form of a combination of aperiodic and integrating links connected in different ways is taken as an example. The integrating link is necessary to give the system astatic properties. As a delay, we will use the approximation of the range of different orders. The link of delay gives the system a non-stable character.