Reviewerly/exHarmony huggingface dataset:
Meta-Learned Invariant Risk Minimization

Empirical Risk Minimization (ERM) based machine learning algorithms have suffered from weak generalization performance on data obtained from out-of-distribution (OOD). To address this problem, Invariant Risk Minimization (IRM) objective was suggested to find invariant optimal predictor which is less affected by the changes in data distribution. However, even with such progress, IRMv1, the practical formulation of IRM, still shows performance degradation when there are not enough training data, and even fails to generalize to OOD, if the number of spurious correlations is larger than the number of environments. In this paper, to address such problems, we propose a novel meta-learning based approach for IRM. In this method, we do not assume the linearity of classifier for the ease of optimization, and solve ideal bi-level IRM objective with Model-Agnostic Meta-Learning (MAML) framework. Our method is more robust to the data with spurious correlations and can provide an invariant optimal classifier even when data from each distribution are scarce. In experiments, we demonstrate that our algorithm not only has better OOD generalization performance than IRMv1 and all IRM variants, but also addresses the weakness of IRMv1 with improved stability.
PERILAKU KONSUMEN TERHADAP KEPUTUSAN PEMBELIAN BERAS MERAH DI PASAR TEMBOK SURABAYA

The method uses analysis in the form of test equivalence, reliability test, multiple linear regression, coefficient of determination and hypothesis testing with the f test and the T test with the help of the program (SPSS) 16.0. Based on the results of the analysis, the regression equation Y = 0.150 + 0.365X1 + 0.370X2 + 0.390X3 + 0.433X4 + e constant is 0.150. meaning that if the free value is 0, then the Y value is 0.150 with the coefficient of cultural variables (X1) of 0.365, social (X2) of 0.370, personal (X3) of 0.390 and psychological (X4) of 0.433 which means that it is positive. From the results of the t hypothesis test, it was obtained that the tcount of cultural variables was 2.562, social variables were 1.911, personal variables were 1.715, and psychological variables were 1.542. Only cultural variables have an effect with a value of 2.562 so that the tcount&gt; is compared with the ttable value of 2.05954. Based on the simultaneous F test, the value of Fcount is 5,628&gt; F table 2,7822 and the significance is 0,000
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain, where the compactness is measured by underlying similarity metrics that belong to another training domain with different statistics. Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains. The network parameters are further enforced to learn generalized features under domain shift, in a model-agnostic learning pipeline. Unlike the recent work of Meta Face Recognition, our method does not require careful hard-pair sample mining and filtering strategy during training. Extensive experiments on various face recognition benchmarks show the superiority of our method in handling variations, compared to baseline and the state-of-the-art methods.
The Obstacles and Countermeasures Faced by the Informationization of College English Education

Informatization” is one of the important keywords in the context of the current era. In many industries and fields, informatization has brought unprecedented thinking inspiration and technical impact, and in the field of English teaching, informatization has also brought profound Impact and rare opportunities. When it comes to the informatization of English teaching reform in colleges and universities, most people first think of specific technologies, products or application systems, such as micro-classes, mu-classes, multimedia technology, and TEP oral test system. In the background of the rapid development of Internet technology and mobile terminal technology, through the introduction of these information technology products, colleges and teachers can better use the efficient and convenient technical advantages of information technology, breaking the time and space limitations in traditional English teaching, And further cater to the information acquisition methods and English learning habits of students under the premise of conforming to the development trend of the times, so that the introduction of technology in English classrooms is at the forefront of the technology of education informatization, effectively ensuring the overall efficiency, quality and teaching of English teaching Standardization and data accuracy can reach a higher level.
Enriching Web Services Tags to Improve Data-Driven Web Services Composition

Due to the large number of existing services and complexity of manual composition, automatic service composition is provided to enable automatic search of the service compositions for the given queries. Many solutions for automatic service composition have been developed, including integer programming, graph planning, artificial intelligence, and so on in this paper, a heuristic method is proposed to improve the data-driven composition of web services by enriching tags based on tags semantic. To do so, firstly, useful information on web services is collected from various sources and is turned into collections of tags. In the next step, using the hierarchical clustering algorithm, the tags are clustered based on semantic similarity. Thereafter, for services which do not have enough tags, enrichment of the tag is carried out and finally, using an algorithm, composition solutions based on QoS parameters are extracted, which can formulate user targets or even provide potential compositions. Moreover, a series of tests were conducted on the web services, which validate the efficiency of the proposed approach. The experimental results confirm the effectiveness of the proposed service composition method and high quality of tag enriching strategies.&#x0D;
River Culture in Nepal

Most of the world civilizations are developed in the river basins. However, we do not have too big rivers in Nepal, though Nepalese culture is closely related with water and rivers. All the sacraments from birth to the death event in Nepalese society are related with river. Rivers and ponds are the living places of Nepali gods and goddesses. Jalkanya and Jaladeviare known as the goddesses of rivers. In the same way, most of the sacred places are located at the river banks in Nepal. Varahakshetra, Bishnupaduka, Devaghat, Triveni, Muktinath and other big Tirthas lay at the riverside. Most of the people of Nepal despose their death bodies in river banks. Death sacrement is also done in the tirthas of such localities. In this way, rivers of Nepal bear the great cultural value. Most of the sacramental, religious and cultural activities are done in such centers. Religious fairs and festivals are also organized in such a places. Therefore, river is the main centre of Nepalese cultural activities and we can find the remains of ld cultural remains in river basins.
The Big Deal Is Dead! Long Live The Big Deal!

In many countries, the proclamation "The king is dead, long live the king" heralds the demise of the old monarch and the accession of a new one. This tradition ensures that the throne never remains empty while facilitating a smooth transition of power. When the "Big Deal" journal subscription model debuted in 1996, few suspected the extent to which academic libraries would come to rely upon it, or that it would become the primary channel by which academic libraries procure academic journal content. As budget cuts take their toll on libraries, the demise of the Big Deal model seems inevitable as the true value of all-inclusive packages becomes less evident. But is it? Collection analysis reveals that many titles included within these Big Deal packages remain unused or underutilized, significantly decreasing the overall value of serial subscription packages. SPARC's Big Deal Cancellation Tracker shows an increasing number of libraries and consortia forgoing this model in favor of regaining local control over their collections and budgets. Binghamton University Libraries is no exception. Recent curriculum changes and financial developments have prompted us to adopt an ongoing evaluation of our users' information needs and proactively negotiate and cancel deals in order to better serve our constituents. This session described our fact finding, workflow modifications, and data analysis processes as well as the outcomes of our adventures in pursuing and planning for the cancellation of Big Deal agreements based on local collection development priorities and serials budget realities.
Completely Randomized Block Design for Assessment of the Gender and Personality Effect on Reading Comprehension Ability

The objectives of the current study were to analyze the following problems: First, to determine if there was any significant difference between male and female engineering students’ reading comprehension; and second, whether there was any significant difference in engineering students’ reading comprehension ability among different types of personalities. The participants comprised 190 engineering students at Sumatra Institute of Technology, South Lampung Indonesia, taking English for Specific Purposes. The data were collected through an experiment, where the design experiment used in this study was Completely Randomized Block Design, in which the departments were considered blocks. The data were analyzed by using analysis of variance and the results showed there was no difference in students’ RC ability between genders; but there were significant differences in their RC ability across different types of personalities.
Common names and proper nouns: Morphosyntactic evidence of a complete nominal paradigm

The terms "common noun" and "proper name" encode two dichotomies that are often conflated. This paper explores the possibility of the other combinations—"common name" and "proper noun"—and concludes that both exist on the basis of their morphosyntactic behavior. In support of common names, inflectional regularization is determined to result from a "name" layer in the structure, meaning that common nouns that regularize are, in fact, common names (computer mouses, tailor’s gooses). In support of proper nouns, there are bare singular count nouns in English that receive definite interpretations and seem to be licensed as arguments by the same null determiner as proper names (I left town, she works at home). Not only does a four-way distinction between nouns, names, proper nouns, and proper names achieve greater empirical coverage, but it also captures the independent morphosyntactic effects of [PROPER] and [NAME] as features on D and N, respectively.
Two Timescale Hybrid Federated Learning with Cooperative D2D Local Model Aggregations.

Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid federated learning (TT-HF), which is a hybrid between the device-to-server communication paradigm in federated learning and device-to-device (D2D) communications for model training. In TT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus formation of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of O(1/t) while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity.
Russian Universities After the Pandemic: Ways of Transformation

The article is devoted to the analysis of the key challenges faced by Russian higher education during the pandemic and the prospects for its further development. The article highlights the key problems of higher education that have arisen in connection with the pandemic situation (reduced academic mobility, weakened partnerships, inability to complete research on time, reduced number of applicants, suspension of educational activities), examines the actions of governments and measures taken by universities to adapt to work in the context of the pandemic (development of appropriate legislation, provision of consulting and financial assistance, measures to support foreign students who found themselves in the country during the pandemic, etc.). Hypotheses are put forward regarding the ways of transformation of university education in the "post-covid" period, possible trends are proposed, including digitalization, changes in the content of the educational process, scientific activity, increasing the social role and the "third mission" of universities. The problems of online education, the development of digital technologies and new forms of education by teachers and students, and the creation of a new system of social and educational work corresponding to new educational formats are considered. Conclusions are drawn that the Russian higher education system as a whole has managed to overcome the difficulties that have arisen. The situation of the pandemic, on the one hand, has accelerated a number of transformational processes that began in the higher education system before it, and on the other – has revealed new challenges, the solution of which is necessary for the effective functioning of universities in force majeure situations. We should not expect a complete return of universities to the "pre-covid" situation, since certain mechanisms have been launched to accelerate the modernization of domestic higher education.
Study Habits of Students Attending Full Day School

Learning habits are methods or techniques of learning that are carried out repeatedly so as to produce learning skills that remain in students and students will get used to doing it without coercion. This research is motivated by the phenomenon of students who have poor study habits. This study aims to describe the learning habits of students attending full day school. This research is a type of descriptive research using quantitative methods. The population in this study were students of class X and XI of SMAN 1 Coastal Domains totaling 573 students and the sample in this study amounted to 239 students selected using proportional random sampling techniques. The instrument used in this study was a questionnaire about student learning habits with a Likert scale model. Data analysis techniques using descriptive statistical techniques. This study describes (1) students' learning habits based on aspects of making schedules and their implementation in the very good and good category (39.7%), (2) student learning habits based on aspects of reading and taking notes are in the very good category (54.0%), (3) students 'learning habits based on repeating aspects of learning materials are in good categories (46%), (4) students' learning habits based on concentration aspects are in quite good category (43,5%), (5) learning habits students based on aspects of doing the assignment are in good category (48.5%).
Reading Interest and its Relationship with Reading Performance: A Study of English as Second Language Learners in Malaysia

This study aims to specifically examine reading interest among English as a Second Language (ESL) learners and determine its relationship with their performance in ESL classrooms. This study is based on cross-sectional survey design, comprising a randomly selected sample of 351 ESL learners from the state of Terengganu. The questionnaire was used as a data-gathering instrument, consisting of pre-test and post-test items using Know Want Learn (KWL) reading method.: The findings revealed that most of the ESL learners read English materials for academic purposes rather than news and entertainment. There is also a significant relationship between ESL learners’ reading interests and their performance in ESL classrooms. This study consists of empirical evidence regarding students’ reading interests and their correlation with their performance in the ESL classroom. This could help ESL teachers to rectify the students’ problems in reading English materials. This study provides valuable information on the importance of reading interests in determining students’ performance, particularly in ESL classrooms.
Pregroup Grammars, Their Syntax and Semantics

Pregroup grammars were developed in 1999 and stayed Lambek’s preferred algebraic model of grammar. The set-theoretic semantics of pregroups, however, faces an ambiguity problem. In his latest book, Lambek suggests that this problem might be overcome using finite dimensional vector spaces rather than sets. What is the right notion of composition in this setting, direct sum or tensor product of spaces?
Poetry Writing in EFL Classrooms: Learning from Indonesian Students’ Strategies

Hanuer’s Meaningful Literacy has been used as a major justification for including poetry writing as part of pedagogical practices in second and foreign language contexts. Unfortunately, within the EFL context, many teachers are still reluctant to include poetry writing in their teaching practices due to the common assumption that writing poems in a second language is too difficult for students to deal with and therefore will be out of their reach and interest. This qualitative study, which collected data through observations, documentation and surveys, investigated how 171 Indonesian EFL students successfully write their poetry books in creative writing classes. Employing an ethnographic case study approach, the findings showed that students use the following strategies in writing their English poems: (1) Using popular poem templates as idea starters; (2) Creating a vocabulary bank for writing rhyming poems; and (3) Building emotions through personal story sharing and later channeling them through poetry writing. This study concludes that EFL students could enjoy poetry writing as expressive pedagogy and thus, debunks the negative assumption related to EFL students’ lack of interest and appreciation in poetry writing.&#x0D; Keywords: Creative writing, English as a Foreign Language (EFL), expressive pedagogy, poetry writing, Indonesian students
Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras

While an exciting diversity of new imaging devices is emerging that could dramatically improve robotic perception, the challenges of calibrating and interpreting these cameras have limited their uptake in the robotics community. In this work we generalise techniques from unsupervised learning to allow a robot to autonomously interpret new kinds of cameras. We consider emerging sparse light field (LF) cameras, which capture a subset of the 4D LF function describing the set of light rays passing through a plane. We introduce a generalised encoding of sparse LFs that allows unsupervised learning of odometry and depth. We demonstrate the proposed approach outperforming monocular, stereo and conventional techniques for dealing with 4D imagery, yielding more accurate odometry and depth maps and delivering these with metric scale. We anticipate our technique to generalise to a broad class of LF and sparse LF cameras, and to enable unsupervised recalibration for coping with shifts in camera behaviour over the lifetime of a robot. This work represents a first step toward streamlining the integration of new kinds of imaging devices in robotics applications.
Enhanced anchored neighborhood regression for image super resolution based on maximized utilization on dictionary atoms and training sample patches

The two recent effective image super resolution algorithms, Anchored Neighborhood Regression (ANR) and its improved variation, Adjusted ANR (A+), have provided state-of-the-art performance and fast execution speed. In this paper, we further explored the improvable points of A+ and proposed the enhanced ANR (EA) algorithm. First, for a better approximation of input image patches, EA learns the dictionary with least sparsity limit to ensure more patches adhere to these atoms. Second, EA uses the correlation rather than the Euclidean distance to measure the similarity between training sample patches and dictionary atoms when searching neighbors. Third, sample patches are not only clustered to compute the regressors, but also seen as potential atoms in our EA. These potential atoms work together with dictionary atoms, help find the atom most similar with the input image patch as far as possible. We demonstrate the results on commonly used datasets, showing both better visual performance and higher index values compared to state-of-the-art methods.
FakeMix Augmentation Improves Transparent Object Detection

Detecting transparent objects in natural scenes is challenging due to the low contrast in texture, brightness and colors. Recent deep-learning-based works reveal that it is effective to leverage boundaries for transparent object detection (TOD). However, these methods usually encounter boundary-related imbalance problem, leading to limited generation capability. Detailly, a kind of boundaries in the background, which share the same characteristics with boundaries of transparent objects but have much smaller amounts, usually hurt the performance. To conquer the boundary-related imbalance problem, we propose a novel content-dependent data augmentation method termed FakeMix. Considering collecting these trouble-maker boundaries in the background is hard without corresponding annotations, we elaborately generate them by appending the boundaries of transparent objects from other samples into the current image during training, which adjusts the data space and improves the generalization of the models. Further, we present AdaptiveASPP, an enhanced version of ASPP, that can capture multi-scale and cross-modality features dynamically. Extensive experiments demonstrate that our methods clearly outperform the state-of-the-art methods. We also show that our approach can also transfer well on related tasks, in which the model meets similar troubles, such as mirror detection, glass detection, and camouflaged object detection. Code will be made publicly available.
Detection of main persons in snapshots using deep neural networks

Detection of main persons in a snapshot is proposed in this paper. The proposed method uses feature maps from Mask RCNN [1] and depth information from the depth estimation model [2] to detect main persons in the photo. Our research aims to construct a deep neural network model to estimate the main person degree map from these two inputs. Based on the estimated importance map, we performed post-processing and confirmed that only important persons could be extracted.