Reviewerly/exHarmony huggingface dataset:
Gradient-based Hyperparameter Optimization Over Long Horizons

Gradient-based hyperparameter optimization has earned a widespread popularity in the context of few-shot meta-learning, but remains broadly impractical for tasks with long horizons (many gradient steps), due to memory scaling and gradient degradation issues. A common workaround is to learn hyperparameters online, but this introduces greediness which comes with a significant performance drop. We propose forward-mode differentiation with sharing (FDS), a simple and efficient algorithm which tackles memory scaling issues with forward-mode differentiation, and gradient degradation issues by sharing hyperparameters that are contiguous in time. We provide theoretical guarantees about the noise reduction properties of our algorithm, and demonstrate its efficiency empirically by differentiating through $\sim 10^4$ gradient steps of unrolled optimization. We consider large hyperparameter search ranges on CIFAR-10 where we significantly outperform greedy gradient-based alternatives, while achieving $\times 20$ speedups compared to the state-of-the-art black-box methods. Code is available at: \url{https://github.com/polo5/FDS}
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent

Although the optimization objectives for learning neural networks are highly non-convex, gradient-based methods have been wildly successful at learning neural networks in practice. This juxtaposition has led to a number of recent studies on provable guarantees for neural networks trained by gradient descent. Unfortunately, the techniques in these works are often highly specific to the problem studied in each setting, relying on different assumptions on the distribution, optimization parameters, and network architectures, making it difficult to generalize across different settings. In this work, we propose a unified non-convex optimization framework for the analysis of neural network training. We introduce the notions of proxy convexity and proxy Polyak-Lojasiewicz (PL) inequalities, which are satisfied if the original objective function induces a proxy objective function that is implicitly minimized when using gradient methods. We show that stochastic gradient descent (SGD) on objectives satisfying proxy convexity or the proxy PL inequality leads to efficient guarantees for proxy objective functions. We further show that many existing guarantees for neural networks trained by gradient descent can be unified through proxy convexity and proxy PL inequalities.
Comparing Text Representations: A Theory-Driven Approach

Much of the progress in contemporary NLP has come from learning representations, such as masked language model (MLM) contextual embeddings, that turn challenging problems into simple classification tasks. But how do we quantify and explain this effect? We adapt general tools from computational learning theory to fit the specific characteristics of text datasets and present a method to evaluate the compatibility between representations and tasks. Even though many tasks can be easily solved with simple bag-of-words (BOW) representations, BOW does poorly on hard natural language inference tasks. For one such task we find that BOW cannot distinguish between real and randomized labelings, while pre-trained MLM representations show 72x greater distinction between real and random labelings than BOW. This method provides a calibrated, quantitative measure of the difficulty of a classification-based NLP task, enabling comparisons between representations without requiring empirical evaluations that may be sensitive to initializations and hyperparameters. The method provides a fresh perspective on the patterns in a dataset and the alignment of those patterns with specific labels.
LibGuides: NHLI FUTURE LEARNING PATHWAYS: Strategy/ resources/ Tools

FUTURE LEARNING PATHWAY DEVELOPMENT: PLACING STUDENTS AT THE CENTER AND INSPIRING HOPE FOR THE FUTURE.
EFFECT OF POULTRY FARMING PROJECTS ON YOUTH’S LIVING CONDITIONS IMPROVEMENT. A CASE OF LOW-INCOME YOUTH OF MUSANZE DISTRICT, RWANDA

The purpose of the study was to examine the effect of poultry farming projects on youths living conditions improvement in Musanze District, Rwanda. Data was collected from 96 smallholder farmers by using questionnaires, and 4 veterinarians in charge of livestock development. In order to analyze the data; regression models, relationship and correlation techniques were used, which indicated the positive effect of poultry farming projects on living conditions of the youths. The results of study indicated significant effects of predictors on response variables. Keywords : Poultry Farming, Training, Input Support, Poultry Product Trade, Leadership Role CITATION Habumugisha, J., &amp; Dushimimana, J. D. (2021). Effect of poultry farming projects on youth’s living conditions improvement. A case of low-income youth of Musanze District, Rwanda. The Strategic Journal of Business &amp; Change Management, 8 (4), 707 – 719.
An Econometric Study of the Determinants of Foreign Direct Investment in Sudan During the Period (1999 – 2019) by using the Autoregressive Distributed Lag Model (ARDL): دراسة قياسية اقتصادية لمحددات الاستثمار الأجنبي المباشر في السودان خلال الفترة (1999– 2019) باستخدام نموذج الانحدار الذاتي للفجوات الزمنية الموزعة (ARDL)

The study aimed at measuring the impact of determinants of foreign direct investment in Sudan during the period (1999 – 2019). The study problem lies in the main question: Why wasn’t the foreign investment in Sudan at the required level, despite the availability of investment prospects and obstacles-free environment that was supposed to attract investors? The descriptive analytical method was used in the theoretical part of the study, while the statistical method based on econometrics tools analysis, (ARDL) was used in the practical part. The study’s data was collected from the Central Bureau of Statistics and Central Bank of the Sudan. The most important hypothesis the study assumed that, there was a reverse relationship between inflation and direct investment in Sudan. The study concluded that, there is a negative relationship between inflation and foreign direct investment in Sudan. It also concluded that, there is a negative relationship between inflation and foreign direct investment in Sudan. Moreover, the study reached that, the exchange rate and financing cost on foreign direct investment equal % 70. This means that the independent variables have 70% effect on the dependent variable. The study recommended that, financial and monetary authorities have to maintain economic stability, curb inflation, set a fixed exchange rate and reduce the economy’s financing cost.
Revisiting the Retranslation Hypothesis Supported by Insights in Cognitive Linguistics and Language Complexity

This study delves into the analysis of the common practice of literary retranslation, which can be defined as a translation of a previously translated text into the same target language (Berman 1990: 1). Despite being such a quotidian issue, retranslation still remains fuzzy as a concept and as a theory. Similarly, there is no solid or unified methodology that can help scholars understand the complex phenomenon of literary retranslation or how to fully apprehend its infamous Retranslation Hypothesis. Therefore, this investigation examines how Retranslation studies can benefit from using Cognitive Linguistics theoretical conceptualizations in order to establish a robust reconceptualization of the field and stronger methodological underpinnings.
Fair Classification with Adversarial Perturbations

We study fair classification in the presence of an omniscient adversary that, given an $\eta$, is allowed to choose an arbitrary $\eta$-fraction of the training samples and arbitrarily perturb their protected attributes. The motivation comes from settings in which protected attributes can be incorrect due to strategic misreporting, malicious actors, or errors in imputation; and prior approaches that make stochastic or independence assumptions on errors may not satisfy their guarantees in this adversarial setting. Our main contribution is an optimization framework to learn fair classifiers in this adversarial setting that comes with provable guarantees on accuracy and fairness. Our framework works with multiple and non-binary protected attributes, is designed for the large class of linear-fractional fairness metrics, and can also handle perturbations besides protected attributes. We prove near-tightness of our framework's guarantees for natural hypothesis classes: no algorithm can have significantly better accuracy and any algorithm with better fairness must have lower accuracy. Empirically, we evaluate the classifiers produced by our framework for statistical rate on real-world and synthetic datasets for a family of adversaries.
Transfer Learning based Kannada Text Detection and Recognition in Natural Scene Images

With the ascent in deep learning, computer vision problems have taken center stage among the research community. Text recognition from natural images is an extremely complex task and entails detecting and locating text inside an image by producing the co-ordinates of a bounding box that holds it. You Only Look Once (YOLO) algorithm is proven for object detection by creating a bounding box around a detected object and providing its location. And Transfer learning in Deep Learning has been gaining momentum for complex text and speech detection tasks. Taking cue from this, in this paper we implement the YOLO algorithm on AksharaNet, a convolution neural network (CNN) based classification model and train it on a large Kannada Scene Individual Character (KSIC) dataset. On testing the pre-trained network with the South Indian language dataset, we observe that it classifies Kannada characters with an impressive testing accuracy of 90.17% with an area under the curve (AUC) of 0.932. On comparing the proposed model with existing models, it is observed that pre-trained AksharaNet with YOLO implementation returns the best precision, recall and F1-scores of 88%, 90% and 93.83% respectively, which is greater than 8%, 4% and 5% on an average compared to other models.
CSMI-AW: Computational System for Medical Image Authentication Using Watermarking

The enormous growth of digital data in the era of information technology (IT) has provided broader scope towards improving the quality of life. However, the technological aspects in various data-driven solutions also resulted in data duplication and malevolent manipulation during the digital data distributions through networks, especially when it comes to images. Thereby the demand to protect the privacy of image data is highly envisioned and a crucial concern in the current scenario. Currently, medical facilities also require protecting medical image data ownership with copyright protection and content authentication to preserve the authenticity of the patient clinical details. The study in this regard introduces a computationally efficient technique, namely-computational system for medical image authentication using watermarking (CSMI-AW) to protect the ownership of the patient chest x-ray images. The system uses the potential factors associated with the spectral domain techniques, such as with Discrete Cosine Transformation (DCT) approach with non-blind detection. The optimized procedure of DCT-based significant technique for message image/watermarking and substitution methods for embedding process makes the CSMI-AW more robust to retain the medical image watermark and watermarked image perceptual quality. It also simplifies the algorithm steps in such a way where it optimizes the performance of CSMI-AW with a proper balance between robustness, computational cost, and embedding efficiency. However, the CSMI-AW system not only ensures a higher degree of security for digital x-ray images but also attains better computational performance with higher perceptual quality of the watermark. It attains approximately 40% better outcome of PSNR and 60% computational improvement. The comparative performance analysis for the traditional approaches shows its effectiveness in terms of computational efficiency and peak-signal-to-noise ratio (PSNR).
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics

Several works in implicit and explicit generative modeling empirically observed that feature-learning discriminators outperform fixed-kernel discriminators in terms of the sample quality of the models. We provide separation results between probability metrics with fixed-kernel and feature-learning discriminators using the function classes $\mathcal{F}_2$ and $\mathcal{F}_1$ respectively, which were developed to study overparametrized two-layer neural networks. In particular, we construct pairs of distributions over hyper-spheres that can not be discriminated by fixed kernel $(\mathcal{F}_2)$ integral probability metric (IPM) and Stein discrepancy (SD) in high dimensions, but that can be discriminated by their feature learning ($\mathcal{F}_1$) counterparts. To further study the separation we provide links between the $\mathcal{F}_1$ and $\mathcal{F}_2$ IPMs with sliced Wasserstein distances. Our work suggests that fixed-kernel discriminators perform worse than their feature learning counterparts because their corresponding metrics are weaker.
Choose Settings Carefully: Comparing Action Unit detection at Different Settings Using a Large-Scale Dataset

In this paper, we investigate the impact of some of the commonly used settings for (a) preprocessing face images, and (b) classification and training, on Action Unit (AU) detection performance and complexity. We use in our investigation a large-scale dataset, consisting of ~55K videos collected in the wild for participants watching commercial ads. The preprocessing settings include scaling the face to a fixed resolution, changing the color information (RGB to gray-scale), aligning the face, and cropping AU regions, while the classification and training settings include the kind of classifier (multi-label vs. binary) and the amount of data used for training models. To the best of our knowledge, no work had investigated the effect of those settings on AU detection. In our analysis we use CNNs as our baseline classification model.
The Analytical Study on Learning Outcomes of University Students Majoring in Social Welfare

The purpose of this study is to find way to improve the learning outcomes of university students majoring in social welfare of Korea. This study analyzed the relations of learning outcome, satisfaction (social welfare major education), interaction with professors, learning support service of university, perception of cooperative learning activity. The research data used NASEL(National Assessment of Student Engagement in Learning) data from the Korea Educational Development Institute in 2015, and the target of analysis was 776 students majoring in social welfare. The analysis method was a multiple regression analysis was conducted. In the results of analysis, participated in the community volunteer activities as a part of lesson, judged their own learning outcome as high. And the students who performed the cooperative learning activities like actively sharing opinions with other people and seeking for solutions to problems during class hours and non-class hours, judged their learning outcome as high, compared to other students. Also the students who frequently met and discussed with professors for the matter of career or not, and also used the mentoring service for the adaptation to university life, cognized their learning outcome as high, compared to other students. Based on such results of this study, the measures for the improvement of learning outcome of university students majoring in social welfare were suggested.
Exploración del desarrollo de competencias para la vida en adolescentes a través de un ambiente escolar con y sin apoyo de las TIC

espanolEste estudio analiza las competencias para la vida (OMS) que alcanzaron estudiantes de secundaria de dos instituciones educativas en Colombia, antes y despues de una intervencion pedagogica. Empleando un metodo con diseno de preprueba/posprueba con grupo de control, donde la variable manipulable fue el uso o no de TIC durante la intervencion. De acuerdo a los resultados de las pruebas estadisticas los grupos de estudiantes no presentan diferencias significativas entre si. EnglishThis study analyzed the life skill levels obtained by adolescent from two Colombian public schools. A quasi-experimental method with pretest-posttest control group was used. The instrument measured ten life skills. After students received two pedagogical interventions for learning life skills (one of them is based on the use of ICT and the other without ICT), nonparametric statistical tests were applied. No significant difference between these two groups was found.
Parallel-sequential adder-subtractor with the highest digits forward on neurons

The article deals with the development of a parallel-sequential adder-subtractor that performs arithmetic operations of addition and subtraction of binary numbers in the format with a fixed comma with the highest digits forward. The result of performing arithmetic operations is the sum and difference of binary numbers in the direct code of eight digits. The sum and difference of numbers is calculated on neuropositive elements, the transfer to the highest digits when summing and the loan from the highest digits when subtracting is determined by the majority elements. The algorithm for adding numbers in direct codes allows you to get the result in direct code. The signed digits of numbers determine which operation should be performed on numbers using the sum modulo two operation. If the characters are the same, the result will be zero. Otherwise, the result will be one. After that, the addition or subtraction operation is selected. Summation is performed if the numbers have the same signs, the result is assigned the sign of the first number. Subtraction is performed if the numbers have different signs, the result is assigned the sign of a larger modulo number. The adder-subtracter senior digits forward on neurons contains: block input, block comparatii, the block parallel-serial addersubtracter, the unit registers a larger number, the unit of determining the transfer and loan, a unit registers a smaller number of unit registers a result, the control unit, majority, threshold and neural elements. The device can be used as an arithmetic co-processor in a computer system. It significantly speeds up calculations of both simple arithmetic operations and results of various mathematical functions.
Emerging Gender Role and Hate Speech Representation in Indian web series OTT media Content

Abstract: In India, the media is an essential component of daily life. The impact of the media may be understood since the great majority of people across the world rely on various media platforms to get information for enjoyment and to increase consciousness and knowledge about a variety of other global events. Denis McQuail (2000) believes that the media is accountable for illuminating and fostering systematic exposure, as well as influencing ideas and ideals. Furthermore, according to the BARC (Broadcast Audience Research Council) Report, 2019, web series are defined as popular shows with high viewing that cover a wide range of socio-cultural elements of people's life. There are several types of Over the Top (OTT) platforms for entertainment that broadcast 24 hours a day, seven days a week. India, like other jurisdictions, has been grappling with the question of how to govern hate speech in the internet environment. Regulating internet hate speech is a complicated problem. The purpose of this study is to evaluate web series in the perspective of Indian society. The stereotyped image of women and hate speech, including rude and abusive language, and discrimination on the basis of caste and religion, have been depicted in Indian web series content .The current study intends to investigate how the content structure of Indian web series continues to impact gender representation and hate speech on modern and digital platforms.
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows

Normalizing flows are generative models that provide tractable density estimation by transforming a simple base distribution into a complex target distribution. However, this technique cannot directly model data supported on an unknown low-dimensional manifold, a common occurrence in real-world domains such as image data. Recent attempts to remedy this limitation have introduced geometric complications that defeat a central benefit of normalizing flows: exact density estimation. We recover this benefit with Conformal Embedding Flows, a framework for designing flows that learn manifolds with tractable densities. We argue that composing a standard flow with a trainable conformal embedding is the most natural way to model manifold-supported data. To this end, we present a series of conformal building blocks and apply them in experiments with real-world and synthetic data to demonstrate that flows can model manifold-supported distributions without sacrificing tractable likelihoods.
비대면 의사소통 영어 수업에 대한 만족도와 인식

This study is a case analysis on non-face-to-face communicative English language learning conducted in a university during the COVID-19 pandemic. The purpose of this study is to draw the direction of effective communicative language learning approaches by investigating the students satisfaction and perceptions of the two online teaching types:Asynchronous pre-recorded ‘Video lecture and synchronous ‘Real-time lecture’. This study used both a questionnaire and interviews to examine perceptions and satisfaction of 114 university students (58 students for Video lecture/56 students for Real-time lecture) taking an online general English conversation program. The results showed that the satisfaction level of students in the Video lecture was more statistically significant than that in the Real-time lecture. The students in the Video lecture perceived that the class is effective in terms of ‘interest in learning’ ‘concentration on learning’ and ‘self-directed learning’. The students in the real-time lecture were satisfied with the teacher-student interaction while they revealed difficulties in dealing with the technological issues with small group online activities. In terms of language learning, the Video lecture was helpful to improve listening, speaking and reading skills while the Real-time lecture was helpful to improve listening and speaking skills. Educational implications of this study are discussed. (200 words)
Score-based Generative Modeling in Latent Space

Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. However, they are usually applied directly in data space and often require thousands of network evaluations for sampling. Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space, relying on the variational autoencoder framework. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space, resulting in fewer network evaluations and faster sampling. To enable training LSGMs end-to-end in a scalable and stable manner, we (i) introduce a new score-matching objective suitable to the LSGM setting, (ii) propose a novel parameterization of the score function that allows SGM to focus on the mismatch of the target distribution with respect to a simple Normal one, and (iii) analytically derive multiple techniques for variance reduction of the training objective. LSGM obtains a state-of-the-art FID score of 2.10 on CIFAR-10, outperforming all existing generative results on this dataset. On CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while outperforming them in sampling time by two orders of magnitude. In modeling binary images, LSGM achieves state-of-the-art likelihood on the binarized OMNIGLOT dataset.