Reviewerly/exHarmony huggingface dataset:
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings

Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further improve multilingual contextualized representations for downstream cross-lingual transfer. This approach does not require word-alignment pairs prior to fine-tuning that may lead to sub-optimal matching and instead learns the word alignments within context in an unsupervised manner. It also allows different types of mappings due to soft matching between source and target sentences. We benchmark our proposed method on two tasks (XNLI and XQuAD) and achieve improvements over baselines as well as competitive results compared to similar recent works.
Applying the principles of governance in Directorate-General of Education in Governorate North Al Batinah in Sultanate of Oman from its employee's perspective: تطبيق مبادئ الحوكمة في المديرية العامة للتربية والتعليم بمحافظة شمال الباطنة بسلطنة عُمان من وجهة نظر موظفيها

The study aimed to identify the reality of applying the principles of governance in the Directorate-General of Education in North Al Batinah Governorate in Sultanate of Oman from its employee's perspective. The researcher used the quantitative approach by the descriptive and analytical method. The research tool consisted of a questionnaire distributed on three axes: disclosure, transparency, accountability, and participation. A random sample was included in the study from North Al Batinah Educational Staff, (306) employees, of whom (45) leaders and (261) employees, and the researcher used the statistical processors' program (SPSS) to deal with the data.The results of the study showed the application of governance principles in the directorate to a moderate degree, with an overall average (1.73 out of 3), at the level of major axes: The accountability axis got the highest arithmetic average (1.94), followed by the disclosure and transparency axis with an average (1.80), both of which have an application degree (medium), and finally, the participation axis got an application score (low) with a mean (1.40). The results also showed statistically significant differences in favor of leaders in the disclosure axis, transparency, while no differences appeared in the axes' rest. The study recommends the need to spread the culture of governance among North Al Batinah Governorate employees and enhance the administrative system with the integration of electronic work mechanisms that ensure ease of disclosure and transparent application of the rules and laws of accountability of all without exception. The study also recommends activating the control mechanisms and freedom of expression to ensure the right of participation for the directorate's employees in policy and decision making.
Digital Format of Professional and Business Education: Foreign Language Course Changes

The article examines the problem of adapting the English language course, taught in universities, following the requirements of the digital format of professional and business education, which emerged in 2020 due to the COVID-19 pandemic, causing the need for a remote format of teaching disciplines. An online format of education requires making a foreign language teaching more focused on the needs of a specific group of students using the most effective tasks. The article proposes an option for changing the English language course by identifying the problems experienced by students due to the need to study a foreign language and the subsequent adaptation of the specified problems to the most interesting tasks from the point of students’ view. The gradation of problems and tasks is given by conducting a survey and identifying the most difficult aspects when studying a foreign language. Conclusions are made on the most popular types of tasks.
“Inverted Classroom” – Lesson in Geography and Economics with Innovative Elements

The article examines the possibilities that offers the Flipped Clarssroom to students and teachers in Geography and economics subject. The approach of the inverted classroom changes the places of the homework and the class lessons when gaining new knowledge. First, the students receive information on the curriculum independently, and then the teacher, stepping on the laid foundation, clarifies concepts and structures the respective volume of teaching material. Through this approach the student steps in an active position. There is increase of learning motivation. New models of communication between teachers and students are being formed. Individual and group forms of work are combined. Applying the inverted classroom method in Geography and economics subject, the teacher turns his role into a mentor who helps students consolidate what they have learned through discussions, games, teamwork and making mind maps.
The Truth About Computers

I’m going to now share with you the shocking truth about computers—computers are really, really stupid. Many people get enamored with these devices and start to believe things about computers that just aren’t true. They may see some amazing graphics, some fantastic data manipulation, and some outstanding artificial intelligence and assume that there is something amazing happening inside the computer. In truth, there is something amazing, but it isn’t the intelligence of the computer.
Red Knight and the 8 Pages

Author(s): Gonzalez, Jorge; Juan, Gaito; Cameron, West | Abstract: Red Knight and the 8 Pages is a horror game that combines the aspects of two pouplar games: Pac Man and Slender Man. The player will play as the Red Knight and their goal is to collect all eight pages that are within the maze. However, this task will not be easy because they will be chased by Slender Man throughout the maze preventing the player from collecting all of the pages.
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition

To help agents reason about scenes in terms of their building blocks, we wish to extract the compositional structure of any given scene (in particular, the configuration and characteristics of objects comprising the scene). This problem is especially difficult when scene structure needs to be inferred while also estimating the agent's location/viewpoint, as the two variables jointly give rise to the agent's observations. We present an unsupervised variational approach to this problem. Leveraging the shared structure that exists across different scenes, our model learns to infer two sets of latent representations from RGB video input alone: a set of latents, corresponding to the time-invariant, object-level contents of the scene, as well as a set of frame latents, corresponding to global time-varying elements such as viewpoint. This factorization of latents allows our model, SIMONe, to represent object attributes in an allocentric manner which does not depend on viewpoint. Moreover, it allows us to disentangle object dynamics and summarize their trajectories as time-abstracted, view-invariant, per-object properties. We demonstrate these capabilities, as well as the model's performance in terms of view synthesis and instance segmentation, across three procedurally generated video datasets.
Towards Verifiable Remote Voting with Paper Assurance.

We propose a protocol for verifiable remote voting with paper assurance. It is intended to augment existing postal voting procedures, allowing a ballot to be electronically constructed, printed on paper, then returned in the post. It allows each voter to verify that their vote has been correctly cast, recorded and tallied by the Electoral Commission. The system is not end-to-end verifiable, but does allow voters to detect manipulation by an adversary who controls either the voting device, or (the postal service and electoral commission) but not both. The protocol is not receipt-free, but if the client honestly follows the protocol (including possibly remembering everything), they cannot subsequently prove how they voted. Our proposal is the first to combine plain paper assurance with cryptographic verification in a (passively) receipt-free manner.
Women Participation in Nigeria: Does Governance Matter?

This study examines the effect of governance on women's participation using annual time-series data for 29 years spanning from 1990 - 2019. The ARDL Bounds test discloses the existence of a long-run co-integration relationship between accountability, control of corruption, and effectiveness of government and women participation in the labor force. The empirical results obtained revealed that both in the short-run and long-run accountability and the percentage of female employment have a positive and statistically significant effect on women's participation in Nigeria. Although, both in the short-run and long-run the effectiveness of government shows negative and statistically insignificant, the control of corruption exerted a negative and statistically significant impact both in the short-run and long-run. Therefore, the study recommends that the government at all levels should ensure that accountability prevailed in every sector, to allow fair play in representatives, employment, and diffusion of decisions to strengthen and energized women's participation.
Improving Narrative Writing Ability Using Model Problem Based Learning Based on Mind Mapping

The purpose of this study is to find out how to increase students' narrative skills by using a mind mapping-based problem-based learning model, to find out how effective the use of mind mapping-based problem-based learning models in writing scientific narratives . Sources of the data were students of class V Elementary School 1 Lambheu totaling 52 students consisting of two classes, namely class A totaling 26 students and class B totaling 26 students. One class as an experimental class by using the problem base learning method based on mind mapping and the other class as a control class is a class whose learning is not given treatment. The data processing tool is using statistics with the help of SPSS for Windows Version of the Standard 16.0. The results showed that the ability of students to write narratives who received learning using the problem base learning method based on mind mapping was better than students who received conventional narrative learning as a whole.
No-Press Diplomacy from Scratch

Prior AI successes in complex games have largely focused on settings with at most hundreds of actions at each decision point. In contrast, Diplomacy is a game with more than 10^20 possible actions per turn. Previous attempts to address games with large branching factors, such as Diplomacy, StarCraft, and Dota, used human data to bootstrap the policy or used handcrafted reward shaping. In this paper, we describe an algorithm for action exploration and equilibrium approximation in games with combinatorial action spaces. This algorithm simultaneously performs value iteration while learning a policy proposal network. A double oracle step is used to explore additional actions to add to the policy proposals. At each state, the target state value and policy for the model training are computed via an equilibrium search procedure. Using this algorithm, we train an agent, DORA, completely from scratch for a popular two-player variant of Diplomacy and show that it achieves superhuman performance. Additionally, we extend our methods to full-scale no-press Diplomacy and for the first time train an agent from scratch with no human data. We present evidence that this agent plays a strategy that is incompatible with human-data bootstrapped agents. This presents the first strong evidence of multiple equilibria in Diplomacy and suggests that self play alone may be insufficient for achieving superhuman performance in Diplomacy.
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity

Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) (Mescheder et al., 2017). SGDA is known to converge to a stationary point for specific classes of games, but current convergence analyses require a bounded variance assumption. SCO is used successfully for solving large-scale adversarial problems, but its convergence guarantees are limited to its deterministic variant. In this work, we introduce the expected co-coercivity condition, explain its benefits, and provide the first last-iterate convergence guarantees of SGDA and SCO under this condition for solving a class of stochastic variational inequality problems that are potentially non-monotone. We prove linear convergence of both methods to a neighborhood of the solution when they use constant step-size, and we propose insightful stepsize-switching rules to guarantee convergence to the exact solution. In addition, our convergence guarantees hold under the arbitrary sampling paradigm, and as such, we give insights into the complexity of minibatching.
Tensor Deblurring and Denoising Using Total Variation.

We consider denoising and deblurring problems for tensors. While images can be discretized as matrices, the analogous procedure for color images or videos leads to a tensor formulation. We extend the classical ROF functional for variational denoising and deblurring to the tensor case by employing multi-dimensional total variation regularization. Furthermore, the resulting minimization problem is calculated by the FISTA method generalized to the tensor case. We provide some numerical experiments by applying the scheme to the denoising, the deblurring, and the recoloring of color images as well as to the deblurring of videos.
Cloud Computing Governance Readiness Assessment: Profiling Turkana University College, Kenya

As a tool and as a service, Cloud Computing makes the dream of computing a reality. With its immense strength and benefits, this internet-based ongoing technology that has offered flexibility, capacity, and processing power has realized the service-oriented philosophy and has established a new ecosystem in the computer world. Cloud computing is transforming the Information Technology (IT) industry by allowing businesses to have more cost flexibility by purchasing a service rather than owning their assets. This will allow universities to move their processing and storage to the cloud, making it easier for students to access. Nonetheless, IT decision-makers struggle to evaluate Cloud services because there are no recommendations or a structured form to utilize when deciding which Cloud services to adopt. Cloud computing has emerged as a critical platform for companies looking for new methods to save money while also improving the reliability and value of their information systems. To gain the many benefits of cloud computing, an organization must have a clear cloud governance framework in place, which must be upgraded regularly to accommodate new cloud computing concerns. Many cloud users have extended their IT governance frameworks to their cloud services, yet these frameworks are insufficient in addressing governance concerns in cloud environments. Furthermore, because most consumers lack quantitative tools to assess their cloud computing governance maturity, they may miss opportunities to improve their cloud governance frameworks and achieve a better maturity level. This study evaluated Turkana University College's cloud computing preparedness by analyzing the many opportunities and difficulties that cloud computing poses to the university. The University College employed path analysis to determine the many aspects that contribute to and impact effective cloud governance, as well as the level to which they influence it. In this research, I suggested a model for evaluating Cloud services based on a set of thirty measuring criteria divided into six groups. I demonstrated Google Apps and Microsoft Office 365 to evaluate this proposition. I conducted interviews with clients, vendors, and Cloud service professionals. In addition, I highlighted numerous main elements in cloud computing performance and examined and evaluated cloud performance in various scenarios while taking these factors into account. The results of the path analysis and the established criteria model were utilized to determine the University College's cloud computing maturity level
Sliced Mutual Information: A Scalable Measure of Statistical Dependence

Mutual information (MI) is a fundamental measure of statistical dependence, with a myriad of applications to information theory, statistics, and machine learning. While it possesses many desirable structural properties, the estimation of high-dimensional MI from samples suffers from the curse of dimensionality. Motivated by statistical scalability to high dimensions, this paper proposes \emph{sliced} MI (SMI) as a surrogate measure of dependence. SMI is defined as an average of MI terms between one-dimensional random projections. We show that it preserves many of the structural properties of classic MI, while gaining scalable computation and efficient estimation from samples. Furthermore, and in contrast to classic MI, SMI can grow as a result of deterministic transformations. This enables leveraging SMI for feature extraction by optimizing it over processing functions of raw data to identify useful representations thereof. Our theory is supported by numerical studies of independence testing and feature extraction, which demonstrate the potential gains SMI offers over classic MI for high-dimensional inference.
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise

Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-set noises. In this paper, we empirically show that open-set noisy labels can be non-toxic and even benefit the robustness against inherent noisy labels. Inspired by the observations, we propose a simple yet effective regularization by introducing Open-set samples with Dynamic Noisy Labels (ODNL) into training. With ODNL, the extra capacity of the neural network can be largely consumed in a way that does not interfere with learning patterns from clean data. Through the lens of SGD noise, we show that the noises induced by our method are random-direction, conflict-free and biased, which may help the model converge to a flat minimum with superior stability and enforce the model to produce conservative predictions on Out-of-Distribution instances. Extensive experimental results on benchmark datasets with various types of noisy labels demonstrate that the proposed method not only enhances the performance of many existing robust algorithms but also achieves significant improvement on Out-of-Distribution detection tasks even in the label noise setting.
8 Artificial Neural Networks

The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors. Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts. This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.
How Tight Can PAC-Bayes be in the Small Data Regime?

In this paper, we investigate the question: Given a small number of datapoints, for example N = 30, how tight can PAC-Bayes and test set bounds be made? For such small datasets, test set bounds adversely affect generalisation performance by discarding data. In this setting, PAC-Bayes bounds are especially attractive, due to their ability to use all the data to simultaneously learn a posterior and bound its generalisation risk. We focus on the case of i.i.d. data with a bounded loss and consider the generic PAC-Bayes theorem of Germain et al. (2009) and Begin et al. (2016). While their theorem is known to recover many existing PAC-Bayes bounds, it is unclear what the tightest bound derivable from their framework is. Surprisingly, we show that for a fixed learning algorithm and dataset, the tightest bound of this form coincides with the tightest bound of the more restrictive family of bounds considered in Catoni (2007). In contrast, in the more natural case of distributions over datasets, we give examples (both analytic and numerical) showing that the family of bounds in Catoni (2007) can be suboptimal. Within the proof framework of Germain et al. (2009) and Begin et al. (2016), we establish a lower bound on the best bound achievable in expectation, which recovers the Chernoff test set bound in the case when the posterior is equal to the prior. Finally, to illustrate how tight these bounds can potentially be, we study a synthetic one-dimensional classification task in which it is feasible to meta-learn both the prior and the form of the bound to obtain the tightest PAC-Bayes and test set bounds possible. We find that in this simple, controlled scenario, PAC-Bayes bounds are surprisingly competitive with comparable, commonly used Chernoff test set bounds. However, the sharpest test set bounds still lead to better guarantees on the generalisation error than the PAC-Bayes bounds we consider.