Reviewerly/exHarmony huggingface dataset:
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework

Model smoothing is of central importance for obtaining a reliable teachermodel in the student-teacher framework, where the teacher generates surrogatesupervision signals to train the student. A popular model smoothing method isthe Temporal Moving Average (TMA), which continuously averages the teacherparameters with the up-to-date student parameters. In this paper, we proposeSpatial Ensemble, a novel model smoothing mechanism in parallel with TMA.Spatial randomly picks up a small fragment of the student model todirectly replace the corresponding fragment of the teacher model.Consequentially, it stitches different fragments of historical student modelsinto a unity, yielding the Ensemble effect. Spatial obtainscomparable student-teacher learning performance by itself and demonstratesvaluable complementarity with temporal moving average. Their integration, namedSpatial-Temporal Smoothing, brings general (sometimes significant) improvementto the student-teacher learning framework on a variety of state-of-the-artmethods. For example, based on the self-supervised method BYOL, it yields +0.9%top-1 accuracy improvement on ImageNet, while based on the semi-supervisedapproach FixMatch, it increases the top-1 accuracy by around +6% on CIFAR-10when only few training labels are available. Codes and models are available at:https://github.com/tengteng95/Spatial_Ensemble.
Penerapan Metode Gabor Wavelet Dalam Pengenalan Wajah Pada Rekaman CCTV

Human facial recognition is an important field of research, and there are many applications today that can apply it. Through the development of a technique such as the Gabor Wavelet, computers can now outpace the human brain in a variety of tasks in facial recognition, particularly those that require searching large facial databases. The face recognition system is used to compare an input face image with a face database and produce the face that best matches that image if detected. Meanwhile, face authentication is used to test the authenticity / similarity of a face with face data that has been stored in the previous database. Applications have advantages over conventional methods. In case it is not easily stolen or used by unauthorized users. The purpose of this final project is to create a facial recognition software from CCTV footage using Gabor Wavelet to reduce the dimensions of facial images as well as to determine the capabilities of the technique. In this final project, the software created uses a still image as input. To build this application using Matlab. The system test results show that the use of Gabor Wavelet for facial recognition on CCTV footage provides a fairly high level of accuracy. Test results The results obtained will then be entered into the Gabor Wavelet Method to determine the identity of the face owner. For facial images that were included in the exercise, it was possible to get 85.71% correct identification
Model-Based Domain Generalization

Despite remarkable success in a variety of applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data. Toward addressing this challenge, we consider the domain generalization problem, wherein predictors are trained using data drawn from a family of related training domains and then evaluated on a distinct and unseen test domain. We show that under a natural model of data generation and a concomitant invariance condition, the domain generalization problem is equivalent to an infinite-dimensional constrained statistical learning problem; this problem forms the basis of our approach, which we call Model-Based Domain Generalization. Due to the inherent challenges in solving constrained optimization problems in deep learning, we exploit nonconvex duality theory to develop unconstrained relaxations of this statistical problem with tight bounds on the duality gap. Based on this theoretical motivation, we propose a novel domain generalization algorithm with convergence guarantees. In our experiments, we report improvements of up to 30 percentage points over state-of-the-art domain generalization baselines on several benchmarks including ColoredMNIST, Camelyon17-WILDS, FMoW-WILDS, and PACS.
대학교육의 고 효능을 위한 교수학습 혁신방안 : 미국사례의 검토를 통한 새로운 디자인

The purpose of this study was to introduce high impact strategies which can lead to significant learning of college students as a part of innovating colleges and universities in terms of the quality of courses. First, Kolb’s experiential learning theory and constructivism were reviewed as guiding theories for college students’ experiential learning. Next, more than ten instructional learning design strategies were introduced for instructors to adopt and implement in their course designing based on the review of examples in the United States. Next, several suggestions were made for colleges and universities to support instructors to utilize the reviewed strategies. Next, some innovative trends and examples that colleges and universities can consider to educate college students as desirable global and digital citizens. Therefore, this study can provide a basis for higher education institutes to design and integrate high impact learning strategies into courses for improving college students’ experiential learning opportunities.
Evolution Of Artificial Intelligence In Marketing, Comparison With Traditional Marketing

Business being dynamic is evolving day by day, such innovations can be seen in each and very sphere of the business. The marketing sphere has also not left un touched of it, with the change in the mode of the communication and integration the conventional marketing has been evolving into the non-conventional marketing through the inculcation of the Artificial Intelligence in the marketing, the development of this had lead to major change in the marketing to study the evolution of the AI in the marketing and along with the adoption and the perspective of the customers toward AI marketing has been desired to study through the medium of this research, for this the survey has been conducted through the questionnaire system composed of different mix of the customer to know their awareness toward the AI in marketing and along with that the factors which lead to their adoption of the AI. The study has concluded that yes although there has been major inclement toward the AI but still its proper implementation would take some time for the over all adoption on the level of the customers also and on the level of companies also.
Meaning in Communication Games

This paper addresses two related questions: How can we capture different degrees of sharing a language with pre-specified meanings? And, how do we expect such a language to be used?
RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models.

We describe a robotic learning system for autonomous navigation in diverse environments. At the core of our method are two components: (i) a non-parametric map that reflects the connectivity of the environment but does not require geometric reconstruction or localization, and (ii) a latent variable model of distances and actions that enables efficiently constructing and traversing this map. The model is trained on a large dataset of prior experience to predict the expected amount of time and next action needed to transit between the current image and a goal image. Training the model in this way enables it to develop a representation of goals robust to distracting information in the input images, which aids in deploying the system to quickly explore new environments. We demonstrate our method on a mobile ground robot in a range of outdoor navigation scenarios. Our method can learn to reach new goals, specified as images, in a radius of up to 80 meters in just 20 minutes, and reliably revisit these goals in changing environments. We also demonstrate our method's robustness to previously-unseen obstacles and variable weather conditions. We encourage the reader to visit the project website for videos of our experiments and demonstrations this https URL
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing

In the problem of active sequential hypotheses testing (ASHT), a learner seeks to identify the true hypothesis from among a known set of hypotheses. The learner is given a set of actions and knows the random distribution of the outcome of any action under any true hypothesis. Given a target error $\delta>0$, the goal is to sequentially select the fewest number of actions so as to identify the true hypothesis with probability at least $1 - \delta$. Motivated by applications in which the number of hypotheses or actions is massive (e.g. genomics-based cancer detection), we propose efficient (greedy, in fact) algorithms and provide the first approximation guarantees for ASHT, under two types of adaptivity. Both of our guarantees are independent of the number of actions and logarithmic in the number of hypotheses. We numerically evaluate the performance of our algorithms using both synthetic and real DNA mutation data, demonstrating that our algorithms outperform previous heuristic policies by large margins.
Survey of Confidentiality and trust in recommender systems

Recommender systems has an important role in social networks. With the growth and development ofsocial networks, this issue is becoming more and more important. Recommending systems try to predict the user's interests and then suggest the closest items to the user's tastes. Recommender systems analyze the user’s behavior and suggest the most appropriate items. By collecting user information, the system categorizes and summarizes them, allowing users to access more relevant information in less time. Recommender system is an intelligent system that creates appropriate suggestions for each person by discovering and analyzing user information.In this paper, we will investigate recommending systems in three sections: types of recommendingsystems, information confidentiality and trust in recommender systems. We will refer to the relatedworks in each section, review the challenges of them, and present our results and evaluation on thesemethods
Trajectory Prediction &amp; Path Planning for an Object Intercepting UAV with a Mounted Depth Camera

A novel control &#x0026; software architecture using ROS C++ is introduced for object interception by a UAV with a mounted depth camera and no external aid. Existing work in trajectory prediction focused on the use of off-board tools like motion capture rooms to intercept thrown objects. The present study designs the UAV architecture to be completely on-board capable of object interception with the use of a depth camera and point cloud processing. The architecture uses an iterative trajectory prediction algorithm for non-propelled objects like a ping-pong ball. A variety of path planning approaches to object interception and their corresponding scenarios are discussed, evaluated &#x0026; simulated in Gazebo. The successful simulations exemplify the potential of using the proposed architecture for the onboard autonomy of UAVs intercepting objects.
The Difficulty of Passive Learning in Deep Reinforcement Learning

Learning to act from observational data without active environmental interaction is a well-known challenge in Reinforcement Learning (RL). Recent approaches involve constraints on the learned policy or conservative updates, preventing strong deviations from the state-action distribution of the dataset. Although these methods are evaluated using non-linear function approximation, theoretical justifications are mostly limited to the tabular or linear cases. Given the impressive results of deep reinforcement learning, we argue for a need to more clearly understand the challenges in this setting. In the vein of Held & Hein's classic 1963 experiment, we propose the tandem experimental paradigm which facilitates our empirical analysis of the difficulties in offline reinforcement learning. We identify function approximation in conjunction with fixed data distributions as the strongest factors, thereby extending but also challenging hypotheses stated in past work. Our results provide relevant insights for offline deep reinforcement learning, while also shedding new light on phenomena observed in the online case of learning control.
Self-Consistent Models and Values

Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment. In particular, models enable planning, i.e. using more computation to improve value functions or policies, without requiring additional environment interactions. In this work, we investigate a way of augmenting model-based RL, by additionally encouraging a learned model and value function to be jointly \emph{self-consistent}. Our approach differs from classic planning methods such as Dyna, which only update values to be consistent with the model. We propose multiple self-consistency updates, evaluate these in both tabular and function approximation settings, and find that, with appropriate choices, self-consistency helps both policy evaluation and control.
Coordinated Proximal Policy Optimization

We present Coordinated Proximal Policy Optimization (CoPPO), an algorithm that extends the original Proximal Policy Optimization (PPO) to the multi-agent setting. The key idea lies in the coordinated adaptation of step size during the policy update process among multiple agents. We prove the monotonicity of policy improvement when optimizing a theoretically-grounded joint objective, and derive a simplified optimization objective based on a set of approximations. We then interpret that such an objective in CoPPO can achieve dynamic credit assignment among agents, thereby alleviating the high variance issue during the concurrent update of agent policies. Finally, we demonstrate that CoPPO outperforms several strong baselines and is competitive with the latest multi-agent PPO method (i.e. MAPPO) under typical multi-agent settings, including cooperative matrix games and the StarCraft II micromanagement tasks.
ВЛИЯНИЕ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ В ПРЕПОДАВАНИИ ПРИРОДОВЕДЕНИЯ В НАЧАЛЬНОЙ ШКОЛЕ

The article deals with the problem o f the use o f information technologies ineducational process o f nature-study lessons. Peculiarities, the causes o f need to implementmodern information technologies in the learning process o f students, and also revealed thebenefits o f modern technology, both fo r teachers and for students.
Variational Multi-Task Learning with Gumbel-Softmax Priors

Multi-task learning aims to explore task relatedness to improve individual tasks, which is of particular significance in the challenging scenario that only limited data is available for each task. To tackle this challenge, we propose variational multi-task learning (VMTL), a general probabilistic inference framework for learning multiple related tasks. We cast multi-task learning as a variational Bayesian inference problem, in which task relatedness is explored in a unified manner by specifying priors. To incorporate shared knowledge into each task, we design the prior of a task to be a learnable mixture of the variational posteriors of other related tasks, which is learned by the Gumbel-Softmax technique. In contrast to previous methods, our VMTL can exploit task relatedness for both representations and classifiers in a principled way by jointly inferring their posteriors. This enables individual tasks to fully leverage inductive biases provided by related tasks, therefore improving the overall performance of all tasks. Experimental results demonstrate that the proposed VMTL is able to effectively tackle a variety of challenging multi-task learning settings with limited training data for both classification and regression. Our method consistently surpasses previous methods, including strong Bayesian approaches, and achieves state-of-the-art performance on five benchmark datasets.
Evaluating the Robustness of Neural Language Models to Input Perturbations.

High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks. However, results for common benchmark datasets often do not reflect model reliability and robustness when applied to noisy, real-world data. In this study, we design and implement various types of character-level and word-level perturbation methods to simulate realistic scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. Conducting comprehensive experiments on different NLP tasks, we investigate the ability of high-performance language models such as BERT, XLNet, RoBERTa, and ELMo in handling different types of input perturbations. The results suggest that language models are sensitive to input perturbations and their performance can decrease even when small changes are introduced. We highlight that models need to be further improved and that current benchmarks are not reflecting model robustness well. We argue that evaluations on perturbed inputs should routinely complement widely-used benchmarks in order to yield a more realistic understanding of NLP systems’ robustness.
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning

We provide improved gap-dependent regret bounds for reinforcement learning in finite episodic Markov decision processes. Compared to prior work, our bounds depend on alternative definitions of gaps. These definitions are based on the insight that, in order to achieve a favorable regret, an algorithm does not need to learn how to behave optimally in states that are not reached by an optimal policy. We prove tighter upper regret bounds for optimistic algorithms and accompany them with new information-theoretic lower bounds for a large class of MDPs. Our results show that optimistic algorithms can not achieve the information-theoretic lower bounds even in deterministic MDPs unless there is a unique optimal policy.
Blockchain and Government

Blockchain technology is a route bridge to assist government in all its usual processes and decisions. Nowadays, people as well as all electro-mechanical devices are in some way connected to the internet toward sharing and receiving data efficiently. Network servers may act as authorities and help to enhance efficiency in workflow, control, and enforcement of government laws as amended from time to time. Also, blockchain-based distributed ledger is secure, readily auditable, and immutable to keep track of government data where data is secure and unaltered to eliminate forgery for personal interest. Governance may establish streamlined procedures for various government activities through emerging technologies such as blockchain. It removes the involvement of unethical third parties and corruption from most government activities – namely, the election process, resource procurement, political mergers, fund allocation and distribution, responsibilities of various representatives (leaders) and government political employees, government assets management, to name a few. It is expected that the operations of government will be transparent, managed efficiently, and controlled through this emerging technology. Though blockchain is a nascent technology, it has promise for changes for a new generation of politics. Blockchain is a technology created by humans for humans with the transparency for the involved stakeholders where fraud is not possible and politicians may get better trust of civilians for their activities. Many countries are promoting this technology for government activities to reap benefits. This technology makes government employees’ perspective much clearer and concise, while politicians make use of this platform to better serve the nation. This chapter covers the relevant aspects of blockchain toward suitable governance.
LibGuides: Landing Your Dream Job: Social Media

This guide is based on information provided by Jan-Marie Lanuzza, Experiential Education Coordinator and Jean Moats, Director of Library Services.
Re-examining Aristotle's Categories as a Knowledge Organization System

In his Categories, Aristotle details the kinds of being that exist, along with what can be understood and predicated of existing things. Most notably within this work, Aristotle advances a set of ten, top-level categories that can be used to classify all kinds of being. Even today, the influence of the Categories is felt in many domains, particularly in knowledge organization (KO). Here, Aristotle’s Categories bear deep, long-standing connections with works examining categorization, subject analysis, and theory of classification. Though its relation to ontology might seem obvious, connections to KO perspectives on knowledge organization systems (KOSs) and ontological modeling are curiously lacking. The aim of this work is to offer a re-examination of the Categories as a KOS, particularly through the lens of the KO field’s understandings of ontology. Utilizing Zeng’s classification of KOSs as a theoretical framework, this study draws parallels between the first two sections of the Categories and the defining features of ontologies and offers an initial ontological model of this work. The results of this re-examination stand to offer a new view of a fundamental work in the KO canon, draw further connections between past and present perspectives in KO, and further contribute to the theoretical grounding of contemporary KOS research and practice.
Littlestone Classes are Privately Online Learnable

We consider the problem of online classification under a privacy constraint. In this setting a learner observes sequentially a stream of labelled examples $(x_t, y_t)$, for $1 \leq t \leq T$, and returns at each iteration $t$ a hypothesis $h_t$ which is used to predict the label of each new example $x_t$. The learner's performance is measured by her regret against a known hypothesis class $\mathcal{H}$. We require that the algorithm satisfies the following privacy constraint: the sequence $h_1, \ldots, h_T$ of hypotheses output by the algorithm needs to be an $(\epsilon, \delta)$-differentially private function of the whole input sequence $(x_1, y_1), \ldots, (x_T, y_T)$. We provide the first non-trivial regret bound for the realizable setting. Specifically, we show that if the class $\mathcal{H}$ has constant Littlestone dimension then, given an oblivious sequence of labelled examples, there is a private learner that makes in expectation at most $O(\log T)$ mistakes -- comparable to the optimal mistake bound in the non-private case, up to a logarithmic factor. Moreover, for general values of the Littlestone dimension $d$, the same mistake bound holds but with a doubly-exponential in $d$ factor. A recent line of work has demonstrated a strong connection between classes that are online learnable and those that are differentially-private learnable. Our results strengthen this connection and show that an online learning algorithm can in fact be directly privatized (in the realizable setting). We also discuss an adaptive setting and provide a sublinear regret bound of $O(\sqrt{T})$.
Parametricity Features and their Requirements.

In this note, we discuss a number of parametricity features and what their requirements are in terms of complexity of the type system and its model.