Reviewerly/exHarmony huggingface dataset:
Uniform Sampling over Episode Difficulty

Episodic training is a core ingredient of few-shot learning to train models on tasks with limited labelled data. Despite its success, episodic training remains largely understudied, prompting us to ask the question: what is the best way to sample episodes? In this paper, we first propose a method to approximate episode sampling distributions based on their difficulty. Building on this method, we perform an extensive analysis and find that sampling uniformly over episode difficulty outperforms other sampling schemes, including curriculum and easy-/hard-mining. As the proposed sampling method is algorithm agnostic, we can leverage these insights to improve few-shot learning accuracies across many episodic training algorithms. We demonstrate the efficacy of our method across popular few-shot learning datasets, algorithms, network architectures, and protocols.
Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation

Knowledge distillation is classically a procedure where a neural network is trained on the output of another network along with the original targets in order to transfer knowledge between the architectures. The special case of self-distillation, where the network architectures are identical, has been observed to improve generalization accuracy. In this paper, we consider an iterative variant of self-distillation in a kernel regression setting, in which successive steps incorporate both model outputs and the ground-truth targets. This allows us to provide the first theoretical results on the importance of using the weighted ground-truth targets in self-distillation. Our focus is on fitting nonlinear functions to training data with a weighted mean square error objective function suitable for distillation, subject to $\ell_2$ regularization of the model parameters. We show that any such function obtained with self-distillation can be calculated directly as a function of the initial fit, and that infinite distillation steps yields the same optimization problem as the original with amplified regularization. Finally, we examine empirically, both in a regression setting and with ResNet networks, how the choice of weighting parameter influences the generalization performance after self-distillation.
Support vector machines and linear regression coincide with very high-dimensional features

The support vector machine (SVM) and minimum Euclidean norm least squares regression are two fundamentally different approaches to fitting linear models, but they have recently been connected in models for very high-dimensional data through a phenomenon of support vector proliferation, where every training example used to fit an SVM becomes a support vector. In this paper, we explore the generality of this phenomenon and make the following contributions. First, we prove a super-linear lower bound on the dimension (in terms of sample size) required for support vector proliferation in independent feature models, matching the upper bounds from previous works. We further identify a sharp phase transition in Gaussian feature models, bound the width of this transition, and give experimental support for its universality. Finally, we hypothesize that this phase transition occurs only in much higher-dimensional settings in the $\ell_1$ variant of the SVM, and we present a new geometric characterization of the problem that may elucidate this phenomenon for the general $\ell_p$ case.
Path Outlining of a KUKA LBR Iiwa 14 R820 using Hybrid Optimization

In this paper, the inverse kinematics of more advanced degree of freedom robot is computed with the support of MATLAB. This paper deals with the study of control modeling of the KUKA robot. The path planning of robot end effector has been a research area for years. In this paper the new hybrid optimization techniques are used to solve the forward and inverse kinematics solution of KUKA LBR iiwa 14 R820. For Forward Kinematics SHO (selfish herd optimization), KHO (krill herd optimization)hybrid optimization technique is used and for Inverse Kinematics, hybrid ALO (ant lion optimization), ACO (ant colony optimization) technique is used. This proposed work also incorporates the itinerary of the robot in a workspace area with multiple obstacles by using a Hybrid GWO (Grey wolf optimization), GOA (grasshopper optimization). The optimized route discovered by applying the hybrid optimizer is energy efficient. Graphic User Interface is used to strut the optimized path into 3-Dimensional form. Forged consequences are administered to exemplify the effeteness and virtue of the propounding method. While using the different optimizing techniques the optimized path is found, with obstacle and without obstacle with less execution time and in less number of iterations. The Graphic user Interface is used to represent the different paths. In this work, the multi-objective functions such as collision free motion, jerk, travelling time and acceleration are considered for the planning of trajectory of the robot manipulator. The fitness function is reducing the multi-objective function.
Evaluation of QualityEarly Childhood Education Program Services UsingCIPP Model

The purpose of this evaluation study is to analyzing the effect of context, inputevaluation, the process of the quality of the ECE program in Semarang city. To find thisout, quantitative research was conducted with the exposure facto research approach. Thisevaluative research will be conducted ECE in Semarang city with a population of 553 ECEeducators, sample in the study are 120 ECE educators. Data collection techniques used isobservation and questionnaire. The analysis technique used in this study uses StructuralEquitation Modeling software, AMOS 24.0. The results of the study show: contextevaluation, input evaluation, and process evaluation has a positive and significant effecton the quality of ECE services in Semarang city. So the decreasing quality of the processin ECE services will have difficulties in improving the quality of ECE services, andcontext, input, and process evaluation together have a significant effect on the quality ofECE service in Semarang city.
Extracción potencial de níquel y cobalto con mineral laterítico de mina “Pinares de Mayarí” en la tecnología Caron. Parte I

The leaching of a reduced lateritic mineral (RLM) sample in ammonia carbonate solution was experimentally studied to validate the effect of the specific air flow (K), in m3∙t-1∙min-1, on the potential extraction of nickel and cobalt. The reduction was carried out in a Selas furnace, at 700oC and adding fuel-oil additive; for its part, for leaching, a suspension at 6,6 % by weight of solids was prepared, with 90 g/L of dissolved ammonia and a NH3/CO2 ratio of 1,8. The RLM was characterized by Atomic Absorption Spectrophotometry and had an average content of nickel of 1,03 %, cobalt 0,124 % and iron 55,9 %. It was confirmed that it is valid to apply, for a redox potential Eh≤-455 mV, K=1,3; and for Eh>-455 mV, K=1,8; and nickel extractable increased by 1,4%, and cobalt between 1,0 and 3,6% with respect to the traditional specific flow. The inverse of ferrous iron in the RLM had a statistically significant relationship with the redox potential and the extractable, which suggested assuming the measurement of Eh as a physical test to estimate the extraction potential.
Chapter 2. Navigating the Toolkit

Chapter 2 of Library Technology Reports (vol. 57, no. 8), “Using the Toward Gigabit Libraries Toolkit,” covers the self-guided portions of the toolkit (sections 4–8), showing examples of toolkit resources and how they support technology troubleshooting efforts.
Technological change in Agriculture: From urban to rural path for Agridrones

Two main domains of Indian economy are agriculture and manufacturing. More than 50 percent of the total employment force directly, indirect depends on agriculture which contributes around 17-18 percent to the country’s GDP [1] from agriculture. Uncertainty in production, changing government policies, urbanization effects this percentage and an estimated drop in this work force would be 25.7 percent by 2050 [2]. With the challenges which are may or may not be controlled by human intervention but use of different machines and technology can work coping against those natural factors in one or another way. Unmanned Air Vehicles with sensors, a synonym for Flying Agriculture Networks is a new technology for agriculture is widely used for different chore in agriculture and prove their efficiency to improve the production. This technological transformation supports the farming communities for improvement, can become a strong key to feed the growing population of world. With increasing availability of affordable systems, use of smart systems may boost the production towards rising consumer expectation and cares the rapid growth of agriculture production. India accounted for 55 per cent market share of the US$ 200-250 billion global services sourcing business in 2019-20 [3], which is the largest sourcing destination; on the other hand, India accounted for only 8 percent for smart agriculture devices. The use of technology in India for agriculture is a big challenge due to traditional believes, social and political issues. This paper survey is about Indian perspective for the use of FANET in agriculture, their challenges, and opportunities. A detailed survey is conducted with 2000 farmers across India for availability and challenges facing them to use technology. The Paper is also discussing Government support, initiatives in the last few years and current reports for use of UAV’s in Indian Agriculture.
Kebiasaan Membaca Santri di Pondok Pesantren Sirojut Tholibin Tulungagung

Reading is one of the activities that are beneficial for people life. Reading has various benefits in everyday life. By reading, people can find various information, insights, and knowledge. By reading, a person can solve the problems of his life. Therefore, the habit of reading must be grown and improved, especially in educational institutions such as Islamic boarding schools. This research aims to describe the reading habits of the Islamic boarding schools students and the factors that influence these reading habits. The method used in this research is qualitative method. The data collection technique used is distribute questionnaires onto research subjects. The subjects of this research are 10 female students of the Sirojut Tholibin Islamic Boarding School Tulungagung. The analysis technique used is descriptive analysis. Based on the results of the research and discussion, it can be concluded: (1) The reading habits of the students of the Sirojut Tholibin Islamic Boarding School Tulungagung can be analyzed from the duration of reading, the time used in reading books, the types of books that the students prefer (2) Factors that influence the reading habits of the students include the reading motivation of the students, the schedule of the boarding school, and the attitude of the students towards reading activities.
PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages.

NLP applications for code-mixed (CM) or mix-lingual text have gained a significant momentum recently, the main reason being the prevalence of language mixing in social media communications in multi-lingual societies like India, Mexico, Europe, parts of USA etc. Word embeddings are basic build-ing blocks of any NLP system today, yet, word embedding for CM languages is an unexplored territory. The major bottleneck for CM word embeddings is switching points, where the language switches. These locations lack in contextually and statistical systems fail to model this phenomena due to high variance in the seen examples. In this paper we present our initial observations on applying switching point based positional encoding techniques for CM language, specifically Hinglish (Hindi - English). Results are only marginally better than SOTA, but it is evident that positional encoding could bean effective way to train position sensitive language models for CM text.
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning

Large natural language models (such as GPT-3 or T5) demonstrate impressive abilities across a range of general NLP tasks. Here, we show that the knowledge embedded in such models provides a useful inductive bias, not just on traditional NLP tasks, but also in the nontraditional task of training a symbolic reasoning engine. We observe that these engines learn quickly and generalize in a natural way that reflects human intuition. For example, training such a system to model block-stacking might naturally generalize to stacking other types of objects because of structure in the real world that has been partially captured by the language describing it. We study several abstract textual reasoning tasks, such as object manipulation and navigation, and demonstrate multiple types of generalization to novel scenarios and the symbols that comprise them. We also demonstrate the surprising utility of \textit{compositional learning}, where a learner dedicated to mastering a complicated task gains an advantage by training on relevant simpler tasks instead of jumping straight to the complicated task.
Formalizing the Generalization-Forgetting Trade-off in Continual Learning

We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two-player sequential game. In this approach, player 1 maximizes the cost due to lack of generalization whereas player 2 minimizes the cost due to catastrophic forgetting. We show theoretically that a balance point between the two players exists for each task and that this point is stable (once the balance is achieved, the two players stay at the balance point). Next, we introduce balanced continual learning (BCL), which is designed to attain balance between generalization and forgetting and empirically demonstrate that BCL is comparable to or better than the state of the art.
YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews

Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets. Such an evaluation is limited, and may not reflect true performance on sites like Amazon or Yelp that host diverse reviews from many domains. To address this gap, we present YASO – a new TSA evaluation dataset of open-domain user reviews. YASO contains 2,215 English sentences from dozens of review domains, annotated with target terms and their sentiment. Our analysis verifies the reliability of these annotations, and explores the characteristics of the collected data. Benchmark results using five contemporary TSA systems show there is ample room for improvement on this challenging new dataset. YASO is available at https://github.com/IBM/yaso-tsa.
Consistent Non-Parametric Methods for Maximizing Robustness

Learning classifiers that are robust to adversarial examples has received a great deal of recent attention. A major drawback of the standard robust learning framework is there is an artificial robustness radius $r$ that applies to all inputs. This ignores the fact that data may be highly heterogeneous, in which case it is plausible that robustness regions should be larger in some regions of data, and smaller in others. In this paper, we address this limitation by proposing a new limit classifier, called the neighborhood optimal classifier, that extends the Bayes optimal classifier outside its support by using the label of the closest in-support point. We then argue that this classifier maximizes the size of its robustness regions subject to the constraint of having accuracy equal to the Bayes optimal. We then present sufficient conditions under which general non-parametric methods that can be represented as weight functions converge towards this limit, and show that both nearest neighbors and kernel classifiers satisfy them under certain conditions.
Challenges in Everyday Use of Mobile Visualizations

This chapter illustrates challenges resulting from the everyday use of mobile visualizations in three categories: logistical challenges relating to situated use, privacy challenges involved with potential data disclosures, and ethical challenges surrounding increased access and decreased evidence. In spite of these challenges, introducing visualizations in everyday life can lead to positive experiences viewing and reflecting on data in their natural contexts. Using scenarios to depict use opportunities and challenges, this chapter introduces a set of considerations for designers and researchers looking to develop mobile visualizations for everyday contexts.
A Novel Framework for Robotic Chess

In the subject of human-robot interaction, robotic chess is a new issue domain. The author introduces a robotic chess-playing robotic system capable of detecting all conceivable chess board states, creating following moves, and executing those moves. Existing chess-playing robots combine a camera to detect current game conditions and an interlocked framework arm to move the chess pieces across the board, making them very complicated and costly. In this proposed robotic chess, an X-Y plotter system is designed to transport chess pieces on the chessboard. In addition, the current game state is determined by sixty-four magnetic reed switches. Use of stepper motors, linear bearings, and a microcontroller-based chess engine for the system, improves the compactness and portability of the prototype.
HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data

The recent success of machine learning methods applied to time series collected from Intensive Care Units (ICU) exposes the lack of standardized machine learning benchmarks for developing and comparing such methods. While raw datasets, such as MIMIC-IV or eICU, can be freely accessed on Physionet, the choice of tasks and pre-processing is often chosen ad-hoc for each publication, limiting comparability across publications. In this work, we aim to improve this situation by providing a benchmark covering a large spectrum of ICU-related tasks. Using the HiRID dataset, we define multiple clinically relevant tasks developed in collaboration with clinicians. In addition, we provide a reproducible end-to-end pipeline to construct both data and labels. Finally, we provide an in-depth analysis of current state-of-the-art sequence modeling methods, highlighting some limitations of deep learning approaches for this type of data. With this benchmark, we hope to give the research community the possibility of a fair comparison of their work.
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives

Despite the potential of reinforcement learning (RL) for buildinggeneral-purpose robotic systems, training RL agents to solve robotics tasksstill remains challenging due to the difficulty of exploration in purelycontinuous action spaces. Addressing this problem is an active area of researchwith the majority of focus on improving RL methods via better optimization ormore efficient exploration. An alternate but important component to considerimproving is the interface of the RL algorithm with the robot. In this work, wemanually specify a library of robot action primitives (RAPS), parameterizedwith arguments that are learned by an RL policy. These parameterized primitivesare expressive, simple to implement, enable efficient exploration and can betransferred across robots, tasks and environments. We perform a thoroughempirical study across challenging tasks in three distinct domains with imageinput and a sparse terminal reward. We find that our simple change to theaction interface substantially improves both the learning efficiency and taskperformance irrespective of the underlying RL algorithm, significantlyoutperforming prior methods which learn skills from offline expert data. Codeand videos at https://mihdalal.github.io/raps/
THE EFFECTIVENESS OF PODCAST TO IMPROVE LISTENING SKILL IN NARRATIVE TEXT

The purpose of the study is to find out the effectiveness of Podcast  for improving  listening skill and building comprehension for students on Narrative text. This research uses quasy experimental design. They were 18 tenth grade students of SMK Darul Ulum Kuningan as the research subject . they were devided into two groups ;  Experimental and Control Group . Pre-test and post-test was used as the instruments in order to take data collection. Furthermore, the researcher analyzed and interpreted through quantitative procedure by SPSS 23.0 version. The result showed in the post-test of experiment group was higher than the mean score of the pre-test, which the mean of post-test was 84.00, and the pre-test was 58.75.Based on the statistical analysis using T-test, the result indicates that the significant value (sig-2 tailed) was 0,000 and it was lower than 0.05 0.000 t-table 2.145. It represented that there was a significant improvement  of students` listening skill on Narrative text before and after being taught by using Podcast. the researcher takes the conclusion that  Podcast is effective used in teaching learning listening at the tenth grade students of SMK Darul Ulum Kuningan.
Natural Language Generation Requirements for Social Robots in Sub-Saharan Africa

Robots are deployed in Africa mainly in manufacturing, yet they may assist in society as future oriented technologies as well. They may ameliorate, e.g., service delivery issues and skills shortages. In this discussion paper, several uses and use cases relevant to Sub-Saharan Africa are described and requirements identified. We zoom in on human-robot interaction in Niger-Congo B (‘bantu’) languages. Use cases for healthcare and education elucidate specific requirements for the natural language generation component of robots in society. In contrast to typical generation systems, it demands i) combining data-to-text and knowledge-to-text in one system, ii) generating different types of sentences so as to switch between written and spoken language, and iii) processing non-trivial numbers.
Perturbation Theory for the Information Bottleneck

Extracting relevant information from data is crucial for all forms of learning. The information bottleneck (IB) method formalizes this, offering a mathematically precise and conceptually appealing framework for understanding learning phenomena. However the nonlinearity of the IB problem makes it computationally expensive and analytically intractable in general. Here we derive a perturbation theory for the IB method and report the first complete characterization of the learning onset, the limit of maximum relevant information per bit extracted from data. We test our results on synthetic probability distributions, finding good agreement with the exact numerical solution near the onset of learning. We explore the difference and subtleties in our derivation and previous attempts at deriving a perturbation theory for the learning onset and attribute the discrepancy to a flawed assumption. Our work also provides a fresh perspective on the intimate relationship between the IB method and the strong data processing inequality.