Across the world on Sunday, examinees will sit the Japanese Language Proficiency Test, a lengthy comprehension test that is the most widely taken Japanese exam by foreign nationals and whose upper levels can open opportunities in employment and education. Many companies seeking to hire foreign workers with Japanese ability specify the top two of the test's five levels among their requirements. But the JLPT's multiple-choice format of passive reading and listening skills, with no sections for speaking or writing, casts doubt over its suitability as a standard for gauging candidates looking to work in a Japanese environment. Established in 1984 and administered jointly by the Japan Foundation and Japan Educational Exchanges and Services, the JLPT boasts huge examinee numbers that were surging before the pandemic. In 2013, 571,075 people in 65 countries and regions took the test, rising to a record 1,168,535 examinees in 87 countries and regions in 2019.

On the second day of Amazon Web Services (AWS) re:Invent, Swami Sivasubramanian, vice president of AWS Data and Machine Learning (ML) revealed the latest innovations during his keynote. To start, Sivasubramanian announced the launch of Amazon Athena for Apache Spark, which he said will provide organizations with a more intuitive way to run complex data analytics. He noted that Apache Spark will run three times faster on AWS. The next product announcement was of the general availability of Amazon DocumentDB Elastic Clusters, a fully-managed solution to quickly scale document workloads of any size. Amazon SageMaker now supports Geospatial ML, giving access to multiple new kinds of data.

Complete Blender Creator: Learn 3D Modelling for Beginners - Use Blender to Create Beautiful 3D models for Video Games, 3D Printing & More. This course is in the process of being completely remastered in Blender 3.2. Currently both the new and original content are in this course, once the remaster is complete students will be able to access the original 2.8 content in a separate archive course. Blender is a fantastic platform which enables you to make AAA-quality models which can be exported to any game engine, 3D printer, or other software. Here are some of the reasons why you want to learn Blender with this online tutorial... Create assets for video games.

This paper describes the system developed at the Universitat Polit\`ecnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation techniques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers' baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings.

Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning from demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.

The introductory programming sequence has been the focus of much research in computing education. The recent advent of several viable and freely-available AI-driven code generation tools present several immediate opportunities and challenges in this domain. In this position paper we argue that the community needs to act quickly in deciding what possible opportunities can and should be leveraged and how, while also working on how to overcome or otherwise mitigate the possible challenges. Assuming that the effectiveness and proliferation of these tools will continue to progress rapidly, without quick, deliberate, and concerted efforts, educators will lose advantage in helping shape what opportunities come to be, and what challenges will endure. With this paper we aim to seed this discussion within the computing education community.

Peer grading is a powerful pedagogical tool. It benefits students by giving them exposure to others' perspectives; helping them to internalize evaluation criteria by applying them critically to peer work Lu and Law (2012); and offering them feedback from equal-status learners Topping (2009). Just as importantly, it gives instructors a way to make classes more scalable by shifting (some) grading workload away from course staff; effectively, this again benefits students, by giving them more opportunities for their work to be evaluated. In order for peer grading systems to be both useful to instructors and acceptable to students, they must produce grades that are sufficiently similar to those that an instructor would have given. This is a challenging task because individual peer graders will be biased (consistently give generous or harsh grades); noisy (the same grader could grade an assignment differently on different days); and potentially strategic (some students will enter insincere peer grades unrelated to a submission's quality if they can get away with it). Addressing these interrelated challenges has been a topic of academic study in Computer Science for at least the last two decades. The first methods for aggregating peer grades--and many others introduced more recently--produce point estimates of each assignment's grade and each grader's quality (Walsh, 2014; Chakraborty et al., 2018; Prajapati et al., 2020; de Alfaro and Shavlovsky, 2014; Hamer et al., 2005). At their best, methods that produce point estimates maximize the likelihood of the data given a model, e.g., by assigning each grader a "reliability" parameter and iteratively updating these parameters to best describe the reported grades.

Deep reinforcement learning algorithms have succeeded in several challenging domains. Classic Online RL job schedulers can learn efficient scheduling strategies but often takes thousands of timesteps to explore the environment and adapt from a randomly initialized DNN policy. Existing RL schedulers overlook the importance of learning from historical data and improving upon custom heuristic policies. Offline reinforcement learning presents the prospect of policy optimization from pre-recorded datasets without online environment interaction. Following the recent success of data-driven learning, we explore two RL methods: 1) Behaviour Cloning and 2) Offline RL, which aim to learn policies from logged data without interacting with the environment. These methods address the challenges concerning the cost of data collection and safety, particularly pertinent to real-world applications of RL. Although the data-driven RL methods generate good results, we show that the performance is highly dependent on the quality of the historical datasets. Finally, we demonstrate that by effectively incorporating prior expert demonstrations to pre-train the agent, we short-circuit the random exploration phase to learn a reasonable policy with online training. We utilize Offline RL as a launchpad to learn effective scheduling policies from prior experience collected using Oracle or heuristic policies. Such a framework is effective for pre-training from historical datasets and well suited to continuous improvement with online data collection.

Online learning naturally arises in many statistical and machine learning problems. The most widely used methods in online learning are stochastic first-order algorithms. Among this family of algorithms, there is a recently developed algorithm, Recursive One-Over-T SGD (ROOT-SGD). ROOT-SGD is advantageous in that it converges at a non-asymptotically fast rate, and its estimator further converges to a normal distribution. However, this normal distribution has unknown asymptotic covariance; thus cannot be directly applied to measure the uncertainty. To fill this gap, we develop two estimators for the asymptotic covariance of ROOT-SGD. Our covariance estimators are useful for statistical inference in ROOT-SGD. Our first estimator adopts the idea of plug-in. For each unknown component in the formula of the asymptotic covariance, we substitute it with its empirical counterpart. The plug-in estimator converges at the rate $\mathcal{O}(1/\sqrt{t})$, where $t$ is the sample size. Despite its quick convergence, the plug-in estimator has the limitation that it relies on the Hessian of the loss function, which might be unavailable in some cases. Our second estimator is a Hessian-free estimator that overcomes the aforementioned limitation. The Hessian-free estimator uses the random-scaling technique, and we show that it is an asymptotically consistent estimator of the true covariance.

In this paper, we present PARTIME, a software library written in Python and based on PyTorch, designed specifically to speed up neural networks whenever data is continuously streamed over time, for both learning and inference. Existing libraries are designed to exploit data-level parallelism, assuming that samples are batched, a condition that is not naturally met in applications that are based on streamed data. Differently, PARTIME starts processing each data sample at the time in which it becomes available from the stream. PARTIME wraps the code that implements a feed-forward multi-layer network and it distributes the layer-wise processing among multiple devices, such as Graphics Processing Units (GPUs). Thanks to its pipeline-based computational scheme, PARTIME allows the devices to perform computations in parallel. At inference time this results in scaling capabilities that are theoretically linear with respect to the number of devices. During the learning stage, PARTIME can leverage the non-i.i.d. nature of the streamed data with samples that are smoothly evolving over time for efficient gradient computations. Experiments are performed in order to empirically compare PARTIME with classic non-parallel neural computations in online learning, distributing operations on up to 8 NVIDIA GPUs, showing significant speedups that are almost linear in the number of devices, mitigating the impact of the data transfer overhead.