Reviewerly/exHarmony huggingface dataset:
Online Shielding for Reinforcement Learning

Besides the recent impressive results on reinforcement learning (RL), safety is still one of the major research challenges in RL. RL is a machine-learning approach to determine near-optimal policies in Markov decision processes (MDPs). In this paper, we consider the setting where the safety-relevant fragment of the MDP together with a temporal logic safety specification is given and many safety violations can be avoided by planning ahead a short time into the future. We propose an approach for online safety shielding of RL agents. During runtime, the shield analyses the safety of each available action. For any action, the shield computes the maximal probability to not violate the safety specification within the next $k$ steps when executing this action. Based on this probability and a given threshold, the shield decides whether to block an action from the agent. Existing offline shielding approaches compute exhaustively the safety of all state-action combinations ahead of time, resulting in huge computation times and large memory consumption. The intuition behind online shielding is to compute at runtime the set of all states that could be reached in the near future. For each of these states, the safety of all available actions is analysed and used for shielding as soon as one of the considered states is reached. Our approach is well suited for high-level planning problems where the time between decisions can be used for safety computations and it is sustainable for the agent to wait until these computations are finished. For our evaluation, we selected a 2-player version of the classical computer game SNAKE. The game represents a high-level planning problem that requires fast decisions and the multiplayer setting induces a large state space, which is computationally expensive to analyse exhaustively.
Visualizing Contributor Code Competency for PyPI Libraries: Preliminary Results

Python is known to be used by beginners to professional programmers. Python provides functionality to its community of users through PyPI libraries, which allows developers to reuse functionalities to an application. However, it is unknown the extent to which these PyPI libraries require proficient code in their implementation. We conjecture that PyPI contributors may decide to implement more advanced Pythonic code, or stick with more basic Python code. Are complex codes only committed by few contributors, or only to specific files? The new idea in this paper is to confirm who and where complex code is implemented. Hence, we present a visualization to show the relationship between proficient code, contributors, and files. Analyzing four PyPI projects, we are able to explore which files contain more elegant code, and which contributors committed to these files. Our results show that most files contain more basic competency files, and that not every contributor contributes competent code. We show how~our visualization is able to summarize such information, and opens up different possibilities for understanding how to make elegant contributions.
Understanding How Model Size Affects Few-shot Instruction Prompting

Large Language Models are affected by the phenomena of memorizing and forgetting their training data. But how do these vary by model size? We work towards this question by investigating how the model size affects the model's ability to discriminate a word's meaning in a given context. We introduce a dataset called DeltaWords, which evaluates a model's ability to follow instructions to select a sentence which replaces the target word with its antonym. We show a weak inverse scaling trend, where task accuracy degrades as model size increase, under extremely few-shot prompting regimes. We show that increasing the number of examples tend to disproportionately benefit larger models than smaller models.
Applying Multilingual Models to Question Answering (QA)

We study the performance of monolingual and multilingual language models on the task of question-answering (QA) on three diverse languages: English, Finnish and Japanese. We develop models for the tasks of (1) determining if a question is answerable given the context and (2) identifying the answer texts within the context using IOB tagging. Furthermore, we attempt to evaluate the effectiveness of a pre-trained multilingual encoder (Multilingual BERT) on cross-language zero-shot learning for both the answerability and IOB sequence classifiers.
Hierarchical Policy Blending As Optimal Transport

We present hierarchical policy blending as optimal transport (HiPBOT). HiPBOT hierarchically adjusts the weights of low-level reactive expert policies of different agents by adding a look-ahead planning layer on the parameter space. The high-level planner renders policy blending as unbalanced optimal transport consolidating the scaling of the underlying Riemannian motion policies. As a result, HiPBOT effectively decides the priorities between expert policies and agents, ensuring the task's success and guaranteeing safety. Experimental results in several application scenarios, from low-dimensional navigation to high-dimensional whole-body control, show the efficacy and efficiency of HiPBOT. Our method outperforms state-of-the-art baselines -- either adopting probabilistic inference or defining a tree structure of experts -- paving the way for new applications of optimal transport to robot control. More material at https://sites.google.com/view/hipobot
Effects of environment correlations on the onset of collective decay in waveguide QED

We calculate the dynamics of one and two two-level atoms interacting with the electromagnetic vacuum field in the vicinity of an optical nanofiber without making either the Born or the Markov approximations. We use a constant dielectric function and the Drude-Lorentz model, observing deviations from the standard super- and sub-radiant decays. Despite the non-trivial environment correlations, we discuss the validity of approximating the speed of atom-atom communication to the group velocity of the guided field. Our work presents a deeper understanding of the validity of commonly used approximations in recent platforms for quantum optics applications in the context of waveguide QED.
Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation

Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological process. A listener needs to find out events which cause a speaker's emotions (emotion cause extraction), project the events into some experience (knowledge extension), and express empathy in the most appropriate way (communication mechanism). To this end, we propose a novel approach, which integrates the three components - emotion cause, knowledge graph, and communication mechanism for empathetic response generation. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and show that incorporating the key components generates more informative and empathetic responses.
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight

This paper strives to recognize activities in the dark, as well as in the day. We first establish that state-of-the-art activity recognizers are effective during the day, but not trustworthy in the dark. The main causes are the limited availability of labeled dark videos to learn from, as well as the distribution shift towards the lower color contrast at test-time. To compensate for the lack of labeled dark videos, we introduce a pseudo-supervised learning scheme, which utilizes easy to obtain unlabeled and task-irrelevant dark videos to improve an activity recognizer in low light. As the lower color contrast results in visual information loss, we further propose to incorporate the complementary activity information within audio, which is invariant to illumination. Since the usefulness of audio and visual features differs depending on the amount of illumination, we introduce our `darkness-adaptive' audio-visual recognizer. Experiments on EPIC-Kitchens, Kinetics-Sound, and Charades demonstrate our proposals are superior to image enhancement, domain adaptation and alternative audio-visual fusion methods, and can even improve robustness to local darkness caused by occlusions. Project page: https://xiaobai1217.github.io/Day2Dark/
Syntactic Multi-view Learning for Open Information Extraction

Open Information Extraction (OpenIE) aims to extract relational tuples from open-domain sentences. Traditional rule-based or statistical models have been developed based on syntactic structures of sentences, identified by syntactic parsers. However, previous neural OpenIE models under-explore the useful syntactic information. In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures. To better fuse heterogeneous information from both graphs, we adopt multi-view learning to capture multiple relationships from them. Finally, the finetuned constituency and dependency representations are aggregated with sentential semantic representations for tuple generation. Experiments show that both constituency and dependency information, and the multi-view learning are effective.
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection

Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems. Most of the previous studies focused on the detection of OOD samples in the multi-class classification task. However, OOD detection in the multi-label classification task, a more common real-world use case, remains an underexplored domain. In this research, we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task. Object detection models have an inherent ability to distinguish between objects of interest (in-distribution) and irrelevant objects (e.g., OOD objects) in images that contain multiple objects belonging to different class categories. These abilities allow us to convert a regular object detection model into an image classifier with inherent OOD detection capabilities with just minor changes. We compare our approach to state-of-the-art OOD detection methods and demonstrate YolOOD's ability to outperform these methods on a comprehensive suite of in-distribution and OOD benchmark datasets.
End-to-end Recording Device Identification Based on Deep Representation Learning

Deep learning techniques have achieved specific results in recording device source identification. The recording device source features include spatial information and certain temporal information. However, most recording device source identification methods based on deep learning only use spatial representation learning from recording device source features, which cannot make full use of recording device source information. Therefore, in this paper, to fully explore the spatial information and temporal information of recording device source, we propose a new method for recording device source identification based on the fusion of spatial feature information and temporal feature information by using an end-to-end framework. From a feature perspective, we designed two kinds of networks to extract recording device source spatial and temporal information. Afterward, we use the attention mechanism to adaptively assign the weight of spatial information and temporal information to obtain fusion features. From a model perspective, our model uses an end-to-end framework to learn the deep representation from spatial feature and temporal feature and train using deep and shallow loss to joint optimize our network. This method is compared with our previous work and baseline system. The results show that the proposed method is better than our previous work and baseline system under general conditions.
Human-in-the-Loop Hate Speech Classification in a Multilingual Context

The shift of public debate to the digital sphere has been accompanied by a rise in online hate speech. While many promising approaches for hate speech classification have been proposed, studies often focus only on a single language, usually English, and do not address three key concerns: post-deployment performance, classifier maintenance and infrastructural limitations. In this paper, we introduce a new human-in-the-loop BERT-based hate speech classification pipeline and trace its development from initial data collection and annotation all the way to post-deployment. Our classifier, trained using data from our original corpus of over 422k examples, is specifically developed for the inherently multilingual setting of Switzerland and outperforms with its F1 score of 80.5 the currently best-performing BERT-based multilingual classifier by 5.8 F1 points in German and 3.6 F1 points in French. Our systematic evaluations over a 12-month period further highlight the vital importance of continuous, human-in-the-loop classifier maintenance to ensure robust hate speech classification post-deployment.
Matrix factorization with neural networks

Matrix factorization is an important mathematical problem encountered in the context of dictionary learning, recommendation systems and machine learning. We introduce a new `decimation' scheme that maps it to neural network models of associative memory and provide a detailed theoretical analysis of its performance, showing that decimation is able to factorize extensive-rank matrices and to denoise them efficiently. We introduce a decimation algorithm based on ground-state search of the neural network, which shows performances that match the theoretical prediction.
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning

Convergence bounds are one of the main tools to obtain information on the performance of a distributed machine learning task, before running the task itself. In this work, we perform a set of experiments to assess to which extent, and in which way, such bounds can predict and improve the performance of real-world distributed (namely, federated) learning tasks. We find that, as can be expected given the way they are obtained, bounds are quite loose and their relative magnitude reflects the training rather than the testing loss. More unexpectedly, we find that some of the quantities appearing in the bounds turn out to be very useful to identify the clients that are most likely to contribute to the learning process, without requiring the disclosure of any information about the quality or size of their datasets. This suggests that further research is warranted on the ways -- often counter-intuitive -- in which convergence bounds can be exploited to improve the performance of real-world distributed learning tasks.
Momentum Decoding: Open-ended Text Generation As Graph Exploration

Open-ended text generation with autoregressive language models (LMs) is one of the core tasks in natural language processing. However, maximization-based decoding methods (e.g., greedy/beam search) often lead to the degeneration problem, i.e., the generated text is unnatural and contains undesirable repetitions. Existing solutions to this problem either introduce randomness prone to incoherence or require a look-ahead mechanism that demands extra computational overhead. In this study, we formulate open-ended text generation from a new perspective, i.e., we view it as an exploration process within a directed graph. Thereby, we understand the phenomenon of degeneration as circular loops within the directed graph. Based on our formulation, we propose a novel decoding method -- \textit{momentum decoding} -- which encourages the LM to \textit{greedily} explore new nodes outside the current graph. Meanwhile, it also allows the LM to return to the existing nodes with a momentum downgraded by a pre-defined resistance function. We extensively test our approach on three benchmarks from different domains through automatic and human evaluations. The results show that momentum decoding performs comparably with the current state of the art while enjoying notably improved inference speed and computation FLOPs. Furthermore, we conduct a detailed analysis to reveal the merits and inner workings of our approach. Our codes and other related resources are publicly available at https://github.com/gmftbyGMFTBY/MomentumDecoding.
B-Spline Quarklets and Biorthogonal Multiwavelets

We show that B-spline quarks and the associated quarklets fit into the theory of biorthogonal multiwavelets. Quark vectors are used to define sequences of subspaces $ V_{p,j} $ of $ L_{2}(\mathbb{R}) $ which fulfill almost all conditions of a multiresolution analysis. Under some special conditions on the parameters they even satisfy all those properties. Moreover we prove that quarks and quarklets possess modulation matrices which fulfill the perfect reconstruction condition. Furthermore we show the existence of generalized dual quarks and quarklets which are known to be at least compactly supported tempered distributions from $\mathcal{S}'(\mathbb{R})$. Finally we also verify that quarks and quarklets can be used to define sequences of subspaces $ W_{p,j} $ of $ L_{2}(\mathbb{R}) $ that yield non-orthogonal decompositions of $ L_{2}(\mathbb{R}) $.
Research on Early Warning and NB-IoT Real-time Monitoring System for Radiation Source Shedding of Gamma Flaw Detection Machine

The system takes the embedded system single chip as the core, and organizes the gamma ray induction module, keying switch, radiation source braid locking mechanism, on-site alarm equipment, NB-IoT communication module, GPS positioning system and other related equipment to realize real-time operators warning and remote alarming to the monitoring platform. Thus, timely management of the fallen radiation source can be realized. What's more, position of radiation source braid is monitored by radiation source braid locking mechanism, gamma-ray induction module and keying switch. This idea solves the bottleneck problem of difficulty in transmitting real-time remote monitoring signal by NB-IoT technology. Single-chip microcontroller is innovatively embedded around the radiation source to monitor whether the source falls off. As a result, when part or all of the source braid are not returned to the storage location of the flaw detector, the gamma ray induction module, keying switch, and radiation source braid locking mechanism remind the operator of the dangerous situation. At the same time, NB-IoT transmission system alarms companies and regulators of safety risks. In conclusion, radiation source can be found timely when it falls off, to avoid radiation accident. Two bottlenecks related to installing GPS positioning system can be solved, that the GPS positioning system is installed on the flaw detector, and the radioactive source is still unable to be controlled, and that the GPS positioning system requires power to transmit the signal back to the regulatory platform or to the platform of the flaw detection enterprise. This article's ideas can solve this problem through the advantages of NB-IoT.
Addressing Distribution Shift at Test Time in Pre-trained Language Models

State-of-the-art pre-trained language models (PLMs) outperform other models when applied to the majority of language processing tasks. However, PLMs have been found to degrade in performance under distribution shift, a phenomenon that occurs when data at test-time does not come from the same distribution as the source training set. Equally as challenging is the task of obtaining labels in real-time due to issues like long-labeling feedback loops. The lack of adequate methods that address the aforementioned challenges constitutes the need for approaches that continuously adapt the PLM to a distinct distribution. Unsupervised domain adaptation adapts a source model to an unseen as well as unlabeled target domain. While some techniques such as data augmentation can adapt models in several scenarios, they have only been sparsely studied for addressing the distribution shift problem. In this work, we present an approach (MEMO-CL) that improves the performance of PLMs at test-time under distribution shift. Our approach takes advantage of the latest unsupervised techniques in data augmentation and adaptation to minimize the entropy of the PLM's output distribution. MEMO-CL operates on a batch of augmented samples from a single observation in the test set. The technique introduced is unsupervised, domain-agnostic, easy to implement, and requires no additional data. Our experiments result in a 3% improvement over current test-time adaptation baselines.
The Effectiveness of a Mobile Learning Strategy via WhatsApp in Developing EFL Prospective Teachers' Reading and Writing Skills

The aim of this study is to investigate the effectiveness of the mobile learning strategy via WhatsApp in developing EFL reading and writing skills among third -year prospective teachers. The participants included (30) third -year prospective teachers at English department at the Faculty of Education, Menoufia University. They are assigned to one study group. To reach the goal of the study, the researcher prepared an integrated reading - writing skills checklist, an integrated reading –writing skills pre-test, an integrated reading–writing skills post-test, and an integrated reading –writing skills rubric. The mobile learning strategy via WhatsApp is designed to develop EFL reading skills and writing skills among third -year Prospective teachers at the Faculty of Education. Results show that mobile learning strategy via WhatsApp emphasized it is effective in improving third year prospective teachers’ reading and writing skills. Thus, the suggested app is effective in developing EFL reading and writing skills among third year prospective teachers.
Is there a finite complete set of monotones in any quantum resource theory?

Entanglement quantification aims to assess the value of quantum states for quantum information processing tasks. A closely related problem is state convertibility, asking whether two remote parties can convert a shared quantum state into another one without exchanging quantum particles. Here, we explore this connection for quantum entanglement and for general quantum resource theories. For any quantum resource theory which contains resource-free pure states, we show that there does not exist a finite set of resource monotones which completely determines all state transformations. We discuss how these limitations can be surpassed, if discontinuous or infinite sets of monotones are considered, or by using quantum catalysis. We also discuss the structure of theories which are described by a single resource monotone and show equivalence with totally ordered resource theories. These are theories where a free transformation exists for any pair of quantum states. We show that totally ordered theories allow for free transformations between all pure states. For single-qubit systems, we provide a full characterization of state transformations for any totally ordered resource theory.