Reviewerly/exHarmony huggingface dataset:
DNA: Proximal Policy Optimization with a Dual Network Architecture

This paper explores the problem of simultaneously learning a value function and policy in deep actor-critic reinforcement learning models. We find that the common practice of learning these functions jointly is sub-optimal, due to an order-of-magnitude difference in noise levels between these two tasks. Instead, we show that learning these tasks independently, but with a constrained distillation phase, significantly improves performance. Furthermore, we find that the policy gradient noise levels can be decreased by using a lower \textit{variance} return estimate. Whereas, the value learning noise level decreases with a lower \textit{bias} estimate. Together these insights inform an extension to Proximal Policy Optimization we call \textit{Dual Network Architecture} (DNA), which significantly outperforms its predecessor. DNA also exceeds the performance of the popular Rainbow DQN algorithm on four of the five environments tested, even under more difficult stochastic control settings.
Supervised learning of random quantum circuits via scalable neural networks

Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are represented via an appropriately designed one-hot encoding of the constituent gates. The prediction accuracy for previously unseen circuits is analyzed, also making comparisons with small-scale quantum computers available from the free IBM Quantum program. The CNNs often outperform the quantum devices, depending on the circuit depth, on the network depth, and on the training set size. Notably, our CNNs are designed to be scalable. This allows us exploiting transfer learning and performing extrapolations to circuits larger than those included in the training set. These CNNs also demonstrate remarkable resilience against noise, namely, they remain accurate even when trained on (simulated) expectation values averaged over very few measurements.
Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo

In this paper, we launch a new Universal Dependencies treebank for an endangered language from Amazonia: Kakataibo, a Panoan language spoken in Peru. We first discuss the collaborative methodology implemented, which proved effective to create a treebank in the context of a Computational Linguistic course for undergraduates. Then, we describe the general details of the treebank and the language-specific considerations implemented for the proposed annotation. We finally conduct some experiments on part-of-speech tagging and syntactic dependency parsing. We focus on monolingual and transfer learning settings, where we study the impact of a Shipibo-Konibo treebank, another Panoan language resource.
Visualizing and Understanding Contrastive Learning

Contrastive learning has revolutionized the field of computer vision, learning rich representations from unlabeled data, which generalize well to diverse vision tasks. Consequently, it has become increasingly important to explain these approaches and understand their inner workings mechanisms. Given that contrastive models are trained with interdependent and interacting inputs and aim to learn invariance through data augmentation, the existing methods for explaining single-image systems (e.g., image classification models) are inadequate as they fail to account for these factors and typically assume independent inputs. Additionally, there is a lack of evaluation metrics designed to assess pairs of explanations, and no analytical studies have been conducted to investigate the effectiveness of different techniques used to explaining contrastive learning. In this work, we design visual explanation methods that contribute towards understanding similarity learning tasks from pairs of images. We further adapt existing metrics, used to evaluate visual explanations of image classification systems, to suit pairs of explanations and evaluate our proposed methods with these metrics. Finally, we present a thorough analysis of visual explainability methods for contrastive learning, establish their correlation with downstream tasks and demonstrate the potential of our approaches to investigate their merits and drawbacks.
Consistent circuits for indefinite causal order

Over the past decade, a number of quantum processes have been proposed which are logically consistent, yet feature a cyclic causal structure. However, there is no general formal method to construct a process with an exotic causal structure in a way that ensures, and makes clear why, it is consistent. Here we provide such a method, given by an extended circuit formalism. This only requires directed graphs endowed with Boolean matrices, which encode basic constraints on operations. Our framework (a) defines a set of elementary rules for checking the validity of any such graph, (b) provides a way of constructing consistent processes as a circuit from valid graphs, and (c) yields an intuitive interpretation of the causal relations within a process and an explanation of why they do not lead to inconsistencies. We display how several standard examples of exotic processes, including ones that violate causal inequalities, are among the class of processes that can be generated in this way; we conjecture that this class in fact includes all unitarily extendible processes.
Ideal User Group: A New Perspective on Item Representation for Recommender Systems

Abstract Item representation is a significant building block of recommender systems (RSs). Most existing methods are based on the inherent features of the item, such as its type, shape, and color. In this paper, we propose a new perspective to characterize items by building ideal user groups (IUGs). We define the IUG as the most suitable user group for an item, such as athletes are the IUG of fitness equipment. The construction of an IUG begins with separating user groups according to every single demographic attribute (gender, occupation, etc.), such as male or female groups by gender attribute. Subsequently, we calculate the average rating of each group based on historical records. The next step is to compare the average ratings of different attribute values for the same attribute (e.g., male and female in gender) of the demographics. Meanwhile , we employ Bayesian averaging to address the issue of certain user groups having fewer members but stronger preferences to achieve a fair comparison. Finally, we combine the attribute values corresponding to the group with the maximum average rating of each attribute to obtain the demographics of an IUG, such as female students in their 20s. To validate the effectiveness of the IUG, we propose an IUG-based neural Collaborative Filtering model. Experiments are taken on two real-world datasets in comparison with nine state-of-the-art methods. Results show that the application of the IUG is effective in terms of two metrics.
Intelligent Blockchain-based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things networks, by enabling task offloading with security enhancement based on blockchain mining. Yet the existing approaches for these enabling technologies are isolated, providing only tailored solutions for specific services and scenarios. To fill this gap, we propose a novel cooperative task offloading and blockchain mining (TOBM) scheme for a blockchain-based MEC system, where each edge device not only handles computation tasks but also deals with block mining for improving system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. To accommodate the highly dynamic environment and high-dimensional system state space, we apply a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. Experimental results demonstrate the superior performance of the proposed TOBM scheme in terms of enhanced system reward, improved offloading utility with lower blockchain mining latency, and better system utility, compared to the existing cooperative and non-cooperative schemes. The paper concludes with key technical challenges and possible directions for future blockchain-based MEC research.
Single-Qubit Reaped Quantum State Tomography

Quantum state tomography is the experimental procedure of determining an unknown state. It is not only essential for the verification of resources and processors of quantum information but is also important in its own right with regard to the foundation of quantum mechanics. Standard methods have been elusive for large systems because of the enormous number of observables to be measured and the exponential complexity of data post-processing. Here, we propose a new scheme of quantum state tomography that requires the measurement of only three observables (acting jointly on the system and pointer) regardless of the size of the system. The system is coupled to a "pointer" of single qubit, and the wavefunction of the system is "reaped" onto the pointer upon the measurement of the system. Subsequently, standard two-state tomography on the pointer and classical post-processing are used to reconstruct the quantum state of the system. We also developed an efficient and scalable iterative maximum likelihood algorithm to estimate states from statistically incomplete data.
GAN2X: Non-Lambertian Inverse Rendering of Image GANs

2D images are observations of the 3D physical world depicted with the geometry, material, and illumination components. Recovering these underlying intrinsic components from 2D images, also known as inverse rendering, usually requires a supervised setting with paired images collected from multiple viewpoints and lighting conditions, which is resource-demanding. In this work, we present GAN2X, a new method for unsupervised inverse rendering that only uses unpaired images for training. Unlike previous Shape-from-GAN approaches that mainly focus on 3D shapes, we take the first attempt to also recover non-Lambertian material properties by exploiting the pseudo paired data generated by a GAN. To achieve precise inverse rendering, we devise a specularity-aware neural surface representation that continuously models the geometry and material properties. A shading-based refinement technique is adopted to further distill information in the target image and recover more fine details. Experiments demonstrate that GAN2X can accurately decompose 2D images to 3D shape, albedo, and specular properties for different object categories, and achieves the state-of-the-art performance for unsupervised single-view 3D face reconstruction. We also show its applications in downstream tasks including real image editing and lifting 2D GANs to decomposed 3D GANs.
Japanese-Chinese Machine Translation of Japanese Determiners Based on Templates

The machine translation of Japanese sentences with determiners, like “shika...nai”, “tyoutto...dakedeha”, “tada...dake” and so on, are more special and regular on sentences structure. The research collects and classifies the Japanese sentences which contain the determiners. The classification is carried out by according to the characteristics of Japanese sentences and translation habit of Chinese sentences. Through further abstraction and simplification, translation templates are extracted by gathering grammar rules information, studying syntax and analysis the collocation mode of sentences. Those determiners express confirmed meaning, and the corresponding translation Chinese sentences have the same characteristic. By analyzing the sentence characteristics with determiners and formalizing the sentences structure, the translation templates are abstracted. By investigating the structure characteristic of original sentences with translation templates, the similarity algorithm was defined. The threshold value of the similarity calculation was obtained by preliminary experiments, and the experiments of Japanese-Chinese translation are carried out by a small corpus. The experimental results for several kinds of Japanese sentences with determiners show the translation accuracy rate is 68.6%, template coverage rate reach 83.3%. At last, through the analysis for the translation errors, following conclusion is drawn: the results of morphological analysis are erroneous, because the error of word segmentation the part of speech tagging also are erroneous, result in the grammar structure cannot match with templates; the original sentences are long and especially complex sentences; the templates are too complicated; the similarity calculation method needs to discuss further, and so on.
Misspelling Semantics In Thai

User-generated content is full of misspellings. Rather than being just random noise, we hypothesise that many misspellings contain hidden semantics that can be leveraged for language understanding tasks. This paper presents a fine-grained annotated corpus of misspelling in Thai, together with an analysis of misspelling intention and its possible semantics to get a better understanding of the misspelling patterns observed in the corpus. In addition, we introduce two approaches to incorporate the semantics of misspelling: Misspelling Average Embedding (MAE) and Misspelling Semantic Tokens (MST). Experiments on a sentiment analysis task confirm our overall hypothesis: additional semantics from misspelling can boost the micro F1 score up to 0.4-2%, while blindly normalising misspelling is harmful and suboptimal.
Stop Overcomplicating Selective Classification: Use Max-Logit

We tackle the problem of Selective Classification where the goal is to achieve the best performance on the desired coverages of the dataset. Recent state-of-the-art selective methods come with architectural changes either via introducing a separate selection head or an extra abstention logit. In this paper, we present surprising results for Selective Classification by confirming that the superior performance of state-of-the-art methods is owed to training a more generalizable classifier; however, their selection mechanism is suboptimal. We argue that the selection mechanism should be rooted in the objective function instead of a separately calculated score. Accordingly, in this paper, we motivate an alternative selection strategy that is based on the cross entropy loss for the classification settings, namely, max of the logits. Our proposed selection strategy achieves better results by a significant margin, consistently, across all coverages and all datasets, without any additional computation. Finally, inspired by our superior selection mechanism, we propose to further regularize the objective function with entropy-minimization. Our proposed max-logit selection with the modified loss function achieves new state-of-the-art results for Selective Classification.
Pengembangan Model Pembelajran PJOK Tematik Terintegratif Tema Diriku (Subtema 3: Aku Merawat Tubuhku) Untuk Peserta Didik Kelas 1 SD Di Kecamatan Buleleng

This study aims, 1) to develop an integrated thematic-based PJOK learning module with the theme myself sub-theme 3: I take care of my body for 1st-grade elementary school students in Buleleng District. 2) knowing the results of the expert validity test on the integrated thematic PJOK game module, sub-theme 3: I take care of my body for 1st-grade elementary school students in Buleleng District. This type of research is research on the development of R&amp;D methods using the Borg and Gall design. The data collection instrument used was a questionnaire. The data analysis technique used is descriptive quantitative and qualitative. The results of the study show that 1) the design of the learning module uses three stages, namely: research and information gathering, planning, and developing a preliminary form of the product. These three stages resulted in an integrated thematic PJOK game module, sub-theme 3: I take care of my body for 1st-grade elementary school students. 2) validation of teaching materials in terms of content aspects with a total percentage of 91.76% being in the very good category. The media aspect with a percentage of 90% is in the very good category. The aspect of field practitioners with a percentage of 85% is in a good category. Based on the results of the study, it can be concluded that the development of an integrated thematic PJOK learning module with the theme of myself sub-theme 3: I take care of my body for 1st-grade elementary school students in Buleleng District is feasible to use.
News classification using light gradient boosted machine algorithm

News classification is a complex issue as people are easily convinced of misleading information and lack control over the spread of fake news. However, we ca n break the problem of spreading fake news with artificial intelligence (AI), which has developed rapidly. This study proposes a news classification model using a light gradient boosted machine (LightGBM) algorithm. The model is analyzed using two feature extraction techniques, count vectorizer and Tfidf vectorize r and compared with a deep learning model using long - short term memory (LSTM). The experimental evaluation showed that all LightGBM models outperform LSTM. The best model is the count vectorizer Li ghtGBM, which achieves an accuracy value of 0.9933 and an area under curve (AUC) score of 0.9999.
Perancangan dan Pengembangan Video Katalog Film Animasi Indonesia

Many Indonesian animations on YouTube are not directly proportional to the National Gross Domestic Product for the Creative Economy sub-sector data. The film, animation, and video sub-sector still considered low and has not contributed much and, caused by many factors, such as not being correctly recorded by animation creators. There are many efforts to record the content creators, such as government involvement, the Associations (AINAKI), stakeholders, and academics. Seeing these problems, we conducted a data through YouTube scraping to be able to find content creator channels of animations. Then we present it in the form of a video catalog of Indonesian animated films. This final video is delivered in 5 minutes, with production stages including pre-production, production, and post-production.
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control

Learned models and policies can generalize effectively when evaluated within the distribution of the training data, but can produce unpredictable and erroneous outputs on out-of-distribution inputs. In order to avoid distribution shift when deploying learning-based control algorithms, we seek a mechanism to constrain the agent to states and actions that resemble those that it was trained on. In control theory, Lyapunov stability and control-invariant sets allow us to make guarantees about controllers that stabilize the system around specific states, while in machine learning, density models allow us to estimate the training data distribution. Can we combine these two concepts, producing learning-based control algorithms that constrain the system to in-distribution states using only in-distribution actions? In this work, we propose to do this by combining concepts from Lyapunov stability and density estimation, introducing Lyapunov density models: a generalization of control Lyapunov functions and density models that provides guarantees on an agent's ability to stay in-distribution over its entire trajectory.
Computation and memory optimized spectral domain convolutional neural network for throughput and energy-efficient inference

Conventional convolutional neural networks (CNNs) present a high computational workload and memory access cost (CMC). Spectral domain CNNs (SpCNNs) offer a computationally efficient approach to compute CNN training and inference. This paper investigates CMC of SpCNNs and its contributing components analytically and then proposes a methodology to optimize CMC, under three strategies, to enhance inference performance. In this methodology, output feature map (OFM) size, OFM depth or both are progressively reduced under an accuracy constraint to compute performance-optimized CNN inference. Before conducting training or testing, it can provide designers guidelines and preliminary insights regarding techniques for optimum performance, least degradation in accuracy and a balanced performance-accuracy trade-off. This methodology was evaluated on MNIST and Fashion MNIST datasets using LeNet-5 and AlexNet architectures. When compared to state-of-the-art SpCNN models, LeNet-5 achieves up to 4.2× (batch inference) and 4.1× (single-image inference) higher throughputs and 10.5× (batch inference) and 4.2× (single-image inference) greater energy efficiency at a maximum loss of 3% in test accuracy. When compared to the baseline model used in this study, AlexNet delivers 11.6× (batch inference) and 5× (single-image inference) increased throughput and 25× (batch inference) and 8.8× (single-image inference) more energy-efficient inference with just 4.4% reduction in accuracy.
What Functions Can Graph Neural Networks Generate?

In this paper, we fully answer the above question through a key algebraic condition on graph functions, called \textit{permutation compatibility}, that relates permutations of weights and features of the graph to functional constraints. We prove that: (i) a GNN, as a graph function, is necessarily permutation compatible; (ii) conversely, any permutation compatible function, when restricted on input graphs with distinct node features, can be generated by a GNN; (iii) for arbitrary node features (not necessarily distinct), a simple feature augmentation scheme suffices to generate a permutation compatible function by a GNN; (iv) permutation compatibility can be verified by checking only quadratically many functional constraints, rather than an exhaustive search over all the permutations; (v) GNNs can generate \textit{any} graph function once we augment the node features with node identities, thus going beyond graph isomorphism and permutation compatibility. The above characterizations pave the path to formally study the intricate connection between GNNs and other algorithmic procedures on graphs. For instance, our characterization implies that many natural graph problems, such as min-cut value, max-flow value, max-clique size, and shortest path can be generated by a GNN using a simple feature augmentation. In contrast, the celebrated Weisfeiler-Lehman graph-isomorphism test fails whenever a permutation compatible function with identical features cannot be generated by a GNN. At the heart of our analysis lies a novel representation theorem that identifies basis functions for GNNs. This enables us to translate the properties of the target graph function into properties of the GNN's aggregation function.
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars

This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL methods are popular approaches for autonomous vehicle (AV) agents. However, most existing methods are sample- and time-inefficient and lack a natural communication channel with the human expert. In this paper, how new human drivers learn from human coaches motivates us to study new ways of human-in-the-loop learning and a more natural and approachable training interface for the agents. We propose incorporating natural language voice instructions (NLI) in model-based deep reinforcement learning to train self-driving cars. We evaluate the proposed method together with a few state-of-the-art DRL methods in the CARLA simulator. The results show that NLI can help ease the training process and significantly boost the agents' learning speed.