Reviewerly/exHarmony huggingface dataset:
Generalized Variable Selection Algorithms for Gaussian Process Models by LASSO-like Penalty

With the rapid development of modern technology, massive amounts of data with complex pattern are generated. Gaussian process models that can easily fit the non-linearity in data become more and more popular nowadays. It is often the case that in some data only a few features are important or active. However, unlike classical linear models, it is challenging to identify active variables in Gaussian process models. One of the most commonly used methods for variable selection in Gaussian process models is automatic relevance determination, which is known to be open-ended. There is no rule of thumb to determine the threshold for dropping features, which makes the variable selection in Gaussian process models ambiguous. In this work, we propose two variable selection algorithms for Gaussian process models, which use the artificial nuisance columns as baseline for identifying the active features. Moreover, the proposed methods work for both regression and classification problems. The algorithms are demonstrated using comprehensive simulation experiments and an application to multi-subject electroencephalography data that studies alcoholic levels of experimental subjects.
Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning

Exploration in sparse-reward reinforcement learning is difficult due to the requirement of long, coordinated sequences of actions in order to achieve any reward. Moreover, in continuous action spaces there are an infinite number of possible actions, which only increases the difficulty of exploration. One class of methods designed to address these issues forms temporally extended actions, often called skills, from interaction data collected in the same domain, and optimizes a policy on top of this new action space. Typically such methods require a lengthy pretraining phase, especially in continuous action spaces, in order to form the skills before reinforcement learning can begin. Given prior evidence that the full range of the continuous action space is not required in such tasks, we propose a novel approach to skill-generation with two components. First we discretize the action space through clustering, and second we leverage a tokenization technique borrowed from natural language processing to generate temporally extended actions. Such a method outperforms baselines for skill-generation in several challenging sparse-reward domains, and requires orders-of-magnitude less computation in skill-generation and online rollouts.
Knowledge Representation on Pharmacotherapy Using Knowledge Ontology

Pharmacotherapy is fundamental knowledge within the medical domain, specifically concerning the utilization of drugs or drug therapy for treating diseases or health disorders. Representation of knowledge from pharmacotherapy is needed to deepen scientific knowledge in that domain. Ontology is an approach in the domain of computer science that can be used to represent knowledge. Ontology has been widely used to represent knowledge in the medical domain. However, the modeling that is done is only focused on the triplet relationship concept between two class or two entities. Even though the knowledge that comes from Pharmacotherapy material is very complex. Therefore, this research used a new ontology model called knowledge ontology to represent information from pharmacotherapy materials. This study uses a dataset from e-book about pharmacotherapy. In this experiment, a total of 200 sentences were represented in knowledge ontology. The results of the representation are then evaluated by Pharmacotherapy’s experts. Based on the experiments that have been carried out, it is known that the proposed ontology model can be used to represent information in the Pharmacotherapy with a testing accuracy of 95%. The results of this study can serve as a valuable resource for students in the field of pharmaceuticals, enabling them to gain a deeper understanding of pharmacotherapy.
Enhanced Modulation Degree of Leakage Wave Induced by IEMI via Nonlinear Circuit Elements

This paper proposes a method to improve the modulation degree of the leakage wave in the threat of electromagnetic information leakage induced by intentional electromagnetic interference. Since the leakage wave from the target device occurs with the same frequency as the continuous wave (CW), the modulation degree of the leakage wave is decreased. This paper shows that the propagation of two CWs to nonlinear circuit elements inside the device causes frequency conversion, generating the leakage wave with a frequency different from the injected CWs. This method prevents degradation in the accuracy of information extracted caused by decreased modulation degree of the leakage wave.
Lothar Voigtländer und sein kompositorisches Werk

The renowned German composer Lothar Voigtländer (* 1943 in Leisnig/Saxony) gained his first musical experience in the Dresden Kreuzchor and studied at the Academy of Music in Leipzig and at the Academy of Arts of the GDR in Berlin, where he has lived since 1973. He worked as a professor of composition at the Dresden Academy of Music. His oeuvre includes scenic, orchestral, choral, and solo music. In France he was awarded for his electro-acoustic experiments. He founded the "Long Night of Electronic Sounds" in Berlin. This volume brings together notations by Lothar Voigtländer on applied design principles as well as texts by other authors on the significance of this composer for contemporary music. <bold>With contributions by</bold> Lothar Voigtländer | Matthias Herrmann | Ekkehard Klemm | Georg-Friedrich Kühn | Ulrike Liedtke | Albrecht von Massow | Anke Stötzner | Yvonne Zitzmann.
Cuboid‐Net: A multi‐branch convolutional neural network for joint space‐time video super resolution

Abstract The demand for high‐resolution videos has been consistently rising across various domains, propelled by continuous advancements in societal. Nonetheless, limitations in imaging and economic factors often result in obtaining low‐resolution images. The currently available space‐time video super‐resolution methods often fail to fully exploit the information existing within the spatio‐temporal domain. To address this problem, the issue is tackled by conceptualizing the input low‐resolution video as a cuboid structure. An innovative methodology called “Cuboid‐Net”, which incorporates a multi‐branch convolutional neural network, is introduced. Cuboid‐Net is designed to collectively enhance the spatial and temporal resolutions of videos, enabling the extraction of rich and meaningful information across both spatial and temporal dimensions. Specifically, the input video is taken as a cuboid to generate different directional slices as input for different branches of the network. The proposed network contains four modules, that is, a multi‐branch‐based hybrid feature extraction module, a multi‐branch‐based reconstruction module, a first‐stage quality enhancement module, and a second‐stage cross frame quality enhancement module for interpolated frames only. Experimental results demonstrate that the proposed method is not only effective for spatial and temporal super‐resolution of video but also for spatial and angular super‐resolution of light field.
BELAJAR MENGURUTKAN ANGKA SECARA VISUAL BERBASIS GAME BERBENTUK GAME PUZZLE: SEBAGAI PENDAHULUAN BELAJAR LOGIKA PEMROGRAMAN

As a learning topic in programming, sorting numbers can be visualized as a puzzle game to increase the engagement of learners in learning the logic of programming through a game. In programming, there are several ways in solving a problem that can be represented by several choices to play a game. In this paper, a sorting game as a puzzle game is chosen in the form of a grid with four rows to four columns. There are four ways to play sorting sixteen arbitrary numbers by exchanging a pair of numbers chosen through the swap as a game feature. The number of steps of each choice can be compared, to which one is the most efficient. So as a game-based learning, sorting numbers as a puzzle game can be used to enhance the engagement of learners to learn introductory programming.
Klasifikasi Judul Berita Clickbait menggunakan RNN-LSTM

Amid technological developments, online news of various life topics is shared across various platforms. Many media often take advantage of this opportunity by uploading their news on several online platforms to increase the traffic and rankings they upload to make much profit. However, many online media attract readers' attention by exaggerating the headlines or news headlines they upload. That way, the news title is often not by the content of the news. This phenomenon is commonly known as "clickbait" among the public. The media usually do this to increase traffic, rankings, and finances. Therefore, this study classified the news with clickbait and non-clickbait titles using the RNN-LSTM architecture. In this study, the classification of clickbait news titles uses the RNN-LSTM architecture. The classification results obtained calculation accuracy of 79% on training data and 77% accuracy on test data.
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks

Gradient inversion attacks are an ubiquitous threat in federated learning as they exploit gradient leakage to reconstruct supposedly private training data. Recent work has proposed to prevent gradient leakage without loss of model utility by incorporating a PRivacy EnhanCing mODulE (PRECODE) based on variational modeling. Without further analysis, it was shown that PRECODE successfully protects against gradient inversion attacks. In this paper, we make multiple contributions. First, we investigate the effect of PRECODE on gradient inversion attacks to reveal its underlying working principle. We show that variational modeling introduces stochasticity into the gradients of PRECODE and the subsequent layers in a neural network. The stochastic gradients of these layers prevent iterative gradient inversion attacks from converging. Second, we formulate an attack that disables the privacy preserving effect of PRECODE by purposefully omitting stochastic gradients during attack optimization. To preserve the privacy preserving effect of PRECODE, our analysis reveals that variational modeling must be placed early in the network. However, early placement of PRECODE is typically not feasible due to reduced model utility and the exploding number of additional model parameters. Therefore, as a third contribution, we propose a novel privacy module -- the Convolutional Variational Bottleneck (CVB) -- that can be placed early in a neural network without suffering from these drawbacks. We conduct an extensive empirical study on three seminal model architectures and six image classification datasets. We find that all architectures are susceptible to gradient leakage attacks, which can be prevented by our proposed CVB. Compared to PRECODE, we show that our novel privacy module requires fewer trainable parameters, and thus computational and communication costs, to effectively preserve privacy.
On the Efficacy of Multi-scale Data Samplers for Vision Applications

Multi-scale resolution training has seen an increased adoption across multiple vision tasks, including classification and detection. Training with smaller resolutions enables faster training at the expense of a drop in accuracy. Conversely, training with larger resolutions has been shown to improve performance, but memory constraints often make this infeasible. In this paper, we empirically study the properties of multi-scale training procedures. We focus on variable batch size multi-scale data samplers that randomly sample an input resolution at each training iteration and dynamically adjust their batch size according to the resolution. Such samplers have been shown to improve model accuracy beyond standard training with a fixed batch size and resolution, though it is not clear why this is the case. We explore the properties of these data samplers by performing extensive experiments on ResNet-101 and validate our conclusions across multiple architectures, tasks, and datasets. We show that multi-scale samplers behave as implicit data regularizers and accelerate training speed. Compared to models trained with single-scale samplers, we show that models trained with multi-scale samplers retain or improve accuracy, while being better-calibrated and more robust to scaling and data distribution shifts. We additionally extend a multi-scale variable batch sampler with a simple curriculum that progressively grows resolutions throughout training, allowing for a compute reduction of more than 30%. We show that the benefits of multi-scale training extend to detection and instance segmentation tasks, where we observe a 37% reduction in training FLOPs along with a 3-4% mAP increase on MS-COCO using a Mask R-CNN model.
Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space, in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities.We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation.To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach.We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git.
On Interactive Coding Schemes with Adaptive Termination

In interactive coding, Alice and Bob wish to compute some function $f$ of their individual private inputs $x$ and $y$. They do this by engaging in an interactive protocol to jointly compute $f(x,y)$. The goal is to do this in an error-resilient way, such that even given some fraction of adversarial corruptions to the protocol, both parties still learn $f(x,y)$. Typically, the error resilient protocols constructed by interactive coding schemes are \emph{non-adaptive}, that is, the length of the protocol as well as the speaker in each round is fixed beforehand. The maximal error resilience obtainable by non-adaptive schemes is now well understood. In order to circumvent known barriers and achieve higher error resilience, the work of Agrawal, Gelles, and Sahai (ISIT 2016) introduced to interactive coding the notion of \emph{adaptive} schemes, where the length of the protocol or the speaker order are no longer necessarily fixed. In this paper, we study the power of \emph{adaptive termination} in the context of the error resilience of interactive coding schemes. In other words, what is the power of schemes where Alice and Bob are allowed to disengage from the protocol early? We study this question in two contexts, both for the task of \emph{message exchange}, where the goal is to learn the other party's input.
Compact: Approximating Complex Activation Functions for Secure Computation

Secure multi-party computation (MPC) techniques can be used to provide data privacy when users query deep neural network (DNN) models hosted on a public cloud. State-of-the-art MPC techniques can be directly leveraged for DNN models that use simple activation functions such as ReLU. However, these techniques are ineffective and/or inefficient for the complex and highly non-linear activation functions used in cutting-edge DNN models. We present Compact, which produces piece-wise polynomial approximations of complex AFs to enable their efficient use with state-of-the-art MPC techniques. Compact neither requires nor imposes any restriction on model training and results in near-identical model accuracy. To achieve this, we design Compact with input density awareness and use an application-specific simulated annealing type optimization to generate computationally more efficient approximations of complex AFs. We extensively evaluate Compact on four different machine-learning tasks with DNN architectures that use popular complex AFs silu, gelu, and mish. Our experimental results show that Compact incurs negligible accuracy loss while being 2x-5x computationally more efficient than state-of-the-art approaches for DNN models with large number of hidden layers. Our work accelerates easy adoption of MPC techniques to provide user data privacy even when the queried DNN models consist of a number of hidden layers and trained over complex AFs.
Embedding structure matters: Comparing methods to adapt multilingual vocabularies to new languages

Pre-trained multilingual language models underpin a large portion of modern NLP tools outside of English. A strong baseline for specializing these models for specific languages is Language-Adaptive Pre-Training (LAPT). However, retaining a large cross-lingual vocabulary and embedding matrix comes at considerable excess computational cost during adaptation. In this study, we propose several simple techniques to replace a cross-lingual vocabulary with a compact, language-specific one. Namely, we address strategies for re-initializing the token embedding matrix after vocabulary specialization. We then provide a systematic experimental comparison of our techniques, in addition to the recently-proposed Focus method. We demonstrate that: 1) Embedding-replacement techniques in the monolingual transfer literature are inadequate for adapting multilingual models. 2) Replacing cross-lingual vocabularies with smaller specialized ones provides an efficient method to improve performance in low-resource languages. 3) Simple embedding re-initialization techniques based on script-wise sub-distributions rival techniques such as Focus, which rely on similarity scores obtained from an auxiliary model.
A, B, C of Three-Qubit Entanglement: Three Vectors to Control It All

In this paper we are focusing on entanglement control problem in a three-qubit system. We demonstrate that vector representation of entanglement, associated with SO(6) representation of SU(4) two-qubit group, can be used to solve various control problems analytically including (i) the transformation between a W-type states and GHZ state, and (ii) manipulating bipartite concurrences and three-tangle under a restricted access to only two qubits, and (iii) designing USp(4)-type quaternionic operations and quantum states.
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering

Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years. However, most existing methods overlook the inherent relational information among the non-independent and non-identically distributed nodes in a graph. Due to the lack of exploration of relational attributes, the semantic information of the graph-structured data fails to be fully exploited which leads to poor clustering performance. In this paper, we propose a novel self-supervised deep graph clustering method named Relational Redundancy-Free Graph Clustering (R$^2$FGC) to tackle the problem. It extracts the attribute- and structure-level relational information from both global and local views based on an autoencoder and a graph autoencoder. To obtain effective representations of the semantic information, we preserve the consistent relation among augmented nodes, whereas the redundant relation is further reduced for learning discriminative embeddings. In addition, a simple yet valid strategy is utilized to alleviate the over-smoothing issue. Extensive experiments are performed on widely used benchmark datasets to validate the superiority of our R$^2$FGC over state-of-the-art baselines. Our codes are available at https://github.com/yisiyu95/R2FGC.
Fuzzy cylinders of finite length

We introduce non-commutative algebras, which can be associated with the function algebra of functions on a finite or half-finite cylinder. The algebras, which depend on a deformation parameter, are crossed product algebras of a partial group action of $\mathbb{Z}$ on an interval of the real line $\mathbb{R}$. Discrete representations of the algebras can be seen as matrix regularizations of the respective function algebra on the finite or half-finite cylinder and therefore as fuzzy space. In a second part of the article, we review crossed product algebras based on partial group actions and derive the results needed in the first part.
SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

Accurate medical image segmentation especially for echocardiographic images with unmissable noise requires elaborate network design. Compared with manual design, Neural Architecture Search (NAS) realizes better segmentation results due to larger search space and automatic optimization, but most of the existing methods are weak in layer-wise feature aggregation and adopt a ``strong encoder, weak decoder" structure, insufficient to handle global relationships and local details. To resolve these issues, we propose a novel semi-supervised hybrid NAS network for accurate medical image segmentation termed SSHNN. In SSHNN, we creatively use convolution operation in layer-wise feature fusion instead of normalized scalars to avoid losing details, making NAS a stronger encoder. Moreover, Transformers are introduced for the compensation of global context and U-shaped decoder is designed to efficiently connect global context with local features. Specifically, we implement a semi-supervised algorithm Mean-Teacher to overcome the limited volume problem of labeled medical image dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate that SSHNN outperforms state-of-the-art approaches and realizes accurate segmentation. Code will be made publicly available.
Continual Robot Learning using Self-Supervised Task Inference

Endowing robots with the human ability to learn a growing set of skills over the course of a lifetime as opposed to mastering single tasks is an open problem in robot learning. While multi-task learning approaches have been proposed to address this problem, they pay little attention to task inference. In order to continually learn new tasks, the robot first needs to infer the task at hand without requiring predefined task representations. In this paper, we propose a self-supervised task inference approach. Our approach learns action and intention embeddings from self-organization of the observed movement and effect parts of unlabeled demonstrations and a higher-level behavior embedding from self-organization of the joint action-intention embeddings. We construct a behavior-matching self-supervised learning objective to train a novel Task Inference Network (TINet) to map an unlabeled demonstration to its nearest behavior embedding, which we use as the task representation. A multi-task policy is built on top of the TINet and trained with reinforcement learning to optimize performance over tasks. We evaluate our approach in the fixed-set and continual multi-task learning settings with a humanoid robot and compare it to different multi-task learning baselines. The results show that our approach outperforms the other baselines, with the difference being more pronounced in the challenging continual learning setting, and can infer tasks from incomplete demonstrations. Our approach is also shown to generalize to unseen tasks based on a single demonstration in one-shot task generalization experiments.