Reviewerly/exHarmony huggingface dataset:
One-shot Visual Imitation via Attributed Waypoints and Demonstration Augmentation

In this paper, we analyze the behavior of existing techniques and design new solutions for the problem of one-shot visual imitation. In this setting, an agent must solve a novel instance of a novel task given just a single visual demonstration. Our analysis reveals that current methods fall short because of three errors: the DAgger problem arising from purely offline training, last centimeter errors in interacting with objects, and mis-fitting to the task context rather than to the actual task. This motivates the design of our modular approach where we a) separate out task inference (what to do) from task execution (how to do it), and b) develop data augmentation and generation techniques to mitigate mis-fitting. The former allows us to leverage hand-crafted motor primitives for task execution which side-steps the DAgger problem and last centimeter errors, while the latter gets the model to focus on the task rather than the task context. Our model gets 100% and 48% success rates on two recent benchmarks, improving upon the current state-of-the-art by absolute 90% and 20% respectively.
Sharing pattern submodels for prediction with missing values

Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. When variables are missing in recurring patterns, fitting separate pattern submodels have been proposed as a solution. However, fitting models independently does not make efficient use of all available data. Conversely, fitting a single shared model to the full data set relies on imputation which often leads to biased results when missingness depends on unobserved factors. We propose an alternative approach, called sharing pattern submodels, which i) makes predictions that are robust to missing values at test time, ii) maintains or improves the predictive power of pattern submodels, and iii) has a short description, enabling improved interpretability. Parameter sharing is enforced through sparsity-inducing regularization which we prove leads to consistent estimation. Finally, we give conditions for when a sharing model is optimal, even when both missingness and the target outcome depend on unobserved variables. Classification and regression experiments on synthetic and real-world data sets demonstrate that our models achieve a favorable tradeoff between pattern specialization and information sharing.
Analisis Keterampilan Menyimak Melalui Pendekatan Saintifik Pada Anak Kelas IV Di SD Negeri 6 Rejang Lebong

In listening skills through a scientific approach, students must understand and develop the four aspects of listening skills, namely, speaking, reading, listening, and writing which must be mastered after we undergo the process of learning to listen. So a scientific approach is needed to analyze the level of students' ability in listening skills through a scientific approach. The purpose of this study was to determine the ability of students in listening skills through a scientific approach for class IV SD Negeri 6 Rejang Lebong, the research method used was descriptive qualitative, research data sources: interviews, LKPD documentation. Research data analysis analysis techniques: data reduction, data presentation, conclusion drawing. The results of this study are: Students who fall into the very good category. totaling 15 students or as much as 57.70%, students belonging to the good category totaled students or as many as 19.2%, there were students who were included in the sufficient category totaling 2 or as much as 7.6%, there were 3 students who entered the less category or as much as 15.40%, and none of the students included in the failed category.
Deep Generative Models on 3D Representations: A Survey

Generative models aim to learn the distribution of observed data by generating new instances. With the advent of neural networks, deep generative models, including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models (DMs), have progressed remarkably in synthesizing 2D images. Recently, researchers started to shift focus from 2D to 3D space, considering that 3D data is more closely aligned with our physical world and holds immense practical potential. However, unlike 2D images, which possess an inherent and efficient representation (\textit{i.e.}, a pixel grid), representing 3D data poses significantly greater challenges. Ideally, a robust 3D representation should be capable of accurately modeling complex shapes and appearances while being highly efficient in handling high-resolution data with high processing speeds and low memory requirements. Regrettably, existing 3D representations, such as point clouds, meshes, and neural fields, often fail to satisfy all of these requirements simultaneously. In this survey, we thoroughly review the ongoing developments of 3D generative models, including methods that employ 2D and 3D supervision. Our analysis centers on generative models, with a particular focus on the representations utilized in this context. We believe our survey will help the community to track the field's evolution and to spark innovative ideas to propel progress towards solving this challenging task.
Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection

Training models dedicated to semantic segmentation requires a large amount of pixel-wise annotated data. Due to their costly nature, these annotations might not be available for the task at hand. To alleviate this problem, unsupervised domain adaptation approaches aim at aligning the feature distributions between the labeled source and the unlabeled target data. While these strategies lead to noticeable improvements, their effectiveness remains limited. To guide the domain adaptation task more efficiently, previous works attempted to include human interactions in this process under the form of sparse single-pixel annotations in the target data. In this work, we propose a new domain adaptation framework for semantic segmentation with annotated points via active selection. First, we conduct an unsupervised domain adaptation of the model; from this adaptation, we use an entropy-based uncertainty measurement for target points selection. Finally, to minimize the domain gap, we propose a domain adaptation framework utilizing these target points annotated by human annotators. Experimental results on benchmark datasets show the effectiveness of our methods against existing unsupervised domain adaptation approaches. The propose pipeline is generic and can be included as an extra module to existing domain adaptation strategies.
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors

Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit. By focusing on shape alignment rather than semantic cues, we can achieve across-category generalization. In this paper, we introduce a novel task, pairwise 3D geometric shape mating, and propose Neural Shape Mating (NSM) to tackle this problem. Given the point clouds of two object parts of an unknown category, NSM learns to reason about the fit of the two parts and predict a pair of 3D poses that tightly mate them together. We couple the training of NSM with an implicit shape reconstruction task to make NSM more robust to imperfect point cloud observations. To train NSM, we present a self-supervised data collection pipeline that generates pairwise shape mating data with ground truth by randomly cutting an object mesh into two parts, resulting in a dataset that consists of 200K shape mating pairs from numerous object meshes with diverse cut types. We train NSM on the collected dataset and compare it with several point cloud registration methods and one part assembly baseline. Extensive experimental results and ablation studies under various settings demonstrate the effectiveness of the proposed algorithm. Additional material is available at: https://neural-shape-mating.github.io/
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus

This paper considers offline multi-agent reinforcement learning. We propose the strategy-wise concentration principle which directly builds a confidence interval for the joint strategy, in contrast to the point-wise concentration principle that builds a confidence interval for each point in the joint action space. For two-player zero-sum Markov games, by exploiting the convexity of the strategy-wise bonus, we propose a computationally efficient algorithm whose sample complexity enjoys a better dependency on the number of actions than the prior methods based on the point-wise bonus. Furthermore, for offline multi-agent general-sum Markov games, based on the strategy-wise bonus and a novel surrogate function, we give the first algorithm whose sample complexity only scales $\sum_{i=1}^mA_i$ where $A_i$ is the action size of the $i$-th player and $m$ is the number of players. In sharp contrast, the sample complexity of methods based on the point-wise bonus would scale with the size of the joint action space $\Pi_{i=1}^m A_i$ due to the curse of multiagents. Lastly, all of our algorithms can naturally take a pre-specified strategy class $\Pi$ as input and output a strategy that is close to the best strategy in $\Pi$. In this setting, the sample complexity only scales with $\log |\Pi|$ instead of $\sum_{i=1}^mA_i$.
Fair Comparison between Efficient Attentions

Transformers have been successfully used in various fields and are becoming the standard tools in computer vision. However, self-attention, a core component of transformers, has a quadratic complexity problem, which limits the use of transformers in various vision tasks that require dense prediction. Many studies aiming at solving this problem have been reported proposed. However, no comparative study of these methods using the same scale has been reported due to different model configurations, training schemes, and new methods. In our paper, we validate these efficient attention models on the ImageNet1K classification task by changing only the attention operation and examining which efficient attention is better.
Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale

The advent of the internet, followed shortly by the social media made it ubiquitous in consuming and sharing information between anyone with access to it. The evolution in the consumption of media driven by this change, led to the emergence of images as means to express oneself, convey information and convince others efficiently. With computer vision algorithms progressing radically over the last decade, it is become easier and easier to study at scale the role of images in the flow of information online. While the research questions and overall pipelines differ radically, almost all start with a crucial first step - evaluation of global perceptual similarity between different images. That initial step is crucial for overall pipeline performance and processes most images. A number of algorithms are available and currently used to perform it, but so far no comprehensive review was available to guide the choice of researchers as to the choice of an algorithm best suited to their question, assumptions and computational resources. With this paper we aim to fill this gap, showing that classical computer vision methods are not necessarily the best approach, whereas a pair of relatively little used methods - Dhash perceptual hash and SimCLR v2 ResNets achieve excellent performance, scale well and are computationally efficient.
Sistem Informasi Rekam Medis Klinik Hewan (Studi Kasus: Klinik Hewan Drh. I Dewa Made Anom)

The Veterinary Clinic Medical Record Information System (Case Study: Veterinary Clinic Drh. I Dewa Made Anom) is a responsive website-based information system with PHP programming language, MariaDB database and Bootstrap framework that aims to provide information and access to medical records at the Veterinary Clinic of Drh. I Dewa Made Anom. In this system there are two users, namely admin as website manager and patient data and animal owners as website users. This system goes through several stages and uses the waterfall method designed with ERD, DFD, context diagrams and conceptual databases. The test method used is the blackbox method with the result that this system runs according to the design and user needs. Testing the target users of the system was carried out by distributing a questionnaire to thirty-two respondents with the result that the system was categorized as "Very Good" with a score of 96%.
Generative Anomaly Detection for Time Series Datasets

Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings.
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning

Recent studies on backdoor attacks in model training have shown that polluting a small portion of training data is sufficient to produce incorrect manipulated predictions on poisoned test-time data while maintaining high clean accuracy in downstream tasks. The stealthiness of backdoor attacks has imposed tremendous defense challenges in today's machine learning paradigm. In this paper, we explore the potential of self-training via additional unlabeled data for mitigating backdoor attacks. We begin by making a pilot study to show that vanilla self-training is not effective in backdoor mitigation. Spurred by that, we propose to defend the backdoor attacks by leveraging strong but proper data augmentations in the self-training pseudo-labeling stage. We find that the new self-training regime help in defending against backdoor attacks to a great extent. Its effectiveness is demonstrated through experiments for different backdoor triggers on CIFAR-10 and a combination of CIFAR-10 with an additional unlabeled 500K TinyImages dataset. Finally, we explore the direction of combining self-supervised representation learning with self-training for further improvement in backdoor defense.
Recycled detection of genuine multiparty entanglement of unlimitedly stretched array of parties and arbitrarily long series of sequential observers

We consider a scenario where spatially separated observers share a genuinely multiparty entangled quantum state with each local observer possessing a single qubit. A particular qubit is acted upon by sequential and independent observers. We study the recycled detection of genuine multipartite entanglement of multiqubit states by any one of the sequential observers and the rest of the spatially separated parties. We show that for it is possible to sequentially detect genuine multiparty entanglement, arbitrarily many times for an arbitrarily large number of parties. Modified genuine multiparty entanglement witness operators for unsharp measurements by sequential observers are deduced, which are then employed to show that an arbitrary number of observers can sequentially detect genuine multisite entanglement of Greenberger-Horne-Zeilinger and cluster states of an arbitrary number of parties. Extensions to multiparty generalized Greenberger-Horne-Zeilinger states and a class of mixed states are also shown to be achievable.
An incremental input-to-state stability condition for a generic class of recurrent neural networks

This paper proposes a novel sufficient condition for the incremental input-to-state stability of a generic class of recurrent neural networks (RNNs). The established condition is compared with others available in the literature, showing to be less conservative. Moreover, it can be applied for the design of incremental input-to-state stable RNN-based control systems, resulting in a linear matrix inequality constraint for some specific RNN architectures. The formulation of nonlinear observers for the considered system class, as well as the design of control schemes with explicit integral action, are also investigated. The theoretical results are validated through simulation on a referenced nonlinear system.
Universal approach to sending-or-not-sending twin field quantum key distribution

We present the method of decoy-state analysis after bit-flip error correction and using confidential observed numbers. Taking this tool we then construct a universal approach to sending-or-not-sending (SNS) protocol of twin-field quantum key distribution. In this improved protocol, the code bits are not limited to heralded events in time windows participated by pulses of intensity $\mu_z$ and vacuum. All kinds of heralded events can be used for code bits to distill the final keys. The number of intensities (3 or 4) and the kinds of heralded events for code bits are automatically chosen by the key rate optimization itself. Numerical simulation shows that the key rate rises drastically in typical settings, up to 80\% improvement compared with the prior results. Also, larger intensity value can be used for decoy pulses. This makes the protocol more robust in practical experiments.
Asymptotics for Outlier Hypothesis Testing

We revisit the outlier hypothesis testing framework of Li \emph{et al.} (TIT 2014) and derive fundamental limits for the optimal test. In outlier hypothesis testing, one is given multiple observed sequences, where most sequences are generated i.i.d. from a nominal distribution. The task is to discern the set of outlying sequences that are generated according to anomalous distributions. The nominal and anomalous distributions are \emph{unknown}. We consider the case of multiple outliers where the number of outliers is unknown and each outlier can follow a different anomalous distribution. Under this setting, we study the tradeoff among the probabilities of misclassification error, false alarm and false reject. Specifically, we propose a threshold-based test that ensures exponential decay of misclassification error and false alarm probabilities. We study two constraints on the false reject probability, with one constraint being that it is a non-vanishing constant and the other being that it has an exponential decay rate. For both cases, we characterize bounds on the false reject probability, as a function of the threshold, for each tuple of nominal and anomalous distributions. Finally, we demonstrate the asymptotic optimality of our test under the generalized Neyman-Pearson criterion.
A survey on security and policy aspects of blockchain technology

This survey talks about the problem of security and privacy in the blockchain ecosystem which is currently a hot issue in the blockchain community. The survey intended to study this problem by considering different types of attacks in the blockchain network with respect to algorithms presented. After a preliminary literature review it seems that some focus has been given to study the first use case while, to the best of my knowledge, the second use case requires more attention when blockchain is applied to study it. The research is also interested in exploring the link between these two use cases to study the overall data ownership preserving accountable system which will be a novel contribution of this work. However, due to the subsequent government mandated secrecy around the implementation of d ata encryption standard ( DES), and the distrust of the academic community because of this, a movement was spawned that put a premium on individual privacy and decentralized control. This movement brought together the top minds in encryption and spawned the technology we know of as blockchain today. This survey explores the genesis of encryption, its early adoption, and the government meddling which eventually spawned a movement which gave birth to the ideas behind blockchain.
Balanced Incomplete Block Design (BIBD) as traceable codes

TraceabilityCodes are CombinatorialObjects introduced by Chor, Fiat and Naor in 1994[7] to be used in traitor tracing to protect Digital Content. Frameproof Codes were given byBoneh and Shaw in 1994 to prevent privacy and gave the idea of c-secure codes withέ-error. Traceable Codes is a strong form of Frameproof Codes. Balanced Incomplete Block Design Codes in form of frameproof codes arealready available in literature [4]. Here in this paper we will define how Balanced Incomplete Block Designs can be used as Traceable Codes and in view of our paper[ ] we have relaxed these conditionsfor being Balanced Incomplete Block Designs to be traceable Code.
Crytocurrency price prediction using machine learning

Cryptocurrencies are a digital way of money in which all transactions are held electronically. It is a soft currency which doesn’t exist in the form of hard notes physically. Here, we are emphasizing the difference of fiat currency which is decentralized that without any third-party intervention all virtual currency users can get the services. However, getting services of these cryptocurrencies impacts on international relations and trade, due to its high price volatility. There are several virtual currencies such as bit-coin, ripple, ethereum, ethereum classic, lite coin, etc. In our study, we especially focused on a popular cryptocurrency, i.e., bitcoin. From many types of virtual currencies, bitcoin has a great acceptance by different bodies such as investors, researchers, traders, and policy-makers. To the best of our knowledge, our target is to implement the efficient deep learning-based prediction models. Specifically long short-term memory (LSTM) and gated recurrent unit (GRU) to handle the price volatility of bitcoin and to obtain high accuracy. Our study involves comparing these two time series deep learning techniques and proved the efficacy in forecasting the price of bitcoin.