Reviewerly/exHarmony huggingface dataset:
Meta-active Learning in Probabilistically-Safe Optimization

Learning to control a safety-critical system with latent dynamics (e.g. for deep brain stimulation) requires taking calculated risks to gain information as efficiently as possible. To address this problem, we present a probabilistically-safe, meta-active learning approach to efficiently learn system dynamics and optimal configurations. We cast this problem as meta-learning an acquisition function, which is represented by a Long-Short Term Memory Network (LSTM) encoding sampling history. This acquisition function is meta-learned offline to learn high quality sampling strategies. We employ a mixed-integer linear program as our policy with the final, linearized layers of our LSTM acquisition function directly encoded into the objective to trade off expected information gain (e.g., improvement in the accuracy of the model of system dynamics) with the likelihood of safe control. We set a new state-of-the-art in active learning for control of a high-dimensional system with altered dynamics (i.e., a damaged aircraft), achieving a 46% increase in information gain and a 20% speedup in computation time over baselines. Furthermore, we demonstrate our system's ability to learn the optimal parameter settings for deep brain stimulation in a rat's brain while avoiding unwanted side effects (i.e., triggering seizures), outperforming prior state-of-the-art approaches with a 58% increase in information gain. Additionally, our algorithm achieves a 97% likelihood of terminating in a safe state while losing only 15% of information gain.
Exploring the Potential of Low-bit Training of Convolutional Neural Networks

In this work, we propose a low-bit training framework for convolutional neural networks, which is built around a novel multi-level scaling (MLS) tensor format. Our framework focuses on reducing the energy consumption of convolution operations by quantizing all the convolution operands to low bit-width format. Specifically, we propose the MLS tensor format, in which the element-wise bit-width can be largely reduced. Then, we describe the dynamic quantization and the low-bit tensor convolution arithmetic to leverage the MLS tensor format efficiently. Experiments show that our framework achieves a superior trade-off between the accuracy and the bit-width than previous low-bit training frameworks. For training a variety of models on CIFAR-10, using 1-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within $1\%$. And on larger datasets like ImageNet, using 4-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within $1\%$. Through the energy consumption simulation of the computing units, we can estimate that training a variety of models with our framework could achieve $8.3\sim10.2\times$ and $1.9\sim2.3\times$ higher energy efficiency than training with full-precision and 8-bit floating-point arithmetic, respectively.
Towards Class-Oriented Poisoning Attacks Against Neural Networks

Poisoning attacks on machine learning systems compromise the model performance by deliberately injecting malicious samples in the training dataset to influence the training process. Prior works focus on either availability attacks (i.e., lowering the overall model accuracy) or integrity attacks (i.e., enabling specific instance-based backdoor). In this paper, we advance the adversarial objectives of the availability attacks to a per-class basis, which we refer to as class-oriented poisoning attacks. We demonstrate that the proposed attack is capable of forcing the corrupted model to predict in two specific ways: (i) classify unseen new images to a targeted "supplanter" class, and (ii) misclassify images from a "victim" class while maintaining the classification accuracy on other non-victim classes. To maximize the adversarial effect as well as reduce the computational complexity of poisoned data generation, we propose a gradient-based framework that crafts poisoning images with carefully manipulated feature information for each scenario. Using newly defined metrics at the class level, we demonstrate the effectiveness of the proposed class-oriented poisoning attacks on various models (e.g., LeNet-5, Vgg-9, and ResNet-50) over a wide range of datasets (e.g., MNIST, CIFAR-10, and ImageNet-ILSVRC2012) in an end-to-end training setting.
An On-Line POMDP Solver for Continuous Observation Spaces

Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for problems with continuous observation spaces remains challenging. Most on-line solvers rely on discretising the observation space or artificially limiting the number of observations that are considered during planning to compute tractable policies. In this paper we propose a new on-line POMDP solver, called Lazy Belief Extraction for Continuous POMDPs (LABECOP), that combines methods from Monte-Carlo-Tree-Search and particle filtering to construct a policy reprentation which doesn't require discretised observation spaces and avoids limiting the number of observations considered during planning. Experiments on three different problems involving continuous observation spaces indicate that LABECOP performs similar or better than state-of-the-art POMDP solvers.
Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives

In this paper, we revisit the problem of private stochastic convex optimization. We propose an algorithm based on noisy mirror descent, which achieves optimal rates both in terms of statistical complexity and number of queries to a first-order stochastic oracle in the regime when the privacy parameter is inversely proportional to the number of samples.
Learnable Graph-regularization for Matrix Decomposition

Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields including computer vision, text mining, bioinformatics and many others. They allow for embedding high-dimensional data into low-dimensional spaces, which mitigates the effects of noise and uncovers latent relations. In order to make the learned representations inherit the structures in the original data, graph-regularization terms are often added to the loss function. However, the prior graph construction often fails to reflect the true network connectivity and the intrinsic relationships. In addition, many graph-regularized methods fail to take the dual spaces into account. Probabilistic models are often used to model the distribution of the representations, but most of previous methods often assume that the hidden variables are independent and identically distributed for simplicity. To this end, we propose a learnable graph-regularization model for matrix decomposition (LGMD), which builds a bridge between graph-regularized methods and probabilistic matrix decomposition models. LGMD learns two graphical structures (i.e., two precision matrices) in real-time in an iterative manner via sparse precision matrix estimation and is more robust to noise and missing entries. Extensive numerical results and comparison with competing methods demonstrate its effectiveness.
Towards Egocentric Person Re-identification and Social Pattern Analysis

Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation.
Analisis Debit Banjir Rancangan dengan Data Curah Hujan di DAS Waru Kabupaten Banggai

The meteorological data is important to design and planning the water resources system and irrigation. The complete and long of data is needed to make sure the accuracy of the water avaibility. Unfortunately, the avaibility of the rainfall data is not always complete. So it’s needed the methods to fill the lacking rainfall data. The location of the research is Waru watershed at the Banggai Regency, Central Sulawesi. The cause of repeated flooding in 2019 and 2020 was the disability of the watershed to accommodate higher rainfall intensity. The goals of the research was to compare the design flood discharge values obtained, using the lacking of rainfall data and rainfall data that had been filled in. In filling in the missing rainfall data, 2 methods are used, that are the Algebraic Average method and the Inverse Square Distance method. Then the calculation continues with the design flood discharge, using the Rational method. The final result is the design flood discharge values from the two rainfall data with different conditions will be compared to see the difference between the both of the methods. The value of the difference in design flood discharge obtained from this comparison is 0.0552 m³/s for a 2 year return period, 37.0313 m³/s for a 5 year return period, 79.3389 m³/s for a 10 year return period, 163.1544 m³/s for a 25 year return period, 255.1732 m³/s for the 50-year return period and 380.8701 m³/s for the 100-year return period.
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning

Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC). However, to achieve this, contemporary NMNs need strong supervision in executing the query as a specialized program over reasoning modules and fail to generalize to more open-ended settings without such supervision. Hence we propose Weakly-Supervised Neuro-Symbolic Module Network (WNSMN) trained with answers as the sole supervision for numerical reasoning based MRC. It learns to execute a noisy heuristic program obtained from the dependency parsing of the query, as discrete actions over both neural and symbolic reasoning modules and trains it end-to-end in a reinforcement learning framework with discrete reward from answer matching. On the numerical-answer subset of DROP, WNSMN out-performs NMN by 32% and the reasoning-free language model GenBERT by 8% in exact match accuracy when trained under comparable weak supervised settings. This showcases the effectiveness and generalizability of modular networks that can handle explicit discrete reasoning over noisy programs in an end-to-end manner.
Stability of a Multilingual Sentiment Analysis based on Word-to-Word Translations

People’s sentiments are known to have a large impact on changes in stock prices, products sales, and trends. Since web users generally state their opinion in various languages, it is important to develop a method of multilingual sentiment analysis for web texts. In this study, we design a multilingual sentiment analysis method based on word-to-word translation. The method classifies sentences by using a sentiment dictionary in a native language. The method consists of three phases: morphological analysis of a sentence, sentiment extraction of each word with the senti-ment dictionary, and sentiment extraction of a sentence based on words sentiments. We conduct sentiment classification experiments for sentences in English, German, French, and Spanish. In the experiments, we compare our method with three previous methods by the evaluation metrics “Accuracy,” “Precision,” “Recall,” and “F1-score.” The experimental results show that our method has an advantage on the stability for variations of languages.
Tıbbi Verilerde Heinz Ortalamasına Dayalı Yeni Sentetik Veriler Üreterek Veri Kümesini Dengeleme

Advances in science and technology have caused data sizes to increase at a great rate. Thus, unbalanced data has arisen. A dataset is unbalanced if the classes are not nearly equally represented. In this case, classifying the data causes performance values to decrease because the classification algorithms are developed on the assumption that the datasets are balanced. As the accuracy of the classification favors the majority class, the minority class is often misclassified. The majority of datasets, especially those used in the medical field, have an unbalanced distribution. To balance this distribution, several studies have been performed recently. These studies are undersampling and oversampling processes. In this study, distance and mean based resampling method is used to produce synthetic samples using minority class. For the resampling process, the closest neighbors for all data points belonging to the minority class were determined by using the Euclidean distance. Based on these neighbors and using the Heinz Mean, the desired number of new synthetic samples were formed between each sample to obtain balance. The Random Forest (RF) and Support Vector Machine (SVM) algorithms are used to classify the raw and balanced datasets, and the results were compared. Additionally, the other well known methods (Random Over Sampling-ROS, Random Under Sampling-RUS, and Synthetic Minority Oversampling TEchnique-SMOTE) are compared with the proposed method. It was shown that the balanced dataset using the proposed resampling method increases classification efficiency as compared to the raw dataset and other methods. Accuracy measurements of RF are 0.751 and 0.799 and, accuracy measurements of SVM are 0.762 and 0.781 for raw data and resampled data respectively. Likewise, there are improvements in the other metrics such as Precision, Recall, and F1 Score.
DETECTION OF SIGNAL TO NOISE RATIO FROM IMAGE CONTOUR -A MATLAB APPLICATION

Image is a combination of thousand number of pixels which is an integral unit of image , every pixel has three primary colors Red,Green,Blue. Contour means border detection of black and white image. Here in this paper our main aim is to reduce noise from black and white image after converting it from color image. Several noises are there, but we have used only few of them as an application to show you the procedure. Here we have added noise then applied filtering method to generate the contour and compared it with original contour of a normal noise free image. In each case original image has been displayed in result section.]
A Variational Hierarchical Model for Neural Cross-Lingual Summarization

The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e.g., English) to a summary in another one (e.g., Chinese). Essentially, the CLS task is the combination of machine translation (MT) and monolingual summarization (MS), and thus there exists the hierarchical relationship between MT\&MS and CLS. Existing studies on CLS mainly focus on utilizing pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. However, it is very challenging for the model to directly conduct CLS as it requires both the abilities to translate and summarize. To address this issue, we propose a hierarchical model for the CLS task, based on the conditional variational auto-encoder. The hierarchical model contains two kinds of latent variables at the local and global levels, respectively. At the local level, there are two latent variables, one for translation and the other for summarization. As for the global level, there is another latent variable for cross-lingual summarization conditioned on the two local-level variables. Experiments on two language directions (English-Chinese) verify the effectiveness and superiority of the proposed approach. In addition, we show that our model is able to generate better cross-lingual summaries than comparison models in the few-shot setting.
Quantum Speedup and Limitations on Matroid Property Problems

This paper initiates the study of quantum algorithms for matroid property problems. It is shown that quadratic quantum speedup is possible for the calculation problem of finding the girth or the number of circuits (bases, flats, hyperplanes) of a matroid, and for the decision problem of deciding whether a matroid is uniform or Eulerian, by giving a uniform lower bound $\Omega(\sqrt{\binom{n}{\lfloor n/2\rfloor}})$ on the query complexity for all these problems. On the other hand, for the uniform matroid decision problem, an asymptotically optimal quantum algorithm is proposed which achieves the lower bound, and for the girth problem, an almost optimal quantum algorithm is given with query complexity $O(\log n\sqrt{\binom{n}{\lfloor n/2\rfloor}})$. In addition, for the paving matroid decision problem, a lower bound $\Omega(\sqrt{\binom{n}{\lfloor n/2\rfloor}/n})$ on the query complexity is obtained, and an $O(\sqrt{\binom{n}{\lfloor n/2\rfloor}})$ quantum algorithm is presented.
Learning to Adapt to Unseen Abnormal Activities under Weak Supervision

We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available. Our work is motivated by the fact that existing methods suffer from poor generalization to diverse unseen examples. We claim that an anomaly detector equipped with a meta-learning scheme alleviates the limitation by leading the model to an initialization point for better optimization. We evaluate the performance of our framework on two challenging datasets, UCF-Crime and ShanghaiTech. The experimental results demonstrate that our algorithm boosts the capability to localize unseen abnormal events in a weakly supervised setting. Besides the technical contributions, we perform the annotation of missing labels in the UCF-Crime dataset and make our task evaluated effectively.
S2RMs: Spatially Structured Recurrent Modules

Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution. While methods that harness spatial and temporal structures find broad application, recent work has demonstrated the potential of models that leverage sparse and modular structure using an ensemble of sparingly interacting modules. In this work, we take a step towards dynamic models that are capable of simultaneously exploiting both modular and spatiotemporal structures. We accomplish this by abstracting the modeled dynamical system as a collection of autonomous but sparsely interacting sub-systems. The sub-systems interact according to a topology that is learned, but also informed by the spatial structure of the underlying real-world system. This results in a class of models that are well suited for modeling the dynamics of systems that only offer local views into their state, along with corresponding spatial locations of those views. On the tasks of video prediction from cropped frames and multi-agent world modeling from partial observations in the challenging Starcraft2 domain, we find our models to be more robust to the number of available views and better capable of generalization to novel tasks without additional training, even when compared against strong baselines that perform equally well or better on the training distribution.
Analisis Kesulitan Pemahaman Menulis Cerita Dongeng Pada Pembelajaran E-Learning Bahasa Indonesia Menggunakan Media Zoom Meeting Di Kelas II SD IT Muhammadiyah Palangkaraya

The purpose of this research was to determine the difficulty of understanding student fairy-tale stories during long distance learning using zoom meetings. The method used to do this research is a qualitative method with descriptive type. As for data collection techniques is observation, and interviews. Analysis of research data uses stages consisting of data reduction, display, data collection and verification. Based on the research discovery, it can be concluded, namely (1) there are some students who do not understand the material to write fairy tales. so students are still not able to write fairy tales well (2) found several factors The causes include the lack of student learning interest due to the learning process online, the teacher in delivering the material is still not optimal due to the constraints of signals, school facilities and infrastructure that are inadequate, especially for long-distance learning activities (e-learning).
Perancangan Media Pembelajaran Perangkat Keras Komputer (Hardware) Berbasis Augmented Reality

The existence of technology, especially smartphones that are growing, must be addressed wisely. The benefits of this technology must continue to be explored for a better human life. The phenomenon of the high number of smartphone users is a challenge and opportunity in education. The challenge is abuse for negative things. Besides being a challenge, smartphones also bring great opportunities to develop technology that is useful in education. One of the benefits that can be taken from the existence of this technology is to use it as an effective, creative and educational learning medium. So that educational application media can continue to be developed, one of which is augmented reality (AR) technology. So, we need a media that is able to provide an understanding of computer hardware (hardware) and can increase student interest in learning. Based on these problems, the researcher aims to create and determine the feasibility of augmented reality-based learning media using Unity on ICT subjects as the subject of computer hardware (hardware). This research uses research and development methods. The results showed that the ICT learning media was the subject of augmented reality-based computer hardware and went through 5 stages of development, namely: (1) Analysis, (2) Design, (3) Development, (4) Implementation, (5) Evaluation. The average feasibility level of the score based on the assessment is: (1) Media expert 4.11 (Very Eligible), (2) Material expert 4.53 (Very Eligible), (3) Testing on students 4,472 (Very Eligible). The results of the feasibility assessment concluded that the ICT learning media on the subject of computer hardware (hardware) based on augmented reality "Very Appropriate" is used as an ICT learning medium on the subject of computer hardware (hardware).
The Analogy of Magnet Rules to Practice Main Verbs and Helping Verbs in English Sentence Predicate

The rule of magnet is the basic condition of every magnet. It has north and south poles. When the north pole of one magnet is closed to the south pole of another magnet, they will pull each other. On the opposite, when north pole is closed to north pole, or south pole is closed to south pole, they will repel each other. These basic rules of magnet can be applied to help students to understand developing English sentence predicate, especially when the sentences are the result of translation from Bahasa Indonesia. The translation result is sometimes not accepted grammatically. It seems that our students still making mistakes when it comes to use helping verbs and main verbs in their sentences. This study is to propose the option of using magnet rules as a teaching trick to help students making sentences in English grammatically by using analogy of magnet rules for main verbs and helping verbs in English.