Reviewerly/exHarmony huggingface dataset:
Analisa Hasil Prediksi Metode Least Square menggunakan Korelasi dan MAPE pada Toko PS

The sale of cat feed in Toko PS is the main concern of Shop Owners because of the very high enthusiasm of cat owners to buy Cat Feed. However, due to business competition, the sale of Cat Feed is not decisive, so the owner has difficulty in procuring goods. This research predicts the selling of food products in Toko PS using the Least Square method. The results were analyzed using the standby method and MAPE to see the prediction alert level. The obtained values ​​obtained were low, namely between 0 - 0.2 and the MAPE values ​​of 41.8% and 56.3% were categorized as fair and inaccurate. So that the Least Square method is not suitable for predicting to predict fluctuating reference data and with a short period, namely the date or day in the case of Toko PS.
COVID-19 MISINFORMATION: HOW DOES SCIENTIFIC INFORMATION LITERACY PREVENT IT?

This article aims to examine the importance of information literacy and scientific literacy skills to prevent exposure to misinformation in the midst of the Covid-19 pandemic. Believing in misinformation encourages behaviour that is detrimental to individuals and groups due to anxiety, fear, uncertainty, and a lack of critical thinking skills. This study uses a qualitative approach with a systematic literature review (SLR) method. Through the SLR method, this article uses various sources of empirical research by collecting data and information to analyze elements in information literacy and scientific literacy that can identify misinformation. Information literacy is considered to be more useful in preventing belief in misinformation compared to the concepts of digital literacy, media literacy, and news literacy. Information literacy skills with information verification, and supported by scientific literacy with intellectual virtue, can recognize misinformation about COVID-19 so that it can prevent individuals from believing in misinformation that can result in errors of action. Scientific information literacy needs practical intervention to the public, one of which is through the role of educational institutions.
Video Class Agnostic Segmentation Benchmark for Autonomous Driving

Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical robotics applications, especially autonomous driving, it is important to segment all objects, including those unknown at training time. We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects. Video class agnostic segmentation can be formulated as an open-set or a motion segmentation problem. We discuss both formulations and provide datasets and benchmark different baseline approaches for both tracks. In the motion-segmentation track we benchmark real-time joint panoptic and motion instance segmentation, and evaluate the effect of ego-flow suppression. In the open-set segmentation track we evaluate baseline methods that combine appearance, and geometry to learn prototypes per semantic class. We then compare it to a model that uses an auxiliary contrastive loss to improve the discrimination between known and unknown objects. Datasets and models are publicly released at https://msiam.github.io/vca/.
TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models

In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA 1 , an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs).We use carefully selected prompt words to guide GPT-2 to generate candidate sentences, from which we select 47K for error annotation.Crowdsourced workers manually check each of these sentences and detect 12k erroneous sentences.We create an error taxonomy to cover 24 types of errors occurring in these erroneous sentences according to the nature of errors with respect to linguistics and knowledge (e.g., common sense).For each erroneous span in PLM-generated sentences, we also detect another span that is closely associated with it.Each error is hence manually labeled with comprehensive annotations, including the span of the error, the associated span, minimal correction to the error, the type of the error, and rationale behind the error.Apart from the fully annotated dataset, we also present a detailed description of the data collection procedure, statistics and analysis of the dataset.This is the first dataset with comprehensive annotations for PLM-generated texts, which facilitates the diagnostic evaluation of PLM-based text generation.Furthermore, we use TGEA as a benchmark dataset and propose a series of automatic diagnosis tasks, including error detection, error type classification, associated span detection, error rationale generation, to further promote future study on the automatic error detection and correction on texts generated by pretrained language models.
Masked Proxy Loss for Text-Independent Speaker Verification

Open-set speaker recognition can be regarded as a metric learning problem, which is to maximize inter-class variance and minimize intra-class variance. Supervised metric learning can be categorized into entity-based learning and proxy-based learning. Most of the existing metric learning objectives like Contrastive, Triplet, Prototypical, GE2E, etc all belong to the former division, the performance of which is either highly dependent on sample mining strategy or restricted by insufficient label information in the mini-batch. Proxy-based losses mitigate both shortcomings, however, fine-grained connections among entities are either not or indirectly leveraged. This paper proposes a Masked Proxy (MP) loss which directly incorporates both proxy-based relationships and pair-based relationships. We further propose Multinomial Masked Proxy (MMP) loss to leverage the hardness of speaker pairs. These methods have been applied to evaluate on VoxCeleb test set and reach state-of-the-art Equal Error Rate(EER).
Pattern Recognition for Data Retrieval using Artificial Neural Network

Data retrieval is an important aspect of data management. In this paper, we design an ANN to recognize the learned patterns. We use a three-layer feed-forward network for the training of patterns (bitmap data). We implement two kinds of recognition: forced recognition and custom-specific recognition. The ANN model developed recognizes the pattern even if there is variation in the applied test patterns from the learned/trained patterns. In particular, we discuss the fault tolerance offered by neural networks. The characteristic of fault tolerance depends upon the type of distribution taken from random numbers. The conventional network is based on the concept of memorization whereas the neural network is based on the concept of generalization.
Unsupervised Visual Representation Learning by Tracking Patches in Video

Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to learn the visual representations. Modelled on the Catch game played by the children, we design a Catch-the-Patch (CtP) game for a 3D-CNN model to learn visual representations that would help with video-related tasks. In the proposed pretraining framework, we cut an image patch from a given video and let it scale and move according to a pre-set trajectory. The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame. We discover that using multiple image patches simultaneously brings clear benefits. We further increase the difficulty of the game by randomly making patches invisible. Extensive experiments on mainstream benchmarks demonstrate the superior performance of CtP against other video pretraining methods. In addition, CtP-pretrained features are less sensitive to domain gaps than those trained by a supervised action recognition task. When both trained on Kinetics-400, we are pleasantly surprised to find that CtP-pretrained representation achieves much higher action classification accuracy than its fully supervised counterpart on Something-Something dataset. Code is available online: github.com/microsoft/CtP.
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints

Convolutional neural networks (CNNs) have achieved state-of-the-art performance on various tasks in computer vision. However, recent studies demonstrate that these models are vulnerable to carefully crafted adversarial samples and suffer from a significant performance drop when predicting them. Many methods have been proposed to improve adversarial robustness (e.g., adversarial training and new loss functions to learn adversarially robust feature representations). Here we offer a unique insight into the predictive behavior of CNNs that they tend to misclassify adversarial samples into the most probable false classes. This inspires us to propose a new Probabilistically Compact (PC) loss with logit constraints which can be used as a drop-in replacement for cross-entropy (CE) loss to improve CNN's adversarial robustness. Specifically, PC loss enlarges the probability gaps between true class and false classes meanwhile the logit constraints prevent the gaps from being melted by a small perturbation. We extensively compare our method with the state-of-the-art using large scale datasets under both white-box and black-box attacks to demonstrate its effectiveness. The source codes are available from the following url: https://github.com/xinli0928/PC-LC.
Glow-WaveGAN: Learning Speech Representations from GAN-based Variational Auto-Encoder For High Fidelity Flow-based Speech Synthesis

Current two-stage TTS framework typically integrates an acoustic model with a vocoder -- the acoustic model predicts a low resolution intermediate representation such as Mel-spectrum while the vocoder generates waveform from the intermediate representation. Although the intermediate representation is served as a bridge, there still exists critical mismatch between the acoustic model and the vocoder as they are commonly separately learned and work on different distributions of representation, leading to inevitable artifacts in the synthesized speech. In this work, different from using pre-designed intermediate representation in most previous studies, we propose to use VAE combining with GAN to learn a latent representation directly from speech and then utilize a flow-based acoustic model to model the distribution of the latent representation from text. In this way, the mismatch problem is migrated as the two stages work on the same distribution. Results demonstrate that the flow-based acoustic model can exactly model the distribution of our learned speech representation and the proposed TTS framework, namely Glow-WaveGAN, can produce high fidelity speech outperforming the state-of-the-art GAN-based model.
Crystalline stenonian time features from earth and beyond

The writings of Niels Stensen (Steno) on mineral growth and modification in his Prodromus, together with his work on time and process in other solids, are here synthesized as five classes of time features defined by changes in the visible continuity of either or both chemistry and orientation. This organization highlights Steno’s implicit recognition of the fractal, scale-invariant nature of natural time features with regard to space, time, and material. The effectiveness and validity of this Stenonian geochronology framework is demonstrated down to atom scale with modern case studies of the U-Pb geochronology mineral zircon from samples originating from the Earth, Moon, and Mars and spanning most of solar system history. Recently discovered nano-scale features, here termed chronostructures, were intimated by Steno in his corpuscular view of mineral behaviour. The remarkable advances in the Prodromus are seen here as a resulting from the intersection of Steno’s highly attuned approach to visual perception, his adoption of Stoic (Senecan) ethics early in his career to guide his natural philosophy, and the influence of the Galilean scientific environment of Florence. It is shown that, by virtue of its scale-invariant and intensive properties, Stenonian geochronology continues to serve as an independent and critical check on the accuracy of absolute geochronology measurements of geologic time given the latter’s inherent dependence on sample volume and the assumption of the chemically closed system. In this way Steno’s scientific legacy continues to help propel human understanding of how we see our place in time.
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks

Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance functions of networks with activation functions used in modern networks remain unknown. Furthermore, while the kernels of deep networks can be computed iteratively, theoretical understanding of deep kernels is lacking, particularly with respect to fixed-point dynamics. Firstly, we derive the covariance functions of multi-layer perceptrons (MLPs) with exponential linear units (ELU) and Gaussian error linear units (GELU) and evaluate the performance of the limiting Gaussian processes on some benchmarks. Secondly, and more generally, we analyse the fixed-point dynamics of iterated kernels corresponding to a broad range of activation functions. We find that unlike some previously studied neural network kernels, these new kernels exhibit non-trivial fixed-point dynamics which are mirrored in finite-width neural networks. The fixed point behaviour present in some networks explains a mechanism for implicit regularisation in overparameterised deep models. Our results relate to both the static iid parameter conjugate kernel and the dynamic neural tangent kernel constructions
Few-Shot Upsampling for Protest Size Detection

We propose a new task and dataset for a common problem in social science research: "upsampling" coarse document labels to finegrained labels or spans.We pose the problem in a question answering format, with the answers providing the fine-grained labels.We provide a benchmark dataset and baselines on a socially impactful task: identifying the exact crowd size at protests and demonstrations in the United States given only orderof-magnitude information about protest attendance, a very small sample of fine-grained examples, and English-language news text.We evaluate several baseline models, including zero-shot results from rule-based and questionanswering models, few-shot models fine-tuned on a small set of documents, and weakly supervised models using a larger set of coarselylabeled documents.We find that our rulebased model initially outperforms a zero-shot pre-trained transformer language model but that further fine-tuning on a very small subset of 25 examples substantially improves out-ofsample performance.We also demonstrate a method for fine-tuning the transformer span on only the coarse labels that performs similarly to our rule-based approach.This work will contribute to social scientists' ability to generate data to understand the causes and successes of collective action.
On the Connections between Counterfactual Explanations and Adversarial Examples.

Counterfactual explanations and adversarial examples have emerged as critical research areas for addressing the explainability and robustness goals of machine learning (ML). While counterfactual explanations were developed with the goal of providing recourse to individuals adversely impacted by algorithmic decisions, adversarial examples were designed to expose the vulnerabilities of ML models. While prior research has hinted at the commonalities between these frameworks, there has been little to no work on systematically exploring the connections between the literature on counterfactual explanations and adversarial examples. In this work, we make one of the first attempts at formalizing the connections between counterfactual explanations and adversarial examples. More specifically, we theoretically analyze salient counterfactual explanation and adversarial example generation methods, and highlight the conditions under which they behave similarly. Our analysis demonstrates that several popular counterfactual explanation and adversarial example generation methods such as the ones proposed by Wachter et. al. and Carlini and Wagner (with mean squared error loss), and C-CHVAE and natural adversarial examples by Zhao et. al. are equivalent. We also bound the distance between counterfactual explanations and adversarial examples generated by Wachter et. al. and DeepFool methods for linear models. Finally, we empirically validate our theoretical findings using extensive experimentation with synthetic and real world datasets.
Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System

With the continuous expansion and innovations in modern software development, the rate at which defects are present in software is directly proportional to how sophisticated and complex the software tends to be.Software fault prediction therefore continues to remain an important area of research in software engineering especially as new modeling algorithms are still emerging.In spite of the fact that the potential implementations of fuzzy set theory in software fault prediction have been explored in the past, to the best knowledge of the authors, it has not yet examined how interval type-2 intuitionistic fuzzy sets with membership and non-membership degrees could be used with parameter optimization in this domain.Therefore, this work aims to adopt an interval type-2 intuitionistic fuzzy logic system to predict fault in the requirement phase of Software Development Life Cycle.Intuitionistic fuzzy logic system deals with uncertainty using separate degrees of membership and non-membership of an element to a set as well as hesitation index, therefore becoming more appropriate and flexible tool to deal with imprecision and vagueness in data.Experimental analyses show that the obtained software prediction outputs using interval type-2 intuitionistic fuzzy logic system are very close to the actual outputs which confirm that the proposed approach is a more realistic alternative for software fault prediction.
Improving Performance of Seen and Unseen Speech Style Transfer in End-to-end Neural TTS

End-to-end neural TTS training has shown improved performance in speech style transfer. However, the improvement is still limited by the training data in both target styles and speakers. Inadequate style transfer performance occurs when the trained TTS tries to transfer the speech to a target style from a new speaker with an unknown, arbitrary style. In this paper, we propose a new approach to style transfer for both seen and unseen styles, with disjoint, multi-style datasets, i.e., datasets of different styles are recorded, each individual style is by one speaker with multiple utterances. To encode the style information, we adopt an inverse autoregressive flow (IAF) structure to improve the variational inference. The whole system is optimized to minimize a weighed sum of four different loss functions: 1) a reconstruction loss to measure the distortions in both source and target reconstructions; 2) an adversarial loss to "fool" a well-trained discriminator; 3) a style distortion loss to measure the expected style loss after the transfer; 4) a cycle consistency loss to preserve the speaker identity of the source after the transfer. Experiments demonstrate, both objectively and subjectively, the effectiveness of the proposed approach for seen and unseen style transfer tasks. The performance of the new approach is better and more robust than those of four baseline systems of the prior art.
How well do you know your summarization datasets?

State-of-the-art summarization systems are trained and evaluated on massive datasets scraped from the web. Despite their prevalence, we know very little about the underlying characteristics (data noise, summarization complexity, etc.) of these datasets, and how these affect system performance and the reliability of automatic metrics like ROUGE. In this study, we manually analyze 600 samples from three popular summarization datasets. Our study is driven by a six-class typology which captures different noise types (missing facts, entities) and degrees of summarization difficulty (extractive, abstractive). We follow with a thorough analysis of 27 state-of-the-art summarization models and 5 popular metrics, and report our key insights: (1) Datasets have distinct data quality and complexity distributions, which can be traced back to their collection process. (2) The performance of models and reliability of metrics is dependent on sample complexity. (3) Faithful summaries often receive low scores because of the poor diversity of references. We release the code, annotated data and model outputs.
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems

We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs). Existing approaches that leverage the standard Taylor Model (TM) arithmetic for approximating the neural-network controller cannot deal with non-differentiable activation functions and suffer from rapid explosion of the remainder when propagating the TMs. POLAR overcomes these shortcomings by integrating TM arithmetic with \textbf{Bernstein B{é}zier Form} and \textbf{symbolic remainder}. The former enables TM propagation across non-differentiable activation functions and local refinement of TMs, and the latter reduces error accumulation in the TM remainder for linear mappings in the network. Experimental results show that POLAR significantly outperforms the current state-of-the-art tools in terms of both efficiency and tightness of the reachable set overapproximation. The source code can be found in https://github.com/ChaoHuang2018/POLAR_Tool
High-precision, absolute earthquake location using source-specific station terms and inter-event waveform similarity

Earth and Space Science Open Archive This work has been accepted for publication in Journal of Geophysical Research - Solid Earth. Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing an older version [v1]Go to new versionHigh-precision, absolute earthquake location using source-specific station terms and inter-event waveform similarityAuthorsAnthonyLomaxiDAlexandrosSavvaidisiDSee all authors Anthony LomaxiDCorresponding Author• Submitting AuthorALomax ScientificiDhttps://orcid.org/0000-0002-7747-5990view email addressThe email was not providedcopy email addressAlexandros SavvaidisiDUniversity of Texas at AustiniDhttps://orcid.org/0000-0001-6373-5256view email addressThe email was not providedcopy email address
Multi-Step Transfer Learning for Sentiment Analysis

In this study, we test transfer learning approach on Russian sentiment benchmark datasets using additional train sample created with distant supervision technique. We compare several variants of combining additional data with benchmark train samples. The best results were obtained when the three-step approach is used where the model is iteratively trained on general, thematic, and original train samples. For most datasets, the results were improved by more than 3% to the current state-of-the-art methods. The BERT-NLI model treating sentiment classification problem as a natural language inference task reached the human level of sentiment analysis on one of the datasets.