Reviewerly/exHarmony huggingface dataset:
Markov modulated Poisson processes for stochastic modelling of background seismicity

&amp;lt;p&amp;gt;We investigate the statistical properties of declustered catalogs as obtained from the application of two different data-driven declustering algorithms, namely the nearest-neighbor method and the stochastic declustering method (Benali et al., Stoch. Environ Res Risk Assess, 2020). The nearest-neighbor method partitions earthquakes into background and clustered components, based on nearest-neighbor distances between earthquakes in the space-time-magnitude domain (Zaliapin and Ben-Zion, J Geophys Res, 2013); the stochastic declustering method classifies earthquakes into background and clustered components through a probabilistic procedure based on the estimation of the space-time ETAS model (Zhuang et al., J Geophys Res, 2004).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Two Italian case studies are considered: North-Eastern Italy (data from OGS Bulletins) and Central Italy (data from ISIDe catalog). For both case studies, the time series of background seismicity are obtained from the two declustering methods. Then we investigate the general assumption according to which the temporal sequence of background seismicity is suitably modelled by the stationary Poisson model. For this purpose, several features and statistical tests are considered to verify the main properties that characterize Poisson processes (e.g. events are independent, exponential inter-arrival times, etc.).&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Whenever the Poissonian hypothesis is rejected, we get evidence of certain heterogeneity in the background sequence, which leads us to rule out the simple Poisson process for background seismicity modeling. As a simple and more suitable alternative, we consider here the Markov Modulated Poisson Process (MMPP model), which allows the Poisson seismicity rate to change over time according to a finite (unknown) number of states of the system. The MMPP model turns out suitable for identifying and quantifying heterogeneities in background seismicity, as well as for comparing them against the two considered declustering algorithms.&amp;lt;/p&amp;gt;
Exploiting Parallel Corpora to Improve Multilingual Embedding based Document and Sentence Alignment.

Multilingual sentence representations pose a great advantage for low-resource languages that do not have enough data to build monolingual models on their own. These multilingual sentence representations have been separately exploited by few research for document and sentence alignment. However, most of the low-resource languages are under-represented in these pre-trained models. Thus, in the context of low-resource languages, these models have to be fine-tuned for the task at hand, using additional data sources. This paper presents a weighting mechanism that makes use of available small-scale parallel corpora to improve the performance of multilingual sentence representations on document and sentence alignment. Experiments are conducted with respect to two low-resource languages, Sinhala and Tamil. Results on a newly created dataset of Sinhala-English, Tamil-English, and Sinhala-Tamil show that this new weighting mechanism significantly improves both document and sentence alignment. This dataset, as well as the source-code, is publicly released.
Challenges in Fighting Hate Speech, False Propaganda and Abuse on Twitter in Indian Context

The research highlights principle and specific challenges associated with the false propaganda, hate speech and abuse on twitter platform in Indian context. The paper speaks about 2 kind of challenges (1) Principle challenge and (2) Specific Challenges.Principle Challenges refer to comments expressed on the platform have varying influence on the users/ followers. Sometimes these have impact on their life and beliefs. More prominent when the content has ill informed facts and biased opinions (intentional or otherwise). Specific challenges refer to review of comments, Google reverse image search and using application programming interface to classify hate speech and abuse revealed key insights associated with limitations of the published algorithms.It also speaks about need for vigilance in regional sensitivities. The regional sensitivities of certain content and its implications on general perceptions could be really detrimental to the region in many cases (eg: Free Kashmir slogans or posters).While some of these challenges are being widely researched in a broader context, there is more specific need for India specific research. This research is intended to contributing to the growing community exhibiting need for regional or linguistic specific research. Besides, such studies could support the larger developer community to explore specific regional solutions.
Interdisciplinarity in theory and in practice: an example of social policy

The article discusses the problems of interdisciplinarity arising both from its theoretical understanding and practical application. The authors show the importance of organizing interdisciplinary interaction as a condition for the synthesis of disciplinary knowledge for making managerial decisions. Using social policy as an example, it is shown that even in a field that combines elements of both disciplinary and interdisciplinary approaches, the formation of a common interdisciplinary knowledge and practice is still problematic, despite the theoretical consensus on its necessity and objective practical prerequisites
Bayesian Estimation for Identifiable Topic Models with Latent Dirichlet Allocation

This paper discusses two effective Bayesian estimation algorithms for identifiable topic models with latent Dirichlet allocation (LDA). Identifying a statistical model and corresponding parameters is important for obtaining a reasonable interpretation from the model in the social sciences. Due to the instability of topic models, several studies have addressed this problem. A reliable solution for the identifiable topic model is to specify anchor words. We employ this anchor word condition and present a simple application in Markov chain Monte Carlo (MCMC) algorithms for identifiable LDA topic models. Simulation studies are performed to investigate the identifiability of standard and correlated topic models, and the results demonstrate that the proposed methods improve the stability of parameter estimation in topic models with LDA.
Strategi Promosi UPT Perpustakaan UMM pada Masa Pandemi Covid-19

&lt;p&gt;Introduction: The Covid-19 pandemic caused university libraries to adopt policies restricting small and large-scale activities, including UPT UMM Library. The outbreak required UPT UMM Library to change the routine of library services in normal time by adjusting library services during the pandemic. Therefore, it requires a strategic library promotion. Methodology: The study employed descriptive method with qualitative approach. Data analysis techniques used Cresswell's descriptive qualitative analysis model. Results: The result showed that promotional strategy conducted UMM Library during the Covid-19 pandemic are to create library programs consisting of user education, information literacy classes, seminars and book reviews. In addition, in collaboration with FPPTI of East Java and also FPPTMA, UMM Library created "myUMM Library" which communicates and serves library users though social medias. Conclusion: As a result, the impact generated by the library promotion strategy during the pandemic has significant increase in digital visits and digital access.&lt;/p&gt;
F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain

Generative adversarial networks built from deep convolutional neural networks (GANs) lack the ability to exactly replicate the high-frequency components of natural images. To alleviate this issue, we introduce two novel training techniques called frequency dropping (F-Drop) and frequency matching (F-Match). The key idea of F-Drop is to filter out unnecessary high-frequency components from the input images of the discriminators. This simple modification prevents the discriminators from being confused by perturbations of the high-frequency components. In addition, F-Drop makes the GANs focus on fitting in the low-frequency domain, in which there are the dominant components of natural images. F-Match minimizes the difference between real and fake images in the frequency domain for generating more realistic images. F-Match is implemented as a regularization term in the objective functions of the generators; it penalizes the batch mean error in the frequency domain. F-Match helps the generators to fit in the high-frequency domain filtered out by F-Drop to the real image. We experimentally demonstrate that the combination of F-Drop and F-Match improves the generative performance of GANs in both the frequency and spatial domain on multiple image benchmarks.
Email Prioritization Using Machine Learning

Personal and business users prefer to use e-mail as one of the crucial sources of communication. The usage and importance of e-mails continuously grow despite the prevalence of alternative means, such as electronic messages, mobile applications, and social networks. Finding out the important mails of the all the mails received on the same day is becoming difficult for any users, as the volume of critical e-mails continues to grow, the need to automate the management of e-mails increases for several reasons, such as spam e-mail classification, phishing e-mail classification, and multi-folder categorization, among others. To achieve the objective of study, analysis and comprehensive review to explore the classification as per the importance of emails as user need to look these mails. Main area of classification is the important mail folder and categorizing it into different classification by using Naive Bayes algorithm. By Giving the weights to words present in mails we can find out the total weight of that mails and by comparing the weights with the other ones we can prioritize the mails so that it will be efficient to users to look for the important mails only. The research directions, research challenges, and open issues in the field of e-mail classification are also presented for future researchers.
Morphometric Characteristics of Cipeles Watershed to Identify Flood Prone Area

Cipeles watershed is located on Sumedang Regency, West Java Province. This research aims to identify the flood-prone area in the Cipeles Watershed based on morphometric characteristics. This research was conducted through studio analysis using Map Info and Global Mapper software and mathematical calculations. The data used in this research are stream network and topography. Digital Elevation Model was used to analyze the slope of the research area. Morphometric aspects used in the mathematical calculations consist of Drainage density, Drainage texture, Ratio of circularity, Ratio of elongation, and Form factor. The research area is a hilly area with high relief and a very steep-slightly steep slope in the west and south, relatively gentle in the middle to the east of the research area. It consists of parallel, subparallel, subradial, rectangular, dendritic, and subdendritic drainage patterns. Cipeles watershed has 38 subwatersheds with predominantly very elongated-circular shape and very coarse-coarse texture. The research area has relatively high rainfall that potentially causes flooding, especially downstream of the Cpl_10, Cpl_11, Cpl_12, Cpl_13, Cpl_14, Cpl_15, Cpl_16, and Cpl_17 subwatersheds. The research area is classified into low and moderate risk levels of flood potential. The low-risk level is located in Tanjungsari and Rancakalong district. The moderate risk level is located in North Sumedang, South Sumedang, Situraja, and Cimalaka district. The results showed that quantitative research could be used to identify flood prone area. It is recommended that the Cipeles Watershed can be well maintained through integrated watershed management and land use management.
Value Iteration in Continuous Actions, States and Time

Classical value iteration approaches are not applicable to environments with continuous states and actions. For such environments, the states and actions are usually discretized, which leads to an exponential increase in computational complexity. In this paper, we propose continuous fitted value iteration (cFVI). This algorithm enables dynamic programming for continuous states and actions with a known dynamics model. Leveraging the continuous-time formulation, the optimal policy can be derived for non-linear control-affine dynamics. This closed-form solution enables the efficient extension of value iteration to continuous environments. We show in non-linear control experiments that the dynamic programming solution obtains the same quantitative performance as deep reinforcement learning methods in simulation but excels when transferred to the physical system. The policy obtained by cFVI is more robust to changes in the dynamics despite using only a deterministic model and without explicitly incorporating robustness in the optimization. Videos of the physical system are available at \url{this https URL}.
Flow-based Attribution in Graphical Models: A Recursive Shapley Approach

We study the attribution problem in a graphical model, wherein the objective is to quantify how the effect of changes at the source nodes propagates through the graph. We develop a model-agnostic flow-based attribution method, called recursive Shapley value (RSV). RSV generalizes a number of existing node-based methods and uniquely satisfies a set of flow-based axioms. In addition to admitting a natural characterization for linear models and facilitating mediation analysis for non-linear models, RSV satisfies a mix of desirable properties discussed in the recent literature, including implementation invariance, sensitivity, monotonicity, and affine scale invariance.
Self-Paced Context Evaluation for Contextual Reinforcement Learning

Reinforcement learning (RL) has made a lot of advances for solving a single problem in a given environment; but learning policies that generalize to unseen variations of a problem remains challenging. To improve sample efficiency for learning on such instances of a problem domain, we present Self-Paced Context Evaluation (SPaCE). Based on self-paced learning, \spc automatically generates \task curricula online with little computational overhead. To this end, SPaCE leverages information contained in state values during training to accelerate and improve training performance as well as generalization capabilities to new instances from the same problem domain. Nevertheless, SPaCE is independent of the problem domain at hand and can be applied on top of any RL agent with state-value function approximation. We demonstrate SPaCE's ability to speed up learning of different value-based RL agents on two environments, showing better generalization capabilities and up to 10x faster learning compared to naive approaches such as round robin or SPDRL, as the closest state-of-the-art approach.
Free Speech Skepticism

If the free speech clause of the First Amendment is interpreted to mean that speech is to be granted special protection not accorded to other forms of conduct, then a free speech principle, distinct from a principle of general liberty, must be posited and must receive a distinct justification. A defense of a free speech principle must explain why the harm principle either does not apply in the case of speech or applies with less force than in the case of all other forms of human conduct. In this article, I argue that none of the defenses of the right to free speech on offer succeeds in showing why even significantly harmful speech is deserving of special protection not afforded non-speech conduct. More work needs to be done to justify a free speech principle and, until such work is done, the belief in the existence of a free speech principle that undergirds and justifies our current free speech practices is no more than an article of faith.
An Efficient Telugu Word Image Retrieval System using Deep Cluster

Optical character recognition (OCR) system has successfully implemented in many applications of text classifications and text retrieval systems. But, due to the complexity of huge number of alphabets (symbols), grammatical punctuations,ithas failed to classify and retrieve many regional languages. Thus, to overcome these problems, word image retrieval is innovated. this article presents the Telugu word image retrieval(TWIR) system using advanced artificial intelligence based multi-layer deep learning convolution neural network (DL-CNN) is developed for extraction of accurate Telugu word features. Then, AlexNet based deep learning model is used for further clustering of features and the procedure named as DeepCluster. Thus, by using DeepCluster method logy various grammar rules of Telugu scripts will be perfectly analyzed. Hence, the feature database is trained with the Telugu word features along with its grammar rules. Thus, the system will effectively use for retrieval Telugu words and useful in all real time applications such as English to Telugu conversions and Telugu web browsing, Telugu speech analysis. The simulation results show that the proposed deep learning model gives the outstanding results compared to the state of art approaches for both Average Precision (mAP) and mean Average Recall (mAR) metrics.
Verifying Quantized Neural Networks using SMT-Based Model Checking.

Artificial Neural Networks (ANNs) are being deployed on an increasing number of safety-critical applications, including autonomous cars and medical diagnosis. However, concerns about their reliability have been raised due to their black-box nature and apparent fragility to adversarial attacks. Here, we develop and evaluate a symbolic verification framework using incremental model checking (IMC) and satisfiability modulo theories (SMT) to check for vulnerabilities in ANNs. More specifically, we propose several ANN-related optimizations for IMC, including invariant inference via interval analysis and the discretization of non-linear activation functions. With this, we can provide guarantees on the safe behavior of ANNs implemented both in floating-point and fixed-point (quantized) arithmetic. In this regard, our verification approach was able to verify and produce adversarial examples for 52 test cases spanning image classification and general machine learning applications. For small- to medium-sized ANN, our approach completes most of its verification runs in minutes. Moreover, in contrast to most state-of-the-art methods, our approach is not restricted to specific choices of activation functions or non-quantized representations.
Sensor Based Human Activity Recognition by Multi-Headed ConvLSTM

The past few years have witnessed a boom in the field of Artificial Intelligence, driving people towards it. Various sectors around the globe are using artificial intelligence, especially the Health-care sector. The healthcare sector today has started using sensor based technology in order to automate the activity of people belonging to any age group. As the name suggests, the sensor based method can be elaborated as an approach to record or “sense” activities of various age groups, and sensor data is being used for the same. Earlier, this was carried out using the machine learning models, but as the techniques progressed, researchers became more inclined towards the latest techniques rather than the traditional ones, the most recent one being Deep Learning. This particular paper proceeds in that direction as well. The basic idea behind this paper is to bring forth a new technique for Human Activity Recognition (HAR). The technique used here is “MultiHeaded ConvLSTM”, to build a deep neural architecture for improved accuracy of HAR.
Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?

Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions. In light of this steady progress, can we claim that the performance gap between the two is closed? Starting from this question, we present a systematic comparison between state-of-the-art systems representative of the two paradigms. Focusing on three language directions (English-German/Italian/Spanish), we conduct automatic and manual evaluations, exploiting high-quality professional post-edits and annotations. Our multi-faceted analysis on one of the few publicly available ST benchmarks attests for the first time that: i) the gap between the two paradigms is now closed, and ii) the subtle differences observed in their behavior are not sufficient for humans neither to distinguish them nor to prefer one over the other.
Fair Normalizing Flows.

Fair representation learning is an attractive approach that promises fairness of downstream predictors by encoding sensitive data. Unfortunately, recent work has shown that strong adversarial predictors can still exhibit unfairness by recovering sensitive attributes from these representations. In this work, we present Fair Normalizing Flows (FNF), a new approach offering more rigorous fairness guarantees for learned representations. Specifically, we consider a practical setting where we can estimate the probability density for sensitive groups. The key idea is to model the encoder as a normalizing flow trained to minimize the statistical distance between the latent representations of different groups. The main advantage of FNF is that its exact likelihood computation allows us to obtain guarantees on the maximum unfairness of any potentially adversarial downstream predictor. We experimentally demonstrate the effectiveness of FNF in enforcing various group fairness notions, as well as other attractive properties such as interpretability and transfer learning, on a variety of challenging real-world datasets.