Reviewerly/exHarmony huggingface dataset:
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering

Hidden Markov Model (HMM) combined with Gaussian Process (GP) emission can be effectively used to estimate the hidden state with a sequence of complex input-output relational observations. Especially when the spectral mixture (SM) kernel is used for GP emission, we call this model as a hybrid HMM-GPSM. This model can effectively model the sequence of time-series data. However, because of a large number of parameters for the SM kernel, this model can not effectively be trained with a large volume of data having (1) long sequence for state transition and 2) a large number of time-series dataset in each sequence. This paper proposes a scalable learning method for HMM-GPSM. To effectively train the model with a long sequence, the proposed method employs a Stochastic Variational Inference (SVI) approach. Also, to effectively process a large number of data point each time-series data, we approximate the SM kernel using Reparametrized Random Fourier Feature (R-RFF). The combination of these two techniques significantly reduces the training time. We validate the proposed learning method in terms of its hidden-sate estimation accuracy and computation time using large-scale synthetic and real data sets with missing values.
An Exploration of Embodied Visual Exploration

Embodied computer vision considers perception for robots in novel, unstructured environments. Of particular importance is the embodied visual exploration problem: how might a robot equipped with a camera scope out a new environment? Despite the progress thus far, many basic questions pertinent to this problem remain unanswered: (i) What does it mean for an agent to explore its environment well? (ii) Which methods work well, and under which assumptions and environmental settings? (iii) Where do current approaches fall short, and where might future work seek to improve? Seeking answers to these questions, we first present a taxonomy for existing visual exploration algorithms and create a standard framework for benchmarking them. We then perform a thorough empirical study of the four state-of-the-art paradigms using the proposed framework with two photorealistic simulated 3D environments, a state-of-the-art exploration architecture, and diverse evaluation metrics. Our experimental results offer insights and suggest new performance metrics and baselines for future work in visual exploration. Code, models and data are publicly available: https://github.com/facebookresearch/exploring_exploration
Classification of human activity recognition using smartphones

Smartphones have been the most popular and widely used devices among means of communication. Nowadays, human activity recognition is possible on mobile devices by embedded sensors, which can be exploited to manage user behavior on mobile devices by predicting user activity. To reach this aim, storing activity characteristics, Classification, and mapping them to a learning algorithm was studied in this research. In this study, we applied categorization through deep belief network to test and training data, which resulted in 98.25% correct diagnosis in training data and 93.01% in test data. Therefore, in this study, we prove that the deep belief network is a suitable method for this particular purpose.
Quotient graphs and amalgam presentations for unitary groups over cyclotomic rings

Suppose $4|n$, $n\geq 8$, $F=F_n=\mathbb{Q}(\zeta_n+\bar{\zeta}_n)$, and there is one prime $\mathfrak{p}=\mathfrak{p}_n$ above $2$ in $F_n$. We study amalgam presentations for $\operatorname{PU_{2}}(\mathbb{Z}[\zeta_n, 1/2])$ and $\operatorname{PSU_{2}}(\mathbb{Z}[\zeta_n, 1/2])$ with the Clifford-cyclotomic group in quantum computing as a subgroup. These amalgams arise from an action of these groups on the Bruhat-Tits tree $\Delta =\Delta_{\mathfrak{p}}$ for $\operatorname{SL_{2}}(F_\mathfrak{p})$ constructed via the Hamilton quaternions. We explicitly compute the finite quotient graphs and the resulting amalgams for $8\leq n\leq 48$, $n\neq 44$, as well as for $\operatorname{PU_{2}}(\mathbb{Z}[\zeta_{60}, 1/2])$.
Clustering Binary Data by Application of Combinatorial Optimization  Heuristics

We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters. Five new and original methods are introduced, using neighborhoods and population behavior combinatorial optimization metaheuristics: first ones are simulated annealing, threshold accepting and tabu search, and the others are a genetic algorithm and ant colony optimization. The methods are implemented, performing the proper calibration of parameters in the case of heuristics, to ensure good results. From a set of 16 data tables generated by a quasi-Monte Carlo experiment, a comparison is performed for one of the aggregations using L1 dissimilarity, with hierarchical clustering, and a version of k-means: partitioning around medoids or PAM. Simulated annealing perform very well, especially compared to classical methods.
Stance Detection Benchmark: How Robust Is Your Stance Detection?

Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search. However, while stance is easily detected by humans, machine learning models are clearly falling short of this task. Given the major differences in dataset sizes and framing of StD (e.g. number of classes and inputs), we introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning (MDL) setting, as well as from related tasks via transfer learning. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets. Yet, the models still perform well below human capabilities and even simple adversarial attacks severely hurt the performance of MDL models. Deeper investigation into this phenomenon suggests the existence of biases inherited from multiple datasets by design. Our analysis emphasizes the need of focus on robustness and de-biasing strategies in multi-task learning approaches. The benchmark dataset and code is made available.
Snappy: Fast On-chain Payments with Practical Collaterals

Permissionless blockchains offer many advantages but also have significant limitations including high latency. This prevents their use in important scenarios such as retail payments, where merchants should approve payments fast. Prior works have attempted to mitigate this problem by moving transactions off the chain. However, such Layer-2 solutions have their own problems: payment channels require a separate deposit towards each merchant and thus significant locked-in funds from customers; payment hubs require very large operator deposits that depend on the number of customers; and side-chains require trusted validators. In this paper, we propose Snappy, a novel solution that enables recipients, like merchants, to safely accept fast payments. In Snappy, all payments are on the chain, while small customer collaterals and moderate merchant collaterals act as payment guarantees. Besides receiving payments, merchants also act as statekeepers who collectively track and approve incoming payments using majority voting. In case of a double-spending attack, the victim merchant can recover lost funds either from the collateral of the malicious customer or a colluding statekeeper (merchant). Snappy overcomes the main problems of previous solutions: a single customer collateral can be used to shop with many merchants; merchant collaterals are independent of the number of customers; and validators do not have to be trusted. Our Ethereum prototype shows that safe, fast (<2 seconds) and cheap payments are possible on existing blockchains.
Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation

Breaking down the structure of long texts into semantically coherent segments makes the texts more readable and supports downstream applications like summarization and retrieval. Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model -- a neural architecture consisting of two hierarchically connected Transformer networks -- is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones. The proposed model, dubbed Coherence-Aware Text Segmentation (CATS), yields state-of-the-art segmentation performance on a collection of benchmark datasets. Furthermore, by coupling CATS with cross-lingual word embeddings, we demonstrate its effectiveness in zero-shot language transfer: it can successfully segment texts in languages unseen in training.
Aleatoric and Epistemic Uncertainty with Random Forests

Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties with random forests. More specifically, we show how two general approaches for measuring the learner's aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose.
First image then video: A two-stage network for spatiotemporal video  denoising

Video denoising is to remove noise from noise-corrupted data, thus recovering true signals via spatiotemporal processing. Existing approaches for spatiotemporal video denoising tend to suffer from motion blur artifacts, that is, the boundary of a moving object tends to appear blurry especially when the object undergoes a fast motion, causing optical flow calculation to break down. In this paper, we address this challenge by designing a first-image-then-video two-stage denoising neural network, consisting of an image denoising module for spatially reducing intra-frame noise followed by a regular spatiotemporal video denoising module. The intuition is simple yet powerful and effective: the first stage of image denoising effectively reduces the noise level and, therefore, allows the second stage of spatiotemporal denoising for better modeling and learning everywhere, including along the moving object boundaries. This two-stage network, when trained in an end-to-end fashion, yields the state-of-the-art performances on the video denoising benchmark Vimeo90K dataset in terms of both denoising quality and computation. It also enables an unsupervised approach that achieves comparable performance to existing supervised approaches.
Restricting the Flow: Information Bottlenecks for Attribution

Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide. We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings. The method's information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision. For reviews: https://openreview.net/forum?id=S1xWh1rYwB For code: https://github.com/BioroboticsLab/IBA
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis

Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Given the large size of these images and the increase in the number of potential cancer cases, an automated solution as an aid to histopathologists is highly desirable. In the recent past, deep learning-based techniques have provided state of the art results in a wide variety of image analysis tasks, including analysis of digitized slides. However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis. We propose a deep learning-based framework for histopathology tissue analysis. We demonstrate the generalizability of our framework, including training and inference, on several open-source datasets, which include CAMELYON (breast cancer metastases), DigestPath (colon cancer), and PAIP (liver cancer) datasets. We discuss multiple types of uncertainties pertaining to data and model, namely aleatoric and epistemic, respectively. Simultaneously, we demonstrate our model generalization across different data distribution by evaluating some samples on TCGA data. On CAMELYON16 test data (n=139) for the task of lesion detection, the FROC score achieved was 0.86 and in the CAMELYON17 test-data (n=500) for the task of pN-staging the Cohen's kappa score achieved was 0.9090 (third in the open leaderboard). On DigestPath test data (n=212) for the task of tumor segmentation, a Dice score of 0.782 was achieved (fourth in the challenge). On PAIP test data (n=40) for the task of viable tumor segmentation, a Jaccard Index of 0.75 (third in the challenge) was achieved, and for viable tumor burden, a score of 0.633 was achieved (second in the challenge). Our entire framework and related documentation are freely available at GitHub and PyPi.
Hubungan Antara Minat Baca Terhadap Hasil Belajar Ekonomi Siswa SMK Mardi Bakti Jakarta

The purpose of this study was to determine the relationship between reading interest on Economics learning outcomes at SMK Mardi Bakti Jakarta. The author uses a korelational quantitative method. Population of this research is all of X AK student which are 34 student. The sampling technique used in this research is nonprobability sampling which is saturated sample, so the sample in this reseach are 34 student The variables of this study consisted of two variables, namely variable (X) Interest in Reading and Variable (Y) namely Learning Outcomes. The data analysis techniques carried out by the t-test formula at a significant level of 0.05. The results of this study indicate that tcount =1.11&lt; t table =2.037, it is concluded that there is no relationship between reading interest on student Economic learning outcomes at SMK Mardi Bakti Jakarta.
Pagine al vento. Le identità letterarie della Patagonia in una prospettiva geografica

The analysis of the Patagonian literature from a geographical perspective allows to show some recurring themes, with which the characters of the various works measure and confront each other, constructing and affirming their identity in relation to the places. Among the recognized themes is that of regional citizenship in a condition of cross-border marginality, in which the internal and international migratory phenomenon overlaps with the reshaping and rediscovery of the identities of pre-existing populations, while tourism commercializes the imaginary.
ANALYSIS OF THE POTENTIAL OF WIND ENERGY IN THE PUGER COAST OF THE THREE BLADES DARRIEUS WIND TURBINE PERFORMANCE

Wind energy is one of the renewable energy sources that can still be explored today. The advantage obtained by utilizing wind energy is that the energy source is easy to obtain because it comes from nature. The form of utilization of wind energy in the form of wind power plants is one of the alternative technologies as an effort to meet the increasing electricity needs of the community, especially in coastal areas. Therefore, to advance this abilityl, A tool that transforms wind energy into electrical energy, such as a wind turbine, is requsite. This analysis aims to survey and assess the wind energy potential of the three blade wind turbine off the coast of Puger. The research uses research methods Research and Information Collection method and data collection techniques with needs analysis and Systematic Literature Review (SLR) by utilizing relevant and valid library sources. In the results of the study, the vertical axis wind turbine with 3 blades produces the highest rotor rotation of 224.65 rpm at a speed of wind of 4,2 m/s, as well as a maximum wind power of 3.50 watts as well as a maximum generator power of 2.28 watts at a maximum speed of 2.28 watts. maximum wind 6 m/s. Therefore, this 3-blade Darrieus wind turbine is in accordance with the wind potential that exists on the Puger coast.
Vietnam's Higher Education in the Era of Industrial Revolution 4.0: Opportunities, Challenges and Innovation

The industrial revolution 4.0 with its rapid development speed and far-reaching impacts on the development of each country, if being left behind of this revolution, the lags behind the development is also inevitable. On the contrary, if making good use of the advantages of this revolution, the opportunities are huge. Industry 4.0 is based on people, in which high-quality human resources are the core for development. This is also the development direction that each country needs to prepare for incessant changes in the future. This article will present the issue of "Vietnam's higher education in the era of Industrial Revolution 4.0: Opportunities, challenges, and innovations”
Management of Praja's Reading Culture Development Through Website Media Promotion at The Campus Library of Institut Pemerintahan Dalam Negeri Jatinangor

Management of reading culture development starts from reading - thinking - writing activities. The use of websites can take advantage of librarians to encourage interest in reading and writing for users. The librarian's task is to act as a driving force for collaborative innovation in literacy creation in the use of libraries in the form of a reading fondness movement. This article aims to describe the function of reading culture management through the promotion of media websites and IPDN digital library catalogs. The method used in this research is a qualitative method with literature study and direct observation. Using an interdisciplinary scientific approach. The results of the study found that librarians as literacy drivers and university libraries that facilitated the movement of reading fondness played a very important role in supporting the reading culture of students/users in maximizing library utilization by promoting through websites that display attractive websites, online journal portals, Kubuku applications, and Ebsco used to attract readers to be interested in reading
ON THE WAYS OF FRAGMENTED WRITING (SEVEN FRAGMENTS)

I offer an ensemble of seven fragments featuring modernist and postmodernist authors who practiced fragmentary writing. Fragmented writing is seen as a way of addressing the epistemological problem of part and whole, the aesthetic architectonics of part and whole, as well as existential issues (death, end / nonfinalism). Accordingly, through the prism of modernist artists (Schwitters, Bellmer, Rilke, Mandelstam), the attitude to fragment and whole is demonstrated, which forms a special poetics of dissociation of speech, reconstruction and deconstruction of artifact body and novel character, subversion of narrative mechanisms, including problematization of visual work frame, beginning and end of narrative text. It is revealed that the destructed, fragmented image of the world is often expressed in traditional genres or exploited by stylizing and parodying them. Wittgenstein, Benjamin, Adorno and Hamacher are considered as philosophers and philologists who have chosen the form of fragment (collection of aphorisms, notes and extracts, quotations). In their modus operandi of reflection and style there is a connection with the early Romantic tradition of the fragment, according to which the fragment is both an isolated, completed short text and part of the whole (in the pragmatic sense, an ensemble of fragments; in the epistemological sense, an absolute whole). For Schlegel and Novalis the connection between the fragment and the whole was thought of as an anticipation of the whole in an infinite series of acts of reflection: the whole is unattainable, but the process is infinite. In the 20th and 21st centuries the difficulty, up to complete negation in poststructuralism, of grasping the transcendental signifier evokes a sense of melancholy and nostalgia, and provokes a utopian return to archaic, ascetic escapism. Fragmented writing thus becomes a form of reaction to the modernist acceleration of time, the instrumentalization of the mind and the alienation of the person. The review presented here is stylized in the form of notes-fragments, linked by leitmotifs and a number of ideas that allow not only the construction but also the problematization of fragmented writing.
SAR² - An augmented-reality App for exploration of principles of synthetic aperture radar

SAR² is a prototype educational simulation software for the Microsoft Hololens, developed by students as part of a geoinformatics course. The aim of this software is to provide a tool to introduce and explain the concept of synthetic aperture radar (SAR) to students, as well as the general public, by visualizing and interactively exploring the process of a SAR acquisition in a 3D virtual environment. A distinctive feature of SAR² is that the SAR acquisition procedure is simulated in real time within a Unity Engine environment, using a set of algorithms which replicate the real-life SAR processing algorithms. While this provides a challenge due to the limited computational power available on the Microsoft HoloLens 1 device, it allows maximal freedom to the user in setting whatever configuration they would like to see. This would not have been possible if an approach using a pre-selected set of scenarios was chosen. The augmented-reality (AR) app works in 3 phases: - In the first step, the user is shown a terrain model, and a satellite model inspired by the TerraSAR-X. The user can adjust selected parameters of the acquisition by manipulating the satellite and model using intuitive AR controls (e.g. by physically grabbing and rotating the objects with their hands). - After configuring the parameters, the user launches the acquisition and observes it in real time. The satellite model flies over the terrain, and the "flow" of the data into the storage is immediately visualized. - After the acquisition is finished, the user can explore the focusing procedures that need to be applied to the data - namely the range and azimuth compression. Different geometrical effects (shadowing, layover) can also be explored at this stage. The SAR² app used in concert with conventional educational approaches can reinforce the learned material, clarify misconceptions, and provide intuition for the complicated concepts of synthetic aperture radar