Reviewerly/exHarmony huggingface dataset:
Komputerisasi Sistem Informasi Keanggotaan DPC PORMIKI Solo (SIKPOSO) Berbasis Android

The Solo branch of the Indonesian Association of Medical Recorders and Health Information Professionals (PORMIKI) serves various kinds of correspondence requests from members. Services that are done manually cause the service to be slow. With a well-developed information system, it is hoped that the membership service process will be faster and easier. PORMIKI Solo Membership Information System (SIKPOSO), is an information system in the form of a mobile application with the Android operating system. The form of the mobile application was chosen considering the ease of access for members of PORMIKI (Indonesian Association of Medical Recorders and Health Information Professionals). The design of SIKPOSO includes the preparation of Flowcharts, Use Case Diagrams, Entity Relationship Diagrams, Data Flow Diagrams, Activity Diagrams, Wireframes, and User Interfaces as a reference for developing membership systems that currently do not exist. The method used in the development of this system is the Prototyping method.&#x0D; SIKPOSO is designed to provide 6 service menus, as Request for recommendation of Practice License (SIK), Request for Membership Mutation Letter, Virtual Member Card (KTA), Membership Information, Annual Fee Information and Event Information. Evaluation of the SIKPOSO design was carried out using the SUS (System Usability Scale) questionnaire, getting the final score of 77 indicating that SIKPOSO is on a B grade, have a good rating and can be accepted by user for further development.
Kajian Morfisme Untuk Variasi Kurva Dense Fibonacci Word

The Fibonacci word is one example of a fractal object. The fractal Fibonacci word has the property of being similar to curves with curves. The curve Fibonacci word generated based on the Fibonacci word sequence. The Fibonacci word sequence can be defined by morphism so that a new sequence with the three-digit rule {0,1,2} is called Dense Fibonacci Word. In this research, the variation of the curve Dense Fibonacci Word generated by using the method L-Systems which applies several morphisms. This research method is divided into five stages, the first is the interpretation of the Dense Fibonacci Word and variations of morphism based on the Fibonacci word sequence. Second, the interpretation of fractals Dense Fibonacci Word using the method L-Systems mathematically. Third, the interpretation of fractals Dense Fibonacci Word using the method L-Systems graphically. Fourth, program making and fifth, analysis of results. The results obtained in this study are the visualization of the curve Dense Fibonacci Word with the method L-Systems, the shape of the curve Dense Fibonacci Word varies by applying several morphisms. The variation of the curve Dense Fibonacci Word is compared to each morphism which results in a different shape of the curve Dense Fibonacci Word in the small generation, but the larger the generation the fractal pattern is the same.
$(1+ε)$-Approximate Shortest Paths in Dynamic Streams

Computing approximate shortest paths in the dynamic streaming setting is a fundamental challenge that has been intensively studied during the last decade. Currently existing solutions for this problem either build a sparse multiplicative spanner of the input graph and compute shortest paths in the spanner offline, or compute an exact single source BFS tree. Solutions of the first type are doomed to incur a stretch-space tradeoff of $2\kappa-1$ versus $n^{1+1/\kappa}$, for an integer parameter $\kappa$. (In fact, existing solutions also incur an extra factor of $1+\epsilon$ in the stretch for weighted graphs, and an additional factor of $\log^{O(1)}n$ in the space.) The only existing solution of the second type uses $n^{1/2 - O(1/\kappa)}$ passes over the stream (for space $O(n^{1+1/\kappa})$), and applies only to unweighted graphs. In this paper we show that $(1+\epsilon)$-approximate single-source shortest paths can be computed in this setting with $\tilde{O}(n^{1+1/\kappa})$ space using just \emph{constantly} many passes in unweighted graphs, and polylogarithmically many passes in weighted graphs (assuming $\epsilon$ and $\kappa$ are constant). Moreover, in fact, the same result applies for multi-source shortest paths, as long as the number of sources is $O(n^{1/\kappa})$. We achieve these results by devising efficient dynamic streaming constructions of $(1 + \epsilon, \beta)$-spanners and hopsets. We believe that these constructions are of independent interest.
Analisis Regresi Cox Proportional Hazard Untuk Menentukan Faktor-Faktor yang Memengaruhi Lama Studi Mahasiswa

Higher education has an important role for students, so that students can pursue their studies and can complete their studies on time. Length of study is the time it takes a student to complete his studies. For undergraduate level (S1) is 4 years and no later than 7 years. There are several factors that influence the length of study of students, namely internal factors and student external factors. The survival analysis method is a statistical method that studies the duration of an event or event that occurs. The Cox regression model is a well-known model in survival analysis for explaining the relationship between individual failure at a time and explanatory variables in the presence of censorship. The results of this study indicate that the factors that influence the length of study of students are the factor of GPA&gt; 3, factors majoring in mathematics, majoring in biology and factors majoring in physics.
High-resolution data-driven anomalous event detection from solar farm data using clustering large applications based upon randomized search

Solar energy is a significant and fast-growing source of low-carbon electricity.The usual means of utilityscale solar farm condition monitoring is limited by poor measurement accuracy and low-resolution data collection rates.A micro-synchrophasor measurement unit (µPMU) has been adapted and integrated with a power quality monitor (PQM).This apparatus provides the high-resolution, high-precision, time-stamped data needed by analysts to make solar farms more costeffective and to better understand decentralize grid behaviour.The resulting big data necessitates applying machine learning (ML) for automatic event forecasting, fault detection, and site maintenance.The limited availability of data knowledge, data volume, and performance issues drives the exploration of data-driven based unsupervised ML methods on this occasion.Clustering Large Applications based upon RANdomized Search (CLARANS) algorithm has been employed owing to its suitability to categorise events from the big data.CLARANS has been performed to recognize inefficient voltage phase unbalance.The voltage and current waveforms and related issues such as, voltage dip or voltage sag and phase imbalance events have been considered among multiple data streams and various power distribution issues for this investigation.Ten consecutive days of empirical data have enabled this research.Altogether, 250.92 million power quality data points have been tested and validated.
Microblogging in Denmark and Poland — a contrastive analysis. Part II

This article is the second part of a comparative study of Danish and Polish tweets, inspired by the anthology “Microblogs global”. The first part of the study deals with the social network and microblogging tool Twitter, including the more technical side of microblogging. The many tweet types and the extensive terminology in the field were thoroughly and conscientiously explained. In addition, the contrasts concerning orthography and spoken language were analysed. For the following description, 320 Danish and 320 Polish tweets were collected from randomly selected profiles belonging to various politicians, journalists, and private individuals posting mainly in Danish/ Polish. The analysis covers the period from 30 March to 6 April 2019, and includes differences in tweets in terms of word preference, e.g. colloquialisms, anglicisms, or profanity, to technical and linguistic reductions and the structure of sentences. The degree of graphostilistics in relation to emojis, emoticons, smileys, and iteration is analyzed. Interaction operators are investigated, as well as functional aspects of tweets, i.e. whether tweets can be categorized as messages, comments, statements, or questions. The greatest contrasts occur in such domains as reduction, graphostylistics, and interaction. While the Polish corpus contains only 15 short forms/composites/abbreviations, the Danish corpus contains 3 times more such words (51 examples). Differences in graphostilistics concern above all the number and types of emojis. In addition, emoticons are repeated or concentrated only in the Danish tweets. The divergences in vocabulary and syntax are almost imperceptible.
Language for Description of Worlds

We will reduce the task of creating AI to the task of finding an appropriate language for description of the world. This will not be a programing language because programing languages describe only computable functions, while our language will describe a somewhat broader class of functions. Another specificity of this language will be that the description will consist of separate modules. This will enable us look for the description of the world automatically such that we discover it module after module. Our approach to the creation of this new language will be to start with a particular world and write the description of that particular world. The point is that the language which can describe this particular world will be appropriate for describing any world.
Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations

The abundance of publicly available source code repositories, in conjunction with the advances in neural networks, has enabled data-driven approaches to program analysis. These approaches, called neural program analyzers, use neural networks to extract patterns in the programs for tasks ranging from development productivity to program reasoning. Despite the growing popularity of neural program analyzers, the extent to which their results are generalizable is unknown. In this paper, we perform a large-scale evaluation of the generalizability of two popular neural program analyzers using seven semantically-equivalent transformations of programs. Our results caution that in many cases the neural program analyzers fail to generalize well, sometimes to programs with negligible textual differences. The results provide the initial stepping stones for quantifying robustness in neural program analyzers.
Circular Pattern Matching with $k$ Mismatches

The $k$-mismatch problem consists in computing the Hamming distance between a pattern $P$ of length $m$ and every length-$m$ substring of a text $T$ of length $n$, if this distance is no more than $k$. In many real-world applications, any cyclic rotation of $P$ is a relevant pattern, and thus one is interested in computing the minimal distance of every length-$m$ substring of $T$ and any cyclic rotation of $P$. This is the circular pattern matching with $k$ mismatches ($k$-CPM) problem. A multitude of papers have been devoted to solving this problem but, to the best of our knowledge, only average-case upper bounds are known. In this paper, we present the first non-trivial worst-case upper bounds for the $k$-CPM problem. Specifically, we show an $O(nk)$-time algorithm and an $O(n+\frac{n}{m}\,k^4)$-time algorithm. The latter algorithm applies in an extended way a technique that was very recently developed for the $k$-mismatch problem [Bringmann et al., SODA 2019]. A preliminary version of this work appeared at FCT 2019. In this version we improve the time complexity of the main algorithm from $O(n+\frac{n}{m}\,k^5)$ to $O(n+\frac{n}{m}\,k^4)$.
GAIT: A Geometric Approach to Information Theory

We advocate the use of a notion of entropy that reflects the relative abundances of the symbols in an alphabet, as well as the similarities between them. This concept was originally introduced in theoretical ecology to study the diversity of ecosystems. Based on this notion of entropy, we introduce geometry-aware counterparts for several concepts and theorems in information theory. Notably, our proposed divergence exhibits performance on par with state-of-the-art methods based on the Wasserstein distance, but enjoys a closed-form expression that can be computed efficiently. We demonstrate the versatility of our method via experiments on a broad range of domains: training generative models, computing image barycenters, approximating empirical measures and counting modes.
Space Efficient Algorithms for Breadth-Depth Search

Continuing the recent trend, in this article we design several space-efficient algorithms for two well-known graph search methods. Both these search methods share the same name {\it breadth-depth search} (henceforth {\sf BDS}), although they work entirely in different fashion. The classical implementation for these graph search methods takes $O(m+n)$ time and $O(n \lg n)$ bits of space in the standard word RAM model (with word size being $\Theta(\lg n)$ bits), where $m$ and $n$ denotes the number of edges and vertices of the input graph respectively. Our goal here is to beat the space bound of the classical implementations, and design $o(n \lg n)$ space algorithms for these search methods by paying little to no penalty in the running time. Note that our space bounds (i.e., with $o(n \lg n)$ bits of space) do not even allow us to explicitly store the required information to implement the classical algorithms, yet our algorithms visits and reports all the vertices of the input graph in correct order.
Sequence to sequence deep learning models for solar irradiation forecasting

The energy output a photo voltaic(PV) panel is a function of solar irradiation and weather parameters like temperature and wind speed etc. A general measure for solar irradiation called Global Horizontal Irradiance (GHI), customarily reported in Watt/meter$^2$, is a generic indicator for this intermittent energy resource. An accurate prediction of GHI is necessary for reliable grid integration of the renewable as well as for power market trading. While some machine learning techniques are well introduced along with the traditional time-series forecasting techniques, deep-learning techniques remains less explored for the task at hand. In this paper we give deep learning models suitable for sequence to sequence prediction of GHI. The deep learning models are reported for short-term forecasting $\{1-24\}$ hour along with the state-of-the art techniques like Gradient Boosted Regression Trees(GBRT) and Feed Forward Neural Networks(FFNN). We have checked that spatio-temporal features like wind direction, wind speed and GHI of neighboring location improves the prediction accuracy of the deep learning models significantly. Among the various sequence-to-sequence encoder-decoder models LSTM performed superior, handling short-comings of the state-of-the-art techniques.
A blended distance to define "people-like-me"

Curve matching is a prediction technique that relies on predictive mean matching, which matches donors that are most similar to a target based on the predictive distance. Even though this approach leads to high prediction accuracy, the predictive distance may make matches look unconvincing, as the profiles of the matched donors can substantially differ from the profile of the target. To counterbalance this, similarity between the curves of the donors and the target can be taken into account by combining the predictive distance with the Mahalanobis distance into a `blended distance' measure. The properties of this measure are evaluated in two simulation studies. Simulation study I evaluates the performance of the blended distance under different data-generating conditions. The results show that blending towards the Mahalanobis distance leads to worse performance in terms of bias, coverage, and predictive power. Simulation study II evaluates the blended metric in a setting where a single value is imputed. The results show that a property of blending is the bias-variance trade off. Giving more weight to the Mahalanobis distance leads to less variance in the imputations, but less accuracy as well. The main conclusion is that the high prediction accuracy achieved with the predictive distance necessitates the variability in the profiles of donors.
Neural and gpc operator surrogates: construction and expression rate bounds

Approximation rates are analyzed for deep surrogates of maps between infinite-dimensional function spaces, arising e.g. as data-to-solution maps of linear and nonlinear partial differential equations. Specifically, we study approximation rates for Deep Neural Operator and Generalized Polynomial Chaos (gpc) Operator surrogates for nonlinear, holomorphic maps between infinite-dimensional, separable Hilbert spaces. Operator in- and outputs from function spaces are assumed to be parametrized by stable, affine representation systems. Admissible representation systems comprise orthonormal bases, Riesz bases or suitable tight frames of the spaces under consideration. Algebraic expression rate bounds are established for both, deep neural and spectral operator surrogates acting in scales of separable Hilbert spaces containing domain and range of the map to be expressed, with finite Sobolev or Besov regularity. We illustrate the abstract concepts by expression rate bounds for the coefficient-to-solution map for a linear elliptic PDE on the torus.
TArC: Tunisian Arabish Corpus First complete release

In this paper we present the final result of a project on Tunisian Arabic encoded in Arabizi, the Latin-based writing system for digital conversations. The project led to the creation of two integrated and independent resources: a corpus and a NLP tool created to annotate the former with various levels of linguistic information: word classification, transliteration, tokenization, POS-tagging, lemmatization. We discuss our choices in terms of computational and linguistic methodology and the strategies adopted to improve our results. We report on the experiments performed in order to outline our research path. Finally, we explain why we believe in the potential of these resources for both computational and linguistic researches. Keywords: Tunisian Arabizi, Annotated Corpus, Neural Network Architecture
Edge-preserving Near-light Photometric Stereo with Neural Surfaces

This paper presents a near-light photometric stereo method that faithfully preserves sharp depth edges in the 3D reconstruction. Unlike previous methods that rely on finite differentiation for approximating depth partial derivatives and surface normals, we introduce an analytically differentiable neural surface in near-light photometric stereo for avoiding differentiation errors at sharp depth edges, where the depth is represented as a neural function of the image coordinates. By further formulating the Lambertian albedo as a dependent variable resulting from the surface normal and depth, our method is insusceptible to inaccurate depth initialization. Experiments on both synthetic and real-world scenes demonstrate the effectiveness of our method for detailed shape recovery with edge preservation.
Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis

Video-to-Video synthesis (Vid2Vid) has achieved remarkable results in generating a photo-realistic video from a sequence of semantic maps. However, this pipeline suffers from high computational cost and long inference latency, which largely depends on two essential factors: 1) network architecture parameters, 2) sequential data stream. Recently, the parameters of image-based generative models have been significantly compressed via more efficient network architectures. Nevertheless, existing methods mainly focus on slimming network architectures and ignore the size of the sequential data stream. Moreover, due to the lack of temporal coherence, image-based compression is not sufficient for the compression of the video task. In this paper, we present a spatial-temporal compression framework, \textbf{Fast-Vid2Vid}, which focuses on data aspects of generative models. It makes the first attempt at time dimension to reduce computational resources and accelerate inference. Specifically, we compress the input data stream spatially and reduce the temporal redundancy. After the proposed spatial-temporal knowledge distillation, our model can synthesize key-frames using the low-resolution data stream. Finally, Fast-Vid2Vid interpolates intermediate frames by motion compensation with slight latency. On standard benchmarks, Fast-Vid2Vid achieves around real-time performance as 20 FPS and saves around 8x computational cost on a single V100 GPU.
SIMC 2.0: Improved Secure ML Inference Against Malicious Clients

In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem is first formulated by Lehmkuhl \textit{et al.} with a solution (MUSE, Usenix Security'21), whose performance is then substantially improved by Chandran et al.'s work (SIMC, USENIX Security'22). However, there still exists a nontrivial gap in these efforts towards practicality, giving the challenges of overhead reduction and secure inference acceleration in an all-round way. We propose SIMC 2.0, which complies with the underlying structure of SIMC, but significantly optimizes both the linear and non-linear layers of the model. Specifically, (1) we design a new coding method for homomorphic parallel computation between matrices and vectors. It is custom-built through the insight into the complementarity between cryptographic primitives in SIMC. As a result, it can minimize the number of rotation operations incurred in the calculation process, which is very computationally expensive compared to other homomorphic operations e.g., addition, multiplication). (2) We reduce the size of the garbled circuit (GC) (used to calculate nonlinear activation functions, e.g., ReLU) in SIMC by about two thirds. Then, we design an alternative lightweight protocol to perform tasks that are originally allocated to the expensive GCs. Compared with SIMC, our experiments show that SIMC 2.0 achieves a significant speedup by up to $17.4\times $ for linear layer computation, and at least $1.3\times$ reduction of both the computation and communication overheads in the implementation of non-linear layers under different data dimensions. Meanwhile, SIMC 2.0 demonstrates an encouraging runtime boost by $2.3\sim 4.3\times$ over SIMC on different state-of-the-art ML models.