Reviewerly/exHarmony huggingface dataset:
Rethinking Performance Estimation in Neural Architecture Search

Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space. Since searching an optimal BPE is extremely time-consuming as it requires to train a large number of networks for evaluation, we propose a Minimum Importance Pruning (MIP) approach. Given a dataset and a BPE search space, MIP estimates the importance of hyper-parameters using random forest and subsequently prunes the minimum one from the next iteration. In this way, MIP effectively prunes less important hyper-parameters to allocate more computational resource on more important ones, thus achieving an effective exploration. By combining BPE with various search algorithms including reinforcement learning, evolution algorithm, random search, and differentiable architecture search, we achieve 1, 000x of NAS speed up with a negligible performance drop comparing to the SOTA
Standart Dışı Değerlerin Tespitinde DBSCAN Algoritması ile Seri ve Paralel Programlama Performansının Karşılaştırılması

With the introduction of computers into our lives, digital data sizes are increasing gradually. Non-standard values (outliers) which behave differently from the others can be found in these data produced in the digital world. Detection of these values, especially in big data sets; has great importance in fields such as security, insurance, finance, medicine and genetics. Clustering methods of data mining techniques are frequently used in outlier detection in big data sets. Density based DBSCAN (Density-based spatial clustering of applications with noise) algorithm from clustering algorithms which are sensitive to noisy and outlier values is one of the most important methods in outlier detection. In this study, an application was developed using DBSCAN algorithm in C# programming language for the detection of outliers. In the developed application, 2 data sets with different data numbers were examined and analyzed. For the shortest possible data analysis time, serial and parallel programming techniques were used separately. In order to shorten the analysis time of big data sets, parallel class members in TPL (Task Parallel Library) provided with .Net 4.0 were used. In series of analysis of data sets, it was seen that DBSCAN algorithm produces more accurate results and is more practicable than other selected algorithms in terms of outlier detection. When considered in terms of computing performance, parallel programming has become more efficient as the number of data increases.
MEDIA SOSIAL DAN LITERASI KEBENCANAAN DI BALI

After a large, deadly explosive and effusive eruption during 1963-64, Indonesia's Mount Agung on Bali remained quiet until a new eruption began in November 2017 until 2019. Alert Level III (of four levels) remained in effect throughout the period with a 4 km exclusion radius around the volcano. Massive hysteria occurred when alert level reach the highest level (level IV). The situation get worsed when misinformation has been spreading in social media about Mt. Agung. This research discussing how literate was the Balinese in consuming disaster information. The research was in 2018 used positivisim paradigm. Data collected by using questionnaire with 410 sample from all the regency in Bali Province. Main theory was dependence theory. The result are (1) social media became the most sourceful media to gain disaster information, (2) but the Balinese people didn’t verify the information they gained (3) 30 percent respondent shared the information without verification. It showed Balinese’s literacy in gaining disaster information need to be improve and have a high dependence in using social media.
Tezla, an Intermediate Representation for Static Analysis of Michelson Smart Contracts

This paper introduces Tezla, an intermediate representation of Michelson smart contracts that eases the design of static smart contract analysers. This intermediate representation uses a store and preserves the semantics, ow and resource usage of the original smart contract. This enables properties like gas consumption to be statically verified. We provide an automated decompiler of Michelson smart contracts to Tezla. In order to support our claim about the adequacy of Tezla, we develop a static analyser that takes advantage of the Tezla representation of Michelson smart contracts to prove simple but non-trivial properties.
Some Common Mistakes in the Teaching and Textbooks of Modal Logic

We discuss four common mistakes in the teaching and textbooks of modal logic. The first one is missing the axiom $\Diamond\varphi\leftrightarrow\neg\Box\neg\varphi$, when choosing $\Diamond$ as the primitive modal operator, misunderstanding that $\Box$ and $\Diamond$ are symmetric. The second one is forgetting to make the set of formulas for filtration closed under subformulas, when proving the finite model property through filtration, neglecting that $\Box\varphi$ and $\Diamond\varphi$ may be abbreviations of formulas. The third one is giving wrong definitions of canonical relations in minimal canonical models that are unmatched with the primitive modal operators. The final one is misunderstanding the rule of necessitation, without knowing its distinction from the rule of modus ponens. To better understand the rule of necessitation, we summarize six ways of defining deductive consequence in modal logic: omitted definition, classical definition, ternary definition, reduced definition, bounded definition, and deflationary definition, and show that the last three definitions are equivalent to each other.
Introducing Prosodic Speaker Identity for a Better Expressive Speech Synthesis Control

To have more control over Text-to-Speech (TTS) synthesis and to improve expressivity, it is necessary to disentangle prosodic information carried by the speaker's voice identity from the one belonging to linguistic properties.In this paper, we propose to analyze how information related to speaker voice identity affects a Deep Neural Network (DNN) based multi-speaker speech synthesis model.To do so, we feed the network with a vector encoding speaker information in addition to a set of basic linguistic features.We then compare three main speaker coding configurations: a) simple one-hot vector describing the speaker gender and identifier ; b) an embedding vector extracted from a speaker recognition pre-trained model ; c) a prosodic vector which summarizes information such as melody, intensity, and duration.To measure the impact of the input feature vector, we investigate the representation of the latent space at the output of the first layer of the network.The aim is to have an overview of our data representation and model behavior.Furthermore, we conducted a subjective assessment to validate the result.Results show that the prosodic identity of the speaker is captured by the model and therefore allows the user to control more precisely synthesis.
Survey Pemanfaatan Teknologi Informasi Dalam Pembelajaran Mahasiswa Keperawatan

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Backgroud:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; Information technology has become a key choice in creating a robust organizational information system that is able to produce a competitive advantage amid increasingly stringent competition today. This research aims to determine the perception of respondents which in this case is nursing students about the utilization of information technology in the study of the courses of nursing management information System. &lt;strong&gt;Methods:&lt;/strong&gt; The design of this research is descriptive analytic. The population in this study is odd semester students who take the course of nursing information System, which is as much as 90 people. Sampling using the total sampling technique. Data collection using questionnaires containing student perceptions about the tenology-based learning of information with Google forms. &lt;strong&gt;Results:&lt;/strong&gt; The results showed that students were pleased with the utilization of information technology in learning. The most high student perception is about utilizing Google form in creating a questionnaire with mean 3.189 (SD = 0,598). In addition, a negative statement that has the lowest mean that states that information technology (IT) is difficult with mean 2.900 (SD = 0,704). In addition, 81% of respondents were interested in using IT on learning. IT can be concluded that respondents are not experiencing difficulties in using IT and are pleased with IT utilization in learning. Therefore, it is expected to all nursing lecturers to be able to use information technology in the learning in order to support the 4.0 Industrial Revolutio&lt;/em&gt;n&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;em&gt;Keywords: Technology, information, learning, nursing&lt;/em&gt;&lt;/p&gt;
Comparative analysis of the application of educational technologies for sustainable development

It must be admitted that education is the necessary requirement, and educational technologies are the important tools of implementation for sustainable development. This study is based on a comparative and pedagogical approach. Having used the content analysis method we have considered the data of national reports (2018) from 31 countries on implementing the Strategy of education for sustainable development (ESD) of UNECE (sub-indicator 2.1.3) and defined pedagogical technologies applied at different levels of formal, non-formal and informal education. Besides, the rating of technologies according to the frequency of their usage for 0–8 levels has been made. The revealed differences we explain by age-related psychological features of students and pupils, as well as the national peculiarities of educational systems. Moreover, a brief overview about additional educational technologies and methods that are outside the framework of the current ones (non-formal and informal education in particular) has been prepared. The results of experts’ survey in Russian Federation carried out in 2019 have been introduced. The general evaluation of progress in the field of ESD with the 3-point scale (0.7; in progress) and estimation of indicator 2.1 (0.9; in progress) has been defined. We also characterised the revealed educational-methodical achievements.
Estimation method of photovoltaic power output using extended Lambert‐Beer law

Abstract In recent years, there have been growing needs for accurate estimation of photovoltaic (PV) power due to the widespread use of PV power generation. PV power is usually estimated based on the amount of solar radiation, air temperature, and wind velocity. However, when the PV panel is covered with snow, the solar radiation is blocked by the snow and expected PV output cannot be obtained. In this paper, for accurate PV power estimation, we propose an Extended Lambert‐Beer law, and investigate a method to estimate the degree of attenuation of PV power by snow cover. And, accuracy verification of the proposed method is carried out with actual PV power records from when snow accumulated in the Tokyo Electric Power Company (TEPCO) service area in 2018.
Early Stage LM Integration Using Local and Global Log-Linear Combination

Sequence-to-sequence models with an implicit alignment mechanism (e.g. attention) are closing the performance gap towards traditional hybrid hidden Markov models (HMM) for the task of automatic speech recognition. One important factor to improve word error rate in both cases is the use of an external language model (LM) trained on large text-only corpora. Language model integration is straightforward with the clear separation of acoustic model and language model in classical HMM-based modeling. In contrast, multiple integration schemes have been proposed for attention models. In this work, we present a novel method for language model integration into implicit-alignment based sequence-to-sequence models. Log-linear model combination of acoustic and language model is performed with a per-token renormalization. This allows us to compute the full normalization term efficiently both in training and in testing. This is compared to a global renormalization scheme which is equivalent to applying shallow fusion in training. The proposed methods show good improvements over standard model combination (shallow fusion) on our state-of-the-art Librispeech system. Furthermore, the improvements are persistent even if the LM is exchanged for a more powerful one after training.
Dynamic Refinement Network for Oriented and Densely Packed Object Detection

Object detection has achieved remarkable progress in the past decade. However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task. To resolve the first two issues, we present a dynamic refinement network that consists of two novel components, i.e., a feature selection module (FSM) and a dynamic refinement head (DRH). Our FSM enables neurons to adjust receptive fields in accordance with the shapes and orientations of target objects, whereas the DRH empowers our model to refine the prediction dynamically in an object-aware manner. To address the limited availability of related benchmarks, we collect an extensive and fully annotated dataset, namely, SKU110K-R, which is relabeled with oriented bounding boxes based on SKU110K. We perform quantitative evaluations on several publicly available benchmarks including DOTA, HRSC2016, SKU110K, and our own SKU110K-R dataset. Experimental results show that our method achieves consistent and substantial gains compared with baseline approaches. The code and dataset are available at https://github.com/Anymake/DRN_CVPR2020.
Deep Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition

Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment. This has led to breakthroughs in many complex tasks, such as playing the game "Go", that were previously difficult to solve. However, deep RL requires significant training time making it difficult to use in various real-life applications such as Human-Computer Interaction (HCI). In this paper, we study pre-training in deep RL to reduce the training time and improve the performance of Speech Recognition, a popular application of HCI. To evaluate the performance improvement in training we use the publicly available "Speech Command" dataset, which contains utterances of 30 command keywords spoken by 2,618 speakers. Results show that pre-training with deep RL offers faster convergence compared to non-pre-trained RL while achieving improved speech recognition accuracy.
Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors

In the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore, we measured results for various numbers of threads with ranges depending on a given processor(s) as well as compact and scatter affinities. Based on results we formulate conclusions with respect to optimal parameters and relative performances which can serve as hints for researchers training similar networks using modern processors.
Reservoir Computing for Jurkat T-cell Segmentation in High Resolution Live Cell Ca<sup>2</sup>+ Fluorescence Microscopy

The reservoir computing (RC) paradigm is exploited to detect Jurkat T cells and antibody-coated beads in fluorescence microscopy data. Recent progress in imaging of subcellular calcium (Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> +) signaling offers a high spatial and temporal resolution to characterize early signaling events in T cells. However, data acquisition with dual-wavelength Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + indicators, the photo-bleaching at high acquisition rate, low signal-to-noise ratio, and temporal fluctuations of image intensity entail corporation of post-processing techniques into Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + imaging systems. Besides, although continuous recording enables real-time Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + signal tracking in T cells, reliable automated algorithms must be developed to characterize the cells, and to extract the relevant information for conducting further statistical analyses. Here, we present a robust two-channel segmentation algorithm to detect Jurkat T lymphocytes as well as antibody-coated beads that are utilized to mimic cell-cell interaction and to activate the T cells in microscopy data. Our algorithm uses the reservoir computing framework to learn and recognize the cells - taking the spatiotemporal correlations between pixels into account. A comparison of segmentation accuracy on testing data between our proposed method and the deep learning U-Net model confirms that the developed model provides accurate and computationally cheap solution to the cell segmentation problem.
Adsorption and desorption kinetics of airborne ammonia on chromium-coated wafer in cleanroom depending on humidity and NH3 concentration

Ammonia (NH3) is widely used in semiconductor fabrication as an indispensable chemical substance in chemical mechanical planarization (CMP), etching and cleaning processes. The undesired outgassing of residual NH3 gas results in many severe defects on integrated circuits. The front opening unified pods (FOUPs), which are made of polymers, were introduced as a controlled-microenvironment to protect the processed wafers during transport and storage steps. However, the FOUPs potentially outgas the previous sorbed-NH3 to FOUPs' atmosphere, leading to NH3 cross-contamination. The development of NH3-free liquid phase extraction (LPE) set-up coupled to ionic chromatography (IC) analysis allows sensitive NH3 measurement on wafer surfaces in avoiding the artifacts of airborne NH3 from cleanroom environment. The adsorption and desorption kinetics of NH3 on Cr-coated surface have been determined in cleanroom conditions (21 ± 2°C, 1.0 atm, different NH3 gas concentration (10–35 ppbv) and relative humidity (<1–40%RH). The NH3 adsorption kinetics on Cr-coated wafer correspond to modified Langmuir-type adsorption model. The deposited NH3 amount at saturation level (Cmax 1.94 × 1014 molecule/cm2) and the adsorption constant (kads 0.041 ppbv−1.h−1) are independent of the NH3 airborne concentration. On the contrary, the Cmax and kads linearly depend on the humidity of exposure environment. The desorption kinetics from saturated Cr-wafer are much slower than the adsorption kinetics. Mathematical model of modified Langmuir-type is established and served as prediction tools for the study of cross-contamination between FOUPs–wafer, optimal conditions for wafer/photomask storage.
Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media

We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions. Our model uses deep transformer layers to perform multi-head attentions among the target utterance and the relevant context in the thread. The context-aware models are evaluated on two datasets from social media, Twitter and Reddit, and show 3.1% and 7.0% improvements over their baselines. Our best models give the F1-scores of 79.0% and 75.0% for the Twitter and Reddit datasets respectively, becoming one of the highest performing systems among 36 participants in this shared task.
Combining Distributed Word Representation and Document Distance for Short Text Document Clustering

This paper presents a method for clustering short text documents, such as news headlines, social media statuses, or instant messages. Due to the characteristics of these documents, which are usually short and sparse, an appropriate technique is required to discover hidden knowledge. The objective of this paper is to identify the combination of document representation, document distance, and document clustering that yields the best clustering quality. Document representations are expanded by external knowledge sources represented by a Distributed Representation. To cluster documents, a K-means partitioning-based clustering technique is applied, where the similarities of documents are measured by word mover’s distance. To validate the effectiveness of the proposed method, experiments were conducted to compare the clustering quality against several leading methods. The proposed method produced clusters of documents that resulted in higher precision, recall, F1- score, and adjusted Rand index for both real-world and standard data sets. Furthermore, manual inspection of the clustering results was conducted to observe the efficacy of the proposed method. The topics of each document cluster are undoubtedly reflected by members in the cluster.
Transformations of superpositions by means of incoherent operations

Abstract In this paper we study how the coherence of a superposition of pure states is related with the coherence of its components. We consider two pure initial states and two pure final coherent states, such that the former ones cannot be transformed into the latter ones by means of incoherent transformations. In this situation, we analyze conditions for the existence of superpositions of the initial states that can be transformed into superpositions of the final states. In particular, we consider superpositions formed by quantum states belonging to orthogonal subspaces. By appealing to the majorization theory, we obtain necessary and sufficient conditions for such transformations to be possible. Finally, we provide some examples that illustrate the difference between the obtained conditions and the necessary criterion based on the relative entropy of coherence.