Reviewerly/exHarmony huggingface dataset:
Conditional Frechet Inception Distance.

We consider distance functions between conditional distributions functions. We focus on the Wasserstein metric and its Gaussian case known as the Frechet Inception Distance (FID).We develop conditional versions of these metrics, and analyze their relations. Then, we numerically compare the metrics inthe context of performance evaluation of conditional generative models. Our results show that the metrics are similar in classical models which are less susceptible to conditional collapse. But the conditional distances are more informative in modern unsuper-vised, semisupervised and unpaired models where learning the relations between the inputs and outputs is the main challenge.
SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENYULUH PERTANIAN MENGGUNAKAN METODE ANALITYCHAL HIERARCHY PROCESS

This research was done in Implementing Agency for Agricultural Extension of Fisheries and Forestry Gorontalo City. This research purpose is to design and implementing a Decision Support System to determine agricultural extension agents at the Fishery and Forestry Extension Implementing Agency (BP4K) of Gorontalo City. Researcher will using a multicriteria support decision system named Analytical Hierarchy Process (AHP).With sub-criteria: credit score (K1), duration of promotion (K2), agricultural extension preparation activities (K3), Agricultural extension activities (K4), evaluation activities and agricultural extension reporting in the last 3 years (K5) agriculture in the last 3 years (K6), Professional development activities in the last 3 years (K7), Acquired awards / tandajasa (K8).Based on the results of processing with Analytical Hierarchy Process method is quite effective in accelerating the decision-making process by solving the problem into its parts.
Analysis of the trajectory shapes of moving objects in the video sequence with use of structural description

This article proposes using structural description for graphical objects to solve an urgent task of trajectory analysis. A range of modern trajectory analysis approaches were analyzed and the best that is based on Graph Convolutional Neural Networks and Suffix Tree Clustering algorithm was chosen. Descripted ways to reduce computational sources for this neural network approach. This neural network was adapted to analyze structural description and advantages of this approach are shown.
Capsule Network is Not More Robust than Convolutional Network

The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there are no comprehensive comparisons between these two networks, and it is also unknown which components in the CapsNet affect its robustness. In this paper, we first carefully examine the special designs in CapsNet that differ from that of a ConvNet commonly used for image classification. The examination reveals five major new/different components in CapsNet: a transformation process, a dynamic routing layer, a squashing function, a marginal loss other than cross-entropy loss, and an additional class-conditional reconstruction loss for regularization. Along with these major differences, we conduct comprehensive ablation studies on three kinds of robustness, including affine transformation, overlapping digits, and semantic representation. The study reveals that some designs, which are thought critical to CapsNet, actually can harm its robustness, i.e., the dynamic routing layer and the transformation process, while others are beneficial for the robustness. Based on these findings, we propose enhanced ConvNets simply by introducing the essential components behind the CapsNet's success. The proposed simple ConvNets can achieve better robustness than the CapsNet.
ENHANCED SECURITY FRAMEWORK FOR THE SMART HOME AUTOMATION SYSTEM

Elderly care as well as enhance the quality of life for seniors, the users must be supplied with a lot of personal information about their homes and personal lives. This promising technology is being blocked by results in security and privacy. With the intent to empower end users, we did semi-struct interviews with 42 potential new and novice Smart Home customers. The issues they were concerned with focused on were connected around attacks on Smart Home data and devices, as well as the effect on users in general, to the importance of keeping their functionality balanced with the expectations of the people in their surroundings. In addition, we use measures from an interdisciplinary point of view to deal with the four topics The paper concludes with a few ideas for dealing with users' concerns, along with ideas for supporting application developers in crafting effective user-oriented digital assistants.
Distributed Mining of High Utility Sequential Patterns with Negative Item Values

The sequential pattern mining was widely used to solve various business problems, including frequent user click pattern, customer analysis of buying product, gene microarray data analysis, etc.Many studies were going on these pattern mining to extract insightful data.All the studies were mostly concentrated on high utility sequential pattern mining (HUSP) with positive values without a distributed approach.All the existing solutions are centralized which incurs greater computation and communication costs.In this paper, we introduce a novel algorithm for mining HUSPs including negative item values in support of a distributed approach.We use the Hadoop map reduce algorithms for processing the data in parallel.Various pruning techniques have been proposed to minimize the search space in a distributed environment, thus reducing the expense of processing.To our understanding, no algorithm was proposed to mine High Utility Sequential Patterns with negative item values in a distributed environment.So, we design a novel algorithm called DHUSP-N (Distributed High Utility Sequential Pattern mining with Negative values).DHUSP-N can mine high utility sequential patterns considering the negative item utilities from Bigdata.
Expert System for Tomato Plant Pest Diagnosis Using the Certainty Factor Method

This research is motivated by the failure of tomato harvest one of which is a pest attack for that program will be carried out to identify pest attacks so that crop failure can be avoided. This expert system will be implemented with the certainty factor method can provide accurate results obtained from calculations based on the weight of the symptoms chosen by the user, able to provide answers to problems that are not certain of truth such as problems diagnosing disease risk, and with this method, the expert illustrates the confidence of an expert with giving the weight of confidence in accordance with relevant expert knowledge. so that researchers increasingly believe that this expert system will be easily accessible and used by the community. From the explanation above for that, the writer makes a system, namely expert system for diagnosing oil palm plant diseases using the certainty factor method which is intended to assist the work of farmers in providing information and conclusions from pests suffered on oil palm plants.&#x0D;
Optimizer Fusion: Efficient Training with Better Locality and Parallelism

Machine learning frameworks adopt iterative optimizers to train neural networks. Conventional eager execution separates the updating of trainable parameters from forward and backward computations. However, this approach introduces nontrivial training time overhead due to the lack of data locality and computation parallelism. In this work, we propose to fuse the optimizer with forward or backward computation to better leverage locality and parallelism during training. By reordering the forward computation, gradient calculation, and parameter updating, our proposed method improves the efficiency of iterative optimizers. Experimental results demonstrate that we can achieve an up to 20% training time reduction on various configurations. Since our methods do not alter the optimizer algorithm, they can be used as a general "plug-in" technique to the training process.
Trend Analysis of Technologies Supporting the Availability of Online Content: From Keyword-Based Search to the Semantic Web

Abstract The era of Web 1.0 implied the connection of web-based documents via links, which enabled search engines to scan for information and guarantee the search and availability of webpages. Web 2.0 represented the next evolutionary stage. Known as the collaborative web, the emphasis in this case was on the establishment of services and content by the community. Search options were complemented with labelling and frequently undesirable clickstream analysis coupled with push technology-supported information provision. The semantic web is a revolutionary development, which, in addition to processing information by humans, assures the readability of datasets by machines and facilitates communication between devices. In order to promote data and information processing by machines, the semantic web relies on a special ontology allocating the respective meaning to the given data along with relying on the global indexing and naming schemes of the web. Several ontologies emerged with differing basic guidelines while displaying compatibility to the RDF standard ranging from the more semantic description of bibliographical data in libraries to the description of information gained from social networks and human conversations. While Web 3.0 is often used interchangeably with the semantic web, the former one with its intelligent server function exceeds the semantic web. We have to ask ourselves, however, whether we can rely on the accuracy of the obtained data, and we must explore what progress have libraries – expected to increase reliability – made regarding the implementation of semantic data storage.
Microblogging Sentiment Investor, Return and Volatility in the COVID-19 Era: Indonesian Stock Exchange

The covid-19 pandemic scenario caused the most extensive economic shocks the world has experienced in decades. Maintaining financial performance and economic stability is essential during the pandemic period. In these conditions, where movement is severely restricted, media consumption is considered to be increasing. The social media platform is one of the media online used by the public as a source of information and also expressing their sentiment, including individual investors in the capital market as social media users. Twitter is one of the social media microblogging platforms used by individual investors to share their opinion and get information. This study aims to determine whether microblogging sentiment investors can predict the capital market during pandemics. To analyze microblogging sentiment investors, we classified sentiment using the phyton text mining algorithm and Naive Bayesian text classification into level positive, negative, and neutral from November 2019 to November 2020. This study was on 68 listed companies on the Indonesia stock exchange. A Vector Autoregression and Impulse Response is applied to capture short and long-term impacts along with a causal relationship. We found that microblogging sentiment investor has a significant impact on stock returns and volatility and vice-versa. Also, the response due to shocks is convergent, and microblogging investors in Indonesia are categorized as a news-watcher investor.
Bounded affine permutations I. Pattern avoidance and enumeration

We introduce a new boundedness condition for affine permutations, motivated by the fruitful concept of periodic boundary conditions in statistical physics. We study pattern avoidance in bounded affine permutations. In particular, we show that if $\tau$ is one of the finite increasing oscillations, then every $\tau$-avoiding affine permutation satisfies the boundedness condition. We also explore the enumeration of pattern-avoiding affine permutations that can be decomposed into blocks, using analytic methods to relate their exact and asymptotic enumeration to that of the underlying ordinary permutations. Finally, we perform exact and asymptotic enumeration of the set of all bounded affine permutations of size $n$. A companion paper will focus on avoidance of monotone decreasing patterns in bounded affine permutations.
Derin Sinir Ağları için Hiperparametre Metodlarının ve Kitlerinin İncelenmesi

Automatic machine learning (AutoML) and deep neural networks have many hyperparameters.The recent increasing interest in complex and cost-effective machine learning models has led to the revival of hyperparameter optimization (HPO) research.The beginning of HPO has been around for many years and its popularity has increased with deep learning networks.This article provides important issues related to the revision of the HPO.First, basic hyperparameters related to the training and structure of the model are introduced and their importance and methods for the value range are discussed.Then, it focuses on optimization algorithms and their applicability, especially for deep learning networks, covering their effectiveness and accuracy.Then, it focuses on optimization algorithms and their applicability, especially for deep learning networks, covering their effectiveness and accuracy.At the same time, this study examined the HPO kits that are important for HPO and are preferred by researchers.The most advanced search algorithms of the analyzed HPO kits compare the feasibility and expandability for new modules designed by users with large deep learning tools.Problems that arise when HPO is applied to deep learning algorithms result in prominent approaches for model evaluation with a comparison between optimization algorithms and limited computational resources.
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection

Automated detection of mitotic figures in histopathology images has seen vast improvements, thanks to modern deep learning-based pipelines. Application of these methods, however, is in practice limited by strong variability of images between labs. This results in a domain shift of the images, which causes a performance drop of the models. Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner. Our work is based on the MICCAI-MIDOG challenge 2021 data set, which includes 200 tumor cases of human breast cancer and four scanners. Our work indicates that the domain shift induced not by biochemical variability but purely by the choice of acquisition device is underestimated so far. Models trained on images of the same scanner yielded an average F1 score of 0.683, while models trained on a single other scanner only yielded an average F1 score of 0.325. Training on another multi-domain mitosis dataset led to mean F1 scores of 0.52. We found this not to be reflected by domain-shifts measured as proxy A distance-derived metric.
LEIA: The Lume Pad: 3D Lightfield at your fingertips

When did you last say wow? In this talk I will introduce the Lume Pad, the world’s first switchable 3D Lightfield tablet. It combines the entire Google Play experience in WQHD resolution with Leia’s latest 3D Lightfield experiences. Designed for commercial and creative uses, it also provides powerful consumer features such as the ability to play a wide variety of games, capture, edit and share 3D lightfield pictures, display iPhone “portrait mode” pictures in 3D or convert regular 2D pictures to 3D in one-click. We will review the Nanotechnology and Computer Vision technologies powering the device and present key applications of mobile Lightfields that we think will drive mass adoption.
New and New to You, Part 1: Transitioning to a New Chair Position

The Department ChairVolume 31, Issue 4 p. 8-11 Articles New and New to You, Part 1: Transitioning to a New Chair Position Christopher Barrick, Christopher BarrickSearch for more papers by this author Christopher Barrick, Christopher BarrickSearch for more papers by this author First published: 26 March 2021 https://doi.org/10.1002/dch.30378Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume31, Issue4Spring 2021Pages 8-11 RelatedInformation
Regional geochemical variations in a metamorphosed black shale: a reconnaissance study of the Silurian Smalls Falls Formation, Maine, USA

A reconnaissance geochemical study of 21 samples of sulphidic black phyllite and schist from the Silurian Smalls Falls Formation in Maine was undertaken in order to evaluate compositional changes during regional metamorphism. These samples represent variably metamorphosed black shale. Analyzed samples come from the chlorite zone in northern Maine and the biotite, garnet, and staurolite-andalusite zones in west-central Maine. Strata of the Smalls Falls Formation are distinctive in containing abundant pyrite and/or pyrrhotite (total S = 1.2–9.7 wt%), but only minor organic matter or graphite (TOC = 0.43–1.85 wt%); TOC/S ratios are uniformly low (average = 0.37 ± 0.22). Median enrichment factors were calculated for each element by normalizing the concentration to Ti in each sample to the Ti-normalized median composition of global black shale. In the chlorite zone, moderate to large decreases in enrichment factors (-23.1 to -49.8%) are evident for V, Cr, Cu, Ni, Zn, Pb, Sb, and U, attributed here to various factors during sedimentation plus variable element mobility during diagenesis. With increasing metamorphic grade (biotite through staurolite-andalusite zones), systematic small to extreme decreases (-14.5 to -99.0%) were found for Ba, Sb, Au, and U, together with less-systematic moderate to large decreases (-35.4 to -61.1%) for V and As. Molybdenum shows an extreme decrease (-94.7%) from the garnet to staurolite-andalusite zones. Excluding Ba, these results are interpreted to mainly reflect mobility of trace elements during pyrite recrystallization, and during the metamorphic transformations of organic matter to graphite and of pyrite to pyrrhotite. Moderate to large increases for Rb (+28.1 to +61.5%) and Th (+39.1 to +47.3%) from the biotite to staurolite-andalusite zones likely record the introduction of alkalis and mass loss, respectively, during metamorphism. Three samples from one site in the garnet zone differ in having anomalously high Fe/Al and low La/Yb ratios, attributed here to epigenetic formation of pyrite and related leaching of light rare earth elements during syn-metamorphic, channelized fluid flow.Geologic and geochemical data indicate that strata of the Smalls Falls Formation were deposited during an interval of anoxia on the northwestern flank of the Central Maine Basin, for which detrital sources included an evolved continental arc. Onset of anoxia coincided with deposition of the Mayflower Hill Formation of the Vassalboro Group, on the basin’s southeastern flank, related to emergence of the Brunswick subduction complex. We suggest that this emergence played a role in promoting both lateral and vertical circulation changes, nutrient loading, and deoxygenation through subsequent basin closure that culminated with Acadian deformation and metamorphism. Based on the relatively high contents of total sulphur present in our Smalls Falls samples, sediments in the Black Sea represent the only known plausible candidate among those in modern suboxic to euxinic basins.
Similarity of Trending News A Case Study of Bangladesh

News production and its spreading have rapidly been changed by the social networking sites for instance Facebook.All the Bangla news exists on social media is in textual format which is unstructured as well.Different techniques of Text mining play a vital role in order to convert those Bangla unstructured news into formative knowledge.As there are lack of analysis regarding Bangla news of Facebook posts have been introduced, present study looks for drawing a pattern that refers a constructive knowledge from huge amount of data.To accomplish that, three newspapers have been chosen, namely ProthomAlo, Juganthor and Daily NayaDiganta.Facepager tool has been used to extract data from the Facebook pages of aforementioned newspapers and later data was processed through Spyder, an environment to run Python program.Consequence stated that 'Bangladesh National Election' along with the 'Political Issues' received maximum coverage followed by the recent 'Football World Cup'.Besides, the most frequent newspaper that shares posts on Facebook is Juganthor followed by Daily Naya Diganta and ProthomAlo, respectively.It is also to be said that there is a significant resemblance between Juganthor and Daily Naya Diganta in posting identical posts on Facebook.
LightSpace Technologies: Eye Accommodation Realized by Multifocal Projection

AR headset developer have failed to achieve the promised results. But why? Lack of accommodation is the fundamental flow of current AR headset technology. In this presentation you will hear how the design of AR optics work together with the tools you already have—your eyes.
A language model-based approach for candidates with PhD profiling in a recruiting setting

In the last decade, students facing a PhD course in Europe find terrible difficulties in reaching a permanent position in the academy. The situation gets worse when graduated PhDs have to migrate to public/private organisations that are not always ready to understand and improve the research experience. In such a situation, one of the most critical aspects is encountered immediately in the recruitment phase, since the keywords used in job offers portals are based on the employers' vocabulary and usually do not match the words that a researcher would use to describe her/his experience. Therefore, it is widely recognised that there is a need to define a system that can support a recruiters team in recruiting PhDs. The approach presented in this paper aims at designing a decision support tool able to guide the choices of recruiter of any company in the evaluation of profiles of candidates with PhD.
Chatbot for Query Processing in E-Learning System

Chatbot is a conventional agent which is capable for communicating with users by using natural languages. Today, for every educational institution, learners need an E-Learning portal for acquiring more knowledge. This saves a lot of time and human effort, and a learner can get information regarding the institute and the courses they are interested in. In this scenario a chatbot used with an e-learning portal will be useful for learners to get quick answer for their queries relevant to that system. The present work aims to build a prototype of an intelligence e-learning chatbot to be used with an e-learning portal to facilitate query processing of a learner.
Size Invariant Ship Detection Using SAR Images

Synthetic aperture radar also is known as SAR imagery is considered a very essential source of data for overlooking maritime or naval activities, and its application towards oil spills and ship detection has been a crucial centre of many going on exploration works. Many object detection methods including conventional and deep learning have been contemplated. But, most of them have tremendous computational overloads and are vastly inaccurate. The proposed system is implemented by using a Python web framework Flask, Tensor flow for training the deep learning model, and a database called SQlite3 as a backend for downloading the saved images. The research work undertaken assists the end-user to monitor the activities of the ships, measuring their dimensions, and thus preventing potential mishaps.