Reviewerly/exHarmony huggingface dataset:
HYBRID SORT - A PATTERN-FOCUSED MATRIX REORDERING APPROACH BASED ON CLASSIFICATION

Matrix reordering algorithms permute rows and columns of an input matrix for unveiling patterns that are hidden due to inappropriate permutations.Most reordering algorithms are not pattern-focused and provide low-quality results for revealing some particular patterns.On the other side, few algorithms focus on revealing specific patterns with high-quality results, but they must know a priori that one of these patterns is hidden in the matrix.In this work, we define a matrix reordering algorithm that focuses on revealing a pattern from a pattern set while providing high-quality results.Based on permutation-invariant features of matrices, the algorithm Hybrid Sort uses an empirically-created classifier to classify an input matrix into a canonical data pattern.After that, the algorithm uses this classification to select a pattern-focused reordering algorithm to reorder the input matrix.Therefore, Hybrid Sort inherits the good output quality of the selected reordering algorithms.We report that the classifier reached F2-measures greater or equal to 85% to all patterns in our tests.Besides, we present some examples of this new method applied to synthetic and real-world matrices.
Metadata Formats

This chapter is about metadata formats in the printing industry. First, XMP (Extensible Metadata Platform) is presented. Subsequently, the Job Definition Format (JDF), the Exchange JDF (XJDF) and PrintTalk are explained in detail. The last section of this chapter is about PDF metadata relevant for print production. The basic concepts of these metadata formats are explained, not so much the actual coding.
A NEW PARALLEL HYBRID MULTIOBJECTIVE ANT COLONY ALGORITHM BASED ON OPENMP

Parallel computing constitutes a growing area of interest in solving many complex combinatorial optimization problems.This paper presents a parallel implementation of a hybrid ant colony optimization metaheuristic for the multiobjective knapsack problem using the OpenMP framework.The hybrid algorithm coupled a MultiObjective Ant Colony Optimization (MOACO) algorithm with Tchebycheff based Local Search (TLS) procedure.The parallelization main idea is defined as assuming a shared-memory based on threads in which the initialization phase begins with a single thread called the master thread and executed sequentially.Afterward, a parallel region is defined where many threads are created, each one of them executing its own copy of the proposed ant colony algorithm independently.The threads cooperate through sharing a global archive holding all non-dominated solutions found so far.Experimental results show a significant efficiency of the solutions returned over the sequential implementation.
Research on the Semantic Composite Network Recognition Method of Power Distribution DTU Automation Acceptance Virtual Seat System

Abstract Aiming at semantic inaccurate recognition problem caused by the lack of professional vocabulary in the existing corpus, a composite network recognition method for the semantics of the power distribution DTU acceptance virtual agent system is proposed. This method is used to extract and merge the entity, attribute and relationship information of power equipment from multiple data sources to construct a power distribution DTU acceptance knowledge graph. In addition, the graph database is the main body and the SQL database is the extension, and the corpus is stored in a distributed manner. The acceptance test results show that the overall recognition rate of the system platform for the instructions issued by the acceptance personnel in the complex environment is 92.8%, which reduces the acceptance time by more than 70%.
PERSONALIZED WEB SEARCH USING KEY PHRASE-BASED USER PROFILES

In the context of the personalized web, the structure of user profiles remains a challenge due to its effect on information retrieval efficiency.In creating a user profile, how to extract collections of metadata of documents is a significant point.Although the efficiency of the structures such as keyword-based has proven, there is not research on key phrase based user profiles in the process of personalization.Previous methods to create user profiles emphasize keyword extraction while ignoring the existence of key phrases in the documents.This article proposes the use of keyphrase-based user profiles in personalized web search.We investigate the state of the art keyphrase extraction algorithms by considering different models of supervised and unsupervised methods.This can overcome the problems of missing keyphrases in user profiles and increase the accuracy of information retrieval.Finally, we evaluate keyphrase-based user profiles using a re-ranking algorithm to complete the process of personalization using different datasets.Personalized models based on supervised keyphrase extraction approaches obtained more accuracy by 7% than unsupervised approaches.
Will Automation, AI, and Robots Lead to Widespread Unemployment?

Abstract Automation eliminates certain jobs, as it has for hundreds of years. These disruptions are painful to many, but there are benefits of expanded production, increased quality, and lower costs to consumers, which often increases employment opportunities for many people. Controversies rage about Amazon's rapid growth, facilitated by automation in fulfillment centers, but at the same time their employee numbers have grown ten times in the past decade. While agricultural employment has dropped dramatically in the past hundred years, it has also led to more jobs in food distribution, supermarkets, and restaurants. At the same time, healthcare jobs have grown substantially for nursing, home care, personal training, and medical managers. New industries include software engineering, financial management, and renewable energy systems installation. The challenges are to ensure safety for workers and share the benefits of automation widely.
Image restoration and reconstruction by non-convex total variation and shearlet regularizations

The total variation (TV) model preserves edges well but causes staircase effects and fails to protect textures. To avoid these limitations, an innovative hybrid regularization model that combines minmax-concave TV (and the shearlet sparsity is proposed for simultaneous image deblurring and image reconstruction. Although the proposed cost function is a non-convex L1-regularized optimization problem, it can maintain the convexity of the cost function by giving the proper nonconvexity parameter to minimize it. Then, an alternating iterative scheme using variable splitting and the alternating direction method of multipliers is introduced to optimize the proposed model. The extensive experiments demonstrate the efficiency and viability of the proposed method in terms of both subjective vision and objective measures.
POS-206 Subphenotyping using combined biomarkers and clinical data reveals novel patterns within high-risk patients with diabetic kidney disease

Diabetic kidney disease (DKD) is a heterogeneous condition and subphenotyping DKD with multi-modal data may lead to novel insights. We have previously shown the utility of the KidneyIntelXTM score to stratify individuals with DKD into low, intermediate, and high risk for progressive decline in kidney function. We hypothesized that unsupervised clustering using machine learning, especially in individuals considered high risk by KidneyIntelX, would reveal novel subphenotypes with different profiles.
Cyber Literature Apps Effectiveness in Learning Literature

Undeniable, technology advancement has spread to all fields, with no exception in literature advancement.This phenomenon has resulted various changing in literary field in concrete terms in literary genre.In literature, Indonesia also has applied information and technology in the terms of teaching and advancement.This study is aimed determining the cyber literature application effectiveness in literature learning.This study applied Mix Method.Research respondents were second-year students of the English Literature Study Program taking the Basic Literature course.This study used cognitive theory to determine the students' cognitive development.Based on the pre and post test result, the use of the cyber literature application was quite effective in increasing students' cognitive knowledge in literature.The test results showed that 9 respondents or as much as 40.9% experienced an increased level of cognitive knowledge; 5 respondents or as much as 22.7% experienced a constant level of knowledge; and 8 respondents or as much as 36.3%experienced a decreased level of cognitive knowledge.
Migrating Models: A Decentralized View on Federated Learning

Federated learning (FL) researches attempt to alleviate the increasing difficulty of training machine learning models, when the training data is generated in a massively distributed way. The key idea behind these methods is moving the training to locations of data generation, and periodically collecting and redistributing the model updates. We present our approach for transforming the general training algorithm of FL into a peer-to-peer-like process. Our experiments on baseline image classification datasets show that omitting central coordination in FL is feasible.
Keeping an eye on the bottom line: balancing subscriptions, interlibrary loan, purchase on demand, and patrons needs

In order to view the entire gallery of posters, videos, and other materials, please click "Submissions" below. ATTENTION ATTENDEES: To log in and download the posters & ask questions, please sign in using the link here: [https://www.morressier.com/signup/CLC2020](https://www.morressier.com/signup/CLC2020) To find the Q&A for each poster category, please click "Sessions" below. Further instructions for how to ask a question are included in each section. A handy list of the submissions and their corresponding Q&A Session can be found here: [Submissions by Q&A Category](https://drive.google.com/file/d/1vurahYJIGA9IjA8v94IKfh36QhCFHTQi/view?usp=sharing)
TSAEns: Ensemble Learning for KPI Anomaly Detection

Time series anomaly detection is a critical task in the domain of Artificial Intelligence for IT Operations (AIOps). Large companies that provide Internet-based services need to closely monitor massive time-series data from applications and hardware in real-time and provide timely troubleshooting to keep reliable services and smooth business. However, selecting and ensembling diverse detectors for better detection results is challenging due to the complexity of time series data. In this paper, an selection framework for time series anomaly detection, which can select proper detectors for time series data of diverse characteristics according to the detector’s performance on historical data. Also, we combine active learning methods to propose unseen samples for labeling, which can significantly alleviate the labeling overhead of operators. Experimental results show the effectiveness of the proposed framework.
Cryptocurrency Price Prediction Using FB Prophet Model

A new method encountered for securing cryptocurrency i.e cryptographic algorithms for example Secure Hash Algorithm (SHA-2) and Message Digest (MD5). It uses Blockchain technology to make the transactions secure, transparent, traceable, and immutable. This is the reason cryptocurrencies have gained popularity in almost all sectors, especially in the financial sector. Cryptocurrency price prediction has become a trending research topic globally. Many Machine Learning algorithms have been developed such as Linear Regression, SVM, Random forest, and Facebook Prophet. Facebook Prophet is a time-series forecasting model for predicting the future price of bitcoins. In this paper, Facebook prophet Model is used, and two cryptocurrencies are considered, namely Bitcoin and Litecoin. The result depicts that FB prophet Model accurately predicts the prices of bitcoin cryptocurrencies. We considered the data from yahoofinace.com for BTC-USD and LTC-USD.
USING SEMANTIC RELATIONSHIPS TO ENHANCE NEURAL WORD EMBEDDINGS

Neural language models have significantly improved current natural language understanding tasks.However, distributional semantics, derived from neural language models is less competitive in computing semantic relatedness or similarity than other taxonomy-based methods.Although current researches seek to exploit the handcrafted semantic knowledge in ontology to improve distributional semantics, they often ignore distinguishing different functions of semantic relationships in updating or retrofitting neural word embeddings.This paper proposes retrofitting neural word embedding through semantic relationships encoded in semantic networks such as WordNet and Roget's thesaurus.We employ the hypernym/hyponym relationships to modify the asymmetric distance measure in retrofitting neural embeddings, which can fully transfer the hierarchical semantic information contained in semantic networks.In the evaluation with the gold-standard data sets, our method achieved the Spearman correlation value of 0.80, which is about 8% higher than the state-of-the-art methods in the literature.
Event Time Relation in Natural Language Text

Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing(NLP), "Events" are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP applications like building the summary of news articles, processing health records, and Question Answering System (QA) systems. This paper presents a framework for identifying the events and times from a given document and representing them using a graph data structure. As a result, a graph is derived to show event-time relationships in the given text. Events form the nodes in a graph, and edges represent the temporal relations among the nodes. Time of an event occurrence exists in two forms namely qualitative (like before, after, during, etc.) and quantitative (exact time points/periods). To build the event-time-event structure quantitative time is normalized to qualitative form. Thus obtained temporal information is used to label the edges among the events. Data set released in the shared task EvTExtract of (Forum for Information Retrieval Extraction) FIRE 2018 conference is identified to evaluate the framework. Precision and recall are used as evaluation metrics to access the performance of the work with other methods mentioned in state of the art with 85% of accuracy and 90% of precision.
CROSS-PLATFORM DEVELOPMENT FOR E-COMMERCE

Electronic commerce is generally seen as a competitor for physical stores, however, this paper proposes a solution where they work side-by-side to improve the in-store customer experience.An application directed for store retailers was created with the cross-platform framework React Native, making it available for Android, iOS, and the web.Using the application, retailers can automatically view the customer's previous orders, show them products that may be of their interest, and place orders, providing the customer with a unique and faster shopping experience.
PRINCIPLES OF INCREASING EFFICIENCY AND EDUCATION

The general innovation of the general pedagogical system is the process of coordinating its educational process and gathering the most convenient out of a large number of opportunities. It is possible to work in such a complex, dynamic, multifaceted, near-pedagogical system. There are alternatives, as well as targeted ways of organizing flow and educational activities. But only one of them may be right for the specific situation. Finding it is the main task of coordination. This task is solved by comparing possible alternatives and evaluating other available options.
AI &amp; Public Data for Humanitarian and Emergency Response

When an emergency event, or an incident relevant for peacekeeping or humanitarian needs first occurs, getting the right information as quickly as possible is critical in saving lives. When an event is ongoing, information on what is happening can be critical in making decisions to keep people safe and take control of the particular situation unfolding. In both cases, first responders, peacekeepers, and others have to quickly make decisions that include what resources to deploy and where. Fortunately, in most emergencies, people use social media to publicly share information. At the same time, sensor data is increasingly becoming available. But a platform to detect emergency situations and deliver the right information has to deal with ingesting thousands of noisy data points per second: sifting through and identifying relevant information, from different sources, in different formats, with varying levels of detail, in real time, so that relevant individuals and teams can be alerted at the right level and at the right time. In this talk I will describe the technical challenges in processing vast amounts of heterogenous, noisy data in real time from the web and other sources, highlighting the importance of interdisciplinary research and a human-centered approach to address problems in humanitarian and emergency response. I will give specific examples and discuss relevant future research directions in Machine Learning, NLP, Information Retrieval, Computer Vision and other fields, highlighting the role of knowledge combined with Neural and other approaches. This talk will present an overview, and draw from some of our publications at CVPR, AAAI, EMNLP, and others.
A Study of Sign Language Recognition Techniques

Humans communicate with one another in order to share their views, emotions, and stories with other people. For people suffering from communication disorder, it is not the case. Deaf-mute people can interact owing tosign language. The idea of this research is to build a system for identifying sign language that allows people with speech impairments and regular people to communicate, bridging the communication barrier. Furthermore, being able to recognize common sentences that are used frequently becomes crucial for conversations to flow effectively. Such a system is essential in services like banking where privacy would be a toppriority.
Sentiment Analysis of Public Transportation Services on Twitter Social Media Using the Method Naïve Bayes Classifier

Public transportation services in Indonesia, especially Jabodetabek, have used social media, especially Twitter, as a way to improve services. Currently, the use of online transportation services is like a need; it is necessary to conduct a sentiment analysis of online transportation to find out how people respond to these online transportation services. This research was made to analyze community responses with data analysis in the form of tweets that filtered with a public transportation-related keyword then classified into positive and negative classes using the Naïve Bayes Classifier method. Based on the system built, the total sentiment results for the percentage of the occurrence of positive words were 0.507843137, and the sentiment results for the percentage of negative word occurrences were 1.4132493. The results show that the level of negative sentiment from public tweets is greater than the level of positive sentiment.
Analysis on Cryptographic Design Techniques of Stream Ciphers and Attacks

Blockchain plays a vital role in enabling users to securely communicate with reliability and transparency. Cryptographic hashing is the underlying structure of blockchain empowering cryptocurrencies. With the rapid advancement of communication network, cipher algorithms are becoming increasingly important in information security. Multimedia data are widely used in various applications, including health sectors, military, entertainment, education, banking, and communication with recent improvements in internet technologies. Meanwhile, the application background for cipher algorithms is becoming gradually more complicated, posing problems to existing systems and necessitating the development of new appropriate designs. Stream cipher has been a prominent research area in cryptography, and it is appropriate for use in circumstances of huge data transmission or resource limits. This work provides a quick overview of conventional encryption algorithms based on stream ciphers and cryptographic prerequisites. At present, several attacks faced by stream cipher techniques are also discussed.
Penerapan Metode Diskusi Pada Pembelajaran Muatan Matematika Sebagai Upaya Meningkatkan Hasil Belajar Peserta Didik di Kelas V SDN 98/X Rantau Indah Semester Ganjil Tahun Ajaran 2021/2022

This study aims to describe and obtain information on increasing the activity and learning outcomes of mathematics through the discussion method. This research is a classroom action research consisting of two cycles, each cycle consisting of two meetings. Each meeting consists of four stages, namely planning, implementation, observation and reflection. The subjects of this study were students of class V SDN 98/X Rantau Indah, totaling 18 people. This research was conducted in the odd semester of the 2021/2022 academic year. Data collection techniques using tests, observations, and documentation. Data were analyzed using percentages. The results showed that the discussion method could improve the learning outcomes of the fifth grade students of SDN 98/X Rantau Indah's mathematics content.
Reliable Systems Based on Sound Software Engineering Practices

Abstract Reliable HCAI systems are produced by applying sound technical practices to software engineering teams. These technical practices clarify human responsibility, such as audit trails for accurate records of who did what and when, and histories of who contributed to design, coding, testing, and revisions. Other technical practices are improved software engineering workflows that are tuned to the tasks and application domain. Then when prototype systems are ready, verification and validation testing of the programs, and bias testing of the training data can begin. Software engineering practices also include the user experience design processes that lead to explainable user interfaces for HCAI systems.
LAHROUD A PALEO-TETHYS REMNANT IN NORTHWESTERN IRAN, IMPLICATIONS TO: GEOCHEMISTRY, RADIOISOTOPE, GEOCHRONOLOGY AND TECTONIC SETTING

The Lahroud Ophiolite in northwestern Iran contains extensive zones of Paleozoic ophiolite as remnants of the Paleo-Tethys oceanic crust.The principal rock units are gabbro overlain by pillow basalt which is intruded by granites, interbedded with pelagic sedimentary units, which includes radiolarian cherts.Geochemistry and radioisotope studies, supported by Nd, Sm, Sr, and Pb isotope data indicate that the Lahroud ophiolite has been raised from a within plate basaltic mantle source.Isotope studies show that the basalts are derived from Indian type Oceanic mantle sources.Radiogenic data indicates the involvement of subduction related terrigenous materials in the source magma.All rocks are geochemically cogenetic and were generated by fractionation of a melt with a composition of average E-MORB with a calcalkaline signature.Two 40 Ar/ 39 Ar ages of 343±3 and 187.7±7.7 Ma for the muscovite minerals and glasses respectively, suggest that metamorphic and basaltic rocks were formed during the Late Paleozoic to Early Jurassic, respectively.Microfossil studies show the presence of Paleozoic biostratigraphy.The crystallization process and rifting into the oceanic crust in the Lahroud ophiolite probably began in the Carboniferous, with volcanic activity continuing during the late Triassic.