Reviewerly/exHarmony huggingface dataset:
Analisis Optimasi Algoritma Backpropagation Momentum Dalam Memprediksi Jenis Tingkat Kejahatan Di Kecamatan Tambusai Utara

Crime is conduct that disobeys the law and contravenes social norms in a way that society finds objectionable. There is no system that can forecast the kind and quantity of crimes that will happen in the future as a guide in the process of preventing crime, according to the North Tambusai Police. However, the public service process in the form of complaints has been done digitally. Backpropagation is an iterative method that works well even with complex and convoluted data. Backpropagation is an artificial neural network with several levels (multi-layer). Data processing is done on the types and numbers of crimes that took place in North Tambusai District between 2015 and 2020. The first step in the data processing procedure is to normalize the data and choose the network training parameters. Crime data and levels were used in the artificial neural network research, which used a 5-5-1 design. The test results reveal that the average prediction accuracy rate is 92.66%, with the greatest prediction accuracy rate being 99.6% and the lowest forecast accuracy rate being 90.01 percent. Theft had the highest weighting (Curat) of crimes the next year with 15 cases, while fraud, crime, and extortion/threats each had the lowest weighting (1 case). The prediction findings exhibit a sufficiently high level of accuracy to serve as a basis for evaluation.
Hidden Markov Model Applied for Vehicles Density Prediction

The Gempol-Pandaan toll is a strategic center for regional growth. Number of vehicles passing through this toll has increased in 2017. Because of the passing of numerous cars, particularly trucks carrying loads above their capacity, this condition may result in traffic congestion and road damage. As a result, it is essential to predict the number of vehicles coming from not only the Gempol toll gate but also the Kejapanan, Bangil, and Rembang toll gates that will exit through the Pandaan toll gate. The probability and quantity of each vehicle category—I, II, III, IV, and V—are used in this study to analyse vehicle density. The origin gate and vehicle category cannot be observed directly, but the car must pass the toll gate in order to tap the e-toll card, so the Hidden Markov Model (HMM) method was used in this study. The Expectation-Maximization (EM) algorithm and the Bayesian approach are the two estimation techniques used in this study for HMM parameters. The outcome demonstrates that Bayesian parameter estimation for HMM is superior to the EM algorithm. The model is more representative to explain the predicted vehicle density because the Bayesian estimated parameter values are nearer to the input parameters.
Telecom-Wavelength Bright Single Photon Sources with Post-Selected Indistinguishability

We demonstrate a bright telecom-wavelength single photon source based on a tapered nanobeam containing InAs/InP quantum dots. We obtain a first-lens brightness of 21.0% and a post-selected Hong-Ou-Mandel visibility of 0.91 in the telecom O-band.
Predicting Interaction Effort in Web Interface Widgets

The product of good design should render a tool invisible for a user who is executing a task. Unfortunately, web applications are often far from invisible to users, who struggle with poor design of websites and processes in them. We are particularly interested in web processes that involve form filling, so we have been studying how people interact with web forms. Besides cataloguing user interaction problems that are common in web forms, we have noticed that, in many cases, there is a single form element or widget to blame for a certain interaction problem, because such widget is not the most appropriate one for the required input in that particular context. This unfitness of the widget causes an extra burden to the user, which we call \textit{interaction effort}. In this work we propose measuring the interaction effort of a widget with a unified score based on micro-measures automatically captured from interaction logs. We present the micro-measures that were found relevant to predict the interaction effort in 6 different types of web forms widgets. We describe a large data collection process and prediction models, showing that it is indeed possible to automatically predict a widget interaction effort score by learning from expert human ratings. We consequently believe that the interaction effort could be used as an effective metric to compare small variations in a design in terms of user experience.
A Framework to Compute Entity Relatedness in Large RDF Knowledge Bases

The entity relatedness problem refers to the question of exploring a knowledge base, represented as an RDF graph, to discover and understand how two entities are connected. This article addresses such problem by combining distributed RDF path search and ranking strategies in a framework called DCoEPinKB, which helps reduce the overall execution time in large RDF graphs and yet maintains adequate ranking accuracy. The framework allows the implementation of different strategies and enables their comparison. The article also reports experiments with data from DBpedia, which provide insights into the performance of different strategies.
A Contrastive Analysis on Taboo Words in English And Minangkabau Language

This study focuses on a contrastive analysis of taboo words in English and Minangkabau, an Indonesian language spoken in Sumatera Barat. This study aimed to identify, categorize, and compare the differences between taboo terms used in Minangkabau and English. This study's research methodology was descriptive qualitative. Three procedures were used to carry out this study. The data for taboo terms in English was first prepared using literature, film, and native speakers staying in Medan, Sumatera Utara, whereas the data for taboo words in Minangkabau was prepared using literature, research, and local speakers in Bonjol, Pasaman, Sumatera Barat. Second, talking to several informants on the meaning of the banned terms to gain the full meaning. Third, the Wardhaugh theory was used to examine all the data, and the Lado Theory was used to compare the data to identify similarities and contrasts. According to the study's findings, there are five categories of taboo words in the Minangkabau language, including those that are connected to sex, animals, death, and excrement. They were distinct from the English words for sex, animal functions, human bodily parts, death, excrement, and religion.
The impact of network delay on Nakamoto consensus mechanism

&lt;abstract&gt;&lt;p&gt;Nakamoto consensus is prevailing in the world largest blockchain-based cryptocurrency systems, such as Bitcoin and Ethereum. Since then, various attempts have been studied to attack Nakamoto consensus worldwide. In recent years, network delay has won more attention for making inconsistent ledgers in blockchain-based applications by virtue of attacking Nakamoto consensus. However, so far as we know, most of the existing works mainly focus on constructing inconsistent ledgers for blockchain systems, but not offering fine-grained theoretical analysis for how to optimize the success probability by flexibly dividing computational power and network delay from the viewpoint of adversary. The paper first utilizes network delay and the partition of controlled computation power of honest miners for making forks as long as possible. Then, formally analysis is provided to show the success probability of the proposed attack, and compute the optimal network delay and splitting for adversarial computation power in theory. Finally, simulation experiments validate the correctness of the formal analysis.&lt;/p&gt;&lt;/abstract&gt;
Systematic explorations of a new dataset

This chapter, Systematic explorations of a new dataset, leads students through the steps that, in our view, should be taken when first confronted with a new dataset. Time plots, scatter plots, and histograms, as well as simple calculations, are used to examine the data with the aim of both understanding their general character and spotting problems. We take the position that all datasets have problems—errors, data gaps, inconvenient units of measurement, and so forth. Such problems should not scare a person away from data analysis! The chapter champions the use of the reality check checking that observations of a particular parameter really have the properties that we know it must possess. Several case study datasets are introduced, including a hydrograph from the Neuse River (North Carolina, USA), air temperature from Black Rock Forest (New York), and chemical composition from the floor of the Atlantic Ocean.
piNET–An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

&lt;p&gt;In this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and whole slide images (WSI), representing a diverse multi-centre dataset for evaluating Ki67 quantiﬁcation. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefﬁcient R = 0.927. The success of the system can be attributed to several innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images—and it was posed as a detection problem to mimic pathologists’ workﬂow which improves accuracy and efﬁciency. Secondly, the system is trained purely on tumor cells, which reduces false positives from non-tumor cells without needing the usual pre-requisite tumor segmentation step for Ki67quantiﬁcation. Thirdly,theconceptoflearningbackgroundregionsthroughweaksupervisionis introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain “signiﬁcant” activity.&lt;/p&gt;
PROFESSIONAL AND PERSONAL QUALITIES OF A BUSINESS PSYCHOLOGIST

Business needs specialists in personnel work, organizational and personnel consulting. Today they are in demand in banks, large industrial and commercial holdings, medium and small enterprises. In particular, business psychologists are the specialists, who work to ensure the realization of potential opportunities for staff and management of business organizations, promote effective communication in the team, form a personnel reserve, etc. The article notes that professional and personal qualities in the work of a business psychologist are often closely related. The profession leaves an imprint on the lifestyle and personality of the specialist, and personal characteristics are reflected in all types of professional activities of a business psychologist. Possession of the most pronounced professional and personal qualities is a prerequisite for the effective implementation of practical activities at any stage of professional development of a business psychologist personality. Focusing on continuous professional development, self-improvement provides a high level of competitiveness, productivity and, consequently, career growth, self-realization and self-improvement.
Spying and Knowledge

This chapter begins with a brief introduction to the origins and organization of the Ministry of State Security (Stasi) in the German Democratic Republic. It continues with an analysis of Timothy Garton Ash’s memoir The File: A Personal History, where he writes about his own file found in the Stasi’s archives. Using files from the Stasi, the chapter discusses the case of Chilean writer Carlos Cerda, who was exiled in the GDR from 1973 to 1985, and was approached by the Stasi to become an “Unofficial Collaborator” (IM). Next, it analyzes how the short stories “Ferrobádminton” and “Splendor and Agony of the Horses,” written by Cerda during his time in East Germany, engage with the subject of surveillance and exile. Finally, it examines To Die in Berlin, a novel that Cerda finished writing in Chile after his exile.
Is this a Patient or a Wall?—Adapting Robots from an Industrial Context to a Rehabilitation Clinic

An interview with the roboticists at the INTUITIV project. They talk about the challenges of redesigning a robot technology originally intended for industrial use with the aim of creating a robot assistant within the environment of a rehabilitation clinic.
An Empirical Study on How Well Do COVID-19 Information Dashboards Service Users’ Information Needs

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since its first being reported in December 2019, COVID-19 has spread quickly around the world, and becomes a global pandemic. Previous information sources have a number of problems when providing COVID-19 information web services. First, the information from government or traditional media (i.e., TV and newspaper) is not frequently updated. Second, different layers of the government (state and federal government) may provide contradictory information. Also, there are many rumours spread on social media, which makes it difficult for people to know who and what to trust. Finally, the current information about COVID-19 is fragmented. It takes effort for people to aggregate the information they need to see from different places.
A Deep CNN Model For Age Estimation

Age estimation from human faces is an important yet challenging task in computer vision because of the large differences between physical age and apparent age.Although many inspiring works have focused on the age estimation of a single human face through deep learning, the existing methods still have lower performance when dealing with faces in videos because of the differences in head pose between frames.In this paper, a combined system of age estimation and head pose estimation is proposed to improve the performance of age estimation from faces in videos.We use deep regression forests (DRFs) to estimate the age of facial images, while a multi-loss convolutional neural network is also utilized to estimate the head pose.Accordingly, we estimate the age of faces only for head poses within a set degree threshold to enable value refinement.First, we divided the images in the Cross-Age Celebrity Dataset (CACD) and the Asian Face Age Dataset (AFAD) according to the estimated head pose degrees and generated separate age estimates for images with different poses.The experimental results showed that the accuracy of age estimation from frontal facial images was better than that for faces at different angles.Further experiments were conducted on several videos to estimate the age of the same person with his or her face at different angles, and the results show that our proposed combined system can provide more precise and reliable age estimates than a system without head pose estimation.
Affective economies of racism on social media

New media technologies and social networks have not only opened up spaces for civic engagementand democratic participation, but have also offered alternative sites for the proliferation andcirculation of racist, homophobic and xenophobic sentiment. This article draws on Ahmed’s ideaof “affective economies” and Ekman’s (2019) notion of “affective publics” to investigate how whitesupremacist groups in South Africa have used social media to express racist views, attitudes andsentiments. The internet in general and social networks in particular are based on a libertarianlogic that emphasises freedom of speech at the expense of the rights of minorities whose viewsmay not meet the required algorithmic thresholds of specific social media sites. While governmentsaround the world have put in place laws to deal with overt racism and hate speech, online platformsremain new battlegrounds for the articulation of racist views and sentiments. The findings of thestudy show that white supremacist groups in South Africa use social media as a platform to recontextualiseand re-mediate topical issues in South African society and ramp up group solidarityby circulating racist views that undermine and de-legitimise the ruling party (the African NationalCongress) and its policies.
On-chip Reconfigurable Entangled Photon-pair Sources for Quantum Parallel Processing

We performed the generation and manipulation of the optical high-dimensional hyper-entanglement based on the on-chip reconfigurable Sagnac configuration. Our work provides the critical step for the efficient parallel processing of quantum information with integrated photonics.
A Comprehensive Overview of Autoencoder Algorithms to Leverage the Diagnosis of Complex Diseases

Recent advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell, and others, have boosted the understanding of complex diseases. However, extracting biological meaning using the data generated by these methods is not straightforward. Various analysis techniques, including machine learning algorithms, have been proposed recently. These techniques have recently proven to be beneficial in the medical field. Unsupervised learning methods using neural networks, such as autoencoders (AEs) or variational autoencoders (VAEs), have shown promising results among such approaches. Several applications have been presented on various types of data and in different contexts, spanning from cancer to healthy patient tissues. In this book chapter, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, here we discuss their current applications and the improvements achieved in the diagnostic and survival of patients.
Ternary Mathematics Principles Truth Tables and Logical Operators: An approach towards 3 D Placement of Logical Elements Extensions of Boolean Algebra (Extended)

The Boolean algebra generated by the recursive open sets which are regular the algebra of recursively regular open sets, so that the two attempts to define “the recursive elements of” the completion coincide. The approach presented in this article aims at transition between two systems of counting binary and ternary I propose to use ternary math principle in coding the signal Instead of using duos of numbers 01, the study proposes to use triplets (1,0-1) and make a transition from binary to ternary so that the binary code is converted to ternary and vice versa That same principle can be used for building microcircuits where logical elements are placed in a 3 D space instead of a layer. It is concluded that all Ternary approach in Algebra is a promising method as an alternative to an already developed binary approach Its advantage is in potentially bigger calculating power on condition that the theory is well coordinated with technological innovations.
Sketching hope and grief in transition: Situating anticipation in lived futures

In light of current environmental challenges, it often seems that optimism is a required emotional state for addressing our future. This can be seen in how different technological fixes are assumed to sort our futures out at the same time as requiring minimal change in our daily lives. Moving beyond our existing high-carbon and material lives requires not only that we deal with the optimistic end of the spectrum but also that we envision fragile and uncertain futures. In response, this article proposes a designerly format for supporting public anticipation that attends to and cares for tensions between hope and grief, with the aim of nurturing grounds for living with uncertain futures. In contrast to abstract and decontextualized visions and images of the future that can be hard to relate to, the format situates anticipation in lived futures, that are ongoing, emerging and situated in specific locations, environments and experiences. By tending to anticipated losses related to the transition to a post-carbon future, the workshop format created space for confronting shared difficulties and vulnerabilities. Despite the lack of easy solution, the format also opened up for articulating alternatives and less tech-oriented hopeful engagements and practices.
Validation of the L2 Speaking Performance Assessment for Young EFL Learners: Using Many-facet Rasch Measurement

Although performance assessment has been widely used in English education in Korea, scant attention has been focused on the validation of the assessment, especially on the one that targets young language learners. In this regard, the purpose of this study was to validate an L2 speaking performance assessment for elementary school EFL learners, adopting many-facet Rasch measurement (MFRM) as an analytical tool. The validation focused on examinees’ behaviors, to offer an assessment validity argument as to if the result of the assessment can be considered accurate. While it was found that examinees were evaluated relatively accurately, probability curves of rating scales revealed that examinees were not separated appropriately from the current 4-level rating scale, indicating that 2-level to 3-level distinctions would better describe examinees’ achievement. Interaction analyses between Examinee✕Task and Examinee✕Rating Scale showed that examinees displayed irregular patterns when the task comprised question forms and when they were evaluated in terms of delivery of the speech. The findings of the study provide valuable information to teachers as well as researchers, on the design and interpretation of performance assessments.
Image-Caption Model Based on Fusion Feature

The encoder–decoder framework is the main frame of image captioning. The convolutional neural network (CNN) is usually used to extract grid-level features of the image, and the graph convolutional neural network (GCN) is used to extract the image’s region-level features. Grid-level features are poor in semantic information, such as the relationship and location of objects, while regional features lack fine-grained information about images. To address this problem, this paper proposes a fusion-features-based image-captioning model, which includes the fusion feature encoder and LSTM decoder. The fusion-feature encoder is divided into grid-level feature encoder and region-level feature encoder. The grid-level feature encoder is a convoluted neural network embedded in squeeze and excitation operations so that the model can focus on features that are highly correlated to the title. The region-level encoder employs node-embedding matrices to enable models to understand different node types and gain richer semantics. Then the features are weighted together by an attention mechanism to guide the decoder LSTM to generate an image caption. Our model was trained and tested in the MS COCO2014 dataset with the experimental evaluation standard Bleu-4 score and CIDEr score of 0.399 and 1.311, respectively. The experimental results indicate that the model can describe the image in detail.
Exploring the Integration of Lisp into a Modern Reinforcement Learning Project Through the Use of Hy

This paper explores the usage of Lisp in a small modern Reinforcement Learning (RL) project. The Lisp dialect, Hy programming language, is used to incorporate the traditional libraries and packages in up-to-date workflows. This project is centered around the usage of NetHack for RL. The MiniHack sandbox framework and NetHack Learning Environment (NLE) are used to create custom training/testing environments and tasks. The MiniHack sandbox framework creates a simple level editor and creation interface for use in the training and evaluation process of the agent. NLE is chosen as the working environment. For the agent model, this project adopts Torchbeast’s PolyBeast, a PyTorch implementation of the IMPALA architecture. The usage of Hy within this project is forefront, and so it is implemented as much as possible to accomplish the tasks.