Reviewerly/exHarmony huggingface dataset:
“Hello! How May I Helo You?”: How (Corrected) Errors Humanize a Communicator

Today more than ever before, online text-based interactions (e.g., text messages, emails, social media) have become a primary means of communication. Because written communication lacks human nonverbal cues such as appearance, voice, and identity, consumers may struggle to distinguish whether they are interacting online with a human or a chatbot. The current research investigates how typographical errors (“typos”), a common yet overlooked feature in text communication, can humanize a communicator. Across five experiments (N = 2,515) that used ambiguous conversational counterparts (i.e., customer service agents that might be bots), agents (either chatbots or real humans) who made and subsequently corrected a typo were perceived to be more humanlike than ones who made no typo or did not correct the typo. Participants consequently perceived those agents as warmer and more capable of understanding and helping their issues, were more likely to endorse a reward for the agent, and even perceived the company they represented more favorably. These findings provide novel insights into how conversational features may influence customers’ perception of online agents and the brands that use them. The authors discuss theoretical implications for anthropomorphism and social perception and practical implications for companies wishing to humanize their customer service agents.
Collocation lexicon of Slovene academic discourse Aleks

Aleks is a lexical database with 463 entries typical for general Slovene academic discourse. The entries include typical context examples (collocations and examples of use) taken from KAS, a corpus of Slovene academic texts, i.e. a morphosyntactically tagged synchronous and monolingual corpus, containing more than 1.5 billion words.
Measurement of mental rotation ability and construction of internal information visualization system for echography

Echography is inexpensive and indispensable equipment in every medical field but examination with Echography need the expertise and experience of an operator. So, person who is not skill in echography not easy inspect. We have constructed an echography support system for reducing the burden of doctor and patient so far. We estimated the mental rotation matrix by focusing on the echogram change and probe movement. We focused on the acoustic window can be taken clear images avoiding bones and organs. Also, experts have focused on placing probes on the acoustic window and have been working on building probe contact position and orientation measurement systems. In this paper, we report on the system which visualizes the location of the acoustic window and the internal organ information on the body surface. Also, we propose system for examination acoustic window by estimating range of motion of probe for examination with desired echography.
Accuracy Improvement for Fine-Grained Image Classification with Semi-Supervised Learning

Semi-supervised learning is adopted for fine-grained image classification with convolutional networks. Compared with the traditional approach of distillation, we obtain accuracy improvement of ∼3 percent points under the upper limit of supervised learning on cassava-disease dataset.
Implementasi Jaringan Syaraf Tiruan Metode Backpropagation untuk Prediksi Penjualan Mobil Bekas

Increased sales are needed in line with the rapid economic growth in Indonesia. Procurement of goods and services is an activity needed to realize developments in the area. Manually predictive calculations have a high level of risk and hinder the sales performance process. To handle this, a system is needed to be able to calculate the predictions of the number of used cars sold and reduce the risk of very heavy losses. With the application of this prediction system, it is expected to overcome these problems. The method used is the Backpropogation Method, a training method that uses multilayer perceptron to solve complex problems with supervised training methods, namely pairs that use input-output and which will be calculated is the weight, the desired output target. With the method of Backpropagation Artificial Neural Network (ANN) calculation. The results show that the application system created can produce predictive results that are accurate enough to get results that are not much different from actual sales, ie with MSE = 23.84.
A secret file sharing scheme with chaos based encryption

This paper proposes a secret sharing scheme with encryption using chaotic map. The scheme is demonstrated for different types of files like text, jpeg and pdf. Chaotic maps are utilized for introducing a key based deterministic randomization needed for encryption and decryption. An established ( $t, n$ ) secret sharing scheme is used for generating $n$ number of shares. Out of these $n$ shares, the assembly of any minimum ( $t$ ) number of shares leads to the recovery of the original secret file. Performance analysis of the proposed scheme is presented with the help of certain standard parameters.
Effects of Outlining on the Genre-based Summaries: A Corpus-based Analysis

This study explored the effects of outlining on the summaries of thirty six L2 college students across three text genres and their opinions on the use of outlining in writing the summaries. Over the eight weeks, the students were divided into an outlining group (experimental group) and a keyword group (controlled group) wrote three summaries after reading articles across three genres (e.g. argumentative, expository, and narrative) at two weeks intervals. Additionally, a follow-up questionnaire on the use of outlining in writing summaries was conducted. Data were analyzed by the Syntactic Complexity Analyzer (Lu, 2010) under 14 indices, and the responses to the questionnaire were analyzed by percentages. The results found a statistically significant difference appearing only in the aspect of CT (i.e. clause per T-unit). In addition, a majority of students utilizing outlining for summary writing believed that they were helped not only to understand the contents and the structure of the articles, but to write summaries with their own words. These results have a pedagogical meaning in that outlining can be more efficient for specific reading genres when teaching summarization in L2 classes.
On the Robustness of Active Learning

Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with. When applied correctly, it can be a very powerful tool to counteract the immense data requirements of Artificial Neural Networks. However, we find that it is often applied with not enough care and domain knowledge. As a consequence, unrealistic hopes are raised and transfer of the experimental results from one dataset to another becomes unnecessarily hard. In this work we analyse the robustness of different Active Learning methods with respect to classifier capacity, exchangeability and type, as well as hyperparameters and falsely labelled data. Experiments reveal possible biases towards the architecture used for sample selection, resulting in suboptimal performance for other classifiers. We further propose the new ”Sum of Squared Logits” method based on the Simpson diversity index and investigate the effect of using the confusion matrix for balancing in sample selection.
DLS-Forgetter: An Implementation of the DLS Forgetting Calculus for First-Order Logic

DLS-Forgetter is a reasoning tool that aims to compute restricted views of a knowledge base of first-order logic formulae via semantic forgetting. Semantic forgetting achieves this by eliminating predicate symbols in an equivalence preserving way up to the remain- ing symbols. Forgetting has many applications such as information hiding, explanation generation and computing logical difference. DLS-Forgetter combines ideas from two Ackermann-based approaches: the DLS algorithm and a modal logic inference system inspired by the algorithm. The tool enhances the DLS algorithm by incorporating an ordering over the symbols in the forgetting signature. This allows more control over the forgetting process and the application of the elimination rules. The theory behind the tool is provided by a first-order Ackermann calculus, a first-order generalisation of the rules outlined by the modal logic inference system. The purpose of the tool is to provide the research community with an experimental tool to allow further research to be conducted in the area. This paper describes the DLS-Forgetter tool along with its underlying calculus, it outlines the forgetting process used in the implementation, and presents results of an empirical evaluation.
Motion state estimation of surrounding objects using Dynamic Occupancy Grid Map

Autonomous vehicles have been developed to reduce traffic accidents and use as means of transporting the elderly. Autonomous vehicles require surrounding environment recognition with LIDAR to detect obstacles. In the conventional method, if dynamic obstacles are detected, dynamic state is estimated using Kalman filter, and if static obstacles are detected, map of static environment is made. However, this method sometimes cannot be consistent between dynamic obstacles and static obstacles. Therefore, this paper introduce Dynamic Occupancy Grid Map using LIDAR information. In Dynamic Occupancy Grid Map, the environment is represented by 2D grid, and this method is able to estimate velocity and object existence probability on each grid, so it is possible to recognize dynamic obstacles and static obstacles at the same time. In this paper, after making the map using actual driving data, we consider the estimation results of dynamic objects and static objects.
STUDI PERAMBATAN KESALAHAN SISTEMATIS LUAS LAHAN PARKIR KAMPUS MENGGUNAKAN PENDEKATAN LUAS SEGITIGA SEMBARANG

Parking is a condition of not moving a vehicle that is temporary because it was abandoned by the driver. Included in the definition of parking is every vehicle that stops at certain places whether stated by traffic signs or not, and not solely for the benefit of raising and / or lowering people and / or goods.Campus 3 Lumajang State Community Academy has facilities and infrastructure prepared by the Lumajang Regency government. However, the parking lots provided cannot accommodate vehicles optimally because of the ratio of the number of vehicles and the area of the parking area that is not appropriate. This is because the area of the parking lot is not analyzed by data error when measuring.Each measurement data is assumed to have errors both systematic errors, random errors, and large errors (blunders), so that in the measurement of parking lots certainly there are errors. From this the authors intend to conduct research to find out how the propagation of systematic errors and the large systematic errors of the area of campus parking lot 3 Lumajang Community Academy.The methods used in this study include preparing materials and tools, making land sketches, decomposing them, determining distances using theodolite, determining land area equations, and finding systematic error propagation. So that the final goal in this study is to find large systematic errors in the parking area of Campus 3 of the Lumajang State Community Academy
Tutorial for using the netCDF Data Curation Primer

This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #1 co-located with the Digital Library Federation (DLF) Forum 2018 in Las Vegas, Nevada on October 17-18, 2018.
MODELING SETS OF DISJOINT SUBGRAPHS FOR COMPLEX BRANCHED NETWORKS

An approach to modeling the set of disjoint subgraphs for complex branched networks is presented.
Simulated Annealing Meta-heuristic for Addition Chain Optimization

In this work, a simulated annealing (SA) algorithm is implemented in the Python programming language with the aim of minimizing addition chains of the "star-chain" type. The strategies for generating and mutating individuals are similar to those used by the evolutionary programming (EP) and genetic algorithms (GA) methods found in the literature [1]-[3]. The proposed variant is the acceptance mechanism that is based on the simulated annealing meta-heuristic (SA). The hypothesis is that with the proposed acceptance mechanism, diversity is obtained in the search-space through a simple strategy that allows finding better solutions compared to the deterministic method Optimized Window. The simulations were performed with exponents in the range 218-234 and were compared with the results reported in [3], where a GA is proposed to get optimal addition chains. It is concluded that the proposed algorithm is able to find chains of shorter length than those found with the Optimized Window method and with a performance similar to that of the GA proposed in [3].
Remote Person Authentication using a Super-telephoto Video Tracking System

Walking persons at a distance of 300 m are authenticated in video-shooting through a f = 1600 mm lens on a super-telephoto tracking system, which can control a mirror-drive pan-tilt system with high-speed visual feedback. Experimental results show the effectiveness of supertelephoto tracking in remote authentication for very distant persons in real scenarios.
The peculiarities of studies of academic integrity in the United States of America and Ukraine

The paper deals with the issues of arrangement of pedagogical studies concerning the sphere of academic integrity in Ukraine with respect to the experience of various countries of the world. The USA is considered as a country with the significant theoretical and practical background being of great value for Ukraine. The paper is aimed at specifics description of academic integrity studies in the United States of America and Ukraine. The paper outlines the fact that the implementation of various codes of behavior for Ukrainian students and academicians, as well as psychological, pedagogical and methodical conditions of their efficiency is an issue requiring the serious studies, particularly of experimental type. The studies should be aimed at considering the value component to ensure the proper efficiency level for a code of behavior for modern young people. The study enables drawing up a conclusion that academic integrity studies concerning the prospectives of experience of the United States of America use in Ukraine obtain the systematic features. This is supported by distinguishing such research branches as philosophic, pedagogical and legislative. Pedagogical studies cover the wide range of issues being considered at theoretical and experimental levels, so they may be described as extremely urgent for the process of optimization of academic culture development of Ukrainian educational institutions.
LibGuides: Academic Areas of Interest: Public Service

The Harper College Library supports the College’s Academic Areas of Interest with a wide range of resources, study spaces and services--including research consultations and 24/7 online chat.
How to put into practice the Integration Methods

Till graduating bachelor's degree at University it is admittable fact that most graduators essentially practice only on receiving specific knowledge, resulted that the lack of Utilisation of educational intelligence that is way it is purposed to lead the fresh bachelors of pedagogical units of High Education getting on how to deliver their learners obtained substances. It is directed that analyzing of Integration that promotable comprehensive Methodology on Teaching English language. Furthermore, the strategies of the integrating skills and the techniques about how to present knowledge during the procedure of lesson at GES (general education schools). It is brilliant brief instruction not only for the new teachers who just starting working with learners but also for the experienced teachers.
A diachronic cross-disciplinary study of Readability in Literature Review Section in Research Articles

The purpose of this study is to investigate the comprehensibility of the text of the literature review section in research articles over the last 30 years. In other words, this study aims to investigate the trend of the change of LR text readability from 1988 to 2018. In this study, readability is defined as the level of difficulty of understanding a text based on the style of writing. This study follows previous studies to use the Flesch Reading Ease for measuring readability of the literature review section of research articles from 1988 to 2018. The literature review section of each selected research article was copied to a word file and their readability index of each was determined using the Flesch Reading Ease of the three distinct periods over the last 30 years. Results indicated an increase in the difficulty level of the text of the LR section over the last 30 years. Results also revealed a significant difference in the difficulty of the three distinct areas of research articles. Readability is generally considered one of the most important characteristics of effective writing. Yet little is known about the readability of the literature review section of academic articles in different journals. It is suggested that more research be conducted on the topic to shed more light on the stated gap.
Improvement of Object Tracking Accuracy in an Optical Flow Driven Particle Filter by Switching a Tracking Model

In recent years, researches on object tracking, which targets children’s monitoring and automotive applications, have been actively conducted. In object tracking, modeling the motion of an object to be tracked is an important task for determining the performance of the tracking algorithm. Therefore, a method was developed to model the motion of the tracked object using optical flow and track it with a particle filter. In this paper, the comparative experiments of tracking accuracy with the conventional technique are conducted by switching the model by an optical flow, and the experimental result is reported.
Is EEBO-TCP / LION suitable for attribution studies?

The value of digital resources to authorship studies seems undeniable, and MacDonald Jackson, that frequent pioneer, recognised their potential  in an essay published in 2001. It is an accepted fact that, due to the intense competition between Elizabethan theatre companies, dramatists regularly wrote under time pressure and were prone to repeat words and phrases that they had used before. One basic method in attribution work is to  search for verbal parallels between a text of known authorship and one where the authorship is unknown, and the provision of these vast databases seems an ideal resource. When attribution scholars search for repeated phrases and collocations stretching over several lines of verse, how do they know that they have found all the relevant data? In this essay I wish to raise some doubts about its efficacy by reviewing two recent studies using EEBO on the Shakespeare canon, by MacDonald Jackson, Anna Pruitt.
An improved non-rigid registration algorithm for SAR image based on thin plate spline

SAR image registration is a key step in SAR data processing, and it plays an important role in change detection, image mosaic fusion and target recognition. The traditional rigid registration model can only realize the rotation and translation transformation of a whole image. However, for SAR images with coherent speckle noise and complex deformation, the local transformation result can’t meet the accuracy requirement of the later data processing. In this paper, the existing SAR image registration method is improved. After finishing SAR image feature matching by means of SURF and RANSAC algorithms, we construct an image conversion model with thin plate spline, which can decompose the space transformation of SAR image into global affine transformation and local non affine transformation. In this way, the whole and local mapping transformation of images can be realized simultaneously without the local distortion. The experimental results show that the improved method can achieve better transformation and obtain higher registration accuracy in the whole and local processing of SAR images.
Multi-Objective Reinforcement Learning Algorithm Updating Actor by Using Maximum TD-error Extracted from Some Critics

Multi-agent system (MAS) is constructed by many autonomous agents. Conflicts occur in MAS because of complex interactions among many agents. An agent needs to carry out a task and to avoid conflicts at same time. That is, each agent has to achieve the contradicting purposes. Therefore, this paper proposes a new approach by using multi-objective reinforcement learning as decision making system of an agents. We investigate the efficiency of the proposed approach through a simulation experiment that two agents pass each other in the narrow path.
Migrating to Full Text Finder: A Case Study

University Libraries at The University of Alabama implemented EBSCO’s Discovery Service (EDS) in 2011. This integration added to the use of EBSCO as our serials vendor and provider for usage data. In August 2017, University Libraries migrated to EBSCO’s Full Text Finder from a competing system. Consolidation of these tools under one vendor improved integration and functionality for all of the electronic resources holdings and access management. Following the successful migration to Full Text Finder, the oversight of EBSCO’s Discovery System management was assigned to staff responsible for electronic resources allowing for the benefits of having administrative control of both Full Text Finder and EBSCO’s Discovery System under one manager. The following presents the experience of moving to Full Text Finder.
Efficient Approximate Big Data Clustering: Distributed and Parallel Algorithms in the Spectrum of IoT Architectures

Clustering, the task of grouping together similar items, is a frequently used method for processing data, with numerous applications. Clustering the data generated by sensors in the Internet of Things, for instance, can be useful for monitoring and making control decisions. For example, a cyber physical environment can be monitored by one or more 3D laser-based sensors to detect the objects in that environment and avoid critical situations, e.g. collisions. With the advancements in IoT-based systems, the volume of data produced by, typically high-rate, sensors has become immense. For example, a 3D laser-based sensor with a spinning head can produce hundreds of thousands of points in each second. Clustering such a large volume of data using conventional clustering methods takes too long time, violating the time-sensitivity requirements of applications leveraging the outcome of the clustering. For example, collisions in a cyber physical environment must be prevented as fast as possible. The thesis contributes to efficient clustering methods for distributed and parallel computing architectures, representative of the processing environments in IoT- based systems. To that end, the thesis proposes MAD-C (abbreviating Multi-stage Approximate Distributed Cluster-Combining) and PARMA-CC (abbreviating Parallel Multiphase Approximate Cluster Combining). MAD-C is a method for distributed approximate data clustering. MAD-C employs an approximation-based data synopsis that drastically lowers the required communication bandwidth among the distributed nodes and achieves multiplicative savings in computation time, compared to a baseline that centrally gathers and clusters the data. PARMA-CC is a method for parallel approximate data clustering on multi-cores. Employing approximation-based data synopsis, PARMA-CC achieves scalability on multi-cores by increasing the synergy between the work-sharing procedure and data structures to facilitate highly parallel execution of threads. The thesis provides analytical and empirical evaluation for MAD-C and PARMA-CC.