Reviewerly/exHarmony huggingface dataset:
2021. A study on expression of ‘taste’ in restaurant reviews

The purpose of this study is to examine the linguistic characteristics of the expression of ‘taste’ in online restaurant review texts. Although restaurant reviews are written language, they are highly spoken, and they are written in a horizontal relationship, both producers and consumers of information. Therefore, without being bound by existing norms, they write their own experiences as if they were speaking, and freely use various new words and expressions to vividly convey the subjective evaluation of taste. Therefore, examining the linguistic characteristics related to the taste of restaurant review texts can contribute to broadening the understanding of new genres and broadening the study of Korean vocabulary. There are a lot of new vocabulary used in restaurant reviews, including abbreviations, foreign languages, dialects and North Koreans, reiterative, compound words, vowels added or syllables repeated forms, and idioms. In addition, figurative expressions are actively used, and various taste expressions related to texture, coffee, and sweetness are used.
Analysis of Teachers’ Code Switching in Teaching English at MTsN 7 Agam

Code-switching is one of sociolinguistics aspects, generally defined as a shift from one language to another by the speaker during the speech.In EFL learning, many teachers use code switching to teach their students.This research investigated the reasons behind the use of code-switching by the teachers in EFL classrooms.This research was drawn from the results of interview, observation, video-recording, and note taking.The participant was the teachers at MTsN 7 Agam.This was a descriptive research with a qualitative approach because it described what happened in human being and the phenomena directly from the classroom.The results revealed that the teachers code-switched for different purposes and reasons while teaching English.The findings indicate that code-switching in interaction during learning process is used to serve quotation, addressee specification, reiteration or repetition, message qualification, clarification, emphasis and checking.
Study of mineral chemistry, geothermobarometry and tectonic setting of gabbroic rocks from north-west Hamedan, Iran

The study area is geologically located in the northern area of the Sanandaj-Sirjan metamorphic zone. Based on petrographic study, the main minerals consists of plagioclase, pyroxene, amphibole and biotite and the accessory minerals are chlorite, muscovite, ilmenite and zircon. The obtained results from the mineral chemistry in this study indicate that the amphiboles are calcic amphiboles, and in terms of chemical composition they can be called pargasit, tschermakite and magnesiohomblende. Plagioclases are also andesian. Due to the low Na2O content, it can be concluded, that tectonically these amphiboles are of S-Amph type and are related to the subduction zones. Based on the high levels of Al2O3, the origin of amphiboles is mantle-type. Therefore, due to the ratio of Fetot (Fetot + Mg+2) to AlIv, which is less than 0.6, the oxygen fugacity was high. On the other hand, the water levels vary from 2 to 2.3 for hornblende crystallization. Different methods were used to perform thermobarometry, the best of which showed that the formation temperature of gabbroic mass has been in the range of 700°C (in average) and a pressure of 4.45- 7.52 Kb. According to the estimated pressures, the magma was originated at a depth of 25-30 Km, which is near the Moho discontinuity.
EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition

Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR$\rightarrow$TTS direction is equipped with a language model reward to penalize the ASR hypotheses before forwarding it to TTS. 2) In the TTS$\rightarrow$ASR direction, a hyper-parameter is introduced to scale the attention context from synthesized speech before sending it to ASR to handle out-of-domain data. Training strategies and the effectiveness of the EAT model are explored under out-of-domain data conditions. The results show that EAT reduces the performance gap between supervised and self-supervised training significantly by absolute 2.6\% and 2.7\% on Librispeech and BABEL respectively.
An Overview of Earnings Management Detection Approaches

Earnings management could be considered as a matter ofserious concernby the financial accounting literature.Several approaches for detecting earnings management have been provided by the previous studies. Thisstudy aimsto review the different approaches used to identify earnings management and makes a criticalevaluation on the weaknesses and strengths of these methods. Despite the fact that accrual-based earningsmanagement is the most widely used approach for detecting earnings management, it has a number ofdrawbacks. We have attempted to shed more light on the strengths and drawbacks that should be consideredwhen identifying earnings management behaviour.
Existential Tradition in Psychological Counseling (the Case of Psychological Counseling in the View of the Gestalt Approach)

The article analyzes natural realization of the principles of the existential tradition in psychological counseling. The existential tradition is taking root in the modern psychological science, and more and more researches and practitioners turn to the phenomenological method being the key one. The urgency of the topic has been justified by the analysis of the authoritative scientific sources. The Gestalt method being clearly phenomenological in orientation has been used as an example of for studying the evidence of the existential tradition in psychological counseling. The cycle of contact of psychological counseling in the framework of the Gestalt approach reflects the content of phenomenological reduction. The stages of the interaction of a counseling psychologist with a client prove to be essentially phenomenological (suggest empathy, “presence”, etc.), with the principle of dialogism being the key one. Besides, psychologist’s appealing to the subjective experience of the client is of great importance.
Identifying Cancer Characteristics Utilizing Handwriting Method

Handwriting is an action administered by the brain like each and every other action. This procedure is frequently insensible and is closely tied to instincts from brain. Any kind of sickness affects the kinetic movement and reflects in a person’s handwriting. To recognize the health and mental problems, it is important to focus on how the person writes instead of what person writes. This also makes the procedure of handwriting analysis is independent of at all languages. Person handwriting is scientific proof that whatsoever person writes subconsciously it affects in handwriting. The structures related to motion, time and pressure have been used for analysis of person health. Cancer is the second top cause of death globally, and is accountable for an estimated 9.8 million deaths in 2019. Universally, around 1 in 6 deaths is due to cancer. On an approximation 72% of deaths due to cancer are in middle and low salaried countries. One third deaths from cancer are due to 5 foremost dietary and behavioural risks that are low fruit and vegetable intake, lack of physical activity, high body mass index, tobacco use, and consumption of alcohol. Cancer can be cured if the person gets to know as soon as possible. So, substitute method to patterned whether the person is diagnosed from a cancer or not, can be done by handwriting sample. For this testing 100 various person sample are used for diverse handwriting data samples. To find a solution to this mounting problem we propose the method of cancer characteristics detection by utilizing handwritten text by machine learning, SVM. Various machine learning methods were used to find a model, which can discriminate statistically Cancer patients with approximately 90%accuracy. The classification we use to discriminate are SVM, Naïve Bayes algorithms.
LANGUAGE OF INFORMALITY WITHIN ELT CONTEXT

This research paper explores aspects of informality in the English language, the target hearers referred to by the aspects, and the functions the aspects convey in verbal interactions or communication, and its contribution to English Language Teaching (ELT) in the world. The research data are retrieved, obtained, and verified from informal-English-affiliated URLs or Websites, i.e. Web-Data as sources of informal English. As the findings, informal English involves the notorious, casual, heteregeneous, inconsistent, irregular, unorganized, incomplete, shorter, cut-down, reversed-up, and speaker-dependent aspects, and has the direct and literal nature. Informal English with its relatively any topics, personal and private, is used for close people to confirm their in-group solidarity or membership of a social group within relaxed situations and unofficial contexts. Knowledge and practices of informal English are necessary for teachers as well as learners. ELT teachers should include informal English in their teaching-syllabus material with particularly-set circumstances. Learning the knowledge and practices of informal English through ELT classrooms should prevent English learners from uncontrolled learning exposures to various informal morally-dangerous settings. This way will expectedly encourage English learners to be alert and careful in using informal English, hence encouraging them to maintain interpersonal and social harmony to some broader extent.
Development of Hotel English Educational Contents utilizing NCS Learning Modules

The purpose of the study is to suggest how to utilize National Competency Standards (NCS) in developing educational contents for teaching and learning Hotel English. NCS Learning Modules in the field of accommodation services contain abundant materials suitable for Hotel English education since they present various spoken sentences and dialogues by hotel workers and guests. The study shows how to create educational contents under the theme of handling guests’ requests based on a particular NCS Learning Module as an example. First, a series of spoken sentences and a vocabulary list were selected and modified as needed. Then, various activities involving interactions and communications among activity participants were suggested with the materials. The developed contents can be used as teaching and learning plans for regular classes of a related course. The study also presents a syllabus for a whole course related to Hotel English based on NCS Learning Modules in the field of accommodation services.
Programming with .NET for Apache Spark

In this chapter, we will be writing our first .NET for Apache Spark application that we can execute and even debug in our favorite .NET IDE. We will cover what it is we need to do in our project and then what we need to do so that we can execute the program using Apache Spark.
On Mignotte Secret Sharing Schemes over Gaussian Integers

Secret Sharing Schemes (SSS) are methods for distributing a secret among a set of participants. One of the first Secret Sharing Schemes was proposed by M. Mignotte, based on the Chinese remainder theorem over the ring of integers. In this article we extend the Mignotte's scheme to the ring of Gaussian Integers and study some of its properties. While doing this we aim to solve a gap in a previous construction of such extension. In addition we show that any access structure can be made through a SSS over $ \mathbb{Z}[i]$.
OdeNet: Compiling a GermanWordNet from other Resources

The Princeton WordNet for the English language has been used worldwide in NLP projects for many years. With the OMW initiative, wordnets for different languages of the world are being linked via identifiers. The parallel development and linking allows new multilingual application perspectives. The development of a wordnet for the German language is also in this context. To save development time, existing resources were combined and recompiled. The result was then evaluated and improved. In a relatively short time a resource was created that can be used in projects and continuously improved and extended.
Integrasi Budaya Lokal Dalam Pengembangan LKPD Untuk Mewujudkan Gerakan Literasi Sekolah

Abstract&#x0D; The purpose of this research was to develop a Student Worksheet that integrates local wisdom to support the School Literacy Movement. Development research approach and method using ADDIE model namely: 1) Analysis, 2) Design, 3) Development, 4) Implementation, 5) Evaluation. The stages that can be carried out in this research, the analysis stage, initial product planning, and product development. The researched location at SDN Prawit 1 is located in the Banjarsari subdistrict. Data collection techniques using documentation, questionnaires, interviews, and observations. Data analysis techniques used are LKPD study analysis based on local wisdom, analysis of expert validation results, analysis of observation results, and examination of learners' readability. Data validation questionnaire on local wisdom-based LKPD is analyzed descriptively quantitatively using percentages. The results of research that can be carried out during the covid-19 pandemic show that: 1) LKPD based on local wisdom is very feasible according to media experts with a percentage of 84.375%, 2) LKPD based on local wisdom with a percentage of 88.46% according to the material expert is declared very feasible, 3) according to local wisdom-based LKPD linguists with a percentage of 85.4% declared very feasible, and 4) based on the results of readability of LKPD based on local wisdom by learners obtained a score of 4.4 with very decent results. So, LKPD based on local wisdom with the aim of realizing GLS is very feasible to be disseminated to the field.
The Memphis Plenary

This is the transcript of Jesse James Garrett’s closing plenary address, delivered March 22, 2009 at the 10th ASIS&T Information Architecture Summit in Memphis, Tennessee. The talk addresses infighting and division in the professional community over the labels “information architecture” and “interaction design” for the work practitioners do. Garrett’s solution is to forgo both labels for a third: “There are only, and only ever have been, user experience designers.” Other topics include creating a body of knowledge and a language of critique for information architecture; ethical implications of user experience design work; the dimensions of human experience as media for design; embracing the community’s role as a cultural force.
Analyzing a human-in-the-loop's decisions for the detection of data poisoning

Human-in-the-loop (HITL) is the process of combining the power of a machine or computer system and human intelligence to develop human-aware machine learning (ML) models. The HITL process creates a continuous feedback loop between human and machine, enabling the trained model to continuously improve as edge cases present themselves without the need to fine-tune the model from scratch. Several advantages of utilizing HITL ML systems are to avoid bias, ensure consistency and accuracy, improve efficiency, and provide transparency. However, adding human involvement also invites human mistakes. Occasionally, a HITL system may actually degrade the algorithm rather than improve it: mislabeling an object in an object detection algorithm, incorrectly scoring an algorithm’s output, making misclicks and typos, and other human errors cause the HITL to make a mistake based on the facts presented to it. These errors being made by the user, intentional or not, can be considered a form of data poisoning. To understand the effects of a HITL’s choices on an ML model, several pieces of information during the HITL process can be observed, i.e.,: the time taken by the user to provide input on an output or on a specific object class, as well as an evaluation of the consistency of submitted valid inputs, among other factors. Information extracted from the HITL’s decision-making process can provide insights into whether poor choices are being made by the user (i.e., data poisoning) and identify where, when, and why these choices are being made. Many state-of-the-art models can be utilized for this work, such as ResNet-50, DarkNet-53, Xception, among others. However, for this work, we are less focused on the model being used and more focused on the procedure for tracking HITL performance to maximize model improvement. Nevertheless, this work will consider a pretrained model, though the approach will be model agnostic. The dataset used in this research is the publicly available “Flowers Recognition” dataset available on Kaggle.<sup>1</sup>
Reconstruction Error based Anomaly Detection Defects Using GAN

Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behavior such as defects or unusual situations. Various generative adversarial network (GAN) and autoencoder based methods have been suggested and shown much progress in this field. ABC[5], an existing representative supervised anomalies detection method, generates blur images for normal data, and the distribution of reconstruction errors for normal and abnormal overlaps considerably. In order to solve these problems, we proposed an improved anomaly detection method using autoencoder and GAN. The proposed method combine various loss functions of both supervised and unsupervised anomaly detection utilizing both normal and abnormal training data. We demonstrate the proposed GAN based anomaly detection by performing experiments on Fashion-MNIST, and real-world industrial dataset – metal surface defects. Compared to ABC[5], our model is superior to the previous approach in terms of average area under the ROC curve (AUROC) and distribution of the reconstruction errors for normal and anomaly data.
Segmentation and Positioning of Lecturers in the Department of Computer Science at Pakuan University Based on Student Assessment

Determining the segmentation and positioning of the lecturers in selecting the thesis supervisor is very important to do. It is because, with this information, the supervision process in thesis writing can run well. This study intends to analyze the segmentation and positioning of lecturers related to determine the thesis supervisor using the Clusterwise Bilinear Spatial Multidimensional Scaling Model (CBSMSM) method. The data used is survey data for fifth-semester bachelor students of the 2019/2020 academic year of the Department of Computer Science, Pakuan University. One hundred sixty-one student observations provide an assessment of 10 attributes regarding the characteristics of 32 lecturers of the department. Furthermore, the estimation of the segment coordinate parameters, lecturer coordinates, dimensions, and attributes simultaneously uses the alternating least square (ALS) algorithm. The number of segments and dimensions are selected based on the smallest sum square error (SSE) value for combining segments and other dimensions. As a result, we get four segments and four dimensions with an SSE value of 4864.003. Furthermore, the department can use this result to illustrate student assessments of their lecturers' characteristics regarding thesis supervision.
Implementing CNN Layers on the Manticore Cluster-Based Many-Core Architecture

This document presents implementations of fundamental convolutional neural network (CNN) layers on the Manticore cluster-based many-core architecture and discusses their characteristics and trade-offs.
Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java

Based on data from the Directorate General of Disease Prevention and Control of the Ministry of Health of the Republic of Indonesia, in 2017, new leprosy cases that emerged on Java Island were the highest in Indonesia compared to the number of events on other islands. The purpose of this study is to compare Poisson regression to a negative binomial regression model to be applied to the data on the number of new cases of leprosy and to find out what explanatory variables have a significant effect on the number of new cases of leprosy in Java. This study's results indicate that a negative binomial regression model can overcome the Poisson regression model's overdispersion. Variables that significantly affect the number of new cases of leprosy based on the results of negative binomial regression modeling are total population, percentage of children under five years who had immunized with BCG, and percentage of the population with sustainable access to clean water.
Are Works of Artificial Intelligence in Need for Further Protection?

Due to technological advancement artificial intelligence can create works of art and literature, which easily meet the threshold of creativity copyright laws demand for protection. Traditionally, however, copyright legislation, notably in continental Europe, focuses on work created and conveyed by the human spirit. The missing protection of machined-authored work has led to the question of whether the legal framework still meets the spirit of the time, or rather, needs to be adjusted to face the spread of creative artificial intelligence. This opinion considers if free access to machine-made work should be constrained.
Fairness-by-design dempster-shafer reasoning system

Dempster Shafer theory (DST) of evidence is an effective approach for decision analysis, especially in cases of high uncertainty. It is an evidence based probabilistic reasoning technique which differs from the traditional decision support methods by introducing belief functions not only on sets of propositions but also on all corresponding subsets which provides the capability of distinguishing beliefs on propositions from potential uncertainties among them. However,a significant factor that determines the reliability of reasoning systems is the Fairness that characterizes the processes and the outcomes of the systems. In this paper, it is proposed a modified fairness – by – design Dempster – Shafer reasoning system that quantitative fairness metrics are taken into consideration within the algorithmic procedure. For each evidence provided, a dedicated fairness estimation function determines whether the evidence is compliant with the pre – defined Ethics/Legal regulations of a given Fairness framework. Each fairness estimation function acts as a doubt factor for the evidence and reduces the belief value of the corresponding hypothesis and increases the related to the hypothesis uncertainty. This way unfairness limits the trustfulness of the corresponding evidence and as a result weakens its contribution. The proposed solution is tested against a simulated queue surveillance use case scenario, where 2 CCTV cameras input are used for inferring malicious behavior of people in the queue. For proof of concept, the one of the two cameras, introduces discrimination bias which violates the pre-defined Fairness regulation. Results show that the modified DST systems tolerates unfairness effectively while retaining algorithmic accuracy to a satisfying level.
Identifying Distributional Perspective Differences from Colingual Groups

Perspective differences exist among different cultures or languages. A lack of mutual understanding among different groups about their perspectives on specific values or events may lead to uninformed decisions or biased opinions. Automatically understanding the group perspectives can provide essential background for many downstream applications of natural language processing techniques. In this paper, we study colingual groups and use language corpora as a proxy to identify their distributional perspectives. We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages. On a held out set of diverse topics including marriage, corruption, democracy, our model achieves high correlation with human judgements regarding intra-group values and inter-group differences.