Reviewerly/exHarmony huggingface dataset:
REFINING THE PIE METHOD (PRESELECTED ITEMS EVALUATION) IN TRANSLATOR TRAINING

This paper explains how to use the PIE method, a criterion- and norm-referenced analytical translation evaluation method, with particular emphasis on translator training. In addition, it sheds light on the test construct and the preparation phase that precedes the PIE evaluation. The source text and item selection, as well as the dichotomous categorisation of translation solutions are discussed in detail. This paper also clarifies and refines two contentious issues in the psychometric component of the PIE method. Firstly, the p-value range, which the authors have revised. Secondly, the calculation of the d-index by means of the extreme groups method. The authors propose two additional methods to calculate the d-index, viz., the adjusted and unadjusted item-total correlation, which, unlike the extreme groups method, take into account all test takers rather than just a set percentage of test takers. Keywords: PIE, Preselected Items Evaluation, translation evaluation, translator trainingl test takers rather than just a set percentage of test takers.
Keynote Speech 2: An overview of Decentralized Autonomous Organizations in the blockchain

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Blockchain technology has enabled a new kind of distributed system. It has also allowed the emergence of novel new ways of governance and coordination. The most relevant of these are the socalled Decentralized Autonomous Organizations (DAOs). DAOs typically implement decision-making systems to make it possible for their online community to reach agreements. As a result of these agreements, the DAO operates automatically by executing the appropriate portion of code on the blockchain network (e.g., hire people, delivers payments, invests in financial products, etc). In the last few years, several platforms such as Aragon, DAOstack and DAOhaus, have emerged to facilitate the creation of DAOs. As a result, hundreds of new organizations have appeared, with their communities interacting mediated by blockchain. However, academia has yet to appropriately explore empirically this phenomenon. In this talk, we aim to shed light on the state of the DAO ecosystem. For doing so, we will review the three main platforms that facilitate the creation and management of DAOs nowadays: Aragon, DAOstack and DAOhaus. We will introduce their main similarities and differences, compare their adoption and how they are evolving. We will also review some statistics about the DAOs that these platforms host. This overview will also help us to understand which kind of communities are populating the blockchain and what are the similarities and differences with other online communities.
Making Music, Finishing Music – An Inquiry Into the Music-Making Practice of Popular Electronic Music Students in the “Laptop-Era”

In this study we seek to present a description of how bachelor and masters students in popular electronic music experience making original music in their chosen Digital Audio Workstation (DAW). The chapter focuses on how the participants understand their role while making music afforded by the DAW environment, their strategies for getting started when making music, and the challenges they experience when finishing music. In the study we interviewed six students at bachelor and masters level. We see a tendency in how participants attribute the technical component of music making as the defining aspect of the producer role. The respondents seem to understand themselves as primarily producers when making music in the DAW environment. When starting out with a new song, most of the respondents describe an experience of flow that gradually dissolves as the structure of the song emerges and their inner critique gains foothold. The respondents concur on the challenges of finishing music in the rich decision-making environment that the DAW affords. We conclude by emphasizing the importance of students developing their own creative strategies suited to their unique music-making practice. We argue that the students need to become self-aware of their strengths and weaknesses in order to develop such creative strategies. Arguably, teaching practice that facilitates such meta-learning is therefore highly relevant in higher electronic music education. This is especially relevant in the DAW environment where discipline is required in order to stop fiddling with details and release their music to the world.
Directional Adversarial Training for Robust Ownership-Based Recommendation System

Machine learning algorithms are susceptible to cyberattacks, posing security problems in computer vision, speech recognition, and recommendation systems.So far, researchers have made great strides in adopting adversarial training as a defensive strategy.Single-step adversarial training methods have been proposed as viable solutions for improving model generality and resilience.However, there has been little study to address this issue in the context of ownership-based recommendations, which may fail to capture stable results.In this work, we adapt the single-step adversarial training for ownership recommendation systems.Our main technical contributions are as follows: (1) We propose Adversarial Consumption and Production Relationship (ACPR), a model that combines factorization machine and single-step adversarial training for ownership recommendations.It enables us to take advantage of modeling consumption-production interactions with a factorization machine instead of the conventional matrix factorization method for ownership recommendations.(2) We enrich the ACPR technique with directional adversarial training and call our technique Adversarial Consumption and Production Relationship-Aware Directional Adversarial Model (ACPR-ADAM).The idea behind our ACPR-ADAM is that instead of the worst perturbation direction, the perturbation direction in the embedding space is restricted to other examples in the current embedding space, allowing us to incorporate the collaborative signal into the training process.Lastly, through extensive evaluations on Reddit and Pinterest, we demonstrate that our proposed method outperforms state-of-the-art methods.Compared with CPR and ACPR on Reddit and Pinterest datasets, our proposed ACPR-ADAM achieves 93%, 88%, and 72%, 69% enhancement in terms of AUC and HR, respectively.
Semantic and context features integration for robust object tracking

Siamese network-based object tracking learns features of a target object marked in the first frame and that of the object in subsequent frames simultaneously and then measures similarity between two features to recognize and locate the object. Owing to their efficiency and high accuracy, Siamese networks have attracted much attention recently. However, tracking accuracy decreases significantly when there are scale changes, occlusion, and pose variations due to the way that Siamese networks estimate feature similarity. To address this issue, the authors propose a tracking algorithm, named Semantic and context features integration for robust object tracking that integrates local and global features of the object. Local features provide context information for tracking parts of the object, while global features contain semantic information for tracking the object. The authors meticulously design local and global classification and regression heads and integrate them into one uniform framework to achieve integration tracking. This method effectively alleviates low accuracy in complex scenes such as scale changes, deformation, and occlusion. Numerous experiments demonstrate that this method achieves state-of-art (SOTA) performance with 45 FPS on a single RTX2060 Super GPU on public tracking datasets, including VOT2016, VOT2019, OTB100, GOT-10k, and LaSOT, and its effectiveness and efficiency is confirmed.
Improving segmentation reliability of multi-scanner brain images using a generative adversarial network

Magnetic resonance (MR) images generated by different scanners generally have inconsistent contrast properties, making it difficult to perform a combined quantitative analysis of images from a range of scanners. In this study, we aimed to develop an automatic brain image segmentation model to provide a more reliable analysis of MR images taken with different scanners.The spatially localized atlas network tiles-27 (SLANT-27) deep learning model was used to train the automatic segmentation module, based on a multi-center dataset of 1,917 three-dimensional (3D) T1-weighted MR images. Subsequently, a framework called Qbrain, consisting of a new generative adversarial network (GAN) image transfer module and the SLANT-27 segmentation module, was developed. Another 3D T1-weighted MRI interscan dataset of 48 participants who were scanned in 3 MRI scanners (1.5T Siemens Avanto, 3T Siemens Trio Tim, and 3T Philips Ingenia) on the same day was used to train and test the Qbrain model. Volumetric T1-weighted images were processed with Qbrain, SLANT-27, and FreeSurfer (FS). The automatic segmentation reliability across the scanners was assessed using test-retest variability (TRV).The reproducibility of different segmentation methods across scanners showed a consistent trend in the greater reliability and robustness of QBrain compared to SLANT-27 which, in turn, showed greater reliability and robustness compared to FS. Furthermore, when the GAN image transfer module was added, the mean segmentation error of the TRV of the 3T Siemens vs. 1.5T Siemens, the 3T Philips vs. 1.5T Siemens, and the 3T Siemens vs. 3T Philips scanners was reduced by 1.57%, 2.01%, and 0.56%, respectively. In addition, the segmentation model improved intra-scanner variability (0.9-1.67%) compared with that of FS (2.47-4.32%).The newly developed QBrain method combined with GAN image transfer module and a SLANT-27 segmentation module was shown to improve the reliability of whole-brain automatic structural segmentation results across multiple scanners, thus representing a suitable alternative quantitative method of comparative brain tissue analysis for individual patients.
Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution

Despite significant recent progress in coreference resolution, the quality of current stateof-the-art systems still considerably trails behind human-level performance.Using the CoNLL-2012 and PreCo datasets, we dissect the best instantiation of the mainstream end-toend coreference resolution model that underlies most current best-performing coreference systems, and empirically analyze the behavior of its two components: mention detector and mention linker.While the detector traditionally focuses heavily on recall as a design decision, we demonstrate the importance of precision, calling for their balance.However, we point out the difficulty in building a precise detector due to its inability to make important anaphoricity decisions.We also highlight the enormous room for improving the linker and show that the rest of its errors mainly involve pronoun resolution.We propose promising next steps and hope our findings will help future research in coreference resolution.
Evaluating the Learning Procedure of CNNs through a Sequence of Prognostic Tests Utilising Information Theoretical Measures

Deep learning has proven to be an important element of modern data processing technology, which has found its application in many areas such as multimodal sensor data processing and understanding, data generation and anomaly detection. While the use of deep learning is booming in many real-world tasks, the internal processes of how it draws results is still uncertain. Understanding the data processing pathways within a deep neural network is important for transparency and better resource utilisation. In this paper, a method utilising information theoretic measures is used to reveal the typical learning patterns of convolutional neural networks, which are commonly used for image processing tasks. For this purpose, training samples, true labels and estimated labels are considered to be random variables. The mutual information and conditional entropy between these variables are then studied using information theoretical measures. This paper shows that more convolutional layers in the network improve its learning and unnecessarily higher numbers of convolutional layers do not improve the learning any further. The number of convolutional layers that need to be added to a neural network to gain the desired learning level can be determined with the help of theoretic information quantities including entropy, inequality and mutual information among the inputs to the network. The kernel size of convolutional layers only affects the learning speed of the network. This study also shows that where the dropout layer is applied to has no significant effects on the learning of networks with a lower dropout rate, and it is better placed immediately after the last convolutional layer with higher dropout rates.
A new utility‐aware anonymization model for privacy preserving data publishing

Abstract Most of data in various forms contain sensitive information about individuals and so publishing such data might violate privacy. Privacy preserving data publishing (PPDP) is an essential for publishing useful data while preserving privacy. Anonymization, which is a utility based privacy preserving approach, helps hiding the identities of data subjects and also provides data utility. Since data utility is effective on the accuracy of analysis model, new anonymization algorithms to improve data utility are always required. Mondrian is one of the near‐optimal anonymization models that presents high data utility and is frequently used for PPDP. However, the upper bound problem of Mondrian causes a decrease in potential data utility. This article focuses on this problem and proposes a new utility‐aware anonymization model (u‐Mondrian). Experimental results have shown that u‐Mondrian presents an acceptable solution to the upper bound problem, increases total data utility and presents higher data utility than Mondrian for different partitioning strategies and datasets.
Do We Know What We Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0

Understanding when a text snippet does not provide a sought after information is an essential part of natural language understanding.Recent work (SQuAD 2.0, Rajpurkar et al., 2018) has attempted to make some progress in this direction by enriching the SQuAD dataset for the Extractive QA task with unanswerable questions.However, as we show, the performance of a top system trained on SQuAD 2.0 drops considerably in out-of-domain scenarios, limiting its use in practical situations.In order to study this we build an out-of-domain corpus, focusing on simple event-based questions and distinguish between two types of IDK questions: competitive questions, where the context includes an entity of the same type as the expected answer, and simpler, noncompetitive questions where there is no entity of the same type in the context.We find that SQuAD 2.0-based models fail even in the case of the simpler questions.We then analyze the similarities and differences between the IDK phenomenon in Extractive QA and the Recognizing Textual Entailments task (RTE, Dagan et al., 2013) and investigate the extent to which the latter can be used to improve the performance. 1
On the Potential of Modular Voice Conversion for Virtual Agents

Virtual Agents are a way to give humans a familiar way to interact with the computer. An important component in the design of virtual agents is the voice with which they express themselves. The voice is not only a mere medium for information transfer, but also contains non-verbal functions such as the transmission of emotions. Additionally, in the context of virtual agents, it is important that the user accepts the voice of the agent and considers it consistent. To make this possible, it is necessary that such voices are highly customisable and adaptable. Current systems for generating speech from text are conceptually limited by the fact that a large part of their task is to model the semantics of what is spoken. Systems in the field of voice conversion, however, are decoupled from this, as they only need to model non-verbal features. Such systems become particularly efficient when they are limited to the transformation of dedicated, single characteristics. This paper proposes that the use of such voice conversion systems, and furthermore the exploitation of the possibility to cascade them, can be an immense improvement for conventional Text-to-Speech systems for virtual agents.
Users Versus Non-users: The Impact of Experience on Hotel Guests’ Attitudes Towards Service Robots in Hotels

Abstract The use of robotics and artificial intelligence have created a shift in the ways the service-based hospitality and tourism industry can fulfill the needs and wants of consumers that were earlier fulfilled only by humans. Robots have added the automation and self-service experience that play a vital role in the improvements of efficiency, speed, and the overall experience for the guests using technology. While there are many benefits of using robots in the industry, there are also risks associated with the excessive usage of robots on guest experience. As a result of the pros and cons on the topic, it is very important to gather data and analyze the results to further investigate and understand what the outcomes will be for the industry, its employees, and its customers. The purpose of this study is to examine the perceptions of the use of robots in the hotels as perceived by hotel guests who used a service robot and who did not. A self-administered survey was developed, and 939 usable responses were collected from hotel guests. Factor analysis showed that five factors emerged in the study: Advantages, Attitudes, Disadvantages, Pandemic Related, and Fear. Guests recognize the opportunities that service robots are bringing to their experience while voicing their concerns and fears about the use of them. Findings also showed that there are significant differences between users and non-users.
The Role of Internal Communication, Work Discipline, And Employee Loyalty on Employee Performance

This study aims to determine and examine the significant influence of internal communication, work discipline, and employee loyalty on the performance of PT.Perkebunan Nusantara XII (Persero).This research is categorized as explanatory research.The population of this research is all field employees as many as 305 employees and the sample taken is 200 employees.The data analysis method used in this research is the Structural Equation model using PLS.The results of this study indicate that internal communication has a significant effect on the performance of the field employees of PT.Perkebunan Nusantara XII (Persero), work discipline has a significant effect on the performance of the field employees of PT.Perkebunan Nusantara XII (Persero), and employee loyalty have a significant effect on the performance of the field employees of PT.Perkebunan Nusantara XII (Persero).
HUBUNGAN ANTARA KEBIASAAN BELAJAR DAN MOTIVASI BELAJAR DENGAN PRESTASI BELAJAR MATEMATIKA SISWA SMP SELAMA PEMBELAJARAN DARING

The purpose of this study was to determine the correlation between study habits and mathematics learning outcomes, the correlation between learning motivation and mathematics learning outcomes, and the correlation between learning habits and motivation to learn mathematics outcomes during online learning. The total population is 185 students and the sample size is 123 students with the sampling technique using the Slovin formula approach. Hypothesis testing using multiple regression analysis techniques, Pearson Correlation test, F test, and a coefficient of determination of 5%. Learning habits and motivation are measured by a questionnaire that has been declared valid by the validator and has been tested for validity and reliability on 60 respondents outside the research subject. Data collection to measure learning habits and motivation is done by distributing questionnaires, while for student achievement it is obtained from documentation of math scores. The results showed that: there was a positive and significant correlation between study habits and students' mathematics learning outcomes. The correlation between the two is moderately correlated; there is a positive and significant correlation between learning motivation and student outcomes in mathematics. The correlation between the two is strongly correlated; there is a positive and significant correlation between study habits and learning motivation with students' mathematics learning outcomes. The correlation between the two is strongly correlated. The contribution of study habits and learning motivation simultaneously to mathematics learning outcomes is 30.7%, while 69.3% is influenced by other factors not examined by the researchers in this study.&#x0D;
A BERT and Topic Model Based Approach to reviews Requirements Analysis

With the rise of mobile applications, user reviews are an important avenue of user feedback in which users may mention different issues in using the software. For example, unresponsiveness, low-level privacy, etc. In order to extract effective requirement information and problematic feedback from these huge user reviews, this paper proposes a review non-functional requirement analysis method based on BERT model and topic model (NRABL). Firstly, we use BERT model to classify the reviews with multi-labels classification and then use LDA (latent Dirichlet allocation) to extract topic and review analysis, this method can help developers to quickly understand the user's requirements and the specific usage problems.
Benchmark construction and experimental evaluations for incoherent ontologies

As the core building blocks of the Semantic Web, ontologies provide shared vocabularies and conceptual knowledge for specific application fields. At the same time, ontologies can restrict their individuals and relationships through their semantic schema. However, logical conflicts of an ontology are often inevitably unavoidable in actual application scenarios when the ontology contains any form of disjointness or negation. Generally, logical conflicts can be divided into incoherence and inconsistency. Reasoning with incoherent ontologies may obtain many redundant relationships, and incoherence is a potential cause of inconsistency which seriously affects the correctness of semantic reasoning Therefore, handling incoherence is imperative, which mainly involves the fields of conflicts detection, ontology repair and justification computation. Various incoherent ontologies are indispensable to evaluate the proposed methods for handling incoherence. We provide a survey of relevant research works to study the proposed construction methods of incoherent ontologies. It is observed that incoherent ontologies in existing works still have the following limitations: (1) Lots of web pages used to download ontologies were temporarily constructed are now inaccessible; (2) Most of the existing incoherent ontologies were constructed in a simple way such as randomly adding disjointness axioms or merging ontologies through their alignments without considering all possible incoherence cases. To address the limitations, we propose a general framework to construct incoherent ontologies and design a hybrid algorithm to instantiate this framework. With the implemented construction methods, a comprehensive benchmark containing 116 ontologies is constructed. In our evaluations, incoherent ontologies in the benchmark are measured with 11 classic metrics. We then compare 5 representative ontology debugging systems and 3 repair methods. The evaluation results reveal that these ontologies could reflect different characteristics of each ontology system and repair method. All observations could guide researchers to select incoherent ontologies. The availability of our benchmark makes a contribution to the community of ontology debugging and repair fields.