Reviewerly/exHarmony huggingface dataset:
Mobile Phone Usage Habits of Chinese Graduate Students and the Training of MTI Interpreters

Starting from the mobile phone usage habits of Chinese graduate students, this paper explores the problems of mobile phone usage habits and the training of MTI interpreters in China, find out that most of Chinese MTI-students use mobile phone more than three hours every day and reveals how to give full play to the positive role of mobile phones and other mobile media to promote the training of talents and the development of professional interpreters.
Relationship between Mammographic Findings and Breast Abnormalities in a Nigerian Population

This study aims to describe the mammographic findings in a population of Nigerian women and to explore the relationships between abnormal mammographic findings, breast malignancy, and breast composition. This was a retrospective study of consecutive mammograms carried out at Union Diagnostics and Clinical Services in Lagos, Nigeria from 2016 to 2018. Demographic information, indications for and findings on mammographic evaluation were obtained. A logistic regression fit model was used to establish the correlation between mammographic findings, breast density, and suspicion for breast malignancy (higher BIRADS scores). P ≤ 0.05 represented a statistically significant result. A total of 304 patients were involved in this study (age range 20-80 years, mean age 49.0 ± 10.5 years). The patients between 40 and 49 years formed the largest age group with 128 patients (42.4%). Most patients were referred for a breast mass/lump (115/304-38.6%); 56 patients (18.8%) presenting for routine screening. The most common finding on the mammograms was BIRADS 4 in both breasts in 96 patients (31.6%). Most patients had heterogeneous breast density (195 patients - 64.1%). Multivariate logistic regression analysis showed a significant correlation between history of mass, poorly defined margins, and suspicion of malignancy. There was no statistically significant association between abnormal mammographic findings and higher breast density. Poorly defined margins were positively correlated with BIRADS ratings suspicious for malignancy. The presence of a breast mass was positively correlated with a higher BIRADS score when other possible cofounding variables were not accounted for. Patient age did not correlate with breast density in this study.
A COMPARATIVE ANALYSIS OF SPECTRAL INFORMATION EXTRACTION ALGORITHMS FOR TARGET DETECTION OF HYDROTHERMAL ALTERATION ZONES USING ASTER SATELLITE IMAGE DATA

Abstract. The focus of this paper is to evaluate the performance of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for target detection of hydrothermal alteration zones associated with geothermal (GT) springs as proxy for narrowing areas of interest. The study employed the Per-pixel Spectral Angle Mapper (SAM) and the Sub-pixel Linear Spectral Unmixing (LSU) algorithms for spectral information extraction by using the ASTER satellite image data. In both cases, image endmember spectra specifically for kaolinite, alunite, and illite and calcite zones were selected and extracted by using the Analytical Imaging and Geophysics (AIG)-developed processing methods. The results of the analysis show that both SAM and LSU discriminated targets of interest better when employing image spectra and poorly when using library spectra. However, the Per-pixel SAM is unsuitable for target detection and more suited where the objective of the investigation is to classify whole scene and not particular targets as in this case. The LSU was found to be effective for discriminating alterations associated with the thermal springs especially where image endmember spectra are employed for analysis, thus recommended for prefeasibility mapping of GT related resources.
Discovering of Personal Name Prefix Patterns in Thai Researcher Corpus and Its Application

In the context of information extraction, a person's name is one of the important named entities to be extracted which are applied to the question-answering and summarizing tasks. However, the boundary of a person's name is still ambiguous since there are several writing patterns of a person's name from online public data sources such as news, events, and researcher corpora. To extract, identify, and unify the person's name, discovering the name prefix can be applied as clue words or phrases to such processes. In this paper, the name prefix discovering framework is proposed for collecting the integrated researcher corpus from various data sources and extracting name prefix patterns. Four main functions of the proposed framework are collecting data from data sources, tagging entities, preprocessing the researcher's names, and finding the pattern of the personal name prefix. In this work, six data sources are gathered and ten entities related to the research domain are focused. The preprocessing data uses three sub-processes to provide the researcher's name. The result shows that the 408 personal name prefixes are extracted. Moreover, the API development for extracting a person or researcher's name is implemented using a Flask Python framework. The output of this work can be used to support the researcher's name identification from the integrated researcher corpus.
Estimating subjective video quality while using smartphone

In this paper, we investigate to estimate the subjective video quality of mobile users in a real situation of daily life. We conduct an experiment to collect bio-signals and sensor data from three subjects for a week as well as their contexts by questionnaire, and machine learning algorithms including Random Forest and k-Nearest Neighbor are applied for the estimation. The experimental results show that the subjective video quality is estimated accurately by Random Forest, while the contribution of users’ context is not confirmed. It suggests that the subjective video quality can be well estimated by bio-signals and sensor data of mobile users in a real situation.
Work-in-Progress—Designing a Multi-User Virtual Reality Environment for Conversational Skills Training of Dietetic Students

Nutritional diseases severely impair the everyday life of affected patients and represent a growing proportion of global disease burden. The diagnosis and treatment of these diseases is mainly provided by trained dietitians. Their profession requires highly developed conversational skills. Immersive virtual reality offers learning environments, which could hardly be delivered by classical teaching methods. Previous research suggests that virtual reality can be successfully used for social skills training. However, we are not aware of existing virtual reality applications that are directed at conversational skills training for dietitians. Therefore, this work-in-progress paper presents the development of an immersive learning environment for dietetics. We have set out to design a multi-user VR environment that enables the training of professional conversations between patients and dietitians in a virtual hospital room. Testing a sample of 30 students, we will evaluate the usability of the learning environment. Furthermore, we are investigating whether subjective feelings of social presence, and cognitive load predict learning satisfaction and task performance. We expect that multi-user virtual reality enables effective conversational skills trainings for dietetics. Consequently, its implementation could benefit curricula that are typically constrained by limited in-situ training opportunities.
Can programmers escape the gentle tyranny of call/return?

Although the call/return architectural style has served as the foundation of much of computing since its existence, it no longer matches a large proportion, probably the majority, of the programs or systems created today.
Doctoral Colloquium—Exploring the Benefits of Using 360<sup>0</sup> Video Immersion to Enhance Motivation and Engagement in System Modelling Education

This paper describes the use of a 360°-video case study to enhance experiential learning in an ICT systems analysis class. We hypothesize that the use of a visual case study combined with virtual instructions can lead to learning motivation and enhanced learner engagement. This examination follows the conceptualization phase of a methodology to investigate the practical uses of the intervention deriving in learning engagement. A sample of 24 participants from an Australian University was considered. The findings of the study reveal a positive impact on the measures provoking learning engagement and motivation among the participants.
Humanizing Headless Communication : Marriage between Graphical and Voice Interface

This paper aims to prove the hypothesis set which is to humanize headless communication. The voice user interface is the application of headless communication which will be covered in this paper and the idea is to find a perfect symbiotic relationship between the voice user interface and the graphical user interface. There are many technologies which has emerged in the market to give a face to voice to make the solution more valuable and impactful for the customers, but each of these technologies have some pros and cons. This paper aims to discuss all those possible options to create a human centered intelligent user interface by weighing the plus and minus of all the technologies available in the market. The main purpose is to share the conceptual idea which has been designed by the team to create an affordable and effective solution for the customers. The team also went a step ahead to visualize and implement the conceptual idea and created a working prototype of the minimum viable product, which is further elaborated in the paper to showcase the demonstration.
Applying hard positive mining and its evaluation for person re-identification

“Person Re-identification” technology has attracted more attention because of the proliferation of surveillance cameras and the increased awareness of crime prevention. However, there are various problems. One of them is that the same person is likely to be recognized erroneously as a different person due to the differences in persons’ appearance. Therefore, in this paper, we propose a method of hard positive mining to achieve better performance in identifying the same person images that are difficult to identify as the same person. The evaluation results using MARS dataset show that the CMS by applying the proposed hard positive mining is higher than the existing method of CNN with temporal pooling. It suggests that it is effective to apply hard positive mining in MARS dataset.
Atom-canonicity in varieties of cylindric algebras with applications to omitting types in multi-modal logic

Fix 2<n<ω and let CAn denote the class of cylindric algebras of dimension n. Roughly, CAn is the algebraic counterpart of the proof theory of first-order logic restricted to the first n variables which we denote by Ln. The variety RCAn of representable CAns reflects algebraically the semantics of Ln. A variety V of Boolean algebras with operators is atom-canonical, if whenever A∈V is atomic, then its Dedekind–MacNeille completion is also in V. We show using a so-called blow up and blur construction that for any k≥2n, any variety of the form SNrnCAk containing (and including) RCAn (when k=ω) is not atom-canonical. We show that a restricted form of that the celebrated Henkin–Orey omitting types theorem, which we refer to below as Vaught's Theorem (VT), fails dramatically for Ln even if we allow only so-called m-square models for 2n≤m≤ω. We deduce that many multi-modal logics, like S5n, the m clique guarded fragments and packed fragments of Ln are not Sahlqvist. In contrast, we prove a positive OTT for Ln theories with respect to usual semantics, by imposing extra conditions (such as quantifier elimination) on possibly uncountable theories considered and/or the non-principal types omitted (that are also allowed to be possibly uncountable) such as completeness.ast is a maximality condition delineating the edge of an independent statement to a provable one. is shown that the maximality condition cannot be removed even to prove the weaker VT for uncountable theories; and this construction is used to reprove a celebrated result of Hirsch and Hodkinson, namely, that the class of completely representable CAns is not el.
Securing Asynchronous Exceptions

Language-based information-flow control (IFC) techniques often rely on special purpose, ad-hoc primitives to address different covert channels that originate in the runtime system, beyond the scope of language constructs. Since these piecemeal solutions may not compose securely, there is a need for a unified mechanism to control covert channels. As a first step towards this goal, we argue for the design of a general interface that allows programs to safely interact with the runtime system and the available computing resources. To coordinate the communication between programs and the runtime system, we propose the use of asynchronous exceptions (interrupts), which, to the best of our knowledge, have not been considered before in the context of IFC languages. Since asynchronous exceptions can be raised at any point during execution-often due to the occurrence of an external event-threads must temporarily mask them out when manipulating locks and shared data structures to avoid deadlocks and, therefore, breaking program invariants. Crucially, the naive combination of asynchronous exceptions with existing features of IFC languages (e.g., concurrency and synchronization variables) may open up new possibilities of information leakage. In this paper, we present MACasync, a concurrent, statically enforced IFC language that, as a novelty, features asynchronous exceptions. We show how asynchronous exceptions easily enable (out of the box) useful programming patterns like speculative execution and some degree of resource management. We prove that programs in MACasync satisfy progress-sensitive non-interference and mechanize our formal claims in the Agda proof assistant.
QR image feature extraction effectiveness based on metrics using spectral clustering and grey level Co-Occurrence matrix algorithm

Generation of key for data security is an important feature in cloud environment. Developing key from extracted feature is used to overcome the problem of mathematical attack and vulnerable. The proposed framework differs from prior work in that user-dependent Quick Response (QR) image are utilized to generate more compact and distinguishable features. This extensive paper aims to make analysis on GLCM (Grey level co-occurrence matrix) Algorithm features of QR image with spectral clustering algorithm features of QR image based on time factor. Which is used to the cloud user chooses the best mechanism based on their resource and requirements and help to make their extraction process effectively.
Thermal Image-Based Object Classification for Guiding the Visually Impaired

Abstract Thermal sensors are now being an emerging technology in image processing applications such as face recognition, fault detection, object detection and classification, navigation, etc. Owing to its versatility, it has been an influential concern for many researchers recently. Thermal sensors have proficiency of sensing the object heedless of the lighting conditions. Due to this added leverage of thermal sensors, we propose a novel scheme for spotting the object, which is targeted by a specific thermal camera. The accomplishment of this task paves the opportunity for guiding the visually impaired (VI) people within the indoor environment adequately. Augmenting the obstacles in the user’s path is requisite for the VI people’s navigation. The image of the object is captured using the thermal camera and pre-processed for enhancing the quality of that image by suppressing the background, tuning the colour channels, etc. Noise in the thermal image is eradicated to a certain extent using Gaussian smoothing process followed by Markov random field for constructing the Gaussian mixture model. Further, the pattern is deduced and classified based on the least-squares support-vector machine. The experiment is tested for disparate timing and distance, and the optimum solution is obtained. To enact the accurate outcome with short estimation period in affordable size and cost is the main added logic behind this fused concept.
A Neoteric Speaker Verification System Using Ant Colony Optimization Algorithm

Automatic Speaker Verification (ASV) systems are mostly being used for authentication of a known identity based on different voice features. The ASV involves a comparison of a speaker's voice with pre-stored training data or speaker model captured during the enrolment phase and also with an impersonator model of non-identical voices differing in gender as well as phone types. Thereafter, a score is assigned by the system and decides whether the test speaker is an enrolled speaker or an imposter. In the comprehensive classification of a certain problem, one of the most important processes is feature subset selection. It includes dimensionality reduction, attribute subset selection and variable subset selection and also an important step to reduce overfitting of data. This paper presents one of the methods to solve this feature selection problem using Ant Colony Optimization algorithm. This algorithm subsumes the performance of the classification into state transition rule. The optimal feature set is determined with the help of the pheromone from the ant colony. Finally, a feature set with maximum classification accuracy and small size is selected.
Panel session: How could we build a community for sharing annotation activities, methods, and outcomes?

Summary form only given, as follows. A complete record of the panel discussion was not made available for publication as part of the conference proceedings. we discuss means to develop the sustainability, reusability and ultimately the impact of our work. In so doing, we draw insight from the approaches taken in other domains. For example, the fields of metadata and subject indexing publish and regularly update design artefacts such as taxonomies, ontologies and knowledge schemas. The sustainability of data publication is limited by the long-term availability of the platforms on which they are published and the technologies on which they depend. A prior study has found that many knowledge structures are not formally published, leading to low preservation of these artefacts. Standardisation efforts may significantly increase the likelihood of uptake and ongoing availability of these artefacts, which are expensive and often time-consuming to create. What can we learn from practices in other fields, or gain from making use of the outcomes of prior investigations in this area? Knowledge structures take time and effort to produce, test and validate: are there emerging 'de facto' standards in the field that may benefit from being brought forward toward formal standardisation?
A Comparative Analysis of Capsule Networks in Content based Image Retrieval tasks: CapsCBIR

This paper presents a comparative study and analysis for content-based image retrieval tasks by employing two deep learning architectures - Capsule Siamese Network, and Capsule Triplet Network. The performance of these architectures are compared with various conventional feature extraction techniques as well as state-of-the-art methods in content-based image retrieval and Capsule Network as a standalone feature extractor.
Image Retrieval Using Bat Optimization and Image Entropy

Technological advancements have increased the size of image databases and their applications in different fields. This has also increased the importance of image processing and its subfields like image classification, image segmentation, image retrieval, image enhancement, image compression, image restoration etc. This is also the reason behind the increase in the number of research works on image retrieval. It has inspired people to develop new techniques on image retrieval. Text-based retrieval techniques were gradually replaced by content based image retrieval techniques. Content based image retrieval techniques primarily based on the retrieval of images based on primitive features like colour, shape, texture etc. The proposed method considers image entropy and the Bat algorithm for image retrieval. Entropy helps to find the degree of randomness in the images which can be analysed to obtain their similarity. Bat algorithm is applied to obtain optimal values based on which most similar images are retrieved. For experimental analysis of the proposed technique, both medical and nonmedical images are considered. The results obtained prove the effectiveness of the proposed approach in the retrieval of different categories of images.
L<sup>2</sup>- A Mini Game for Learning Indian Language Vocabulary

Language Learning can lead to the development of one's communication abilities and helps individuals to express, read, write, and think. Many tools and technologies have been proposed to make language learning an easy and enjoyable process. Games are one of the ways that aid in an informative, exciting, and fun learning experience. Thus, we propose L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to help novice language learners to grasp the Indian language vocabulary based on the clues of object images. L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is a 3D game where the player is provided with a cloud of letters and an image of object to interpret. Observing and understanding the image, the player chooses a set of letters that would form a word corresponding to it. On choosing the right set of letters, the player gets promoted to the next level; otherwise, the player is asked to restart the level. We implemented L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for two Indian languages, namely Hindi and Telugu, using Unity 3D. We evaluated this game with 22 participants and got a favourable response from 89% of the users.
A Survey for Validation Concepts to Measure Quality as Well Their Application on the Maintainability of ISO

To reduce the cost of maintenance, software quality is more and more studied. In this area, to present software quality, many models have appeared, among ISO 25000. In other side, to evaluate software quality, several metrics have appeared. The two axes do not have a clear link to assert a complete evaluation. Our work consists to establish the link between this two axes by adding an intermediate layer containing the quality concepts. This work was applied to ISO 25000 model, and to validate it we were establish a survey on this application. In this paper, we present a new results on our survey that aims to validate our work.
Governance and Organizational Structure

Governance of a simulation program refers to the structure and processes through which the program ensures it is doing the right things in the right way. The right things include ensuring: that program activities are supportive of the mission; that activities comply with applicable laws and regulations as well as with program and organizational policies; and that information and communication flows allow consistent, accurate monitoring of program activities and outcomes.
Synthesizing user interfaces using functional reactive web abstractions

In this paper, we describe a novel approach to synthesizing web user interfaces (UI) from declarative specifications given in a domain-specific language (DSL) that can later be read and written by natural language processing (NLP) techniques via human language interaction. We make use of F#'s metaprogramming capabilities and our reactive forms library developed for WebSharper. Our ultimate goal is to enable end users to describe entire web application UIs and carry on a conversation with our system, describing incremental changes to apply on an ongoing specification.