Reviewerly/exHarmony huggingface dataset:
A Bayesian nonparametric topic model for repeated measured data

Topic models are currently used in many fields, particularly for marketing or medical science data analysis, often where an individual subject is repeatedly measured. A topic tracking model (TTM) that can consider the persistency of topics of individual subjects has been already proposed. Although the TTM estimates several parameters for each timepoint through online learning, offline learning should be utilized for analyses of preexisting data sets. Additionally, when a topic model is used, the number of topics should be decided in advance. However, deciding an appropriate number of topics is often difficult. Therefore, we propose a TTM with offline learning and a Bayesian nonparametric TTM (BNPTTM) for time-series data sets where data from individual subjects are repeated measures. The performance of the proposed topic model is evaluated using an actual prescription data set. Our results suggest that the TTM with offline learning has better predictive ability than the existing TTM, and the BNPTTM can deduce the number of topics from a given data set.
Linguistic Evidence of Ageing in the Pratchett Canon

A corpus of literary works authored by Sir Terry Pratchett is analyzed from the perspective of linguistic variables which from the literature on natural ageing, one might expect to show effects of gradual change and periods without change. Aspects of lexical complexity exhibit trends that diverge from patterns associated healthy ageing. Background: Past study of linguistic healthy ageing has analyzed corpora of individual professional writers over time and cross-sectional corpora constructed with writing samples from individuals in distinct age groups. Among other effects, negative linear correlations have been found between the use of both first person singular and first person plural pronouns and age and between past-tense verb forms and age; positive linear correlations have been found between cognitive complexity features and age. Main goal: This study seeks to contribute to understanding of whether Alzheimer's disease is accompanied by the same, accelerated or distinct patterns of change in linguistic features associated with healthy ageing. Method: A corpus of works published by Sir Terry Pratchett is analyzed with respect to pronoun use and linguistic signals of cognitive complexity, such as embedding words and lexical variety, testing correlations between those quantities and author age. Results: The Pratchett corpus exhibits effects in the opposite direction of those associated with healthy ageing for: first person pronoun use and long words. Lexical variety strongly diminishes over the corpus. Conclusions: Linguistic data produced by individuals diagnosed with Alzheimer's disease appears to contain signals of the fact, when analyzed post hoc. This suggests further work to identify inflection points and to study further features that may be indicative as the linguistic data is produced.
Cascade regression based on extreme learning machine for face alignment

Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data are available in advance. When new training samples arrive, the static model must be retrained from scratch, which is excessively time-consuming and memory-consuming. It results in the limitation of its performance on sequential images with extensive variations. Therefore, the most critical and challenging aspect in this field is how to enhance the predictive capability of pretrained model incrementally. To that end, a fast and accurate online learning algorithm for face alignment is proposed. Particularly, extreme learning machine (ELM) is incorporated into a parallel cascaded regression (CR) framework, which we coin parallel cascade regression based on extreme learning machine (CRELM). The proposed model can be fast updated in an incremental way. It has a stronger prediction capability than conventional CR methods. The experimental results demonstrate that the proposed model is more accurate and efficient on still images or videos compared with the recent state-of-the-art approaches.
Positive Free Higher-Order Logic and Its Automation via a Semantical Embedding

Free logics are a family of logics that are free of any existential assumptions. Unlike traditional classical and non-classical logics, they support an elegant modeling of nonexistent objects and partial functions as relevant for a wide range of applications in computer science, philosophy, mathematics, and natural language semantics. While free first-order logic has been addressed in the literature, free higher-order logic has not been studied thoroughly so far. The contribution of this paper includes (i) the development of a notion and definition of free higher-order logic in terms of a positive semantics (partly inspired by Farmer’s partial functions version of Church’s simple type theory), (ii) the provision of a faithful shallow semantical embedding of positive free higher-order logic into classical higher-order logic, (iii) the implementation of this embedding in the Isabelle/HOL proof-assistant, and (iv) the exemplary application of our novel reasoning framework for an automated assessment of Prior’s paradox in positive free quantified propositional logics, i.e., a fragment of positive free higher-order logic.
Implicit and Explicit Regularization for Optical Flow Estimation

In this paper, two novel and practical regularizing methods are proposed to improve existing neural network architectures for monocular optical flow estimation. The proposed methods aim to alleviate deficiencies of current methods, such as flow leakage across objects and motion consistency within rigid objects, by exploiting contextual information. More specifically, the first regularization method utilizes semantic information during the training process to explicitly regularize the produced optical flow field. The novelty of this method lies in the use of semantic segmentation masks to teach the network to implicitly identify the semantic edges of an object and better reason on the local motion flow. A novel loss function is introduced that takes into account the objects' boundaries as derived from the semantic segmentation mask to selectively penalize motion inconsistency within an object. The method is architecture agnostic and can be integrated into any neural network without modifying or adding complexity at inference. The second regularization method adds spatial awareness to the input data of the network in order to improve training stability and efficiency. The coordinates of each pixel are used as an additional feature, breaking the invariance properties of the neural network architecture. The additional features are shown to implicitly regularize the optical flow estimation enforcing a consistent flow, while improving both the performance and the convergence time. Finally, the combination of both regularization methods further improves the performance of existing cutting edge architectures in a complementary way, both quantitatively and qualitatively, on popular flow estimation benchmark datasets.
A Methodological Reflection: Deconstructing Cultural Elements for Enhancing Cross-Cultural Appreciation of Chinese Intangible Cultural Heritage

This paper presents a practical method of deconstructing cultural elements based on the Human Computer Interaction (HCI) perspective to enhance cross-cultural appreciation of Chinese Intangible Cultural Heritage (ICH). The author pioneered this approach during conducting two case studies as a means to enhance appreciation and engagement with Chinese ICH, such as the extraction of elements from traditional Chinese painting and puppetry with potential to support cross-cultural appreciation, as well as the establishment of an elements archive. Through integrating a series of HCI research methods, this approach provides a specific foundational framework that assists non-Chinese people to better understand the cultural significance of Chinese ICH.
Mental Effort and Usability of Assistance Systems in Manual Assembly – A Comparison of Pick-to-Light and AR Contours Through VR Simulation

In our experimental user test, 25 healthy participants assembled hydraulic valves and geometric figures using two assistive functions (Pick-to-light (PtL) and Augmented Reality (AR) contours at the place of installation). We used virtual reality (VR; HTC Vive) to simulate the workplace and the assistance functions and compared the mental effort and the usability between PtL and AR. We also examined the applicability of our methodology to further studies in this field. The mental effort was higher when using the AR contours than when using the Pick-to-light. The participants rated the usage suitability of both alternatives as approximately equal regarding the perceived usefulness, perceived ease of use and dialogue principles of the ISONORM. However, the System Usability Scale (SUS) showed a better usability with the PtL. We found low to medium negative correlations of mental effort with usability dimensions. In general, VR simulations and the methods applied appear to be suitable to investigate user-centric aspects of the use of digital assistance systems in manual assembly. However, in order to broaden our understanding of health standards and productive work, we recommend further field studies and participants of more varied ages.
Trusted Cloud Computing

This chapter introduces various ideas to deal with insider attacks using the research directions, which are discussed in earlier chapters such as remote attestation, sealed storage, and integrity measurement. Trusted computing dependent on hardware root of trust has been produced by industry to secure computing frameworks and billions of end points. Remote attestation provides a facility to attestation the required platforms using platform configuration registers (PCR), and sealed storage is used to encrypt the consumer sensitive data using cryptographic operations. Integrity measurements are used to measure the given computing components in respective register. Here, the authors concentrated on a trusted computing paradigm to enable cloud service providers to solve the potential insider attacks at cloud premises.
Development of a System Based on a Fuzzy Knowledge Base Filled from Various Formats Documents

The article describes the fuzzy knowledge base developing process. The fuzzy knowledge base content represents a knowledge extracted from set of various documents by subject area. Set of documents contains of the wiki-resources, uml-diagrams, text documents and projects source code. A fuzzy knowledge base was implemented on the Neo4j database. An attempt to implement the mechanism of logical inference based on concepts, relationships and rules of a graph database was made. This mechanism is used for generate the screen forms of the user interface dynamically. The contexts allow to represents the content of the fuzzy knowledge base in space and time. Each unique context was assigned as a linguistic label, for example, low, middle, high. This label determines the competence of the expert in the subject area. Time contexts allows storing the history of knowledge base content changes. It allows holding and obtaining a specific state of the knowledge base contents.
Mathematical Philosophy and Fuzzy Logic

Mathematics in general is an abstraction. Of course, if it is not an easy subject, but in the same time it is rewarding one. We have to keep in mind that Mathematics is a part of our culture. In developed, developing and underdeveloped countries nobody can count himself truly good educated without Mathematical Concepts. Perhaps one can say that all individuals have to understand in some degree what Mathematics is and what it does. Unfortunately it has always been a great problem in Mathematics, despite the publication of hundreds of Mathematical books, to understand the essential meaning of it. Above all it is a human subject with its own concepts, axioms, postulates etc. [1, 2, 4].
How to Design Potential Solutions for a Cross-country Platform that Leverages Students’ Diversity: A User-Centered Design Approach – and Its Challenges

This paper outlines a methodological perspective within the research question of how to design potential solutions for a cross-country platform that leverages students' diversity to address their everyday challenges at the university level. Overall, students' challenges can have different nuances based on academic, social, and psychological factors. This paper provides an applied approach into scenarios, personas, and blueprints. We used the double diamond design process model to design the multi-platform service. This paper will mainly focus on a case study from Mexico within its define and development stages, including suggestions for how to design potential solutions. Our study is based on interviews and workshops with 250 university students and 10 university staff members. Our findings reveal that the service blueprint work was an effective tool to provide an overarching view of the service and its components. The service blueprint allowed the design team to mentally zoom in and out during the design process and helped the larger team to generate new ideas and discuss current elements of the service being offered. However, the service blueprint is also time-consuming and demands significant structured coordination.
A Methodology for Designing Meaningful Interactions in Public Space

As digital computing and embedded technologies are increasingly pervasive and accessible in public spaces, interactions with technology-mediated physical environments become the focus of a growing amount of research in human-computer interaction (HCI). This study develops a new methodology towards designing meaningful interactions in public spaces. Based on the theories of embodied cognition, we conducted case analyses, experience tests on an interactive prototype, and design ideations. The findings show that the methodology is useful for the design of meaningful interaction through linking physical interactions with embodied metaphorical expressions. Related design principles and research limitations are summarized.
Multi-pose Face Recognition Based on Block Adaptation

Considering that the SRC algorithm cannot solve the error offset problem between the testing and training samples, we propose one method as the affine transformation and partition by integrating into the linear reconstruction model, which were named as block adaptive multi-pose face recognition algorithm (BA-SRC). In this method, we first model the pose change using the affine transformation model for the face after the human face was blocked. Then we estimate the initial value of the affine transformation parameter by minimizing the image block reconstruction error, and then compensate the local area error caused by pose change, so as to improve the performance of face recognition. The experiments show that the algorithm proposed in this paper is very robust to pose change, and has a good recognition result.
A kind of artificial intelligence model based on nature without statistic

Abstract Simple rules can generate complexity and human can also learn fast with rules. Can machines learn in a similar way? Can artificial intelligence be independent of statistics? Machine learning is growing rapidly but models are poorly interpretable and depend on statistics. We propose a method by iteration based on causality which is the real one exists in the system. It is composed of fixed goals and basic rules called DNA rules. These DNA rules can be obtained from the definition and is not statistical rules. The causality in rules promises the process to be precise, because the potential attractor of the system is deterministic, because it is subjected to the rules although the system is complex especially under uncertain interference. Such a model not only works well in the traditional deterministic systems like the stable point and limited circle but also can work in some seemingly random and systems which are considered to be stochastic systems. The model is taken to play a game and it makes the machine learns fast and adaptively, and it is also interpretable with the causality and independent from the amount of data for it is based on causal iteration. It learns and even predicts the seemingly random interference in the game. We found such a model is adaptable, and it works well even in out-of-sample situations. The model is compared with an LSTM network in prediction a seemingly random sequence, the result shows the causality-based model also works well. We think that it may help to solve some problems hard for the traditional statistical method and become an enrichment for the current models.
An Exploration of How Artists Use Immersive Technologies to Promote Inclusivity, Diversity and Deep Public Engagement in Ethical Ways

The research investigates the relationship between artists, technology, and their audience.It documents their evolving creative practice as they engage with emerging technologies and where they use these as tools for access, exchange and deep engagement.The benefits and challenges of using immersive technology are explored.This includes user interface / experience, interoperability and standardisation.Methods of collaboration across a range of cultures of usercommunities are explored.How do artists engage with some of these ethical questions as they use digital technologies and the capture of data?Our interviewees include artists, art professionals from galleries, museums and online virtual spaces.Interviewees were approached to include different technical, professional and social backgrounds.Fine Art.Tilt Brush.Artificial intelligence.
On the Importance of Adaptive Operator Training in Human-Swarm Interaction

Human-swarm interaction (HSI) as a research discipline can be seen as a combination of swarm robotics and human factors engineering. In this work, we combine perspectives from cognitive systems engineering and systems science to discuss the importance of operator training in HSI and how training of swarm dynamics may be implemented. The concept of neglect benevolence, i.e. temporal sensitivity in swarm control, is described as a case example for operator training in HSI. We propose the application of adaptive instructional systems to (1) optimize mission performance, (2) increase understanding of swarm dynamics and (3) allocate mental workload to different work demands over a variety of complexities. Future empirical investigations must show the utility of the proposed concepts.
He..ro DB: A Concept for Parallel Data Processing on Heterogeneous Hardware

Due to the growing demand on processing power and energy efficiency by today’s data-intensive applications developers have to deal with heterogeneous hardware platforms composed of specialized computing resources. These are highly efficient for certain workloads but difficult to handle from the software engineering perspective. Even state-of-the-art database management systems do not exploit all heterogeneous hardware components, as their characteristics differ significantly. They are thus hard to integrate within a coherent database architecture. To address this problem, we propose a design concept that is based on a layered system software architecture: He..ro DB transforms a data-flow graph that describes the data-processing application to a task-based execution plan. Task implementations for the different computing resources and a reasonable degree of parallelism are chosen automatically based on available resources. The concept can cover any hardware configuration and application scenario. It is versatile and offers opportunities for independent optimization on each layer.
Interactive AR Models in Participation Processes

It is getting more and more important to enable stakeholders from different backgrounds to collaborate efficiently on joint projects. Physical models provide a better understanding of spatial relationships while using video mapping of suitable visualizations enables a meaningful enrichment of information. We therefore developed a demonstrator using a physical architectural model as base and projected additional data via video mapping onto it. In this paper, we describe the initial situation and the requirements for the development of our demonstrator, its construction, the software developed for this purpose, including the calibration process as well as the implementation of tangible interaction as a means to control data and visualizations. In addition, we describe the whole user interface and lessons learned. Ultimately, we present a platform that encourages discussions and can enrich participation processes.
BoVW model based on adaptive local and global visual words modeling and log-based relevance feedback for semantic retrieval of the images

The core of a content-based image retrieval (CBIR) system is based on an effective understanding of the visual contents of images due to which a CBIR system can be termed as accurate. One of the most prominent issues which affect the performance of a CBIR system is the semantic gap. It is a variance that exists between low-level patterns of an image and high-level abstractions as perceived by humans. A robust image visual representation and relevance feedback (RF) can bridge this gap by extracting distinctive local and global features from the image and by incorporating valuable information stored as feedback. To handle this issue, this article presents a novel adaptive complementary visual word integration method for a robust representation of the salient objects of the image using local and global features based on the bag-of-visual-words (BoVW) model. To analyze the performance of the proposed method, three integration methods based on the BoVW model are proposed in this article: (a) integration of complementary features before clustering (called as non-adaptive complementary feature integration), (b) integration of non-adaptive complementary features after clustering (called as a non-adaptive complementary visual words integration), and (c) integration of adaptive complementary feature weighting after clustering based on self-paced learning (called as a proposed method based on adaptive complementary visual words integration). The performance of the proposed method is further enhanced by incorporating a log-based RF (LRF) method in the proposed model. The qualitative and quantitative analysis of the proposed method is carried on four image datasets, which show that the proposed adaptive complementary visual words integration method outperforms as compared with the non-adaptive complementary feature integration, non-adaptive complementary visual words integration, and state-of-the-art CBIR methods in terms of performance evaluation metrics.
A Real-Time Remote Courses Model for the Improvement of the Overall Learning Experience

The presence of International professors in any faculty enriches the overall experience of the students and provides students different point of views in the subjects they learn. Sometimes it is not possible for a university to have more international faculty members because of many factors, including financial resources. In this work we present a model that introduces students to international professors in real-time via video conference. This model has proven to be very useful in our university as professors are able to teach a group of students in a campus located in the Southern Region of Mexico while being in their own countries. Professors from Japan, Europe and North America have taken part of this implementation in the last few semesters and the results on student satisfaction are shown in this work. The model presented in this work uses a real-time remote teaching scheme, supported by on-campus IT resources that are available in any lecture room of our university so no investment was needed for new hardware, in order to implement our model. The model allows students to develop and strengthen their digital and communication competencies as the courses taught in this model uses technology as a learning tool.