Reviewerly/exHarmony huggingface dataset:
Reflections on sonic digital unreality

This paper explores the possibility of constructing an unreal environment through sound, particularly through the use of contemporary digital tools. The idea of the unreal is approached as a not being-in-the-world experience. That is, an experience which expands the natural embodied physicality. And yet, sound propagation through a physical space, which led to the study of reverberation and spatialization as the main subjects of interest. Consequently, reverb became the main tool to explore the subject in practice. Due to the project’s multidisciplinarity, the subject had different approaches: from the possibility of reverb as a tool for fiction-making, to listener's comprehensibility and readability of the events; exploring spatialization techniques and multichannel reproduction systems to finally conclude with the idea that, in the end, sound relies on the listener’s actual physical experience of the space.
A Short-term Photovoltaic Output Prediction Method Based on Improved PSO-RVM Algorithm

In order to improve the accuracy and efficiency of photovoltaic system output prediction effectively, realize short-term photovoltaic (PV) output forecasting, and to provide auxiliary reference basis for grid dispatching and planning, a short-term PV output prediction method based on improved PSO-RVM algorithm is established. Firstly, the improved fuzzy clustering method is used to obtain the similar day, the wavelet analysis method is used to decompose the similar day PV output by high frequency and low frequency. Then, the improved PSO-RVM algorithm is used to predict the wavelet component of the prediction day respectively. Finally, the predicted results are added together to obtain the predicted values of PV output. The simulation results show that the proposed method can significantly improve the short-term PV output prediction accuracy.
A New Hybrid Method for Gland Segmentation in Histology Images

Gland segmentation has become an important task in biomedical image analysis. An accurate gland segmentation could be instrumental in designing of personalised treatments, potentially leading to improved patient survival rate. Different gland instance segmentation architectures have been tested in the work reported here. A hybrid method that combines two-level classification has been described. The proposed method achieved very good image-level classification results with 100% classification accuracy on the available test data. Therefore, the overall performance of the proposed hybrid method highly depends on the results of the pixel-level classification. Diverse image features reflecting various morphological gland structures visible in histology images have been tested in order to improve the performance of the gland instance segmentation. Based on the reported experimental results, the hybrid approach, which combines two-level classification, achieved overall the best results among the tested methods.
Parallelized Heat Map Algorithm Using Multiple Cores

MapReduce simplifies the programming for large-scale data-parallel applications and greatly reduces the development effort by sparing the programmer from complex issues such as parallel execution, fault tolerance, data management, task scheduling, etc. A heat map is generally used to provide the visual summary of information using a two-dimensional representation of data in which values are represented by colours. More elaborate heat maps allow the viewer to understand complex data sets in an easily viewable form. This paper discusses the advantages and uses of parallelized heat map algorithm which is mainly used in eye tracking data. Parallelized version of heat map has also been proved to be efficient when compared with the serial version.
Variational Optical Flow: Warping and Interpolation Revisited

One of the fundamental problems in computer vision is to attain the apparent motion in image sequences, the optical flow. As evaluations at hand of recent benchmarks show, this field is highly competitive. High ranking variational methods often consist of a combination of techniques, where frequently the presentation in the literature focuses on novel contributions in modelling. In this paper we investigate the warping technique and related algorithmic design choices that are fundamental for practical implementation. At hand of a detailed yet straightforward derivation we discuss different warping variations. These are evaluated in numerical experiments, and furthermore we investigate the impact of a variety of interpolation methods that can be used.
The Mobile Exergames Design Model to Encourage Physical Activity for Sedentary Generation Z

With the advancement of HCI technologies, user's bodily movement can now be placed in the centre of experience. Exergames, a combination of exercise and games, allows users to use their body movement as input devices to reach games' goals. A sedentary lifestyle, one where little physical activity is done, is common nowadays as individuals become dependent on technological aids to assist in running their daily actions. Physical activities can be attained using exergames as it helps to encourage some fitness for individuals through physically entertaining games during leisure times. The latest smartphones are now powerful enough to run game smoothly, displaying engaging contents with augmented reality immersion to players whilst exercising. This paper focuses on a proposed design model for a mobile exergame containing components that are beneficial to encourage productive behavioural change from sedentary to active for sedentary GenZ through planned exercises.
Advanced DBSCAN: A Clustering Algorithm for Personal Credit Reference System

Uncertainties included in soft classification exist in classical mathematics. However, in daily life, the extended fuzzy concept has much information, and the clustering algorithm established on fuzzy set for fuzzy samples has been poorly investigated. To solve above problems, this study made improvements on DBSCAN and proposed a DBSCAN algorithm based on fuzzy numbers. Using this algorithm, classical information included in the samples is extended to fuzzy sets, and fuzzy samples can be clustered by searching the density peak. In other words, this algorithm is a kind of fuzzy clustering algorithm on the basis of fuzzy set. Firstly, the related concepts of fuzzy number were briefly introduced; then, by means of error analysis, improved Euclidean distance between fuzzy numbers was defined based on the definition of traditional Euclidean distance, and some key parameters or operating quantities mainly including cut-off distance and Gaussian Kernal function of fuzzy samples were introduced in detail. By referring to the procedures of DBSCAN, the detailed procedures using algorithm were described. Next, we applied this algorithm to Personal Credit Reference System, compared with other hierarchical clustering algorithms; it can be found that this algorithm has better results. Finally, both advantages and disadvantages of the proposed algorithm were concluded and some recommendations for improvement were put forward, which can provide insightful guidance for further investigations of fuzzy clustering algorithms on fuzzy sets.
Design and Evaluation of an “Athletic” Approach to Software Engineering Education

Modern web application development provides an attractive application area for introductory software engineering education, as students have direct experience with the domain and it provides them with the potential to gain practical, real-world skills. Achieving this potential requires the development of competency with a multiple component tech stack for web application development, which is challenging to acquire within a single semester. In this research, we designed, implemented, and evaluated a new pedagogy called “athletic software engineering” which is intended to help students efficiently and effectively acquire competency with a multiple component tech stack as a precursor to a web application development project. We evaluated the pedagogy over 4 years and six semesters with 286 students and found strong evidence for its effectiveness.
A Fuzzy Logic-Based Method for Evaluating AAL Systems

The Ambient Assisted Living (AAL) domain aims to support the daily life activities of elders, patients with chronic conditions, and disabled people. Several AAL platforms have been developed over the last two decades. Hence, there is a need to identify Quality Criteria (QC) and make it well defined in order to achieve the AAL system purposes. To be able to convince all stakeholders including both technologies and end users of AAL systems, high quality must be guaranteed. The goal of this article is to obtain a set of data quality characteristics that would be applicable to AAL system, and have its performance evaluated using sensors' data. To this end, this work uses the ISO/IEC 25012 and ISO/IEC 25010 standards to extract the most relevant criteria that are apt for AAL systems. As a result, an evaluation approach on an indoor localization platform was made, and an evaluation procedure has been established. This is done by first generating a hierarchical data quality model, and have it evaluated using the metrics, based on the sensors data and the concept of fuzzy logic.
Generalizing Psychological Similarity Spaces to Unseen Stimuli

Abstract The cognitive framework of conceptual spaces proposes to represent concepts as regions in psychological similarity spaces. These similarity spaces are typically obtained through multidimensional scaling (MDS), which converts human dissimilarity ratings for a fixed set of stimuli into a spatial representation. One can distinguish metric MDS (which assumes that the dissimilarity ratings are interval or ratio scaled) from nonmetric MDS (which only assumes an ordinal scale). In our first study, we show that despite its additional assumptions, metric MDS does not necessarily yield better solutions than nonmetric MDS. In this chapter, we furthermore propose to learn a mapping from raw stimuli into the similarity space using artificial neural networks (ANNs) in order to generalize the similarity space to unseen inputs. In our second study, we show that a linear regression from the activation vectors of a convolutional ANN to similarity spaces obtained by MDS can be successful and that the results are sensitive to the number of dimensions of the similarity space.
Broadening the View: Technical Writers and Digital Technologies in India

This article reports on the technological priorities of technical writing practitioners in India. Findings suggest that swift technological change prompts professionals in India to be fluent in a growing list of technology-based tasks, challenging them to find and afford training opportunities. Based on the specific technological skills reported, Indian practitioners are more likely to learn and use programming languages and architecture design tools, rather than websites, presentations, wikis, social media, or even word processing programs. Practitioners also reported anticipating an increased need to learn interactive and automated technologies. Such findings suggest that technical web technologies will continue to be important to technical communication practice in India and that training opportunities are needed. Further, because automated and interactive technologies (AI) were seen as an integral part of future technical communication practice, more research is needed about AI's role in technical communication and the changes AI might bring to the field.
An Adaptive Copy-Move Forgery Detection Using Wavelet Coefficients Multiscale Decay

In this paper, an adaptive method for copy-move forgery detection and localization in digital images is proposed. The method employs wavelet transform with non constant Q factor and characterizes image pixels through the multiscale behavior of corresponding wavelet coefficients. The detection of forged regions is then performed by considering similar those pixels having the same multiscale behavior. The method is pointwise and the length of pixel features vector is image dependent, allowing for a more precise and fast detection of forged regions. The qualitative and quantitative evaluation of the experimental results reveals that the proposed method outperforms some existing transform-based methods in terms of performance and execution time.
Real-Time Lane Detection and Extreme Learning Machine Based Tracking Control for Intelligent Self-driving Vehicle

The rising self-driving technological innovations are viewed as brimming with challenges and opportunities because of its tremendous research territory. One of the challenges for the autonomous vehicle is straight and curve line detection to enhance the assistance in the autonomous characteristics. We will use a unique way of detecting a curve line algorithm in the vehicle based on the Kalman filter as well as the parabola equation model to calculate the parameters of the curve lane. For robust stability and performance, we will use an on-line sequential extreme learning machine method. We present our proposed result through the simulation study.
Weakly supervised video action localisation via two‐stream action activation network

This Letter introduces a weakly supervised method for human action localisation, dubbed Two-stream Action Activation Network (TAAN). In order to generate both action category predictions and temporal location predictions only by video-level annotations, TAAN firstly utilises class response variance to select representative segments as video-level expressions. Then under the supervision of video-level annotations, these expressions are used to train a classification network. Next, temporal class activation maps (T-CAMs) are generated based on the classification response, and finally temporal level predictions are given according to T-CAMs. In addition, the proposed method fuses two-steam predictions in a simple but effective way to further improve the precision of the predictions. Evaluations on THUMOS 2014 dataset show TAAN outperforms state-of-the-art weakly supervised methods.
Learning System for Japanese Kanji Calligraphy with Computerized Supervision

The most popular way of learning oriental calligraphy has been by practicing the calligraphy under the supervision of a human teacher, but finding a good instructor can be difficult. There are a number of studies in the literature that have evaluated calligraphic characters in holistic ways, but such systems do not support detailed supervision of scripting errors. This study proposes a Kanji calligraphy learning system with computerized supervision and analyzes the learning efficiency of the system, where the supervision includes symmetries between strokes. The proposed system compares a written calligraphic character of a user to the model of a human expert, and indicates error spots with explanations. An experiment with 22 participants proved that this system was more efficient at reducing the number of scripting errors in comparison to the traditional manner of a human expert. The main contribution of this paper was to identify and reveal the efficacy of computerized supervision in comparison to a human supervisor. The proposed system decreased the writing-error-rates of learners from 32.7% to 3.4%, whereas the traditional practice reduced the error rates from 31.0% to 6.8%. This result shows that computerized supervision is more efficient than human supervision for learning calligraphy.
Symmetric key-based patient controlled secured electronic health record management protocol

In our progressive era of technology, digitization is becoming the link between businesses, people, processes, and data. Since digitization is now a common practice for storing and retrieving data, Electronic Health Record (EHR) management services are rapidly becoming popular as it allows users to store and access their data anywhere at any time. EHR management is bringing many benefits including easy storage, cost effectiveness, shareable with health professionals to a remote location, etc. However it is very challenging to adopt cloud-based EHR services, due to the risk of data breaches and the resulting compromise of patient data. Since EHR stores very sensitive data, a number of security properties including privacy, secrecy, integrity, authenticity and availability of data must be ensured during data transmission, storing and sharing with health professionals. In this study, we have studied symmetric key based technique in designing EHR management protocol. The protocol allows the patient to handle the responsibility of authorizing data access, thus this is a patient-centric protocol. Attributes (e.g., specialization, location, affiliated hospital, etc.) of a health professional is used to delegate access to EHR of a patient, and thus attribute-based access control has been achieved. The protocol we have designed is then validated using AVISPA, an industry-strength security protocol validation tool. AVISPA has confirmed our protocol free from known attacks and confirmed the desired security properties as well.
Epistemic Logics with Structured Knowledge

Multi-agent Dynamic Epistemic Logic, as a suitable modal logic to reason about knowledge evolving systems, has emerged in a number of contexts and scenarios. The agents knowledge in this logic is simply characterised by valuations of propositions. This paper discusses the adoption of other richer structures to make these representations, as graphs, algebras or even epistemic models. This method of building epistemic logics over richer structures is called “Epistemisation”. On this view a parametric method to build such Epistemic Logics with Public Announcements is introduced. Moreover, a parametric notion of bisimulation is presented, and the modal invariance of the proposed logics, with respect to this relation, are proved. Some interesting application horizons opened with this construction are stated.
Measuring the Effects of Temporal Coherence in Depth Estimation for Dynamic Scenes

This paper presents a new algorithm for enforcing temporal coherence on depth estimation from multi-view videos of dynamic scenes as well as the first substantial quantitative evaluation of the improvement in depth estimation accuracy due to temporal coherence. The proposed algorithm is generally applicable and practical since it bypasses explicit scene flow estimation, which has a very large state space, and relies only on optical flow which is used to impose soft constraints on depth estimation for the next frame. As a result, our algorithm is applicable to scenes with large depth and motion ranges. The output is a sequence of depth maps that can be used for novel view synthesis among other applications. While it is intuitive that enforcing temporal coherence should improve the accuracy of depth estimation, this improvement has never been assessed quantitatively due to the lack of data with ground truth. To overcome this limitation we use the image prediction error as the criterion and show that the benefits of temporal coherence are significant on a diverse set of multi-view video sequences.
On geometric probability distributions on the torus with applications to molecular biology

In this paper, we study a family of probability distributions, alternative to the von Mises family, called Inverse Stereographic Normal Distributions.These distributions are counterparts of the Gaussian Distribution on S 1 (univariate) and T n (multivariate).We discuss some key properties of the models, such as unimodality and closure with respect to marginalizing and conditioning.We compare this family of distributions to the von Mises' family and the Wrapped Normal Distribution.Then, we discuss some inferential problems, introduce a notion of moments which is natural for inverse stereographic distributions and revisit a version of the CLT in this context.We construct point estimators, confidence intervals and hypothesis tests and discuss briefly sampling methods.Finally, we conclude with some applications to molecular biology and some illustrative examples.This study is motivated by the Protein Folding Problem and by the fact that a large number of proteins involved in the DNA-metabolism assume a toroidal shape with some amorphous regions.
General Interaction-Aware Neural Network for Action Recognition

Second order representation, like non-local operation and bilinear pooling, has significantly outperformed the plain counterpart on a wide variety of visual tasks. However, these previous works focus on feature interactions either in spatiotemporal dimension or in channels, both of which have been ignored the joint effect of feature interactions along with different axes. We thus propose a general interaction-aware neural network that captures higher order feature interactions both in spatiotemporal and channel dimensions. In this paper, we illustrate how to implement the second and third order exemplar CNNs in a compacted way and evaluate their performance on action recognition benchmarks. Comprehensive experiments demonstrate that our method can achieve competitive or better performance than recent start-of-the-art approaches and visualization results illustrate that our scheme can generate more discriminative representations, focusing on target regions more properly.
Research on Resultative/Directional Structures Based on the Corpus of International Chinese Textbooks

Chinese resultative/directional structures are distinctive from other languages and have distinct uniqueness. The usage of resultative/directional structures is a very common language phenomenon in international Chinese teaching. It is also the key and difficult content of grammar teaching in international Chinese teaching. Studying various structure types and characteristics of resultative/directional structures is a necessary way to master them. Using Chinese information processing technology to study resultative/directional structures in international Chinese teaching, on the one hand, can improve the efficiency and expand the scope of research, make the research of resultative/directional structures more systematic and accurate, and excavate the tacit knowledge and deep value that traditional research methods are not easy to discover; on the other hand, trying to transform the existing achievements of Chinese information processing into the field of international Chinese teaching is conducive to promoting deep integration in the two fields and achieving common and rapid development. This paper first constructs a knowledge base of resultative/directional structure structural modes by annotating the resultative/directional structures in the corpus of a certain scale of international Chinese textbooks. And then the characteristics of resultative/directional structures in the field of international Chinese teaching are analyzed through the constructed structural mode knowledge base. Finally, on the basis of the structural mode knowledge base, the automatic recognition of the resultative/directional structures in the texts of international Chinese textbooks is studied. The results of automatic recognition are satisfactory and it embodies the scientificity, validity and completeness of the structural mode knowledge base.
Augmenting an Answer Set Based Controlled Natural Language with Temporal Expressions

In this paper we discuss how we can augment an existing controlled natural language in a systematic way with temporal expressions in order to write high-level temporal specifications which require reasoning about action and change. We show that domain-dependent axioms which are necessary to specify time-varying properties, deal with the commonsense law of inertia, and with continuous change can be expressed directly and in a transparent way on the level of the controlled natural language. The resulting temporal specification including the corresponding axioms and the required terminological knowledge can be translated automatically into an executable answer set program and then be used by a linguistically motivated version of the Event Calculus, implemented as an answer set program, for temporal reasoning and question answering.