Reviewerly/exHarmony huggingface dataset:
Power Struggles and Disciplined Designers - A Nexus Analytic Inquiry on Cross-Disciplinary Research and Design

Design is at the heart of Human Computer Interaction research and practice. In the research community, there has emerged an increasing interest in understanding and conceptualizing our research practice, particularly such entailing design. However, reflective discussion around the associated challenges and practicalities is yet limited. Moreover, so far there is limited discussion on the cross-disciplinary nature of our research and design practices: although cross-disciplinarity has been brought up as an ideal and a necessity, its practicalities and complexities remain yet poorly explored. This study examines a cross-disciplinary research project with a number of researcher-designers representing different disciplines acting as 'designers', while having a divergent understanding of it and of who has authority to do it. The study relies on nexus analysis as a sensitizing device and shows how various discourses, epistemologies and histories shape cross-disciplinary research and design. Critical reflection around our research practice entailing design is called for.
Constructive Semigroups with Apartness: Towards a New Algebraic Theory

The theory of constructive semigroups with apartness is a new approach to semigroup theory, and not a new class of semigroups. Of course, our work is partly inspired by classical semigroup theory, but, on the other hand, it is distinguished from it by two significant aspects: first, we use intuitionistic logic rather than classical, secondly, our work is based on the notion of apartness (between elements, elements and sets). Here, the focus is on E. Bishop's approach to constructive mathematics (BISH), [4].
Privacy as a Service: Publishing Data and Models

The main obstacle to the development of sustainable and productive ecosystems leveraging data is the unavailability of robust, reliable and convenient privacy management tools and services. We propose to demonstrate our Privacy-as-a-Service system and Liánchéng, the Cloud system that hosts it. We consider not only the publication of data but also that of models created by parametric and non-parametric statistical machine learning algorithms. We illustrate the construction and execution of privacy preserving workflows using real-world datasets.
Automatic English to Kannada Back-Transliteration Using Combination-Based Approach

The task of mapping graphemes or phonemes of one language into phoneme approximations of another language is known as machine transliteration. The paper describes transliteration of Romanized Kannada words to Kannada script. Our system utilizes a bilingual corpus of around 3 lakh words, which comprise pairs of Romanized Kannada word with its corresponding word in Kannada script and employs orthographic and phonetic information. Romanized Kannada words are segmented into phonemes by predefined rules. These phonemes are transliterated to Kannada script statistically based on probabilities. The paradigm is assessed with 3000 Romanized Kannada test words and obtained an accuracy of 85.4%.
Effect of latitude and sky clearance factor on the effectiveness of solar tracking strategies

Solar energy is gaining more grounds in the power generation industry. The effectiveness of solar tracking systems depends on the location’s latitude. In addition, sky clearance has an effect on the received solar radiation. Therefore, it is important to know the effect of both factors in order to select the most appropriate system for each location. While there are many studies concentrated on either of these effects, there are no studies for their joint effect. Here, the combined effect of latitude and sky clearance on the performance of solar tracking systems is presented using data from seven locations with different latitudes and sky clearance factors. Results show that the effect of the sky clearance can overcome the effect of the latitude in terms of the effectiveness of solar tracking systems. The sky clearance factor suppresses the effect of the latitude as the sky clearance factor falls below 0.55.
A Semi-supervised Classification Approach for Multiple Time-Varying Networks with Total Variation

In recent years, we have seen a surge of research on semi-supervised learning for improving classification performance due to the extreme imbalance between labeled and unlabeled data. In this paper, we innovatively propose a semi-supervised classification model for multiple time-varying networks, i.e., Multiple time-varying Networks Classification with Total variation (MNCT), which can integrate the multiple time-varying networks and select relevant ones. From a numerical point of view, the optimization is decomposed into two sub-problems, which can be solved efficiently under the alternating direction method of multipliers (ADMM) framework. Experimental results on both synthetic and real-world datasets empirically demonstrate the advantages of MNCT over state-of-the-art methods.
Self-Generating Policies for Machine Learning in Coalition Environments

In any machine learning problem, obtaining and acquiring good training data is the main challenge that needs to be overcome to build a good model. When applying machine learning approaches in the context of coalition operations, one may only be able to get data for training machine learning models from coalition partners. However, all coalition partners may not be equally trusted, thus the task of deciding when, and when not, to accept training data for coalition operations remain complex. Policies can provide a mechanism for making these decisions but determining the right policies may be difficult given the variability of the environment. Motivated by this observation, in this paper, we propose an architecture that can generate policies required for building a machine learning model in a coalition environment without a significant amount of human input.
Effect of Orientation on Unistroke Touch Gestures

As touchscreens are the most successful input method of current mobile devices, touch gestures became a widely used input technique. While gestures provide users with advantages to express themselves, they also introduce challenges regarding accuracy and memorability. In this paper, we investigate the effect of a gesture's orientation on how well the gesture can be performed. We conducted a study in which participants performed systematically rotated unistroke gestures. For straight lines as well as for compound lines, we found that users tend to align gestures with the primary axes. We show that the error can be described by a Clausen function with R² = .93. Based on our findings, we suggest design implications and highlight the potential for recognizing flick gestures, visualizing gestures and improving recognition of compound gestures.
Self-Reproducing Video Frame Interpolation

Frame interpolation has recently witnessed success by convolutional neural networks, that are learned from end to end to minimizing the reconstruction loss of dropped frames. This paper introduces a novel self-reproducing mechanism, that the real (given) frames could in turn be interpolated from the interpolated ones, to further substantially improve the consistency and performance of video frame interpolation. Such a consistency constraint accounts for the inherent symmetry between existing and interpolated frames in a video sequence, providing a strong form of self-supervision. We then build a pyramid-like architecture, under which existing interpolation models can plug-and-play as building blocks. Extensive experiments validate its state-of-the-art performance, on both high resolution videos in the wild and public benchmarks.
Blood Gas Predictions for Patients Under Artificial Ventilation Using Fuzzy Logic Models

This paper proposes a new modelling framework for accurate predictions of arterial blood gases (ABG) of the previously developed SOPAVent model (Simulation of Patients under Artificial Ventilation). Three ABG parameters which were elicited from the SOPAVent model are the partial arterial pressure of oxygen (PaO2), the partial arterial pressure of carbon-dioxide (PaCO2) and the acid-base (pH). SOPAVent generate predictions of initial ABG and predictions of ABG after ventilator settings were modified. SOPAVent’s sub-models, the relative dead space (Kd) and the carbon-dioxide production (VCO2) were designed using interval type-2 fuzzy logic system (IT2FLS). Further explorations of the models were carried out using fuzzy c-means clustering (FCM) and tuning of fuzzy parameters using ‘new structure’ particle swarm optimization algorithm (nPSO). The new models were integrated into the SOPAVent system for blood gas predictions. SOPAVent was validated using real intensive care unit (ICU) patient data, obtained from the Royal Hallamshire Hospital and Northern General Hospital, Sheffield (UK). The prediction accuracy of SOPAVent was compared with the pre-existing SOPAVent model where the Kd and VCO2 sub-models were developed using machine learning algorithms. Significant improvements in accuracy and correlation were achieved under this frameworks for PaCO2 and pH for both the initial ABG predictions and the post ventilator settings adjustments.
How Biomedical Science Students Use Their Mobile Devices for Learning

Learning occurs in many forms, and learning through mobile devices is becoming increasingly common in higher education. Potentially, there are many ways students may implement mobile learning to enhance learning, and this may be influenced by students' confidence and preferences. To identify mobile learning behaviours, biomedical students (n=189) were asked in meta-learning assessment tasks to report on self-initiated ways they used mobile devices for learning. Thematic analysis of responses showed students were confident using mobile devices for learning, but students of all confidence levels had similar usage levels. Students most commonly reported using laptops for note-taking, watching lecture recordings, and planning, and using phones for looking up concepts and using applications. This contributed to their self-regulation primarily by aiding students' keeping of records and information seeking behaviors. Students also implemented many new ways of using devices during semester, demonstrating adaptability. Surprisingly, they rarely reported seeking social assistance via mobile devices.
Online Video Object Segmentation via Boundary-Constrained Low-Rank Sparse Representation

Graphcut-based algorithm is adopted in many video object segmentation systems because different terms can be probabilistically fused together in a framework. Constructing spatio-temporal coherences is an important stage in segmentation systems. However, many steps are involved when computing a key term with good discriminative power. If the cascade steps are adopted, the inaccurate output of the previous step will definitely affect the next step, leading to inaccurate segmentation. In this paper, a key term that is computed by a single framework referred to as boundary-constrained low-rank sparse representation (BCLRSR) is proposed to achieve the accurate segmentation. By treating the elements as linear combinations of dictionary templates, low-rank sparse optimization is adopted to achieve the spatio-temporal saliency. For adding the spatial information to the low-rank sparse model, a boundary constraint is adopted in the framework as a Laplacian regularization. A BCLRSR saliency is then obtained by the represented coefficients, which measure the similarity between the elements in the current frame and the ones in the dictionary. At last, the object is segmented by minimizing the energy function, which is formalized by the spatio-temporal coherences. The experiments on some public datasets show that our proposed algorithm outperforms the state-of-the-art methods.
An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching

Training a deep neural network is a non-trivial task. Not only the tuning of hyperparameters, but also the gathering and selection of training data, the design of the loss function, and the construction of training schedules is important to get the most out of a model. In this study, we perform a set of experiments all related to these issues. The model for which different training strategies are investigated is the recently presented SDC descriptor network (stacked dilated convolution). It is used to describe images on pixel-level for dense matching tasks. Our work analyzes SDC in more detail, validates some best practices for training deep neural networks, and provides insights into training with multiple domain data.
A heuristic approach towards variability of HC-128

In this paper we consider the problem of variability of eSTREAM cipher HC-128. By variability of a cipher, we mean the maximum number of distinct key sequences that can be generated by the cipher. We present a heuristic analysis for variability of HC-128. Our analysis indicates that the cipher possesses maximum possible variability.
Performance of biologically inspired algorithms tuned on TiO2 nanoparticle benchmark system

Biologically inspired optimization algorithms (BIOA), such as particle swarm optimization (PSO) and differential evolution (DE), are often applied to a specific problem with the tacit assumptions that each algorithm will have a unique set of “ideal” tuning parameters that provide “best” performance, and that, once tuned, two different optimization algorithms ought to exhibit distinct performance. This work tests those assumptions by systematically comparing PSO and DE applied to identify stable and metastable configurations of a benchmark system, a titania dimer Ti2O4, whose energy is described by a modified Matsui-Akaogi pairwise potential. The hypotheses (H) are formally expressed as: (H1) for both PSO and DE there exists a unique, optimal set of tuning parameters that maximize its performance on a given problem; (H2) when running two different BIOAs, one algorithm must demonstrate superior performance over the other. By identifying and studying an approximate Pareto-optimal set of tuning parameters for DE and PSO, H1 is shown to be strictly false for DE, and tentatively false for PSO, while H2 is shown to be false with respect to certain statistics. We further investigate the correlations between key tuning parameters and algorithm performance. Our conclusion may be implemented to further optimize and develop BIOAs applied to materials systems.
Character Recognition on Palm-Leaf Manuscripts—A Survey

Handwritten recognition is an emerging field in the pattern recognition. Extraction of Sanskrit palm manuscript is challenging task and complicated in character recognition compared to other languages. In order to extract relevant information from the manuscript images efficiently, each step of the process requires reduction of irrelevant data such as noise on the images. This paper explains the importance of preprocessing step to determine the noise reduction, binarization, optimal binarization text, and character segmentation technique for palm manuscript. The objective of this paper is to present a survey of the existing methods for character recognition on palm manuscript which have been developed recently, to handle the historical document images.
Limited Number of Materials Scene Reconstruct from Mojette Projections

The discrete tomography which focuses on the binary image reconstruction from two orthogonal projections, whereas the Mojette transform allows for a more general framework with any kind of values and any number of projections. The line Mojette was derived to focus on the limited number of materials scene reconstructions. Meanwhile, the scene only contained limited number of materials. So projection line might contain a unique material value, and then pixels of this line can be back-projected directly with the unique material value. The merits of the inverse line Mojette compare the Dirac Mojette that it can always solve more than one pixel at a time. In this paper, we discussed the line Mojette algorithm performances for the 2, 3, and 4 materials images. After comparison of analysis, results show the line Mojette performance ought to be paramount in the 3 materials case.
A Data Publishing System Based on Privacy Preservation

For data openness and sharing, we need to publish data and protect sensitive data at the same time. This paper provides the users with a system to realize privacy-preserving data publishing, which is implemented based on differential privacy. It has the following characteristics: (1) the raw data are first imported into a database and then are used to generate synthetic data for publishing; (2) a user can choose different privacy preservation levels for the synthetic data; (3) a subset of the attributes can been chosen to be synthesized while keeping the others untouched.
Discovering Relationship Patterns Among Associated Temporal Event Sequences

Sequential data mining is prevalent in many real world applications, such as gene sequence analysis, consumer shopping log analysis, social networking analysis, and banking transaction analysis. Contrast sequence data mining is useful in describing the differences between two sets (classes) of sequences. However, in prior studies, little work has been done in how to mine the patterns from sequences formed by associated temporal events, where there exist relationships in chronological order between any two events in a sequence. To fill this gap, we consider the problem of mining associated temporal relationship pattern (ATRP) and propose a method, called ATTEND (AssociaTed Temporal rElationship patterN Discovery), to discover ATRPs with top contrast measure from two sets of associative temporal event sequences. Moreover, we design several heuristic strategies to improve the efficiency of ATTEND. Experiments on both real and synthetic data demonstrate that ATTEND is effective and efficient.
The Influence of Coauthorship in the Interpretation of Multimodal Interfaces

This paper presents a model to codesign interpretively flexible artefacts. We present the case study of Beatfield, a multimodal system that allows users to control audiovisual material by means of tangible interaction. The design of the system was coauthored by individuals with different background and interests to encourage a range of difference interpretations. The capability of Beatfield to foster multiple interpretations was evaluated in a qualitative study with 21 participants. Elaborating on the outcome of this study, we present a new design model that can be used to stimulate heterogeneous interpretations of interactive artefacts.
Deep Reinforcement Learning for Optimization

Deep reinforcement learning (DRL) has transformed the field of artificial intelligence (AI) especially after the success of Google DeepMind. This branch of machine learning epitomizes a step toward building autonomous systems by understanding of the visual world. Deep reinforcement learning (RL) is currently applied to different sorts of problems that were previously obstinate. In this chapter, at first, the authors started with an introduction of the general field of RL and Markov decision process (MDP). Then, they clarified the common DRL framework and the necessary components RL settings. Moreover, they analyzed the stochastic gradient descent (SGD)-based optimizers such as ADAM and a non-specific multi-policy selection mechanism in a multi-objective Markov decision process. In this chapter, the authors also included the comparison for different Deep Q networks. In conclusion, they describe several challenges and trends in research within the deep reinforcement learning field.
The social spaces of hearing apps: problems, partners and intermediaries

This article offers an analysis of the development of three hearing and communication apps, drawing on interviews with people involved in their production. While a central figure in the media studies literature on apps is the self-managing individual health consumer, this article argues that physical and social environments and relationships within them are central to the way the hearing apps are produced, circulated and used. Often emerging from commercial start-ups, hearing apps become aligned with – or indeed stand in for – various kinds of governmental initiatives, not only in health but also in education and economic development. Partnerships between government, research and commercial organisations and the need to work through app intermediaries to find their end users shaped the way apps create recognisable ‘problems’ to address. This problematising function of apps and its impact on the uptake and use of apps are the key areas for future research.
Inconsistency Detection on Data Communication Standards Using Information Extraction Techniques: The ABP Case

The present research aims mainly, at establishing an error tolerant procedure that extracts information from Natural Language (NL) Communication Standard Documents along with storing error knowledge. The error knowledge will contain information about the detected errors and inconsistencies as well as the actions taken to solve them. It will act as a key tool for solving the detected errors at various levels of the procedure. As a particular scope, the searching of errors and inconsistencies will be based on comparing results from two NLP tools, parsing and chunking. Information Extraction (IE) technics, aided by some specific-developed heuristic algorithms, are used. The approach has been applied to two different-written texts describing the Alternating Bit Protocol (ABP). A Semantic Net is automatically extracted. The error knowledge provides information to the user about what fragments of the text contained inconsistent structures or words and how they were or not solved. The implemented algorithm solved inconsistencies related to words tagged differently by the NLP tools and showed other errors due to the use of complex syntactic structures. Specific metrics were extracted that permitted identify some features of the texts.
Saliency Priority Using Bottom-up Features for Static and Dynamic Scenes Without Cognitive Bias

A visual attention system includes the procedure of selecting the most interesting areas (known as salient regions) across visual information that humans receive in daily life. It is necessary to understand how different visual cues affect the human visual system to be able to measure the significance (i.e., saliency) of different regions of a frame. To this end, we designed an empirically based study to investigate bottom-up features including color, texture, and motion in video sequences to achieve a ranking system stating the saliency priority. In this work, we introduced a saliency detection model using a Bayesian framework for static scenes and considered the feature combination scenarios for dynamic scenes under conditions in which we had no cognitive bias. First, we modeled our test data as videos in a virtual environment to avoid any cognitive bias. Then, we performed an eye-tracking based experiment using human subjects to determine how colors, textures, motion directions, and motion speeds interact with each other to attract human attention. This work provides a benchmark to specify the most salient stimulus with comprehensive information for both static and dynamic scenes. The main goal of this work is to create the ability to assign a saliency priority for the entirety of an image/video frame rather than simply extracting a salient object/area which is widely performed in the state-of-the-art.