Reviewerly/exHarmony huggingface dataset:
Enabling Efficient Communications with Resource Constrained Information Endpoints in Smart Homes

Smart Homes are one of the most promising real applications of Internet of Things and Cyber-Physical Systems. Devices and software components are deployed to create enhanced living environments where physical information is captured by sensors, sent to servers and finally transmitted to information endpoints to be consumed after its processing. These systems usually employ resource constrained components in dense architectures supported by massive machine type communications. Components, to adapt to different scenarios, present several configuration options. In machine type communications, these configuration options should be selected dynamically and automatically. Many works have addressed this situation in relation to sensor-server communications but endpoints are still mostly manually configured. Therefore, in this paper it is proposed an automatic and dynamic configuration algorithm, based on the idea of “efficiency,” for information endpoints in the context of Smart Homes. Different costs associated to endpoint-server communications in Smart Homes are identified and mathematically modelled. Using this model and real measurements, the most efficient configuration is selected for each endpoint at each moment, not only guarantying the interoperability of devices but also ensuring an adequate resource usage, for example, modifying the endpoints’ lifecycle or the information compression mechanism. In order to validate the proposed solution, an experimental validation including both real implementation and simulation scenarios is provided.
Fine-grained image analysis via progressive feature learning

Due to large intra-class variation and inter-class ambiguity, fine-grained object recognition has been a challenging task for decades. A good approach should be able to: (1) discover discriminative local details and (2) align and aggregate these local discriminative patch-level features in an effective manner to facilitate object-level classification. Toward this end, we develop a two-stage framework for fine-grained recognition. In our framework, the first stage aims to discover and align local discriminative features, and the second stage aims to aggregate these features to get classification results. In the first stage, we propose two methods to sequentially discover informative regions. In the second stage, we progressively feed these discovered and aligned regions into a recurrent neural network to obtain object-level representation. Extensive experiments are conducted on three fine-grained image benchmarks, and the results verify the effectiveness of our proposed framework.
Information Retrieval Ranking Using Machine Learning Techniques

Information retrieval is the research area in which many researcher have been done and many are still going on. The rapidly growing web pages make it very crucial to search up to date documents. In continuation of research works on learning to rank, this research focuses on implication of machine learning techniques for IR ranking. SVM, PSO and hybrid of both are the main techniques implemented for IR ranking. In case of SVM, selecting appropriate parameters is difficult, but it gives potential solutions for the ranking. One of the optimization methods i.e. PSO is easy to implement and has global search capability. Thus to find the fitness function to optimize the ranking of document retrieval Hybrid SVM-PSO model is proposed.After the comparative study it has been calculated that the ranking parameters gives best result for RankSVM-PSO over RankPSO and RankSVM. The result has been calculated based on single term queries and multi-term queries. The study shows RankPSO gives the better result than RankSVM and RankSVM –PSO gives better result than RankPSO, so it has been concluded that RankSVM-PSO gives best result among the three techniques.
Towards Augmenting IVR Communication with Physiological Sensing Data

Immersive Virtual Reality (IVR) does not afford social cues for communication, such as sweaty palms to indicate stress, as users can only see an avatar of their collaborator. Prior work has shown that this data is necessary for successful collaboration, which is why we propose to augment IVR communication by (1) real-time capturing of physiological senses and (2) leveraging the unlimited virtual space to display these. We present the results of a focus group (N=7) and a preliminary study (N=32) that investigate how this data may be visualized in a playful interaction and the effects they have on the performances of the collaborators.
Immersive VR Exergames for Health and Wellbeing

This paper describes a Virtual Reality (VR) exergame platform used to investigate player motivation and experience of physical activity through variations of game designs. The VR-Rides platform combines a desk cycle, real-world imagery, an HTC VIVE head-mounted device (HMD) with controllers and a Microsoft wrist band, such that the player can navigate locations in a safe immersive virtual environment. Panorama images come from Google Street View. Two games were developed to explore motivation, enjoyment and their link to player's perceived experiences in immersive VR exergaming setup. The platform acts as a test-bed to iteratively design and evaluate theory driven immersive VR game designs to support activities pertaining to player's health and wellbeing goals.
Low-Resource Hardware Architecture for Semi-Global Stereo Matching

The semi-global matching algorithm is usually used for generating high-quality and real-time disparity maps in stereo vision systems. To reduce the hardware-resource consumption, we present a multi-stage pipeline hardware architecture with timesharing reuse for semi-global stereo matching. Combined with image down-sampling, jumping disparity, and a post processing, the presented architecture is used in a practical advanced driver-assistance system (ADAS), which is implemented on a Zynq-7 FPGA chip. The whole stereo matching architecture consumes 19,603 LUTs and 61.5 BRAM (36 KB), and the throughput is 2857 Million Disparity Estimation per second (MDE/S), which corresponds to a throughput of 31 fps when processing images with 1280∗960 resolution and 75 disparity levels.
Stereoscopic image stitching with rectangular boundaries

This paper proposes a novel algorithm for stereoscopic image stitching, which aims to produce stereoscopic panoramas with rectangular boundaries. As a result, it provides wider field of view and better viewing experience for users. To achieve this, we formulate stereoscopic image stitching and boundary rectangling in a global optimization framework that simultaneously handles feature alignment, disparity consistency and boundary regularity. Given two (or more) stereoscopic images with overlapping content, each containing two views (for left and right eyes), we represent each view using a mesh and our algorithm contains three main steps: We first perform a global optimization to stitch all the left views and right views simultaneously, which ensures feature alignment and disparity consistency. Then, with the optimized vertices in each view, we extract the irregular boundary in the stereoscopic panorama, by performing polygon Boolean operations in left and right views, and construct the rectangular boundary constraints. Finally, through a global energy optimization, we warp left and right views according to feature alignment, disparity consistency and rectangular boundary constraints. To show the effectiveness of our method, we further extend our method to disparity adjustment and stereoscopic stitching with large horizon. Experimental results show that our method can produce visually pleasing stereoscopic panoramas without noticeable distortion or visual fatigue, thus resulting in satisfactory 3D viewing experience.
Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

We introduce a flexible, scalable Bayesian inference framework for nonlinear dynamical systems characterised by distinct and hierarchical variability at the individual, group, and population levels. Our model class is a generalisation of nonlinear mixed-effects (NLME) dynamical systems, the statistical workhorse for many experimental sciences. We cast parameter inference as stochastic optimisation of an end-to-end differentiable, block-conditional variational autoencoder. We specify the dynamics of the data-generating process as an ordinary differential equation (ODE) such that both the ODE and its solver are fully differentiable. This model class is highly flexible: the ODE right-hand sides can be a mixture of user-prescribed or “white-box” sub-components and neural network or “black-box” sub-components. Using stochastic optimisation, our amortised inference algorithm could seamlessly scale up to massive data collection pipelines (common in labs with robotic automation). Finally, our framework supports interpretability with respect to the underlying dynamics, as well as predictive generalization to unseen combinations of group components (also called “zero-shot” learning). We empirically validate our method by predicting the dynamic behaviour of bacteria that were genetically engineered to function as biosensors.
Using Scrum and unified modelling language to analyze and design an automatic course scheduling system

This research used a case study methodology to examine large-scale software projects accomplished despite ambiguous customer requirements. This study adopted Scrum as the agile software development method and used unified modelling language (UML) diagrams to enhance design implementation documents and improve the software development process. This study presented how the case company explored a Scrum-based automatic course scheduling system for elementary and secondary schools. Through interviews, the case company incorporated customers’ requirements by using the corresponding UML diagrams, which helped the project team document the software development process and design the functions to satisfy customer demand. Then, the proposed method was introduced to the automatic elementary and secondary school course scheduling system project undertaken by the case company, verifying the feasibility of the proposed method. A few problems arose in the proposed software development process, and remedies were discussed. Software companies could use these results as a reference when implementing a large-scale software project with ambiguous customer requirements.
On Explainable Recommender Systems Based on Fuzzy Rule Generation Techniques

This paper presents an application of the Zero-Order Takagi-Sugeno-Kang method to explainable recommender systems. The method is based on the Wang-Mendel and the Nozaki-Ishibuchi-Tanaka techniques for the generation of fuzzy rules, and it is best suited to predict users’ ratings. The model can be optimized using the Grey Wolf Optimizer without affecting the interpretability. The performance of the methods has been shown using the MovieLens 10M dataset.
There Is No Safety Without Security and Dependability

Security and safety have been traditionally considered as different disciplines addressed by different professionals. However, a close look at their characteristics and their analogies leads to the conclusion that they are actually interdependent. Specifically, we argue that safety requires system security and dependability.
A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas

Neural somas perform most of the metabolic activities in the neuron and support the chemical process that generates the basic elements of the synapses, and consequently the brain activity. The morphology of the somas is one of the fundamental features for classifying neurons and their functionality. In this paper, we characterize the morphology of the 39 three-dimensional reconstructed human pyramidal somas in terms of their multiresolutional Reeb graph representation, from which we extract a set of directional and linear variables to perform model-based clustering. To deal with this dataset, we introduce the novel Extended Mardia-Sutton mixture model whose mixture components are distributed according to a newly proposed multivariate probability density function that is able to capture the directional-linear correlations. We exploit the capabilities of Bayesian networks in combination with the Structural Expectation-Maximization algorithm to learn the finite mixture model that clusters the neural somas by their morphology and the conditional independence constraints between variables. We also derive the Kullback-Leibler divergence of the Extended Mardia-Sutton distribution to be used as a measure of similarity between soma clusters. The proposed finite mixture model discovered three subtypes of human pyramidal somas. We performed Weltch t-tests and Watson-Williams tests, as well as rule-based identification of clusters to characterize each group by its most prominent features. Furthermore, the resulting model allows us to simulate the 3D virtual representations of somas from each cluster, which can be a useful tool for neuroscientists to reason and suggest new hypotheses.
Privacy Preserving Unstructured Data Publishing (PPUDP) Approach for Big Data

The current trend related to Computer Industry is Big Data Analytics (BDA).The key attributes of Big Data are Volume, Variety, and Velocity.Big Data Analytics mainly focuses on Data collection from heterogeneous sources, Knowledge extraction from collected data and Data storage.In all these process of knowledge discovery from Big Data, Privacy of Big Data is very crucial.As it comprises of different types of data like structured data, unstructured data and semi structured data, a variety of techniques available for preserving privacy for structured and *semi structured data.The major issue with unstructured data is data publishing i.e. lack of preserving privacy because it may contain heterogeneous data like audio, video and text.The Big Data analytics normally carried out by third party, the data provider can be able to classify data as secured or not secured prior to data publishing for Data Analytics and it involves classification of huge volumes of data.
On quantifying and visualizing the potter's personal style

Ceramic-sherds analysis has been concerned with categorizing to types according to vessel shape and size for the description of a given material culture. Yet, the characterization of ceramic variations and their meaning receives little attention in the archaeological study. In the present research, we wished to monitor small-scale variations, searching for the unique signature of the individual potter during production. We thus examined new parameters for distinguishing between trainee potters and monitoring their distinct styles as part of an integrated experimental archaeological study. For the purposes of this research, ceramic students were instructed to produce the same part of a storage jar repeatedly for several days following a strict protocol—with a single prototype and using the same technique in the same workspace. All the produced items were 3-D scanned to extract accurate geometric parameters for classification. Cluster analysis was used to analyze the digital data, in addition to a novel data visualization technique that was developed for detecting ceramic variations. These methods enabled us to distinguish the potters by their individual styles, probably already established in the early stages of learning. Our results show that the novel visualization approach, together with the quantitative method, allows us to efficiently identify the location, on the vessels, of the potters' stylistic fingerprint.
TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes

Accurate risk management and disease prediction are vital in intensive care units to channel prompt care to patients in critical conditions and aid medical personnel in effective decision making. Clinical nursing notes document subjective assessments and crucial information of a patient’s state, which is mostly lost when transcribed into Electronic Medical Records (EMRs). The Clinical Decision Support Systems (CDSSs) in the existing body of literature are heavily dependent on the structured nature of EMRs. Moreover, works which aim at benchmarking deep learning models are limited. In this paper, we aim at leveraging the underutilized treasure-trove of patient-specific information present in the unstructured clinical nursing notes towards the development of CDSSs. We present a fuzzy token-based similarity approach to aggregate voluminous clinical documentations of a patient. To structure the free-text in the unstructured notes, vector space and coherence-based topic modeling approaches that capture the syntactic and latent semantic information are presented. Furthermore, we utilize the predictive capabilities of deep neural architectures for disease prediction as ICD-9 code group. Experimental validation revealed that the proposed Term weighting of nursing notes AGgregated using Similarity (TAGS) model outperformed the state-of-the-art model by 5% in AUPRC and 1.55% in AUROC.
Ranking-Preserving Cross-Source Learning for Image Retargeting Quality Assessment

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Transitions of viewport quality adaptation mechanisms in 360 degree video streaming

Virtual reality has been gaining popularity in recent years fueled by the proliferation of affordable consumer-grade devices such as Oculus Rift, HTC Vive, and Samsung VR. Amongst the various VR applications, 360° video streaming is currently one of the most popular ones. However, it poses a series of challenges to the serving content distribution systems. One challenge is the significantly increased bandwidth requirement for streaming such content in real time. Recent research has shown that only streaming the content that is in the user's (field-of-view) FoV in high quality can lead to strong bandwidth savings. This can be achieved by analyzing the viewers head orientation and movement based on sensor information. Alternatively, historic information from users that watched the content in the past can be considered to prefetch 360° video data in high quality assuming the viewer will direct the FoV to these areas. This paper presents a 360° video streaming system that transitions between sensor- and content-based predictive mechanisms. We evaluate the effects of our system on the Quality of Experience (QoE) of such a VR streaming system and show that the perceived quality can be increased between 50% and 80% compared to systems that only apply either one of the two approaches.
Prevalence of Bad Smells in PL/SQL Projects

Code Smell can be defined as any feature in the source code of a software that may indicate possible problems. In database languages, the term Bad Smell has been used as a generalization of Code Smell, once some features that are not directly related to code also can indicate problems, such as, for instance, the inappropriate type of an index structure or a SQL query written inefficiently. Bearing in mind the recurrence of different Bad Smell, they were catalogued. Along with these catalogs, tools were developed to automatically identify Bad Smell occurrences in a given code. With the help of these tools, it has become possible to perform quick and effective analysis. In this context, this paper proposes an exploratory study about Bad Smell in PL/SQL codes, from free software projects, published on GitHub. We analyzed 20 open-source PL/SQL projects and empirically study the prevalence of bad smells. Our results showed that some smells occur together. Besides, some smells are more frequent than others. Based on this principle, this paper has the potential to aid professionals from the databases area to avoid future problems during the development of a PL/SQL project.
Evidencing How Experience and Problem Format Affect Probabilistic Reasoning Through Interaction Analysis

This paper examines the role that lived experience plays in the human capacity to reason about uncertainty. Previous research shows that people are more likely to provide accurate responses in Bayesian tasks when the data are presented in natural frequencies, the problem in question describes a familiar event, and the values of the data are in line with beliefs. Precisely why these factors are important remains open to debate. We elucidate the issue in two ways. Firstly, we hypothesise that in a task that requires people to reason about conditional probabilities, they are more likely to respond accurately when the values of the problem reflect their own lived experience, than when they reflect the experience of the average participant. Secondly, to gain further understanding of the underlying reasoning process, we employ a novel interaction analysis method that tracks mouse movements in an interactive web application and applies transition analysis to model how the approach to reasoning differs depending on whether data are presented using percentages or natural frequencies. We find 1) that the closer the values of the data in the problem are to people's self-reported lived experience, the more likely they are to provide a correct answer, and 2) that the reasoning process employed when data are presented using natural frequencies is qualitatively different to that employed when data are presented using percentages. The results indicate that the benefits of natural frequency presentation are due to a clearer representation of the relationship between sets and that the 'prior' humans acquire through experience has an overwhelming influence on their ability to reason about uncertainty.