Reviewerly/exHarmony huggingface dataset:
A Study on Cooperation and Navigation Planning for Multi-robot Using Intelligent Water Drops Algorithm

The shortcomings of the existing multi-robot navigation planning methods in an unknown environment are studied extensively. An efficient approach for resolving these issues is addressed in the current study using a novel nature inspired-Intelligent Water Drops (IWD) algorithm [1]. The robustness of the proposed method for multi-robot navigation in an unknown environment is validated through V-Rep simulator. The performance of the algorithm is verified through simulation outcomes in terms of total path travelled, total path deviation, number of turns, and execution time. Further, the efficiency of the proposed method is compared with the existing state of the art to verify its potency. The simulation outcomes reveals that the proposed method takes in an improvement of 9.93%, 35.26%, 33.33%, and 13.04% in terms of the total path travelled, total path deviation, number of turns, and total execution time for all the robots to arrive their target, respectively, as compared to the existing state of the art. Moreover, the current study confirms the superiority of the proposed approach as compared to the existing state of the art in terms of generating optimal and safe navigation path for individual robots.
Ethnographic Methods for Human Factors Researchers

This course offers an introduction to ethnography for Human Factors Research. It covers relevant topics along the research process from decision arguments for the method and study design, up to data collection, analysis and interpretation. Ethical questions will include the researcher's role(s) in the field and modes of data presentation. The collection of multi-dimensional sets of data - a trademark of high-quality ethnographic work - enables inter-weaving threads of HCI perspectives in complex human factors and user research contexts. To achieve this, a comprehensive toolbox of ethnographic methods is introduced along with practical hands-on sessions to familiarize with these methodological instruments.
Parallelizing Convergent Cross Mapping Using Apache Spark

Identifying the causal relationships between subjects or variables remains an important problem across various scientific fields. This is particularly important but challenging in complex systems, such as those involving human behavior, sociotechnical contexts, and natural ecosystems. By exploiting state space reconstruction via lagged embedding of time series, convergent cross mapping (CCM) serves as an important method for addressing this problem. While powerful, CCM is computationally costly; moreover, CCM results are highly sensitive to several parameter values. While best practice entails exploring a range of parameter settings when assessing casual relationships, the resulting computational burden can raise barriers to practical use, especially for long time series exhibiting weak causal linkages. We demonstrate here several means of accelerating CCM by harnessing the distributed Apache Spark platform. We characterize and report on results of several experiments with parallelized solutions that demonstrate high scalability and a capacity for over an order of magnitude performance improvement for the baseline configuration. Such economies in computation time can speed learning and robust identification of causal drivers in complex systems.
Bidirectional Long Short-Term Memory for Automatic English to Kannada Back-Transliteration

Transliteration is the key component in various Natural Language Processing (NLP) tasks. Transliteration is the process of converting one orthographic system to another. This paper demonstrates transliteration of Romanized Kannada words to Kannada script. Our system utilizes a bilingual corpus of around one lakh words, which comprise pairs of Romanized Kannada word with its corresponding word in Kannada script and employs orthographic and phonetic information. Recurrent Neural Networks (RNNs) are widely used Neural Networking model for text and speech processing as they better predict the next word based on past information. Long Short-Term Memory (LSTM) Networks are exceptional kind of RNNs which handles long-term dependencies. A Character level Bidirectional Long Short-Term Memory (BLSTM) paradigm which drives down the perplexity with respect to word-level paradigm has been employed. Knowledge of Characters uncovers structural (dis)similarities among words, thus refining the modeling of uncommon and unknown words. Test data of 3000 Romanized Kannada words is used for model evaluation and we obtained an accuracy of 83.32%.
Idea Bits

Tangible interaction design students often find it difficult to generate ideas for tangible manipulation. They often restrict their explorations to a few familiar possibilities. To our knowledge, there is no design tool that focuses on facilitating the exploration of a variety of manipulation and aiding generation of ideas for manipulation. To address this gap, we designed Idea Bits, a tangible design tool consisting of interactive physical artifacts that enable users to experience a set of manipulations. These artifacts are coupled with digital examples of tangible systems and technical implementation guidance to help users understand how to implement the manipulations. Our work contributes knowledge about the generation of ideas for manipulation and will be useful to tangible interaction design students, instructors, practitioners, and researchers.
Evaluating variability at the software architecture level

Software architecture are designed for developing software systems needed for a diverse of business goals. Consequently, architecture has to deal with a significant amount of variability in functionality and quality attributes to create different products. Due to this variability, the evaluation in software architectures is much more complex, as different alternatives of systems might be developed leading to an expensive and time consuming task. Several methods and techniques have been proposed to evaluate product line architectures (PLAs) aiming to asses whether or not the architecture will lead to the desired quality attributes. However, there is little consensus on the existing evaluations methods is most suitable for evaluating variability in software architectures, instead of only considering PLAs. Understanding and explicitly evaluating variations in architectures is a cost-effective way of mitigating substantial risk to organizations and their software systems. Therefore, the main contribution of this research work is to present the state of the art about means for evaluating software architectures (including, PLAs, software architectures, reference and enterprise architectures) that contain variability information. We conducted a Systematic Mapping Study (SMS) to provide an overview and insight to practitioners about the most relevant techniques and methods developed for this evaluation. Results indicate that most evaluation techniques assess variability as a quality attribute in PLAs through scenario-based; however, little is known about their real effectiveness as most studies present gaps and lack of evaluation, which difficult the usage of such techniques in an industrial environment.
LUNE

LUNE is a displayed lighting object representing time. It provides real-time lighting visualized moon phase. People can recognize the date of month through abstract image of moon. This product was developed to investigate the use of metaphor from lighting to represent time. A diverse set of researches and design initiatives related to time, temporality and slowness has emerged in the DIS and HCI communities. An important area of work is to represent time. The primary objective of this research is to suggest the new perspective of time called sense of time. First, we can understand how people perceive the time and we can also trace recent research related to perspectives of time in HCI. Second, we designed artifacts and investigated the use of moon phase as material to represent time in a new approach.
Effectiveness of Self Normalizing Neural Networks for Text Classification

Self Normalizing Neural Networks (SNN) proposed on Feed Forward Neural Networks (FNN) outperform regular FNN architectures in various machine learning tasks. Particularly in the domain of Computer Vision, the activation function Scaled Exponential Linear Units (SELU) proposed for SNNs, perform better than other non linear activations such as ReLU. The goal of SNN is to produce a normalized output for a normalized input. Established neural network architectures like feed forward networks and Convolutional Neural Networks (CNN) lack the intrinsic nature of normalizing outputs. Hence, requiring additional layers such as Batch Normalization. Despite the success of SNNs, their characteristic features on other network architectures like CNN haven’t been explored, especially in the domain of Natural Language Processing. In this paper we aim to show the effectiveness of proposed, Self Normalizing Convolutional Neural Networks (SCNN) on text classification. We analyze their performance with the standard CNN architecture used on several text classification datasets. Our experiments demonstrate that SCNN achieves comparable results to standard CNN model with significantly fewer parameters. Furthermore it also outperforms CNN with equal number of parameters.
ScaleDial

The teaching of harmonic foundations in music is a common learning objective in many education systems. However, music theory is often considered as a non-interactive subject matter that requires huge efforts to understand. With this work, we contribute a novel tangible device, called ScaleDial, that makes use of the relations between geometry and music theory to provide interactive, graspable and playful learning experiences. Therefore, we introduce an innovative tangible cylinder and demonstrate how harmonic relationships can be explored through a physical set of digital manipulatives, that can be arranged and stacked on top of an interactive chromatic circle. Based on the tangible interaction and further rich visual and auditory output capabilities, ScaleDial enables a better understanding of scales, pitch constellations, triads, as well as intervals. Further, we describe the technical realization of our advanced prototype and show how we fabricate the magnetic, capacitive and mechanical sensing.
Minimizing financial cost of scientific workflows under deadline constraints in multi-cloud environments

In recent years, cloud platforms have been rapidly developed and deployed around the globe and many large-scale scientific workflows have been migrated to multiple clouds for cost-effective data analysis. In such cloud-based workflow applications, financial cost is a major concern in addition to traditional performance requirements such as execution time. In this paper, we formulate a workflow mapping problem to minimize the financial cost of deadline-constrained scientific workflows executed in multi-cloud environments, referred to as MinCost-MC, which is shown to be NP-complete. Within a generic three-layer workflow execution framework, we propose a Workflow Mapping algorithm for Financial Cost Optimization, referred to as WMFCO. This algorithm takes in consideration storage requirements, I/O operations, and data transfers to minimize the financial cost of a given workflow within a specified deadline. Extensive simulation results show that WMFCO exhibits a superior performance over existing algorithms in terms of financial cost in multi-cloud environments.
Spatial Attention Lesion Detection on Automated Breast Ultrasound

Automated Breast Ultrasound (ABUS) is widely applied in breast screening mainly because of its non-invasive, and radiation-free nature, and the high interoperator reproducibility. Due to the complexity and high volume of data, reading ABUS images is a routine but time-consuming task for sonographers. Accordingly, the computer-aided diagnosis (CAD) has been introduced to help, in order to detect breast lesion efficiently. Traditional techniques such as watershed and fuzzy c-means did not perform satisfactorily, due to the strong underlying assumptions and complex image processing. Lately, deep learning has been explored in medical image analysis. However, it often leads to high false positive rates, which is mainly caused by its requirement of abundant training data and the lack of domain knowledge. To address these issues, we propose a novel lesion detection framework based on the U-net segmentation architecture, and explore a novel method using spatial feature map and attention skip connection. We retrospectively evaluate our model on the data of 142 patients with 305 lesions and 70 no-lesion volumes, and it significantly outperforms the comparison methods with the sensitivity of 92.1% with 1.92 false positives per volume. The promising results suggest that our proposed framework is a solid tool to assist ABUS in breast screening.
Improved Logistic Regression Approach in Feature Selection for EHR

Nowadays, population is growing on large scale along with the problems faced by the people are also increasing. Thus, healthcare industry is making lot of technological advancements to provide effective, faster and cheaper treatment to people. Digitization of health records are also expanding in zettabytes. Electronic Health Record (EHR) containing all the patient’s medical history, demographics and other clinical data is also used in hospitals for improved care co-ordination. To avoid critical conditions of people from chronic diseases like hypertension, diabetes, hyperlipidemia etc. there is a need for building a health risk prediction model. But, when whole EHR data is provided to this risk prediction model causes overfitting of features. Overfitting is caused when model learns the details & noise from dataset, thus having negative impact on the performance. Hence, a feature selection approach is proposed for discarding redundant features from EHR. Improved sparse logistic regression method selects the best suitable parameters and forwards to risk prediction model. This regression method improvises the model with the use of logistic loss function that controls the sparsity factor. Neural network is used as a risk prediction model. This paper describes the risk prediction of hypertension disease. Thus, people could take preventive measures for the disease.
Designing for Outdoor Play

There is widespread societal concern regarding the reduction in the amount of time that we all spend playing outdoors. Outdoor play can be important for our social and physical well-being and moreover helps us to connect to space, place and environment. Of course, the CHI community continues to explore play across many contexts; however, specifically designing for outdoor play remains underexplored. This workshop aims to bring together those who are interested in technological, social and design aspects of outdoor play for all ages. We will use participants' insights, energies and expertise to explore the challenges and focus on how we can build a community to share innovative designs, generate knowledge and make actionable research in this context.
Authorization-aware optimization for multi-provider queries

The sharing of sensitive personal information via cloud platforms motivates the need for measures that aim to minimize information leakage to unauthorized users. In this work, we propose a novel SQL optimizer that strikes a balance between query runtime performance and private information exposure. Our approach to ensuring that the access control policies regulating data disclosure are enforced during distributed query execution is based upon a state-of-the-art authorization model from the literature and a preference-aware query optimizer. Our preliminary studies show that our approach outperforms the way the authorization model was originally implemented in terms of query runtime performance which is crucial for the operation on Big Data. To improve it, we adjust the algorithms utilized by a preference-aware query optimizer.
A Novel Approach for Security of Data in IoT Environment

The Internet of Things (IoT) is an emerging technology in this era. The luxury of technology comes with security, which is a noteworthy issue to be addressed. The paper’s soul purpose is to provide security to the data in the IoT environment. Data collected from multiple IoT devices have been provided multilevel security using elliptical curve cryptography (ECC) and fully homomorphic encryption(FHE), mitigating the generalized cryptographic attacks. The data are collected at the roof and on customization sent to the cloud. The proposed method enhances the security by preventing modification of data from any brute-force attack using a short key with less computation power.
Autonomous network traffic control system based on intelligent edge computing

This paper considers an intelligent network traffic control technology for high-reliability IoT services based on edge computing technology featuring modular micro-service computing technology, intelligent traffic analysis and predictive technologies for various applications. This paper proposes an autonomous network traffic control system that takes into account the recycling of edge technologies. Intelligent internet live monitoring service that provides online display of noise, vibration, gas readings, and field images on site as quickly as possible in the event of a civil complaint and efficiently store to prevent on-site images. This intelligent IoT edge computing technology proposes the structure, procedures, configuration and behaviour of intelligent IoT edge computing technologies, including IoT Things, data collection, data analytics capabilities, and gateways for delivering processed information to cloud servers. It applied smart construction monitoring as a representative service example.
Image processing towards the automated identification of nanoparticles in SEM images

SEM images are crucial in the characterisation of material properties. These images can be very hard to interpret without any prior knowledge of the material. This paper discusses a pre-processing method for assisting convolutional Neural Networks in identifying the presence of nanoparticles in composite SEM images. The pre-processing method is developed using a synthetic SEM image.
Wavelet denoising as signal feature-dependent kernel convolution

This work presents a view of wavelet threshold denoising as a convolution process between a noisy signal and a low-pass kernel whose width varies depending on the features of the signal. Wavelet denoising has been widely used for many purposes across a wide range of disciplines, such as in Medical Imaging. However, the procedures are usually presented in a specialized mathematical notation that can be daunting to many researchers, especially those who are not specializing in mathematics. This work seeks to present wavelet denoising in a form more accessible to researchers in other disciplines such as Geographical Information Systems, image processing, and remote sensing.
Prediction of learner’s appropriate online community of practice in question and answering website: similarity in interaction, interest, prior knowledge

The development of information technology and social networks has created new opportunities to access lifelong learning in the form of informal learning. In an informal learning environment, learning takes place via Communities of Practice (CoP). The learning success factors in online CoPs are learners’ similarity in learning interests and learners’ ability to interact with each other in the community. In this paper, a method based on Bayes probability and modified collaborative filtering is proposed to predict next learner topics and suggests appropriate learning questions. In the recommended approach, the learner’s CoP predictions are made based on learners’ behavior in the learning environment and their prior knowledge. The proposed method is evaluated using the dataset of Stack Overflow as a professional Question Answering website. Results show: 1) The proposed method with cosine similarity function, has better performance than traditional Collaborative Filtering, in the prediction of question related to CoP based on co-learner and mentor roles. 2) Using the proposed method can increase the effective appropriate interaction ratio in the learning environment.
InfMatch: Finding isomorphism subgraph on a big target graph based on the importance of vertex

Subgraph matching is an important research topic in the area of graph theory and it has been applied in many areas in nowdays. Filtering and verification are two main processes of subgraph matching algorithms. However, there exists many invalid nodes in candidate matching set after initializing the candidate set for each query node, which may result in a quantity of redundant computation during the filtering period. Regarding the problem mentioned above, in this paper, we propose a subgraph matching algorithm based on node influence, denoted as InfMatch, to improve the performance of subgraph matching on a large target graph. Specially, we find the central node of query graph by calculating the global and local influence value of each query node, after which candidate matching nodes for each query node are found from the neighborhood region of the candidate nodes for the central node. Since the central node we choose connects tightly with other nodes, isolated nodes can′t be added into the candidate matching set for central node and thus a number of unqualified candidate vertices are pruned. To further prune the unqualified candidate nodes, we propose several filter strategies according to the characteristics of our method. What′s more, considering edge limitation, we improve the matching order selection strategy. Extensive experiments demonstrate that our method is more efficient.
Horizontal visibility graphs are uniquely determined by their directed degree sequence

Horizontal visibility graphs (HVGs) are graphs generated from time (or otherwise ordered) series data. Components of the graph reveal key underlying properties of the series data that generated the graph and there are several applications that use HVGs to uncover information about series data. Recently, a bijection between the degree sequence and adjacency matrix of canonical form HVGs was established. This paper extends the relation between the degree sequence and adjacency matrix of HVGs and then presents an algorithm to uniquely construct an HVG from its directed degree sequence.
Human Activities and Postural Transitions Classification using Support Vector Machine and K-Nearest Neighbor Methods

Nowadays, the gyroscope and accelerometer sensors are available on almost all smartphone devices. One of the sensor uses is to sensor is to determine body's position. It is related to human activity that could be related to someone's fitness level, and monitoring it is one of the main focus smart city system. By using classification method, we can determine body's position. The experiment was conducted using k-nearest neighbor (KNN) with n-neighbors 3, 5, 7, and 9, and support vector machine (SVM) which kernels were polynomial, radial basis function (RBF), and sigmoid methods. The results of the K-NN method for all n-neighbor variations were 85.3% - 85.7% for 10 folds of cross validation. While for SVM, only the RBF kernel had a good result with 86.0% for 10 folds of cross-validation. So it can be concluded that K-NN and SVM with kernel RBF had good result.