Reviewerly/exHarmony huggingface dataset:
Influence of domain experience on icon recognition and preferences

With the advance of the Internet of Things (IoT) and smart sensor networks, accurate understanding of complex data via interface icons is critical for effective decision making, operation management and monitoring. In order to understand how the different domain experience among icon designers and users affects icon recognition and preference level among users, this paper takes environmental and operational sensors as a case study and organises two icon design workshops, in which icon recognition and preference tests are conducted based on the standard ISO 9186 methodology. Results reveal that differences in domain experience among designers and users do not impose a significant impact on users’ icon recognition levels. However, users new to the domain significantly prefer icons created by designers who share similar levels of domain experience with them. It provides a little reference for future environmental and operational sensor Graphical User Interface icon designers, decision makers, scientists and engineers to improve the icon design process.
Experimental performance analysis of web crawlers using single and Multi-Threaded web crawling and indexing algorithm for the application of smart web contents

Recovery of information from the web is a complicated task, for which a search engine uses a web crawler. The search engine provides an interface for accessing and retrieving massive web storage information. The software retrieves traverses and indexes the web pages methodically. Web crawler's primary role is to find and retrieve related web pages with multiple algorithms. In this paper, an experimental performance analysis of web crawlers using single and multi-threaded web crawling and indexing algorithm for the application of smart web contents is presented. The simulation work addresses the key parameters for this single, multithreaded and hierarchical clustering strategy, including harvest ratio and execution time. For the corresponding crawling URL web pages, performance metrics of harvest ratio, execution time, precision, recall, harmonic mean and fallacy have been estimated.
Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances

Cloud provider Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 spot instances (SIs) offer spare computational capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that the delay in acquiring spots can be incredible high. Moreover, SIs may not always be available as they can be reclaimed by EC2 at any given time, with a two-minute interruption notice. In this paper, we propose a multi-workflow scheduling algorithm, allied with a container migration-based mechanism, to dynamically construct and readjust virtual clusters on top of non-reserved EC2 pricing model instances. Our solution leverages recent findings on performance and behavior characteristics of EC2 spots. We conducted simulations by submitting real-life workflow applications, constrained by user-defined deadline and budget quality of service (QoS) parameters. The results indicate that our solution improves the rate of completed tasks by almost 20%, and the rate of completed workflows by at least 30%, compared with other state-of-the-art algorithms, for a worse-case scenario.
Explainable Word-Embeddings for Medical Digital Libraries - A Context-Aware Approach

State of the Art Neural Language Models (NLMs) such as Word2Vec are becoming increasingly successful for important biomedical tasks such as the literature-based prediction of com-plex chemical properties or for finding novel drug-disease associations (DDAs). However, NLMs have the disadvantage of being hard to interpret. Therefore, it is notoriously difficult to explain why an artificial neural network learned or predicted some specific association.
SeparateDuino: Design and Fabrication of a Low-Cost Arduino-Based Microcentrifuge Using the Recycled Parts of a Computer DVD Drive

The separation of suspensions and immiscible liquids by the laboratory centrifuge is widely used in such fields as chemistry, biology, biochemistry, and clinical medicine. In this paper, an Arduino-based microcentrifuge was fabricated using a computer DVD (digital versatile disc) drive. The hardware part consisted of inexpensive mechanical and electronic parts such as the recycled parts of a computer DVD drive, an Arduino microcontroller, a motor driver, a magnetic sensor (for speed measurement), and a display. The microcentrifuge provided a wide speed range of 1,000–10,000 rpm. The fabrication process is easy, and the total fabrication cost of the fabricated microcentrifuge is less than $25. Since the fabricated microcentrifuge has open-source hardware and software parts, it is appropriate for educational purposes and laboratory settings.
Clustering of mixed datasets using deep learning algorithm

The performance of a clustering algorithm is highly dependent on the quality and quantity of the training dataset. Deep learning is one of the most popular and successful technique for clustering of datasets with high quality. Typically, most of the datasets contain mixed numeric and categorical data attributes. The clustering of such different types of data is a complex issue. Deep learning methods, the state-of-the-art classifiers, with better learning procedures and computational resources, can fill these gaps. To improve the robustness of clusters, we propose a Constraint-Based Deep Convolutional Generative Adversarial Network (CB-DCGANs) framework for generating simulated data to augment the training set to improve the performance of the clustering algorithm. We evaluated the performance of an end-to-end Deep Convolutional Neural Network (DCNN) in detecting the clusters from given datasets. The results from CB-DCGANs with DCNN yielded baseline accuracies of 0.8853 for heart disease dataset. In chemoinformatics datasets proposed algorithm yielded accuracies of 0.965 for kaggle dataset, 0.987 for factors dataset, 0.952 for kinase dataset. This study shows that using generative adversarial networks for clustering augmentation can significantly improve performance, especially in real-life applications.
Data Parallel Large Sparse Deep Neural Network on GPU

Sparse Deep Neural Network (DNN) is an emerging research area since deploying deep neural networks with limited resources is very challenging. In this work, we provide a scalable solution to the Sparse DNN Challenge-a challenge posed by MIT/IEEE/Amazon GraphChallenge.org-by designing data parallelism on GPUs. We provide a solution based on Python TensorFlow as it is a widely used tool in different scientific applications for deep learning. We use the datasets provided by GraphChallenge, derived from the MNIST handwritten letters. We use the Synthetic DNNs from RadiX-Net with varying number of neurons and layers. We implement a data parallel implementation of Sparse DNN using TensorFlow on GPU. Our solution shows up to 4.7× speedup over the basehne serial MATLAB implementation given in GraphChallenge. In addition to that, our TensorFlow GPU implementation demonstrates a 3-fold speedup over our TensorFloW CPU implementation.
Analyzing the Influence of Bigrams on Retrieval Bias and Effectiveness

Prior work on using retrievability measures in the evaluation of information retrieval (IR) systems has laid out the foundations for investigating the relationship between retrieval effectiveness and retrieval bias. While various factors influencing bias have been examined, there has been no work examining the impact of using bigram within the index on retrieval bias. Intuitively, how the documents are represented, and what terms they contain, will influence whether they are retrievable or not. In this paper, we investigate how the bias of a system changes depending on how the documents are represented using unigrams, bigrams or both. Our analysis of three different retrieval models on three TREC collections, shows that using a bigram only representation results in the lowest bias compared to unigram only representation, but at the expense of retrieval effectiveness. However, when both representations are combined it results in reducing the overall bias, as well as increasing effectiveness. These findings suggest that when configuring and indexing the collection, that the bag-of-words approach (unigrams), should be augmented with bigrams to create better and fairer retrieval systems.
Multi-Term Attention Networks for Skeleton-Based Action Recognition

The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called “Multi-Term Attention Networks” (MTANs), which solves the above problem by extracting temporal features with different time scales. The network consists of a Multi-Term Attention Recurrent Neural Network (MTA-RNN) and a Spatio-Temporal Convolutional Neural Network (ST-CNN). In MTA-RNN, a method for fusing multi-term temporal features are proposed to extract the temporal dependence of different time scales, and the weighted fusion temporal feature is recalibrated by the attention mechanism. Ablation research proves that this network has powerful spatio-temporal dynamic modeling capabilities for actions with different time scales. We perform extensive experiments on four challenging benchmark datasets, including the NTU RGB+D dataset, UT-Kinect dataset, Northwestern-UCLA dataset, and UWA3DII dataset. Our method achieves better results than the state-of-the-art benchmarks, which demonstrates the effectiveness of MTANs.
Global IT Project Management: An Agile Planning Assistance

Planning a global IT project stands for facing two issues, complexity and visibility. So agile methods can be key of success, due to their ability to fast reacting when impacting events come up. In this work, we seek to provide assistance for planning Global IT projects, based on an agile planning approach, by improving the platform we developed for planning usual IT project. We propose a planning assistance platform based on solving the planning problem modelled as a constraint satisfaction problem (CSP). Which should help project managers to analyse the project feasibility and to generate useful schedules and charts. We also allow managers to supervise the project progressing and regenerate the schedule if an adjustment is made or a change has occurred at any phase of the project life cycle.
Generating a Gray code for prefix normal words in amortized polylogarithmic time per word

A prefix normal word is a binary word with the property that no substring has more 1s than the prefix of the same length. By proving that the set of prefix normal words is a bubble language, we can exhaustively list all prefix normal words of length n as a combinatorial Gray code, where successive strings differ by at most two swaps or bit flips. This Gray code can be generated in O(log2⁡n) amortized time per word, while the best generation algorithm hitherto has O(n) running time per word. We also present a membership tester for prefix normal words, as well as a novel characterization of bubble languages.
Maximal structure generation of superstructure for semantic triple generated by DEVS ontology in the process industry

In the process industry, a large number of multi-source heterogeneous data are generated. Ontology combined with resource description framework (RDF) is a great way to describe multi-source heterogeneous data. Due to the characteristics of free extension, no model limitations and few constraints in the calculation process, semantic data have been increasingly used in industrial systems. However, semantic data have not been fully utilized to realize automatic modeling. Lots of time and experience from knowledge workers is still required in the feedstock scheduling. Therefore, this article presents a method of constructing the RDF triple graph data based on the discrete event system specification ontology. Based on the advantage of graph data, the procedure of superstructure generation is greatly simplified by the proposed RDFMSG (maximal structure generation based on resource description framework data) algorithm. In the RDFMSG algorithm, the original structure is no longer pruned and then reorganized; the superstructure can be generated by the traversing RDF data set only once. The effectiveness and efficiency of the proposed methodology are verified through three cases. In addition, the representation of the discrete event system specification ontology can achieve good performance. Furthermore, the combination explosion caused by set operation can be greatly reduced, indicating that the strategy of the RDFMSG plays an important role in the solution structure generation algorithm and the accelerated branch and bound algorithm.
Every Anonymization Begins with k: A Game-Theoretic Approach for Optimized k Selection in k-Anonymization

Privacy preservation is one of the greatest concerns when data is shared between different organizations. On the one hand, releasing data for research purposes is inevitable. On the other hand, sharing this data can jeopardize users' privacy. An effective solution, for the sharing organizations, is to use anonymization techniques to hide the users' sensitive information. One of the most popular anonymization techniques is k-Anonymization in which any data record is indistinguishable from at least k-1 other records. However, one of the fundamental challenges in choosing the value of k is the trade-off between achieving a higher privacy and the information loss associated with the anonymization. In this paper, the problem of choosing the optimal anonymization level for k-anonymization, under possible attacks, is studied when multiple organizations share their data to a common platform. In particular, two common types of attacks are considered that can target the k-anonymization technique. To this end, a novel game-theoretic framework is proposed to model the interactions between the sharing organizations and the attacker. The problem is formulated as a static game and its different Nash equilibria solutions are analytically derived. Simulation results show that the proposed framework can significantly improve the utility of the sharing organizations through optimizing the choice of k value.
An improved dynamic polynomial integrity based QCP-ABE framework on large cloud data security

With the exponential growth of cloud data and network services, the computational resources and cloud data security has become one of the interesting research area of real-time cloud environment. Different types of cloud services are integrated in va
An Adaptive Viewpoint Transformation Network for 3D Human Pose Estimation

Human pose estimation from a monocular image has attracted lots of interest due to its huge potential application in many areas. The performance of 2D human pose estimation has been improved a lot with the emergence of deep convolutional neural network. In contrast, the recovery of 3D human pose from an 2D pose is still a challenging problem. Currently, most of the methods try to learn a universal map, which can be applied for all human poses in any viewpoints. However, due to the large variety of human poses and camera viewpoints, it is very difficult to learn a such universal mapping from current datasets for 3D pose estimation. Instead of learning a universal map, we propose to learn an adaptive viewpoint transformation module, which transforms the 2D human pose to a more suitable viewpoint for recovering the 3D human pose. Specifically, our transformation module takes a 2D pose as input and predicts the transformation parameters. Rather than some hand-crafted criteria, this module is directly learned from the datasets and depends on the input 2D pose in testing phrase. Then the 3D pose is recovered from this transformed 2D pose. Since the difficulty of 3D pose recovery becomes smaller, we can obtain more accurate estimation results. Experiments on Human3.6M and MPII datasets show that the proposed adaptive viewpoint transformation can improve the performance of 3D human pose estimation.
Dengue Fever Detection using Long Short-term Memory Neural Network

In this research, long short-term memory is used for text classification. The LSTM model is used for the detection of dengue fever from symptoms. The inputs of the model are the text of symptoms in Thai language, as well as the sex and age of the patients. For Thai text processing, first, we will token the sentence to words, and then correct the wrong words, and convert the words to vector using Word2Vec model and set as input data for LSTM model training. In addition, we use class balanced cross-entropy loss function for solving class imbalanced data problems. The results show that the G-mean (geometric mean of the accuracy of all classes) of LSTM with class balanced cross-entropy loss of function is greater than LSTM with cross-entropy loss function.
Towards Longevity of Smart Home Systems

Architectures of current smart home systems are not optimized for longevity of their installations. In this paper we analyze scenarios that can render smart home installations useless much sooner than their non-smart counterparts. We analyze current smart home architectures and classify their components and the protocols connecting them. On this basis we present potential threats to the longevity of such smart home installations and determine areas that need more research to provide the longevity and sustainability that users expect from such premium priced products.
Face Detection using Deep Recurrent Learning and SMQT Technique

Now a day's signature is not considered as the best alternative of security hindrance. Some advance version is adding features with a face featuring commands to deal with security. But dynamically this digital proofing is tough for this reliability field. This becomes a platform to think about as confirmation of recognition is efficient in static mode only. So, this study will guide the proofing of the face recognition system and its situation where it is lacking namely incorrect approval and detection failures in a dynamic manner can be reduced. This paper is based on some unique set of technique namely deep recurrent learning, SMQT (Successive Mean Quantization Transform) and K-nearest neighbors (KNN). The technique selected here is wisely operating on the situation like noise, low light, vague and slight tilt in the image. The study will guide towards efficient facial data recognition under versatile effects on the image. These methods have opted as these are like neural networking, and simple machine learning steps are incapable to handle the objective in a fraction of seconds with high accuracy. Result of this study shows 98.72% effectiveness for a video database.
A Context Model for Holistic Monitoring and Management of Complex IT Environments

The increased usage of IoT, containerization, and multiple clouds not only changed the way IT works but also the way IT Operations, i. e., the monitoring and management of IT assets, works. Monitoring a complex IT environment leads to massive amounts of heterogeneous context data, usually spread across multiple data silos, which needs to be analyzed and acted upon autonomously. However, for a holistic overview of the IT environment, context data needs to be consolidated which leads to several problems. For scalable and automated processes, it is essential to know what context is required for a given monitored resource, where the context data are originating from, and how to access them across the data silos. Therefore, we introduce the Monitoring Resource Model for the holistic management of context data. We show what context is essential for the management of monitored resources and how it can be used for context reasoning. Furthermore, we propose a multi-layered framework for IT Operations with which we present the benefits of the Monitoring Resource Model.
Wireless link adaptation with outdated CSI — a hybrid data-driven and model-based approach

Link adaptation provides high spectral efficiency in wireless communications by selecting appropriate transmission parameters, e.g., the modulation and coding scheme (MCS), based on the instantaneous wireless channel. However, link adaptation suffers from impairments due to channel state information (CSI) feedback delay. In this paper, we extend the data-driven MCS selection scheme in our previous work to the case of outdated CSI, by assuming that CSI history is available to the system. We present two approaches that leverage the CSI history to optimally select the MCS for the current channel, i.e., i) an end-to-end (E2E) machine learning approach and ii) a hybrid data-driven and model-based approach. The E2E method uses the CSI history as input to a neural network for MCS selection. Conversely, the hybrid method uses a lower-dimensionality sufficient statistic for the instantaneous CSI, computed from the CSI history, as input to a neural network for MCS selection. We demonstrate that replacing the CSI history with the sufficient statistic comes without loss of generality. Moreover, by means of numerical experiments, we show that both approaches effectively compensate for the feedback delay. However, we advocate the hybrid approach as it comes with the additional benefits of i) a smaller neural network, which in turn requires a lower amount of data and training time, ii) improved explainability, and iii) better insights into optimization choices.
Different Types of Interaction with Augmented Reality Learning Material

In this paper, a study with the focus on interactivity in augmented reality (AR) applications concerning the influence of different forms of interaction with AR learning material is presented. While research on multimedia learning often distinguishes between mental and physical interaction with learning material, other research fields state that physical interaction is necessary to interact mentally. To look at how this distinction may play a role in AR-based learning material, an experimental study with a 2x2 design manipulating mental and physical interaction was conducted, including learning material on the topic of power plants. The data (N = 128) were collected and analyzed, showing that, although not expected, learning was better in groups in which either more physical or more mental interaction was applied, but not in groups in which both were high. The results are discussed under the potential idea of cognitive overload.