Reviewerly/exHarmony huggingface dataset:
PFPMine: A parallel approach for discovering interacting data entities in data-intensive cloud workflows

With the evolution of cloud computing, communities and companies deployed their workflows on cloud to support end-to-end business processes that are usually syndicated with other external services. To improve the efficiency of the system as well as reducing energy consumption, data placement and backup strategies should be carefully designed. One of the most challenging problems is the discovery of interacting data entities in date-intensive workflows. To tackle this challenge, this paper presents a frequent pattern-based approach named FPMine for interacting data entity discovery in cloud workflows. A direct discriminative mining algorithm is first proposed to determine the minimum support threshold, based on which FP-tree is constructed to formulate the frequent item pairs. Next, FP-matrix is applied to avoid traversing the FP-trees during data entity discovery, and a pruning approach is introduced to reduce the redundancy of frequent item pairs. Furthermore, we propose a parallel data entity mining algorithm using MapReduce framework, namely PFPMine, and then design a primitive data placement and backup strategy. Finally, we evaluate the efficiency of our approach by experiments using real-life data, based on which we show that our approach can facilitate the discovery of interacting data entities with efficiency for cloud workflows. Comparing with traditional FP-growth approach, we pay only a multiplicative factor for making our approach able to extract fine-grained frequent item pairs rather than frequent patterns, which can bring significant advantages to data placement. After parallelization, the PFPMine algorithm performs better with high efficiency for both sparse datasets and dense datasets than FP-growth. The results show that PFPMine can reduce the running time by at least 25%, and preforms with significantly higher efficiency than FP-growth approach.
A critical analysis and explication of word sense disambiguation as approached by natural language processing

This paper presents a critical analysis and explication of the approach to the problem of word sense disambiguation by the field of natural language processing. The paper is motivated by the observation that, despite the approach being interesting and promising, the actual research has some problems regarding its conceptual frameworks and methods. The methodology behind the analysis and explication is philosophical, focusing on the identification of conceptual unclarities and methodological incoherencies as well as their (practical) implications. The methodology helps to pinpoint some structural confusion in the research with respect to a central concept and an important set of methods. First, the characterization of the problem of word sense disambiguation as a computational task allows for different interpretations. When a rich interpretation is adopted, the methods developed for performing the task have certain problematic limitations. Secondly, the applicability of methods for the inductive discrimination of word senses is overestimated. The acknowledgement of this confusion makes it possible to understand the sometimes erratic course of the research and underlines the need for one, commonly shared, more refined and integrated conceptual framework and methodology. This paper may be interpreted as a humble onset of some of the necessary groundwork for developing these.
A Feature Importance Study in Ballet Pose Recognition with OpenPose

Movements of the human body can finally be recognised and analysed using computer vision technology. Ballet is an activity that involves various movements and specific poses of the body, making it an attractive candidate for computer vision applications. This paper proposes a feature importance study for determining which body parts play the most significant role in ballet pose recognition. The study is based on the use of OpenPose for feature extraction together with Support Vector Machine, Random Forest and Gradient Boosted Tree classifiers. Recognition accuracies above 95% suggest that the methods are not only feasible but exhibit excellent results. The results also indicate that the body parts that were the most significant for the classification of ballet poses were those situated at the extremities of the body such as the wrists and feet. The study addresses challenges within the ballet domain as it relates to both training and choreography. Furthermore, the study confirms that as technology expands into all areas of life, it is worthwhile to explore the possibilities within artistic fields.
Low Latency and Low Cost Smart Embedded Seismograph for Early Warning IoT Applications

The component technologies and structure of modern seismic network infrastructures shall be capable to fulfil many, sometimes contradictory, requirements and realize various trade-offs in achieving target figures of accuracy, reliability, power autonomy, security, real-time response and costs of installation and data storage and communication. This work describes the design and implementation of a low cost, smart embedded professional seismograph, which can be configured to be efficiently and effectively support both, typical seismological and geophysical data collection networks, as well as recent, real-time event detection and response setups for earthquake early warning applications, by exploiting an elaborate combination of established traditional and newest IoT technologies.
Improving Generative Modelling in VAEs Using Multimodal Prior

In this paper we propose a conditional generative modelling (CGM) approach for unsupervised disentangled representation learning using variational autoencoder (VAE). CGM employs a multimodal/categorical conditional prior distribution in the latent space to learn global uncertainty in data by modelling the variations at local level. Thus, the proposed framework enforces the model to independently estimate the inherent patterns within each category, which improves the interpretability of the latent representations learned by the VAE model. The evidence lower bound objective for training the generative model is maximized using a mutual information criterion between the global latent categorical variable and the encoded inputs. Further, the approach has a built-in mechanism for bounding the information flow between the encoder and the decoder which addresses the problems of posterior collapse in conventional VAE models. Experiments on a variety of datasets demonstrate that our objective can learn disentangled representations and the proposed approach achieves competitive results on various task such as generative modelling, image classification and image denoising.
A refinement of Müller's cube root algorithm

Let p be a prime such that p≡1(mod3). Let c be a cubic residue (modp) such that cp−13≡1(modp). In this paper, we present a refinement of Müller's algorithm for computing a cube root of c [11], which also improves Williams' [14], [15] Cipolla-Lehmer type algorithms. Under the assumption that a suitable irreducible polynomial of degree 3 is given, Müller gave a cube root algorithm which requires 8.5log⁡p modular multiplications. Our algorithm requires only 7.5log⁡p modular multiplications and is based on the recurrence relations arising from the irreducible polynomial h(x)=x3+ct3x−ct3 for some integer t.
A Survey: Mobile Cloud Computing in Education

A new digital technology that is expected to transform the education sector is Cloud computing. Cloud computing has many advantages like simple sharing, fast deployment, reduced cost and convenient processing. The main goal of this paper is to bring out the impact of Cloud computing on the Education system. Nowadays, the usage of mobile phones is increasing day by day and thus they are becoming an essential part of human life. Mobile phones are a very efficient and portable mode of communication which can be used at any time in any place. Most of the applications in mobile phones are connected to cloud like Google cloud for Gmail, Apple iCloud in iPhones and a few others in the education sector information technology plays a vital role. Due to the increase in the educational resources, there should be an architecture developed which should not only store large amounts of data but also process it. Mobile cloud computing is a new approach in education for both teachings as well as learning. Mobile cloud computing is beneficial to educational institutes to store educational data and to help people to access the data at any time from anywhere.
Representing Functional Relationships of Adaptive Instructional Systems in a Conceptual Model

This paper examines the relationships of various functional elements within a class of instructional technologies called adaptive instructional systems (AISs) which include intelligent tutoring systems (ITSs), intelligent mentors or recommender systems, and intelligent instructional media. AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each individual learner or team in the context of domain learning objectives. Under Project 2247.1, The Institute for Electrical and Electronic Engineers (IEEE) is developing standards and guidance for the modeling of AIS to characterize what is and is not an AIS. This paper was composed to document recommendations and generate discussion about the four models that have been proposed as core to the concept of AISs: learner models, adaptive models, domain models and interface models.
Model-based development of smart home scenarios for IoT simulation

The number of Internet of Things (IoT) Devices is rising steadily in the last few years. So it becomes more and more important to analyze their behavior and find the best way for communication between those devices. It is very cost- and time-consuming to test everything on real IoT devices. So it is easier to use a simulation for this purpose. With this simulation the communication between the devices and their behavior in different situations can be analyzed, security-related evaluations can be tested and also the consumed power of the various devices can be calculated without using the real IoT devices. In this paper we present a Model-based development approach of a smart home scenario simulation, which shows the communication between the separate devices and calculates their energy consumption. Also a smart security and health system is implemented.
A Deep Level Tagger for Malayalam, a Morphologically Rich Language

Abstract In recent years, there has been tremendous growth in the amount of natural language text through various sources. Computational analysis of this text has got considerable attention among the NLP researchers. Automatic analysis and representation of natural language text is a step by step procedure. Deep level tagging is one of such steps applied over the text. In this paper, we demonstrate a methodology for deep level tagging of Malayalam text. Deep level tagging is the process of assigning deeper level information to every noun and verb in the text along with normal POS tags. In this study, we move towards a direction that is not much explored in the case of Malayalam language. Malayalam is a morphologically rich and agglutinative language. The morphological features of the language are effectively utilized for the computational analysis of Malayalam text. The language level details required for the study are provided by Thunjath Ezhuthachan Malayalam University, Tirur.
Hybrid Unsupervised Scale-invariant Slide Detection (HUSSD) for Video Presentation

This paper addresses the task of unsupervised scale-invariant slide detection for video presentations, which aims to detect slides and index them with an appropriate location in the presentation video. We propose two new methods that improve on Scale-Invariant Feature Transform (SIFT) in terms of computational cost as well as accuracy. Both methods are instantiations of our Hybrid Unsupervised Scale-invariant Slide Detection paradigm (HUSSD). The first is a feature-based HUSSD algorithm. This method utilizes SIFT as well as Oriented FAST and rotated BRIEF (ORB) tracker to resolve the computation cost issue. Feature-based HUSSD optimizes for speed and substantially improves the runtime to approximately 30 times faster than SIFT at the trivial cost of accuracy falling from 90.06% to 82.89%. Furthermore, our second novel method for slide detection, namely template-based HUSSD, employs a multiscale matching detector and a single scale template matching tracker to improve on SIFT in terms of both speed and accuracy by 7.91 times and to 95.05% respectively.
Fast detection of moving objects based on sequential images processing

Nowadays, moving object detection in sequence images has become a hot topic in computer vision research, and has a very wide range of practical applications in many fields of military and daily life. In this paper, fast detection of moving objects in complex background is studied, and fast detection methods for moving objects in static and dynamic scenes are proposed respectively. Firstly, based on image preprocessing, aiming at the difficulty of feature extraction of moving targets in low illumination at night, Gamma change is used to process. Secondly, for the fast detection of moving objects in static scenes, this paper designs a detection method combining background difference and edge frame difference. Finally, aiming at the fast detection of moving objects in dynamic scenes, a feature matching detection method based on the SIFT algorithm is designed in this paper. Simulation experiments show that the method designed in this paper has good detection performance.
Programmer eXperience: A Set of Heuristics for Programming Environments

The definition of user experience (UX) is broad and covers several aspects. The job of any programmer is very specific and demanding. He/she uses different systems or tools to carry out their programming tasks. We consider a programmer as a specific case of user, who employs programming environments and other software development artifacts. We therefore consider this particular kind of UX as Programmer eXperience (PX). Several authors have defined different aspects of PX, including, among others, language features, programming learning factors or programmer performance. Usability is a relevant aspect of UX, as well as an important aspect of programming environments. Heuristic evaluation is an inspection method that allows evaluating the usability of interactive software systems. We developed a set of heuristics following the methodology proposed by Quiñones et al. We defined a new set of 12 specific heuristics that incorporate concepts of UX and usability of programming environments. These heuristics have been validated following also that methodology. The results obtained in different effectiveness criteria were satisfactory. However, the set of heuristics could be further refined and validate in new scenarios or case studies.
TontineCoin: Murder-Based Proof-of-Stake

Proof-of-stake cryptocurrencies avoid many of the computational and environmental costs associated with proof-of-work protocols. However, they must address the nothing-at-stake problem, where a validator might attempt to sign off on competing blocks, with the hopes of earning coins regardless of which block becomes accepted as part of the blockchain. Cryptocurrencies such as Tendermint resolve this challenge by requiring validators to em bond coins, which can be seized from a validator that is caught signing two competing blocks. Nevertheless, as the number of validators increases, it becomes more and more infeasible to effectively monitor all validators. In this work, we incentivize proper block monitoring by allowing validators to form tontines. Tontines are financial agreements where payouts to each member increase as the number of members decreases. In our system, a tontine is a group of validators that monitor each other's behavior, "murdering" any cheating tontine members to seize their stake. As the number of validators in a tontine is smaller than the number of validators in the currency as a whole, members can effectively police each other. We propose two methods whereby a Tendermint-like currency can be extended to allow for the creation of tontines: a pure proof-of-stake model, and a hybrid proof-of-stake/proof-of-work model. We describe snitch mechanisms for both the inter-and intra-tontine setting, argue our incentive mechanisms increase monitoring, and describe how it handles a variety of possible attacks.
Understanding the Blockchain Technology Adoption from Procurement Professionals’ Perspective - An Analysis of the Technology Acceptance Model Using Intuitionistic Fuzzy Cognitive Maps

Nowadays, the trend towards new technologies has promoted fierce competition in supply chains. Blockchain technology attracts widespread attention in supply chain processes via its potential benefits. Efficiently managed supply chain processes provide operational and organizational advantages, and procurement is one of the critical processes to gain such advantages. With its multi-participant nature, procurement process deserves particular attention in terms of its potential to be transformed by blockchain technology. When evaluating a new technology, variables affecting behavior to use technology should be analyzed carefully. In literature, additional research is needed to obtain a broader understanding of blockchain technology acceptance. This study aims to analyze procurement professionals’ adoption of the blockchain technology with the Technology Acceptance Model (TAM) and Intuitionistic Fuzzy Cognitive Maps (IFCM). The IFCM was used as it has the capacity to model the professionals’ hesitation, and it also copes with incomplete or even conflicting information. Our results indicate that the influences between variables are in line with most of the other studies. However, the findings further strengthens that the procurement professionals give Intention, Job Relevance and Output Quality more importance.
Analysis of the Data from the Twitter account using Machine Learning

Growth is the part where the mining of the opinion and the analysis can happen and can reach our goal to explore the things on various platforms like opinions or text of social media platforms through machine-learning technology. By using analysis called subjective polarity can be achieved. Even then, for one or the other reason the society will keep on using social media sites to get and share information. Here, twitter platform is used where the users use it to send-read posts and to know the tweets. Users read the daily news and comments or tweets their opinion on others post. So this is the good platform to know the opinion of others on their advertisements. Posts in twitter are not in a proper format that is in the unstructured format and it is difficult to analyze. So the proposed scheme helps to combine supervised and unsupervised algorithms. In this paper, a module is proposed that data will be extracted from the twitter application through the permission key then unwanted content will be removed after that data will be fed into different models with positive, negative and natural opinions. Extraction of all Hotel Data is collected from twitter application which is more popular. Machine Learning algorithms are used to perform Analysis on hotels, the task Result is tested through different metrics to get accurate output most likely.
Developing robust nonlinear models through bootstrap aggregated deep belief networks

Deep belief network (DBN) has recently emerged as a powerful tool in building nonlinear data driven models. However, a single DBN model can still lack reliability especially when the amount of data available for modelling is limited. This paper proposes a bootstrap aggregated deep belief network (BAGDBN) to improve model reliability and robustness. In the proposed method, bootstrap re-sampling with replacement is applied to the original modelling data to generate multiple replications. A DBN model is developed on each replication of the original modelling data. These individual DBN models are then combined to form a BAGDBN model. The proposed method is demonstrated on two application examples, modelling of a conic water tank and inferential estimation of polymer melt index in an industrial polypropylene polymerization process. The application results demonstrate that the proposed BAGDBN models can give more reliable estimation and prediction than single DBN models.
Textile Designer Perspective on Haptic Interface Design: A Sensorial Platform for Conversation Between Discipline

Smart textiles have established a foothold in different academic fields, such as in chemistry, engineering, and in human-computer interaction (HCI). Within HCI, smart textiles are present in research in many ways, for example, as context, as means, or as focus. However, interdisciplinary projects tend to leave the implications of and to textile design without notice. How can a project utilise a textile designer's skills to feed back to textile design from an interdisciplinary project? In this paper, we present a case study, where a textile designer's role extends beyond the prototype production, and we analyse the project in light of textile design. Our findings show that textile design can augment data collection and analysis. We conclude with a discussion towards inclusion of textile design in HCI.
Combining exposure indicators and predictive analytics for threats detection in real industrial IoT sensor networks

We present a framework able to combine exposure indicators and predictive analytics using AI-tools and big data architectures for threats detection inside a real industrial IoT sensors network. The described framework, able to fill the gaps between these two worlds, provides mechanisms to internally assess and evaluate products, services and share results without disclosing any sensitive and private information. We analyze the actual state of the art and a possible future research on top of a real case scenario implemented into a technological platform being developed under the H2020 ECHO project, for sharing and evaluating cybersecurity relevant informations, increasing trust and transparency among different stakeholders.
On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images

Selective segmentation is an important aspect of image processing. Being able to reliably segment a particular object in an image has important applications particularly in medical imaging. Robust methods can aid clinicians with diagnosis, surgical planning, etc. Many selective segmentation algorithms use geometric constraints such as information from the edges in order to determine where an object lies. It is still a challenge where there is low contrast present between two objects, and an edge is difficult to detect. Relying on purely edge constraints in this case will fail. We aim to make use of area constraints in addition to edge information in a segmentation model which is robustly capable of segmenting regions in an image even in the presence of low contrast, when given suitable user input. In addition, we implement a deep learning algorithm based on this model, allowing for a supervised, semi-supervised or unsupervised approach, depending on data availability.